diff --git a/.env.template b/.env.template index 3b7c8e887..ba7f752db 100644 --- a/.env.template +++ b/.env.template @@ -23,6 +23,15 @@ WEB_SERVER_PORT=7860 #*******************************************************************# # LLM_MODEL, see /pilot/configs/model_config.LLM_MODEL_CONFIG LLM_MODEL=vicuna-13b-v1.5 +## LLM model path, by default, DB-GPT will read the model path from LLM_MODEL_CONFIG based on the LLM_MODEL. +## Of course you can specify your model path according to LLM_MODEL_PATH +## In DB-GPT, the priority from high to low to read model path: +## 1. environment variable with key: {LLM_MODEL}_MODEL_PATH (Avoid multi-model conflicts) +## 2. environment variable with key: MODEL_PATH +## 3. environment variable with key: LLM_MODEL_PATH +## 4. the config in /pilot/configs/model_config.LLM_MODEL_CONFIG +# LLM_MODEL_PATH=/app/models/vicuna-13b-v1.5 +# LLM_PROMPT_TEMPLATE=vicuna_v1.1 MODEL_SERVER=http://127.0.0.1:8000 LIMIT_MODEL_CONCURRENCY=5 MAX_POSITION_EMBEDDINGS=4096 @@ -46,6 +55,17 @@ QUANTIZE_8bit=True ## Model path # llama_cpp_model_path=/data/models/TheBloke/vicuna-13B-v1.5-GGUF/vicuna-13b-v1.5.Q4_K_M.gguf +### LLM cache +## Enable Model cache +# MODEL_CACHE_ENABLE=True +## The storage type of model cache, now supports: memory, disk +# MODEL_CACHE_STORAGE_TYPE=disk +## The max cache data in memory, we always store cache data in memory fist for high speed. +# MODEL_CACHE_MAX_MEMORY_MB=256 +## The dir to save cache data, this configuration is only valid when MODEL_CACHE_STORAGE_TYPE=disk +## The default dir is pilot/data/model_cache +# MODEL_CACHE_STORAGE_DISK_DIR= + #*******************************************************************# #** EMBEDDING SETTINGS **# #*******************************************************************# @@ -84,6 +104,7 @@ LOCAL_DB_TYPE=sqlite # LOCAL_DB_PASSWORD=aa12345678 # LOCAL_DB_HOST=127.0.0.1 # LOCAL_DB_PORT=3306 +# LOCAL_DB_NAME=dbgpt ### This option determines the storage location of conversation records. The default is not configured to the old version of duckdb. It can be optionally db or file (if the value is db, the database configured by LOCAL_DB will be used) #CHAT_HISTORY_STORE_TYPE=db diff --git a/CODE_OF_CONDUCT b/CODE_OF_CONDUCT new file mode 100644 index 000000000..b7efcc0b3 --- /dev/null +++ b/CODE_OF_CONDUCT @@ -0,0 +1,126 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, caste, color, religion, or sexual +identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the overall + community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or advances of + any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email address, + without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +[INSERT CONTACT METHOD]. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +*Community Impact*: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +*Consequence*: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +*Community Impact*: A violation through a single incident or series of +actions. + +*Consequence*: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or permanent +ban. + +### 3. Temporary Ban + +*Community Impact*: A serious violation of community standards, including +sustained inappropriate behavior. + +*Consequence*: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +*Community Impact*: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +*Consequence*: A permanent ban from any sort of public interaction within the +community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.1, available at +[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at +[https://www.contributor-covenant.org/translations][translations]. diff --git a/README.md b/README.md index 49b006a7b..524541209 100644 --- a/README.md +++ b/README.md @@ -13,9 +13,6 @@ forks - - License: MIT - License: MIT @@ -25,8 +22,8 @@ Open Issues - - Discord + + Discord Open in GitHub Codespaces @@ -34,17 +31,27 @@

-[**简体中文**](README.zh.md) |[**Discord**](https://discord.gg/vqBrcV7Nd) |[**Documents**](https://db-gpt.readthedocs.io/en/latest/)|[**Wechat**](https://github.com/eosphoros-ai/DB-GPT/blob/main/README.zh.md#%E8%81%94%E7%B3%BB%E6%88%91%E4%BB%AC)|[**Community**](https://github.com/eosphoros-ai/community) +[**简体中文**](README.zh.md) | [**Discord**](https://discord.gg/nASQyBjvY) | [**Documents**](https://db-gpt.readthedocs.io/en/latest/) | [**Wechat**](https://github.com/eosphoros-ai/DB-GPT/blob/main/README.zh.md#%E8%81%94%E7%B3%BB%E6%88%91%E4%BB%AC) | [**Community**](https://github.com/eosphoros-ai/community) ## What is DB-GPT? -DB-GPT is an experimental open-source project that uses localized GPT large models to interact with your data and environment. With this solution, you can be assured that there is no risk of data leakage, and your data is 100% private and secure. +DB-GPT is an open-source framework designed for the realm of large language models (LLMs) within the database field. Its primary purpose is to provide infrastructure that simplifies and streamlines the development of database-related applications. This is accomplished through the development of various technical capabilities, including: + +1. **SMMF(Service-oriented Multi-model Management Framework)** +2. **Text2SQL Fine-tuning** +3. **RAG(Retrieval Augmented Generation) framework and optimization** +4. **Data-Driven Agents framework collaboration** +5. **GBI(Generative Business intelligence)** + +DB-GPT simplifies the creation of these applications based on large language models (LLMs) and databases. + +In the era of Data 3.0, enterprises and developers can take the ability to create customized applications with minimal coding, which harnesses the power of large language models (LLMs) and databases. ## Contents -- [install](#install) -- [demo](#demo) +- [Install](#install) +- [Demo](#demo) - [introduction](#introduction) - [features](#features) - [contribution](#contribution) @@ -54,19 +61,11 @@ DB-GPT is an experimental open-source project that uses localized GPT large mode [DB-GPT Youtube Video](https://www.youtube.com/watch?v=f5_g0OObZBQ) ## Demo -Run on an RTX 4090 GPU. +##### Chat Data +![chatdata](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/1f77079e-d018-4eee-982b-9b6a66bf1063) + ##### Chat Excel -![excel](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/0474d220-2a9f-449f-a940-92c8a25af390) -##### Chat Plugin -![auto_plugin_new](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/7d95c347-f4b7-4fb6-8dd2-c1c02babaa56) -##### LLM Management -![llm_manage](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/501d6b3f-c4ce-4197-9a6f-f016f8150a11) -##### FastChat && vLLM -![vllm](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/0c9475d2-45ee-4573-aa5a-814f7fd40213) -##### Trace -![trace_new](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/69bd14b8-14d0-4ca9-9cb7-6cef44a2bc93) -##### Chat Knowledge -![kbqa_new](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/72266a48-edef-4c6d-88c6-fbb1a24a6c3e) +![excel](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/3044e83b-a71e-41fe-a1e2-98e479e0ab59) ## Install ![Docker](https://img.shields.io/badge/docker-%230db7ed.svg?style=for-the-badge&logo=docker&logoColor=white) @@ -75,8 +74,8 @@ Run on an RTX 4090 GPU. ![Windows](https://img.shields.io/badge/Windows-0078D6?style=for-the-badge&logo=windows&logoColor=white) [**Usage Tutorial**](https://db-gpt.readthedocs.io/en/latest/getting_started/install/deploy/deploy.html) -- [**Install**](https://db-gpt.readthedocs.io/en/latest/getting_started/install/deploy/deploy.html) - - [**Install Step by Step**](https://db-gpt.readthedocs.io/en/latest/getting_started/install/deploy/deploy.html) +- [**Install**](https://db-gpt.readthedocs.io/en/latest/getting_started/install/deploy.html) + - [**Install Step by Step**](https://db-gpt.readthedocs.io/en/latest/getting_started/install/deploy.html) - [**Docker Install**](https://db-gpt.readthedocs.io/en/latest/getting_started/install/docker/docker.html) - [**Docker Compose**](https://db-gpt.readthedocs.io/en/latest/getting_started/install/docker_compose/docker_compose.html) - [**How to Use**](https://db-gpt.readthedocs.io/en/latest/getting_started/application/chatdb/chatdb.html) @@ -96,52 +95,47 @@ Run on an RTX 4090 GPU. ## Features -Currently, we have released multiple key features, which are listed below to demonstrate our current capabilities: -- Private KBQA & data processing +At present, we have introduced several key features to showcase our current capabilities: +- **Private Domain Q&A & Data Processing** - The DB-GPT project offers a range of features to enhance knowledge base construction and enable efficient storage and retrieval of both structured and unstructured data. These include built-in support for uploading multiple file formats, the ability to integrate plug-ins for custom data extraction, and unified vector storage and retrieval capabilities for managing large volumes of information. + The DB-GPT project offers a range of functionalities designed to improve knowledge base construction and enable efficient storage and retrieval of both structured and unstructured data. These functionalities include built-in support for uploading multiple file formats, the ability to integrate custom data extraction plug-ins, and unified vector storage and retrieval capabilities for effectively managing large volumes of information. -- Multiple data sources & visualization +- **Multi-Data Source & GBI(Generative Business intelligence)** + + The DB-GPT project facilitates seamless natural language interaction with diverse data sources, including Excel, databases, and data warehouses. It simplifies the process of querying and retrieving information from these sources, empowering users to engage in intuitive conversations and gain insights. Moreover, DB-GPT supports the generation of analytical reports, providing users with valuable data summaries and interpretations. + +- **Multi-Agents&Plugins** + + It offers support for custom plug-ins to perform various tasks and natively integrates the Auto-GPT plug-in model. The Agents protocol adheres to the Agent Protocol standard. + +- **Automated Fine-tuning text2SQL** + + We've also developed an automated fine-tuning lightweight framework centred on large language models (LLMs), Text2SQL datasets, LoRA/QLoRA/Pturning, and other fine-tuning methods. This framework simplifies Text-to-SQL fine-tuning, making it as straightforward as an assembly line process. [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) + +- **SMMF(Service-oriented Multi-model Management Framework)** + + We offer extensive model support, including dozens of large language models (LLMs) from both open-source and API agents, such as LLaMA/LLaMA2, Baichuan, ChatGLM, Wenxin, Tongyi, Zhipu, and many more. - The DB-GPT project enables seamless natural language interaction with various data sources, including Excel, databases, and data warehouses. It facilitates effortless querying and retrieval of information from these sources, allowing users to engage in intuitive conversations and obtain insights. Additionally, DB-GPT supports the generation of analysis reports, providing users with valuable summaries and interpretations of the data. - -- Multi-Agents&Plugins - - Supports custom plug-ins to perform tasks, natively supports the Auto-GPT plug-in model, and the Agents protocol adopts the Agent Protocol standard - -- Fine-tuning text2SQL - - An automated fine-tuning lightweight framework built around large language models, Text2SQL data sets, LoRA/QLoRA/Pturning and other fine-tuning methods, making TextSQL fine-tuning as convenient as an assembly line. [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) - -- Multi LLMs Support, Supports multiple large language models, currently supporting - - Massive model support, including dozens of large language models such as open source and API agents. Such as LLaMA/LLaMA2, Baichuan, ChatGLM, Wenxin, Tongyi, Zhipu, etc. - [Vicuna](https://huggingface.co/Tribbiani/vicuna-13b) - [vicuna-13b-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5) - [LLama2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) - - [baichuan2-13b](https://huggingface.co/baichuan-inc) - - [baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B) + - [baichuan2-13b](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) + - [baichuan2-7b](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat) - [chatglm-6b](https://huggingface.co/THUDM/chatglm-6b) - [chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b) + - [chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b) - [falcon-40b](https://huggingface.co/tiiuae/falcon-40b) - [internlm-chat-7b](https://huggingface.co/internlm/internlm-chat-7b) - - [Qwen-7B-Chat/Qwen-14B-Chat](https://huggingface.co/Qwen/) - - [RWKV-4-Raven](https://huggingface.co/BlinkDL/rwkv-4-raven) - - [CAMEL-13B-Combined-Data](https://huggingface.co/camel-ai/CAMEL-13B-Combined-Data) - - [dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b) - - [h2ogpt-gm-oasst1-en-2048-open-llama-7b](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b) - - [fastchat-t5-3b-v1.0](https://huggingface.co/lmsys/fastchat-t5) - - [mpt-7b-chat](https://huggingface.co/mosaicml/mpt-7b-chat) - - [gpt4all-13b-snoozy](https://huggingface.co/nomic-ai/gpt4all-13b-snoozy) - - [Nous-Hermes-13b](https://huggingface.co/NousResearch/Nous-Hermes-13b) - - [codet5p-6b](https://huggingface.co/Salesforce/codet5p-6b) - - [guanaco-33b-merged](https://huggingface.co/timdettmers/guanaco-33b-merged) - - [WizardLM-13B-V1.0](https://huggingface.co/WizardLM/WizardLM-13B-V1.0) - - [WizardLM/WizardCoder-15B-V1.0](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0) - - [Llama2-Chinese-13b-Chat](https://huggingface.co/FlagAlpha/Llama2-Chinese-13b-Chat) - - [OpenLLaMa OpenInstruct](https://huggingface.co/VMware/open-llama-7b-open-instruct) - - etc. + - [internlm-chat-20b](https://huggingface.co/internlm/internlm-chat-20b) + - [qwen-7b-chat](https://huggingface.co/Qwen/Qwen-7B-Chat) + - [qwen-14b-chat](https://huggingface.co/Qwen/Qwen-14B-Chat) + - [wizardlm-13b](https://huggingface.co/WizardLM/WizardLM-13B-V1.2) + - [orca-2-7b](https://huggingface.co/microsoft/Orca-2-7b) + - [orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) + - [openchat_3.5](https://huggingface.co/openchat/openchat_3.5) + - [zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) + - [mistral-7b-instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) + - [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) - Support API Proxy LLMs - [x] [ChatGPT](https://api.openai.com/) @@ -149,16 +143,16 @@ Currently, we have released multiple key features, which are listed below to dem - [x] [Wenxin](https://cloud.baidu.com/product/wenxinworkshop?track=dingbutonglan) - [x] [ChatGLM](http://open.bigmodel.cn/) -- Privacy and security +- **Privacy and Security** - The privacy and security of data are ensured through various technologies such as privatized large models and proxy desensitization. + We ensure the privacy and security of data through the implementation of various technologies, including privatized large models and proxy desensitization. - Support Datasources | DataSource | support | Notes | | ------------------------------------------------------------------------------ | ----------- | ------------------------------------------- | | [MySQL](https://www.mysql.com/) | Yes | | -| [PostgresSQL](https://www.postgresql.org/) | Yes | | +| [PostgreSQL](https://www.postgresql.org/) | Yes | | | [Spark](https://github.com/apache/spark) | Yes | | | [DuckDB](https://github.com/duckdb/duckdb) | Yes | | | [Sqlite](https://github.com/sqlite/sqlite) | Yes | | @@ -177,43 +171,37 @@ Currently, we have released multiple key features, which are listed below to dem | [StarRocks](https://github.com/StarRocks/starrocks) | No | TODO | ## Introduction -Is the architecture of the entire DB-GPT shown in the following figure: +The architecture of DB-GPT is shown in the following figure:

-The core capabilities mainly consist of the following parts: -1. Multi-Models: Support multi-LLMs, such as LLaMA/LLaMA2、CodeLLaMA、ChatGLM, QWen、Vicuna and proxy model ChatGPT、Baichuan、tongyi、wenxin etc -2. Knowledge Based QA: You can perform high-quality intelligent Q&A based on local documents such as pdf, word, excel and other data. -3. Embedding: Unified data vector storage and indexing, Embed data as vectors and store them in vector databases, providing content similarity search. -4. Multi-Datasources: Used to connect different modules and data sources to achieve data flow and interaction. -5. Multi-Agents: Provides Agent and plugin mechanisms, allowing users to customize and enhance the system's behavior. -6. Privacy & Secure: You can be assured that there is no risk of data leakage, and your data is 100% private and secure. -7. Text2SQL: We enhance the Text-to-SQL performance by applying Supervised Fine-Tuning (SFT) on large language models - -### RAG-IN-Action -

- -

+The core capabilities primarily consist of the following components: +1. Multi-Models: We support multiple Large Language Models (LLMs) such as LLaMA/LLaMA2, CodeLLaMA, ChatGLM, QWen, Vicuna, and proxy models like ChatGPT, Baichuan, Tongyi, Wenxin, and more. +2. Knowledge-Based QA: Our system enables high-quality intelligent Q&A based on local documents such as PDFs, Word documents, Excel files, and other data sources. +3. Embedding: We offer unified data vector storage and indexing. Data is embedded as vectors and stored in vector databases, allowing for content similarity search. +4. Multi-Datasources: This feature connects different modules and data sources, facilitating data flow and interaction. +5. Multi-Agents: Our platform provides Agent and plugin mechanisms, empowering users to customize and enhance the system's behaviour. +6. Privacy & Security: Rest assured that there is no risk of data leakage, and your data is 100% private and secure. +7. Text2SQL: We enhance Text-to-SQL performance through Supervised Fine-Tuning (SFT) applied to Large Language Models (LLMs). ### SubModule -- [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) Text-to-SQL performance by applying Supervised Fine-Tuning (SFT) on large language models. -- [DB-GPT-Plugins](https://github.com/eosphoros-ai/DB-GPT-Plugins) DB-GPT Plugins, Can run autogpt plugin directly +- [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) Text-to-SQL workflow with high performance by applying Supervised Fine-Tuning (SFT) on Large Language Models (LLMs). +- [DB-GPT-Plugins](https://github.com/eosphoros-ai/DB-GPT-Plugins) DB-GPT Plugins that can run Auto-GPT plugin directly - [DB-GPT-Web](https://github.com/eosphoros-ai/DB-GPT-Web) ChatUI for DB-GPT ## Image 🌐 [AutoDL Image](https://www.codewithgpu.com/i/eosphoros-ai/DB-GPT/dbgpt) - - ### Language Switching In the .env configuration file, modify the LANGUAGE parameter to switch to different languages. The default is English (Chinese: zh, English: en, other languages to be added later). ## Contribution -- Please run `black .` before submitting the code. contributing guidelines, [how to contribution](https://github.com/csunny/DB-GPT/blob/main/CONTRIBUTING.md) +- Please run `black .` before submitting the code. +- To check detailed guidelines for new contributions, please refer [how to contribute](https://github.com/csunny/DB-GPT/blob/main/CONTRIBUTING.md) ## RoadMap @@ -224,7 +212,7 @@ The core capabilities mainly consist of the following parts: ### KBQA RAG optimization - [x] Multi Documents - [x] PDF - - [x] Excel, csv + - [x] Excel, CSV - [x] Word - [x] Text - [x] MarkDown @@ -235,7 +223,7 @@ The core capabilities mainly consist of the following parts: - [ ] Graph Database - [ ] Neo4j Graph - [ ] Nebula Graph -- [x] Multi Vector Database +- [x] Multi-Vector Database - [x] Chroma - [x] Milvus - [x] Weaviate @@ -254,7 +242,7 @@ The core capabilities mainly consist of the following parts: - Multi Datasource Support - [x] MySQL - - [x] PostgresSQL + - [x] PostgreSQL - [x] Spark - [x] DuckDB - [x] Sqlite @@ -310,18 +298,7 @@ The core capabilities mainly consist of the following parts: - [x] ChatGLM2 - SFT Accuracy - -As of October 10, 2023, by fine-tuning an open-source model of 13 billion parameters using this project, the execution accuracy on the Spider evaluation dataset has surpassed that of GPT-4! - -| name | Execution Accuracy | reference | -| ----------------------------------| ------------------ | ------------------------------------------------------------------------------------------------------------------------------ | -| **GPT-4** | **0.762** | [numbersstation-eval-res](https://www.numbersstation.ai/post/nsql-llama-2-7b) | -| ChatGPT | 0.728 | [numbersstation-eval-res](https://www.numbersstation.ai/post/nsql-llama-2-7b) | -| **CodeLlama-13b-Instruct-hf_lora**| **0.789** | sft train by our this project,only used spider train dataset ,the same eval way in this project with lora SFT | -| CodeLlama-13b-Instruct-hf_qlora | 0.774 | sft train by our this project,only used spider train dataset ,the same eval way in this project with qlora and nf4,bit4 SFT | -| wizardcoder | 0.610 | [text-to-sql-wizardcoder](https://github.com/cuplv/text-to-sql-wizardcoder/tree/main) | -| CodeLlama-13b-Instruct-hf | 0.556 | eval in this project default param | -| llama2_13b_hf_lora_best | 0.744 | sft train by our this project,only used spider train dataset ,the same eval way in this project | +As of October 10, 2023, through the fine-tuning of an open-source model with 13 billion parameters using this project, we have achieved execution accuracy on the Spider dataset that surpasses even GPT-4! [More Information about Text2SQL finetune](https://github.com/eosphoros-ai/DB-GPT-Hub) @@ -330,8 +307,8 @@ As of October 10, 2023, by fine-tuning an open-source model of 13 billion parame The MIT License (MIT) ## Contact Information -We are working on building a community, if you have any ideas about building the community, feel free to contact us. -[![](https://dcbadge.vercel.app/api/server/vqBrcV7Nd?compact=true&style=flat)](https://discord.gg/vqBrcV7Nd) +We are working on building a community, if you have any ideas for building the community, feel free to contact us. +[![](https://dcbadge.vercel.app/api/server/nASQyBjvY?compact=true&style=flat)](https://discord.gg/nASQyBjvY)

diff --git a/README.zh.md b/README.zh.md index a6119663d..00ead0cc8 100644 --- a/README.zh.md +++ b/README.zh.md @@ -22,24 +22,21 @@ Open Issues - - Discord + + Discord Open in GitHub Codespaces

-[**English**](README.md)|[**Discord**](https://discord.gg/vqBrcV7Nd)|[**文档**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/)|[**微信**](https://github.com/csunny/DB-GPT/blob/main/README.zh.md#%E8%81%94%E7%B3%BB%E6%88%91%E4%BB%AC)|[**社区**](https://github.com/eosphoros-ai/community) +[**English**](README.md)|[**Discord**](https://discord.gg/nASQyBjvY)|[**文档**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/)|[**微信**](https://github.com/csunny/DB-GPT/blob/main/README.zh.md#%E8%81%94%E7%B3%BB%E6%88%91%E4%BB%AC)|[**社区**](https://github.com/eosphoros-ai/community) ## DB-GPT 是什么？ +DB-GPT是一个开源的数据库领域大模型框架。目的是构建大模型领域的基础设施，通过开发多模型管理、Text2SQL效果优化、RAG框架以及优化、Multi-Agents框架协作等多种技术能力，让围绕数据库构建大模型应用更简单，更方便。 -随着大模型的发布迭代，大模型变得越来越智能，在使用大模型的过程当中，遇到极大的数据安全与隐私挑战。在利用大模型能力的过程中我们的私密数据跟环境需要掌握自己的手里，完全可控，避免任何的数据隐私泄露以及安全风险。基于此，我们发起了DB-GPT项目，为所有以数据库为基础的场景，构建一套完整的私有大模型解决方案。 此方案因为支持本地部署，所以不仅仅可以应用于独立私有环境，而且还可以根据业务模块独立部署隔离，让大模型的能力绝对私有、安全、可控。我们的愿景是让围绕数据库构建大模型应用更简单，更方便。 - -DB-GPT 是一个开源的以数据库为基础的GPT实验项目，使用本地化的GPT大模型与您的数据和环境进行交互，无数据泄露风险，100% 私密 - - +数据3.0 时代，基于模型、数据库，企业/开发者可以用更少的代码搭建自己的专属应用。 ## 目录 @@ -55,23 +52,13 @@ DB-GPT 是一个开源的以数据库为基础的GPT实验项目，使用本地 ## 效果演示 -示例通过 RTX 4090 GPU 演示 +##### Chat Data +![chatdata](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/1f77079e-d018-4eee-982b-9b6a66bf1063) ##### Chat Excel -![excel](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/0474d220-2a9f-449f-a940-92c8a25af390) -#### Chat Plugin -![auto_plugin_new](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/7d95c347-f4b7-4fb6-8dd2-c1c02babaa56) -#### LLM Management -![llm_manage](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/501d6b3f-c4ce-4197-9a6f-f016f8150a11) -#### FastChat && vLLM -![vllm](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/0c9475d2-45ee-4573-aa5a-814f7fd40213) -#### Trace -![trace_new](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/69bd14b8-14d0-4ca9-9cb7-6cef44a2bc93) -#### Chat Knowledge -![kbqa_new](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/72266a48-edef-4c6d-88c6-fbb1a24a6c3e) +![excel](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/3044e83b-a71e-41fe-a1e2-98e479e0ab59) #### 根据自然语言对话生成分析图表 -

@@ -80,10 +67,6 @@ DB-GPT 是一个开源的以数据库为基础的GPT实验项目，使用本地

-

- -

- ## 安装 ![Docker](https://img.shields.io/badge/docker-%230db7ed.svg?style=for-the-badge&logo=docker&logoColor=white) @@ -91,80 +74,74 @@ DB-GPT 是一个开源的以数据库为基础的GPT实验项目，使用本地 ![macOS](https://img.shields.io/badge/mac%20os-000000?style=for-the-badge&logo=macos&logoColor=F0F0F0) ![Windows](https://img.shields.io/badge/Windows-0078D6?style=for-the-badge&logo=windows&logoColor=white) -[**教程**](https://db-gpt.readthedocs.io/en/latest/getting_started/install/deploy/deploy.html) -- [**安装**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/install/deploy/deploy.html) - - [**Install Step by Step**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/install/deploy/deploy.html) - - [**Docker安装**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/install/docker/docker.html) - - [**Docker Compose安装**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/install/docker_compose/docker_compose.html) -- [**产品使用手册**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/application/chatdb/chatdb.html) - - [**ChatData**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/application/chatdb/chatdb.html) - - [**ChatKnowledge**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/application/kbqa/kbqa.html) - - [**ChatExcel**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/application/chatexcel/chatexcel.html) - - [**Dashboard**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/application/dashboard/dashboard.html) - - [**LLM 管理**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/application/model/model.html) - - [**Chat Agent**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/application/chatagent/chatagent.html) -- [**如何部署LLM**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/install/cluster/cluster.html) - - [**Standalone**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/install/cluster/vms/standalone.html#) - - [**Cluster**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/install/cluster/vms/index.html) - - [**vLLM**](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/getting_started/install/llm/vllm/vllm.html) +[**教程**](https://www.yuque.com/eosphoros/dbgpt-docs/bex30nsv60ru0fmx) +- [**快速开始**](https://www.yuque.com/eosphoros/dbgpt-docs/ew0kf1plm0bru2ga) + - [**源码安装**](https://www.yuque.com/eosphoros/dbgpt-docs/urh3fcx8tu0s9xmb) + - [**Docker安装**](https://www.yuque.com/eosphoros/dbgpt-docs/glf87qg4xxcyrp89) + - [**Docker Compose安装**](https://www.yuque.com/eosphoros/dbgpt-docs/wwdu11e0v5nkfzin) +- [**产品使用手册**](https://www.yuque.com/eosphoros/dbgpt-docs/tkspdd0tcy2vlnu4) + - [**数据对话**](https://www.yuque.com/eosphoros/dbgpt-docs/gd9hbhi1dextqgbz) + - [**知识库**](https://www.yuque.com/eosphoros/dbgpt-docs/ycyz3d9b62fccqxh) + - [**ChatExcel**](https://www.yuque.com/eosphoros/dbgpt-docs/prugoype0xd2g4bb) + - [**数据库对话**](https://www.yuque.com/eosphoros/dbgpt-docs/wswpv3zcm2c9snmg) + - [**报表分析**](https://www.yuque.com/eosphoros/dbgpt-docs/vsv49p33eg4p5xc1) + - [**插件**](https://www.yuque.com/eosphoros/dbgpt-docs/pom41m7oqtdd57hm) +- [**如何部署模型服务**](https://www.yuque.com/eosphoros/dbgpt-docs/vubxiv9cqed5mc6o) + - [**单机部署**](https://www.yuque.com/eosphoros/dbgpt-docs/kwg1ed88lu5fgawb) + - [**集群部署**](https://www.yuque.com/eosphoros/dbgpt-docs/gmbp9619ytyn2v1s) + - [**vLLM**](https://www.yuque.com/eosphoros/dbgpt-docs/bhy9igdvanx1uluf) - [**如何Debug**](https://db-gpt.readthedocs.io/en/latest/getting_started/observability.html) - [**FAQ**](https://db-gpt.readthedocs.io/en/latest/getting_started/faq/deploy/deploy_faq.html) ## 特性一览 - -目前我们已经发布了多种关键的特性，这里一一列举展示一下当前发布的能力。 - -- 私域问答&数据处理 +- **私域问答&数据处理&RAG** 支持内置、多文件格式上传、插件自抓取等方式自定义构建知识库，对海量结构化，非结构化数据做统一向量存储与检索 - -- 多数据源&可视化 + +- **多数据源&GBI** 支持自然语言与Excel、数据库、数仓等多种数据源交互，并支持分析报告。 -- 自动化微调 +- **自动化微调** 围绕大语言模型、Text2SQL数据集、LoRA/QLoRA/Pturning等微调方法构建的自动化微调轻量框架, 让TextSQL微调像流水线一样方便。详见: [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) -- Multi-Agents&Plugins +- **Data-Driven Multi-Agents&Plugins** 支持自定义插件执行任务，原生支持Auto-GPT插件模型，Agents协议采用Agent Protocol标准 -- 多模型支持与管理 +- **多模型支持与管理** + + 海量模型支持，包括开源、API代理等几十种大语言模型。如LLaMA/LLaMA2、Baichuan、ChatGLM、文心、通义、智谱等。当前已支持如下模型: - 海量模型支持，包括开源、API代理等几十种大语言模型。如LLaMA/LLaMA2、Baichuan、ChatGLM、文心、通义、智谱等。 - - 支持多种大语言模型, 当前已支持如下模型: - [Vicuna](https://huggingface.co/Tribbiani/vicuna-13b) - [vicuna-13b-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5) - [LLama2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) - - [baichuan2-13b](https://huggingface.co/baichuan-inc) - - [baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B) + - [baichuan2-13b](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) + - [baichuan2-7b](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat) - [chatglm-6b](https://huggingface.co/THUDM/chatglm-6b) - [chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b) + - [chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b) - [falcon-40b](https://huggingface.co/tiiuae/falcon-40b) - [internlm-chat-7b](https://huggingface.co/internlm/internlm-chat-7b) - - [Qwen-7B-Chat/Qwen-14B-Chat](https://huggingface.co/Qwen/) - - [RWKV-4-Raven](https://huggingface.co/BlinkDL/rwkv-4-raven) - - [CAMEL-13B-Combined-Data](https://huggingface.co/camel-ai/CAMEL-13B-Combined-Data) - - [dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b) - - [h2ogpt-gm-oasst1-en-2048-open-llama-7b](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b) - - [fastchat-t5-3b-v1.0](https://huggingface.co/lmsys/fastchat-t5) - - [mpt-7b-chat](https://huggingface.co/mosaicml/mpt-7b-chat) - - [gpt4all-13b-snoozy](https://huggingface.co/nomic-ai/gpt4all-13b-snoozy) - - [Nous-Hermes-13b](https://huggingface.co/NousResearch/Nous-Hermes-13b) - - [codet5p-6b](https://huggingface.co/Salesforce/codet5p-6b) - - [guanaco-33b-merged](https://huggingface.co/timdettmers/guanaco-33b-merged) - - [WizardLM-13B-V1.0](https://huggingface.co/WizardLM/WizardLM-13B-V1.0) - - [WizardLM/WizardCoder-15B-V1.0](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0) - - [Llama2-Chinese-13b-Chat](https://huggingface.co/FlagAlpha/Llama2-Chinese-13b-Chat) - - [OpenLLaMa OpenInstruct](https://huggingface.co/VMware/open-llama-7b-open-instruct) + - [internlm-chat-20b](https://huggingface.co/internlm/internlm-chat-20b) + - [qwen-7b-chat](https://huggingface.co/Qwen/Qwen-7B-Chat) + - [qwen-14b-chat](https://huggingface.co/Qwen/Qwen-14B-Chat) + - [wizardlm-13b](https://huggingface.co/WizardLM/WizardLM-13B-V1.2) + - [orca-2-7b](https://huggingface.co/microsoft/Orca-2-7b) + - [orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) + - [openchat_3.5](https://huggingface.co/openchat/openchat_3.5) + - [zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) + - [mistral-7b-instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) + - [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) + - 支持在线代理模型 - [x] [ChatGPT](https://api.openai.com/) - [x] [Tongyi](https://www.aliyun.com/product/dashscope) - [x] [Wenxin](https://cloud.baidu.com/product/wenxinworkshop?track=dingbutonglan) - [x] [ChatGLM](http://open.bigmodel.cn/) -- 隐私安全 +- **隐私安全** 通过私有化大模型、代理脱敏等多种技术保障数据的隐私安全。 @@ -192,22 +169,23 @@ DB-GPT 是一个开源的以数据库为基础的GPT实验项目，使用本地 | [StarRocks](https://github.com/StarRocks/starrocks) | No | TODO | ## 架构方案 -DB-GPT基于 [FastChat](https://github.com/lm-sys/FastChat) 构建大模型运行环境。此外，我们通过LangChain提供私域知识库问答能力。同时我们支持插件模式, 在设计上原生支持Auto-GPT插件。我们的愿景是让围绕数据库和LLM构建应用程序更加简便和便捷。 - 整个DB-GPT的架构，如下图所示 -

-核心能力主要有以下几个部分。 -1. 多模型：支持多LLM，如LLaMA/LLaMA2、CodeLLaMA、ChatGLM、QWen、Vicuna以及代理模型ChatGPT、Baichuan、tongyi、wenxin等 -2. 私域知识库问答: 可以根据本地文档（如pdf、word、excel等数据）进行高质量的智能问答。 -3. 统一数据向量存储和索引: 将数据嵌入为向量并存储在向量数据库中，提供内容相似性搜索。 -4. 多数据源: 用于连接不同的模块和数据源，实现数据的流动和交互。 -5. Agent与插件: 提供Agent和插件机制，使得用户可以自定义并增强系统的行为。 -6. 隐私和安全: 您可以放心，没有数据泄露的风险，您的数据100%私密和安全。 -7. Text2SQL: 我们通过在大型语言模型监督微调（SFT）来增强文本到SQL的性能 +核心能力主要有以下几个部分: +- **RAG(Retrieval Augmented Generation)**，RAG是当下落地实践最多，也是最迫切的领域，DB-GPT目前已经实现了一套基于RAG的框架，用户可以基于DB-GPT的RAG能力构建知识类应用。 + +- **GBI**：生成式BI是DB-GPT项目的核心能力之一，为构建企业报表分析、业务洞察提供基础的数智化技术保障。 + +- **Fine-tune框架**: 模型微调是任何一个企业在垂直、细分领域落地不可或缺的能力，DB-GPT提供了完整的微调框架，实现与DB-GPT项目的无缝打通，在最近的微调中，基于spider的准确率已经做到了82.5% + +- **数据驱动的Multi-Agents框架**: DB-GPT提供了数据驱动的自进化微调框架，目标是可以持续基于数据做决策与执行。 + +- **数据工厂**: 数据工厂主要是在大模型时代，做可信知识、数据的清洗加工。 + +- **数据源**: 对接各类数据源，实现生产业务数据无缝对接到DB-GPT核心能力。 ### RAG生产落地实践架构

@@ -345,16 +323,6 @@ The MIT License (MIT) - SFT模型准确率 截止20231010，我们利用本项目基于开源的13B大小的模型微调后，在Spider的评估集上的执行准确率，已经超越GPT-4! -| 模型名称 | 执行准确率 | 说明 | -| ----------------------------------| ------------------ | ------------------------------------------------------------------------------------------------------------------------------ | -| **GPT-4** | **0.762** | [numbersstation-eval-res](https://www.numbersstation.ai/post/nsql-llama-2-7b) | -| ChatGPT | 0.728 | [numbersstation-eval-res](https://www.numbersstation.ai/post/nsql-llama-2-7b) | -| **CodeLlama-13b-Instruct-hf_lora**| **0.789** | sft train by our this project,only used spider train dataset ,the same eval way in this project with lora SFT | -| CodeLlama-13b-Instruct-hf_qlora | 0.774 | sft train by our this project,only used spider train dataset ,the same eval way in this project with qlora and nf4,bit4 SFT | -| wizardcoder | 0.610 | [text-to-sql-wizardcoder](https://github.com/cuplv/text-to-sql-wizardcoder/tree/main) | -| CodeLlama-13b-Instruct-hf | 0.556 | eval in this project default param | -| llama2_13b_hf_lora_best | 0.744 | sft train by our this project,only used spider train dataset ,the same eval way in this project | - [More Information about Text2SQL finetune](https://github.com/eosphoros-ai/DB-GPT-Hub) ## 联系我们 diff --git a/assets/schema/history.sql b/assets/schema/history.sql deleted file mode 100644 index 3323b73a0..000000000 --- a/assets/schema/history.sql +++ /dev/null @@ -1,18 +0,0 @@ -CREATE DATABASE history; -use history; -CREATE TABLE `chat_feed_back` ( - `id` bigint(20) NOT NULL AUTO_INCREMENT, - `conv_uid` varchar(128) DEFAULT NULL COMMENT '会话id', - `conv_index` int(4) DEFAULT NULL COMMENT '第几轮会话', - `score` int(1) DEFAULT NULL COMMENT '评分', - `ques_type` varchar(32) DEFAULT NULL COMMENT '用户问题类别', - `question` longtext DEFAULT NULL COMMENT '用户问题', - `knowledge_space` varchar(128) DEFAULT NULL COMMENT '知识库', - `messages` longtext DEFAULT NULL COMMENT '评价详情', - `user_name` varchar(128) DEFAULT NULL COMMENT '评价人', - `gmt_created` datetime DEFAULT NULL, - `gmt_modified` datetime DEFAULT NULL, - PRIMARY KEY (`id`), - UNIQUE KEY `uk_conv` (`conv_uid`,`conv_index`), - KEY `idx_conv` (`conv_uid`,`conv_index`) -) ENGINE=InnoDB AUTO_INCREMENT=0 DEFAULT CHARSET=utf8mb4 COMMENT='用户评分反馈表'; \ No newline at end of file diff --git a/assets/schema/knowledge_management.sql b/assets/schema/knowledge_management.sql index 95ed760e6..18f632b14 100644 --- a/assets/schema/knowledge_management.sql +++ b/assets/schema/knowledge_management.sql @@ -1,5 +1,15 @@ -CREATE DATABASE knowledge_management; -use knowledge_management; +-- You can change `dbgpt` to your actual metadata database name in your `.env` file +-- eg. `LOCAL_DB_NAME=dbgpt` + +CREATE DATABASE IF NOT EXISTS dbgpt; +use dbgpt; + +-- For alembic migration tool +CREATE TABLE `alembic_version` ( + version_num VARCHAR(32) NOT NULL, + CONSTRAINT alembic_version_pkc PRIMARY KEY (version_num) +); + CREATE TABLE `knowledge_space` ( `id` int NOT NULL AUTO_INCREMENT COMMENT 'auto increment id', `name` varchar(100) NOT NULL COMMENT 'knowledge space name', @@ -26,6 +36,7 @@ CREATE TABLE `knowledge_document` ( `content` LONGTEXT NOT NULL COMMENT 'knowledge embedding sync result', `result` TEXT NULL COMMENT 'knowledge content', `vector_ids` LONGTEXT NULL COMMENT 'vector_ids', + `summary` LONGTEXT NULL COMMENT 'knowledge summary', `gmt_created` TIMESTAMP DEFAULT CURRENT_TIMESTAMP COMMENT 'created time', `gmt_modified` TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT 'update time', PRIMARY KEY (`id`), @@ -45,6 +56,102 @@ CREATE TABLE `document_chunk` ( KEY `idx_document_id` (`document_id`) COMMENT 'index:document_id' ) ENGINE=InnoDB AUTO_INCREMENT=100001 DEFAULT CHARSET=utf8mb4 COMMENT='knowledge document chunk detail'; + + +CREATE TABLE `connect_config` ( + `id` int NOT NULL AUTO_INCREMENT COMMENT 'autoincrement id', + `db_type` varchar(255) NOT NULL COMMENT 'db type', + `db_name` varchar(255) NOT NULL COMMENT 'db name', + `db_path` varchar(255) DEFAULT NULL COMMENT 'file db path', + `db_host` varchar(255) DEFAULT NULL COMMENT 'db connect host(not file db)', + `db_port` varchar(255) DEFAULT NULL COMMENT 'db cnnect port(not file db)', + `db_user` varchar(255) DEFAULT NULL COMMENT 'db user', + `db_pwd` varchar(255) DEFAULT NULL COMMENT 'db password', + `comment` text COMMENT 'db comment', + PRIMARY KEY (`id`), + UNIQUE KEY `uk_db` (`db_name`), + KEY `idx_q_db_type` (`db_type`) +) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT 'Connection confi'; + +CREATE TABLE `chat_history` ( + `id` int NOT NULL AUTO_INCREMENT COMMENT 'autoincrement id', + `conv_uid` varchar(255) COLLATE utf8mb4_unicode_ci NOT NULL COMMENT 'Conversation record unique id', + `chat_mode` varchar(255) COLLATE utf8mb4_unicode_ci NOT NULL COMMENT 'Conversation scene mode', + `summary` varchar(255) COLLATE utf8mb4_unicode_ci NOT NULL COMMENT 'Conversation record summary', + `user_name` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'interlocutor', + `messages` text COLLATE utf8mb4_unicode_ci COMMENT 'Conversation details', + PRIMARY KEY (`id`) +) ENGINE=InnoDB AUTO_INCREMENT=2 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci COMMENT 'Chat history'; + +CREATE TABLE `chat_feed_back` ( + `id` bigint(20) NOT NULL AUTO_INCREMENT, + `conv_uid` varchar(128) DEFAULT NULL COMMENT 'Conversation ID', + `conv_index` int(4) DEFAULT NULL COMMENT 'Round of conversation', + `score` int(1) DEFAULT NULL COMMENT 'Score of user', + `ques_type` varchar(32) DEFAULT NULL COMMENT 'User question category', + `question` longtext DEFAULT NULL COMMENT 'User question', + `knowledge_space` varchar(128) DEFAULT NULL COMMENT 'Knowledge space name', + `messages` longtext DEFAULT NULL COMMENT 'The details of user feedback', + `user_name` varchar(128) DEFAULT NULL COMMENT 'User name', + `gmt_created` timestamp NULL DEFAULT CURRENT_TIMESTAMP COMMENT 'created time', + `gmt_modified` timestamp NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT 'update time', + PRIMARY KEY (`id`), + UNIQUE KEY `uk_conv` (`conv_uid`,`conv_index`), + KEY `idx_conv` (`conv_uid`,`conv_index`) +) ENGINE=InnoDB AUTO_INCREMENT=0 DEFAULT CHARSET=utf8mb4 COMMENT='User feedback table'; + + +CREATE TABLE `my_plugin` ( + `id` int NOT NULL AUTO_INCREMENT COMMENT 'autoincrement id', + `tenant` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'user tenant', + `user_code` varchar(255) COLLATE utf8mb4_unicode_ci NOT NULL COMMENT 'user code', + `user_name` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'user name', + `name` varchar(255) COLLATE utf8mb4_unicode_ci NOT NULL COMMENT 'plugin name', + `file_name` varchar(255) COLLATE utf8mb4_unicode_ci NOT NULL COMMENT 'plugin package file name', + `type` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'plugin type', + `version` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'plugin version', + `use_count` int DEFAULT NULL COMMENT 'plugin total use count', + `succ_count` int DEFAULT NULL COMMENT 'plugin total success count', + `gmt_created` TIMESTAMP DEFAULT CURRENT_TIMESTAMP COMMENT 'plugin install time', + PRIMARY KEY (`id`), + UNIQUE KEY `name` (`name`) +) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci COMMENT='User plugin table'; + +CREATE TABLE `plugin_hub` ( + `id` int NOT NULL AUTO_INCREMENT COMMENT 'autoincrement id', + `name` varchar(255) COLLATE utf8mb4_unicode_ci NOT NULL COMMENT 'plugin name', + `description` varchar(255) COLLATE utf8mb4_unicode_ci NOT NULL COMMENT 'plugin description', + `author` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'plugin author', + `email` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'plugin author email', + `type` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'plugin type', + `version` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'plugin version', + `storage_channel` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'plugin storage channel', + `storage_url` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'plugin download url', + `download_param` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'plugin download param', + `gmt_created` TIMESTAMP DEFAULT CURRENT_TIMESTAMP COMMENT 'plugin upload time', + `installed` int DEFAULT NULL COMMENT 'plugin already installed count', + PRIMARY KEY (`id`), + UNIQUE KEY `name` (`name`) +) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci COMMENT='Plugin Hub table'; + + +CREATE TABLE `prompt_manage` ( + `id` int(11) NOT NULL AUTO_INCREMENT, + `chat_scene` varchar(100) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'Chat scene', + `sub_chat_scene` varchar(100) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'Sub chat scene', + `prompt_type` varchar(100) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'Prompt type: common or private', + `prompt_name` varchar(512) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'prompt name', + `content` longtext COLLATE utf8mb4_unicode_ci COMMENT 'Prompt content', + `user_name` varchar(128) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'User name', + `gmt_created` timestamp NULL DEFAULT CURRENT_TIMESTAMP COMMENT 'created time', + `gmt_modified` timestamp NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT 'update time', + PRIMARY KEY (`id`), + UNIQUE KEY `prompt_name_uiq` (`prompt_name`), + KEY `gmt_created_idx` (`gmt_created`) +) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci COMMENT='Prompt management table'; + + + CREATE DATABASE EXAMPLE_1; use EXAMPLE_1; CREATE TABLE `users` ( diff --git a/assets/schema/prompt_management.sql b/assets/schema/prompt_management.sql deleted file mode 100644 index b2ed6de23..000000000 --- a/assets/schema/prompt_management.sql +++ /dev/null @@ -1,16 +0,0 @@ -CREATE DATABASE prompt_management; -use prompt_management; -CREATE TABLE `prompt_manage` ( - `id` int(11) NOT NULL AUTO_INCREMENT, - `chat_scene` varchar(100) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT '场景', - `sub_chat_scene` varchar(100) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT '子场景', - `prompt_type` varchar(100) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT '类型： common or private', - `prompt_name` varchar(512) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT 'prompt的名字', - `content` longtext COLLATE utf8mb4_unicode_ci COMMENT 'prompt的内容', - `user_name` varchar(128) COLLATE utf8mb4_unicode_ci DEFAULT NULL COMMENT '用户名', - `gmt_created` datetime DEFAULT NULL, - `gmt_modified` datetime DEFAULT NULL, - PRIMARY KEY (`id`), - UNIQUE KEY `prompt_name_uiq` (`prompt_name`), - KEY `gmt_created_idx` (`gmt_created`) -) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci COMMENT='prompt管理表'; \ No newline at end of file diff --git a/assets/wechat.jpg b/assets/wechat.jpg index 641de1489..7a2ce6dd5 100644 Binary files a/assets/wechat.jpg and b/assets/wechat.jpg differ diff --git a/docker/base/Dockerfile b/docker/base/Dockerfile index 63486e260..e4fad9697 100644 --- a/docker/base/Dockerfile +++ b/docker/base/Dockerfile @@ -28,7 +28,9 @@ WORKDIR /app # RUN pip3 install -i $PIP_INDEX_URL ".[all]" RUN pip3 install --upgrade pip -i $PIP_INDEX_URL \ - && pip3 install -i $PIP_INDEX_URL ".[$DB_GPT_INSTALL_MODEL]" + && pip3 install -i $PIP_INDEX_URL ".[$DB_GPT_INSTALL_MODEL]" \ + # install openai for proxyllm + && pip3 install -i $PIP_INDEX_URL ".[openai]" RUN (if [ "${LANGUAGE}" = "zh" ]; \ # language is zh, download zh_core_web_sm from github diff --git a/docker/compose_examples/cluster-docker-compose.yml b/docker/compose_examples/cluster-docker-compose.yml index b41033458..0ad6be9ae 100644 --- a/docker/compose_examples/cluster-docker-compose.yml +++ b/docker/compose_examples/cluster-docker-compose.yml @@ -7,6 +7,16 @@ services: restart: unless-stopped networks: - dbgptnet + api-server: + image: eosphorosai/dbgpt:latest + command: dbgpt start apiserver --controller_addr http://controller:8000 + restart: unless-stopped + depends_on: + - controller + networks: + - dbgptnet + ports: + - 8100:8100/tcp llm-worker: image: eosphorosai/dbgpt:latest command: dbgpt start worker --model_name vicuna-13b-v1.5 --model_path /app/models/vicuna-13b-v1.5 --port 8001 --controller_addr http://controller:8000 diff --git a/pilot/datasets/oceanbase/OceanBase_Introduction.md b/docker/examples/benchmarks/benchmarks_llm_11k_prompt.txt similarity index 97% rename from pilot/datasets/oceanbase/OceanBase_Introduction.md rename to docker/examples/benchmarks/benchmarks_llm_11k_prompt.txt index 2cb6e9538..25072fa18 100644 --- a/pilot/datasets/oceanbase/OceanBase_Introduction.md +++ b/docker/examples/benchmarks/benchmarks_llm_11k_prompt.txt @@ -1,3 +1,8 @@ +A chat between a curious user and an artificial intelligence assistant, who very familiar with database related knowledge. +The assistant gives helpful, detailed, professional and polite answers to the user's questions. 基于以下已知的信息, 专业、简要的回答用户的问题, + 如果无法从提供的内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题" 禁止胡乱编造, 回答的时候最好按照1.2.3.点进行总结。 + 已知内容: + OceanBase 数据库（OceanBase Database）是一款完全自研的企业级原生分布式数据库，在普通硬件上实现金融级高可用，首创“三地五中心”城市级故障自动无损容灾新标准，刷新 TPC-C 标准测试，单集群规模超过 1500 节点，具有云原生、强一致性、高度兼容 Oracle/MySQL 等特性。 核心特性 @@ -203,4 +208,7 @@ OceanBase 数据库是多租户的数据库系统，一个集群内可包含多 创建租户前，需首先确定租户的资源配置、使用资源范围等。租户创建的通用流程如下： -资源配置是描述资源池的配置信息，用来描述资源池中每个资源单元可用的 CPU、内存、存储空间和 IOPS 等的规格。修改资源配置可动态调整资源单元的规格。这里需要注意，资源配置指定的是对应资源单元能够提供的服务能力，而不是资源单元的实时负载。 创建资源配置的示例语句如下： \ No newline at end of file +资源配置是描述资源池的配置信息，用来描述资源池中每个资源单元可用的 CPU、内存、存储空间和 IOPS 等的规格。修改资源配置可动态调整资源单元的规格。这里需要注意，资源配置指定的是对应资源单元能够提供的服务能力，而不是资源单元的实时负载。 创建资源配置的示例语句如下： + +问题: +请你基于上述内容对 OceanBase 的介绍进行总结，不少于2000字。 \ No newline at end of file diff --git a/docker/examples/dashboard/test_case_mysql_data.py b/docker/examples/dashboard/test_case_mysql_data.py new file mode 100644 index 000000000..e2ef0badd --- /dev/null +++ b/docker/examples/dashboard/test_case_mysql_data.py @@ -0,0 +1,219 @@ +import random +import string +import os +import pymysql +from typing import List + +import pymysql.cursors +from datetime import datetime, timedelta + +# At first you need to create an test database which called dbgpt_test; +# you can use next command to create. +# CREATE DATABASE IF NOT EXISTS dbgpt_test CHARACTER SET utf8; + + +def build_table(connection): + connection.cursor().execute( + """CREATE TABLE user ( + id INT(11) NOT NULL AUTO_INCREMENT COMMENT '用户ID', + name VARCHAR(50) NOT NULL COMMENT '用户名', + email VARCHAR(50) NOT NULL COMMENT '电子邮件', + mobile CHAR(11) NOT NULL COMMENT '手机号码', + gender VARCHAR(20) COMMENT '性别', + birth DATE COMMENT '出生日期', + country VARCHAR(20) COMMENT '国家', + city VARCHAR(20) COMMENT '城市', + create_time TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间', + update_time TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间', + PRIMARY KEY (id), + UNIQUE KEY uk_email (email) + ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='用户信息表';""" + ) + connection.cursor().execute( + """CREATE TABLE transaction_order ( + id INT(11) NOT NULL AUTO_INCREMENT COMMENT '订单ID', + order_no CHAR(20) NOT NULL COMMENT '订单编号', + product_name VARCHAR(50) NOT NULL COMMENT '产品名称', + product_category VARCHAR(20) COMMENT '产品分类', + amount DECIMAL(10, 2) NOT NULL COMMENT '订单金额', + pay_status VARCHAR(20) COMMENT '付款状态', + user_id INT(11) NOT NULL COMMENT '用户ID', + user_name VARCHAR(50) COMMENT '用户名', + create_time TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间', + update_time TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间', + PRIMARY KEY (id), + UNIQUE KEY uk_order_no (order_no), + KEY idx_user_id (user_id) + ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='交易订单表';""" + ) + + +def user_build(names: List, country: str, grander: str = "Male") -> List: + countries = ["China", "US", "India", "Indonesia", "Pakistan"] # 国家列表 + cities = { + "China": ["Beijing", "Shanghai", "Guangzhou", "Shenzhen", "Hangzhou"], + "US": ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"], + "India": ["Mumbai", "Delhi", "Bangalore", "Hyderabad", "Chennai"], + "Indonesia": ["Jakarta", "Surabaya", "Medan", "Bandung", "Makassar"], + "Pakistan": ["Karachi", "Lahore", "Faisalabad", "Rawalpindi", "Multan"], + } + + users = [] + for i in range(1, len(names) + 1): + if grander == "Male": + id = int(str(countries.index(country) + 1) + "10") + i + else: + id = int(str(countries.index(country) + 1) + "20") + i + + name = names[i - 1] + email = f"{name}@example.com" + mobile = "".join(random.choices(string.digits, k=10)) + gender = grander + birth = f"19{random.randint(60, 99)}-{random.randint(1, 12):02d}-{random.randint(1, 28):02d}" + country = country + city = random.choice(cities[country]) + + now = datetime.now() + year = now.year + + start = datetime(year, 1, 1) + end = datetime(year, 12, 31) + random_date = start + timedelta(days=random.randint(0, (end - start).days)) + random_time = datetime.combine(random_date, datetime.min.time()) + timedelta( + seconds=random.randint(0, 24 * 60 * 60 - 1) + ) + + random_datetime_str = random_time.strftime("%Y-%m-%d %H:%M:%S") + create_time = random_datetime_str + users.append( + ( + id, + name, + email, + mobile, + gender, + birth, + country, + city, + create_time, + create_time, + ) + ) + return users + + +def gnerate_all_users(cursor): + users = [] + users_f = ["ZhangWei", "LiQiang", "ZhangSan", "LiSi"] + users.extend(user_build(users_f, "China", "Male")) + users_m = ["Hanmeimei", "LiMeiMei", "LiNa", "ZhangLi", "ZhangMing"] + users.extend(user_build(users_m, "China", "Female")) + + users1_f = ["James", "John", "David", "Richard"] + users.extend(user_build(users1_f, "US", "Male")) + users1_m = ["Mary", "Patricia", "Sarah"] + users.extend(user_build(users1_m, "US", "Female")) + + users2_f = ["Ravi", "Rajesh", "Ajay", "Arjun", "Sanjay"] + users.extend(user_build(users2_f, "India", "Male")) + users2_m = ["Priya", "Sushma", "Pooja", "Swati"] + users.extend(user_build(users2_m, "India", "Female")) + for user in users: + cursor.execute( + "INSERT INTO user (id, name, email, mobile, gender, birth, country, city, create_time, update_time) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)", + user, + ) + + return users + + +def gnerate_all_orders(users, cursor): + orders = [] + orders_num = 200 + categories = ["Clothing", "Food", "Home Appliance", "Mother and Baby", "Travel"] + + categories_product = { + "Clothing": ["T-shirt", "Jeans", "Skirt", "Other"], + "Food": ["Snack", "Fruit"], + "Home Appliance": ["Refrigerator", "Television", "Air conditioner"], + "Mother and Baby": ["Diapers", "Milk Powder", "Stroller", "Toy"], + "Travel": ["Tent", "Fishing Rod", "Bike", "Rawalpindi", "Multan"], + } + + for i in range(1, orders_num + 1): + id = i + order_no = "".join(random.choices(string.ascii_uppercase, k=3)) + "".join( + random.choices(string.digits, k=10) + ) + product_category = random.choice(categories) + product_name = random.choice(categories_product[product_category]) + amount = round(random.uniform(0, 10000), 2) + pay_status = random.choice(["SUCCESS", "FAILD", "CANCEL", "REFUND"]) + user_id = random.choice(users)[0] + user_name = random.choice(users)[1] + + now = datetime.now() + year = now.year + + start = datetime(year, 1, 1) + end = datetime(year, 12, 31) + random_date = start + timedelta(days=random.randint(0, (end - start).days)) + random_time = datetime.combine(random_date, datetime.min.time()) + timedelta( + seconds=random.randint(0, 24 * 60 * 60 - 1) + ) + + random_datetime_str = random_time.strftime("%Y-%m-%d %H:%M:%S") + create_time = random_datetime_str + + order = ( + id, + order_no, + product_category, + product_name, + amount, + pay_status, + user_id, + user_name, + create_time, + ) + cursor.execute( + "INSERT INTO transaction_order (id, order_no, product_name, product_category, amount, pay_status, user_id, user_name, create_time) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)", + order, + ) + + +if __name__ == "__main__": + connection = pymysql.connect( + host=os.getenv("DB_HOST", "127.0.0.1"), + port=int( + os.getenv("DB_PORT", 3306), + ), + user=os.getenv("DB_USER", "root"), + password=os.getenv("DB_PASSWORD", "aa12345678"), + database=os.getenv("DB_DATABASE", "dbgpt_test"), + charset="utf8mb4", + ssl_ca=None, + ) + + build_table(connection) + + connection.commit() + + cursor = connection.cursor() + + users = gnerate_all_users(cursor) + connection.commit() + + gnerate_all_orders(users, cursor) + connection.commit() + + cursor.execute("SELECT * FROM user") + data = cursor.fetchall() + print(data) + + cursor.execute("SELECT count(*) FROM transaction_order") + data = cursor.fetchall() + print("orders:" + str(data)) + + cursor.close() + connection.close() diff --git a/docs/_static/css/custom.css b/docs/_static/css/custom.css new file mode 100644 index 000000000..f3bcfe7b5 --- /dev/null +++ b/docs/_static/css/custom.css @@ -0,0 +1,928 @@ +/* override default colors used in the Sphinx theme */ +:root { + --tabs-color-label-active: #0475DE; + --tabs-color-label-hover: #0475DE; + --buttons-color-blue: #0475DE; + --tabs-color-label-inactive: #9E9E9E; + --tabs-color-overline: #e0e0e0; + --tabs-color-underline: #e0e0e0; + --border-color-gray: #e0e0e0; + --background-color-light-gray:#fafafa; + --background-color-disabled: #9E9E9E; + --pst-color-link: 4, 117, 222; + --pst-color-primary: 4, 117, 222; + --pst-color-text-secondary: #616161; + --blue: #0475DE; + --sidebar-top: 5em; +} + +/* Remove flicker for announcement top bar replacement */ +.header-item.announcement { + background-color: white; + color: white; + padding: 0; +} + +/* Make the book theme secondary nav stick below the new main top nav */ +.header-article { + top: 58px; + z-index: 900 !important; +} + +.toctree-l1.has-children { + font-weight: bold; +} + +.toctree-l2 { + font-weight: normal; +} + +div.navbar-brand-box { + padding-top: 4em; +} + +td p { + margin-left: 0.75rem; +} + +table.longtable.table.autosummary { + table-layout: fixed; +} + +.table.autosummary td { + width: 100%; +} + +tr.row-odd { + background-color: #f9fafb; +} + +/* For Algolia search box + * overflow-y: to flow-over horizontally into main content + * height: to prevent topbar overlap +*/ +#site-navigation { + overflow-y: auto; + height: calc(100vh - var(--sidebar-top)); + position: sticky; + top: var(--sidebar-top) !important; +} + +/* Center the algolia search bar*/ +#search-input { + text-align: center; +} +.algolia-autocomplete { + width: 100%; + margin: auto; +} + +/* Hide confusing "<-" back arrow in navigation for larger displays */ +@media (min-width: 768px) { + #navbar-toggler { + display: none; + } +} + +/* Make navigation scrollable on mobile, by making algolia not overflow */ +@media (max-width: 768px) { + #site-navigation { + overflow-y: scroll; + } + + .algolia-autocomplete .ds-dropdown-menu{ + min-width: 250px; + } +} + +/* sphinx-panels overrides the content width to 1140 for large displays.*/ +@media (min-width: 1200px) { + .container, .container-lg, .container-md, .container-sm, .container-xl { + max-width: 1400px !important; + } +} + +.bottom-right-promo-banner { + position: fixed; + bottom: 100px; + right: 20px; + width: 270px; +} + +@media (max-width: 1500px) { + .bottom-right-promo-banner { + display: none; + } +} + +@media screen and (max-width: 767px) { + .remove-mobile { + display: none; + } + } + + @media screen and (max-width: 767px) { + .row-2-column { + flex-direction: column; + margin-top: 20px; + } + } + +/* Make Algolia search box scrollable */ +.algolia-autocomplete .ds-dropdown-menu { + height: 60vh !important; + overflow-y: scroll !important; +} + +.bd-sidebar__content { + overflow-y: unset !important; +} + +.bd-sidebar__top { + display: flex; + flex-direction: column; +} + +.bd-sidebar li { + position: relative; + word-wrap: break-word; +} + +nav.bd-links { + flex: 1; +} + +nav.bd-links::-webkit-scrollbar-thumb { + background-color: #ccc; +} + +nav.bd-links::-webkit-scrollbar { + width: 5px; +} + +dt:target, span.highlighted { + background-color: white; +} + +div.sphx-glr-bigcontainer { + display: inline-block; + width: 100%; +} + +td.tune-colab, +th.tune-colab { + border: 1px solid #dddddd; + text-align: left; + padding: 8px; +} + +/* Adjustment to Sphinx Book Theme */ +.table td { + /* Remove row spacing on the left */ + padding-left: 0; +} + +.table thead th { + /* Remove row spacing on the left */ + padding-left: 0; +} + +img.inline-figure { + /* Override the display: block for img */ + display: inherit !important; +} + +#version-warning-banner { + /* Make version warning clickable */ + z-index: 1; + margin-left: 0; + /* 20% is for ToC rightbar */ + /* 2 * 1.5625em is for horizontal margins */ + width: calc(100% - 20% - 2 * 1.5625em); +} + +/* allow scrollable images */ +.figure { + max-width: 100%; + overflow-x: auto; +} +img.horizontal-scroll { + max-width: none; +} + +.clear-both { + clear: both; + min-height: 100px; + margin-top: 15px; +} + +.buttons-float-left { + width: 150px; + float: left; +} + +.buttons-float-right { + width: 150px; + float: right; +} + +.card-body { + padding: 0.5rem !important; +} + +/* custom css for pre elements */ +pre { + /* Wrap code blocks instead of horizontal scrolling. */ + white-space: pre-wrap; + box-shadow: none; + border-color: var(--border-color-gray); + background-color: var(--background-color-light-gray); + border-radius:0.25em; +} + +/* notebook formatting */ +.cell .cell_output { + max-height: 250px; + overflow-y: auto; + font-weight: bold; +} + +/* Yellow doesn't render well on light background */ +.cell .cell_output pre .-Color-Yellow { + color: #785840; +} + +/* Newlines (\a) and spaces (\20) before each parameter */ +.sig-param::before { + content: "\a\20\20\20\20"; + white-space: pre; +} + +/* custom css for outlined buttons */ +.btn-outline-info:hover span, .btn-outline-primary:hover span { + color: #fff; +} + +.btn-outline-info, .btn-outline-primary{ + border-color: var(--buttons-color-blue); +} + +.btn-outline-info:hover, .btn-outline-primary:hover{ + border-color: var(--buttons-color-blue); + background-color: var(--buttons-color-blue); +} + +.btn-outline-info.active:not(:disabled):not(.disabled), .btn-outline-info:not(:disabled):not(.disabled):active, .show>.btn-outline-info.dropdown-toggle { + border-color: var(--buttons-color-blue); + background-color: var(--buttons-color-blue); + color: #fff; +} + +.btn-info, .btn-info:hover, .btn-info:focus { + border-color: var(--buttons-color-blue); + background-color: var(--buttons-color-blue); +} + +.btn-info:hover{ + opacity: 90%; +} + +.btn-info:disabled{ + border-color: var(--background-color-disabled); + background-color: var(--background-color-disabled); + opacity: 100%; +} + +.btn-info.active:not(:disabled):not(.disabled), .btn-info:not(:disabled):not(.disabled):active, .show>.btn-info.dropdown-toggle { + border-color: var(--buttons-color-blue); + background-color: var(--buttons-color-blue); +} + + +.topnav { + background-color: white; + border-bottom: 1px solid rgba(0, 0, 0, .1); + display: flex; + align-items: center; +} + +/* Content wrapper for the unified nav link / menus */ +.top-nav-content { + max-width: 1400px; + width: 100%; + margin-left: auto; + margin-right: auto; + padding: 0 1.5rem; + display: flex; + align-items: center; + justify-content: space-between; +} + +@media (max-width: 900px) { + /* If the window is too small, hide the custom sticky navigation bar at the top of the page. + Also make the pydata-sphinx-theme nav bar, which usually sits below the top nav bar, stick + to the top of the page. + */ + .top-nav-content { + display: none; + } + div.header-article.row.sticky-top.noprint { + position: sticky; + top: 0; + } +} + +/* Styling the links and menus in the top nav */ +.top-nav-content a { + text-decoration: none; + color: black; + font-size: 17px; +} + +.top-nav-content a:hover { + color: #007bff; +} + +/* The left part are the links and menus */ +.top-nav-content > .left { + display: flex; + white-space: nowrap; +} + +.top-nav-content .left > * { + margin-right: 8px; +} + +.top-nav-content .left > a, +.top-nav-content .left > .menu > a { + text-align: center; + padding: 14px 16px; + border-bottom: 2px solid white; +} + +.top-nav-content .menu:hover > a, +.top-nav-content .left > a:hover { + border-bottom: 2px solid #007bff; +} + +/* Special styling for the Ray logo */ +.top-nav-content .left > a.ray-logo { + width: 90px; + padding: 10px 0; +} +.top-nav-content .left > a.ray-logo:hover { + border-bottom: 2px solid white; +} + +/* Styling the dropdown menus */ +.top-nav-content .menu { + display: flex; +} +.top-nav-content .menu > a > .down-caret { + margin-left: 8px; +} +.top-nav-content .menu > ul { + display: none; +} + +.top-nav-content > button.try-anyscale > span { + margin: 0 12px; +} + +.top-nav-content .menu:hover > ul { + display: flex; + flex-direction: column; + align-items: flex-start; + box-shadow: 0 5px 15px 0 rgb(0 0 0 / 10%); + padding: 15px; + width: 330px; + position: absolute; + z-index: 2000; + background-color: white; + top: 58px; +} + +.top-nav-content .menu:hover > ul > li { + list-style: none; + padding: 5px 0; +} + +.top-nav-content .menu:hover > ul > li span { + display: block; +} + +.top-nav-content .menu:hover > ul > li span.secondary { + color: #787878; +} + +/* Styling the "Try Anyscale" button */ +.top-nav-content > button.try-anyscale { + float: right; + border-radius: 6px; + background-color: #e7f2fa; + padding-left: 12px; + padding-right: 12px; + margin-left: 12px; + height: 40px; + border: none; + white-space: nowrap; +} + +@media (max-width: 1000px) { + .top-nav-content > button.try-anyscale { + display: none; + } +} + +/* custom css for tabs*/ +.tabbed-set>label,.tabbed-set>label:hover { + border-bottom: 1px solid var(--border-color-gray); + color:var(--tabs-color-label-inactive); + font-weight: 500; +} + +.tabbed-set>input:checked+label{ + border-bottom: 0.125em solid; + color:var(--tabs-color-label-active); +} + + +.tabbed-label{ + margin-bottom:0; +} + +/* custom css for jupyter cells */ +div.cell div.cell_input{ + border: 1px var(--border-color-gray) solid; + background-color: var(--background-color-light-gray); + border-radius:0.25em; + border-left-color: var(--green); + border-left-width: medium; +} + +/* custom css for table */ +table { + border-color: var(--border-color-gray); +} + +/* custom css for topic component */ +div.topic{ + border: 1px solid var(--border-color-gray); + border-radius:0.25em; +} + +.topic { + background-color: var(--background-color-light-gray); +} + +/* custom css for card component */ +.card{ + border-color: var(--border-color-gray); +} + +.card-footer{ + background-color: var(--background-color-light-gray); + border-top-color: var(--border-color-gray); +} + +/* custom css for section navigation component */ +.bd-toc nav>.nav { + border-left-color: var(--border-color-gray); +} + +/* custom css for up and down arrows in collapsible cards */ +details.dropdown .summary-up, details.dropdown .summary-down { + top: 1em; +} + +/* remove focus border in collapsible admonition buttons */ +.toggle.admonition button.toggle-button:focus { + outline: none; +} + +/* custom css for shadow class */ +.shadow { + box-shadow: 0 0.2rem 0.5rem rgb(0 0 0 / 5%), 0 0 0.0625rem rgb(0 0 0 / 10%) !important; +} + +/* custom css for text area */ +textarea { + border-color: var(--border-color-gray); +} + +/* custom css for footer */ +footer { + margin-top: 1rem; + padding:1em 0; + border-top-color: var(--border-color-gray); +} + +.footer p{ + color: var(--pst-color-text-secondary); +} + +/* Make the hover color of tag/gallery buttons differ from "active" */ +.tag.btn-outline-primary:hover { + background-color: rgba(20, 99, 208, 0.62) !important; +} + +span.rst-current-version > span.fa.fa-book { + /* Move the book icon away from the top right + * corner of the version flyout menu */ + margin: 10px 0px 0px 5px; +} + + +/*Extends the docstring signature box.*/ +.rst-content dl:not(.docutils) dt { + display: block; + padding: 10px; + word-wrap: break-word; + padding-right: 100px; +} + +/*Lists in an admonition note do not have awkward whitespace below.*/ +.rst-content .admonition-note .section ul { + margin-bottom: 0; +} + +/*Properties become blue (classmethod, staticmethod, property)*/ +.rst-content dl dt em.property { + color: #2980b9; + text-transform: uppercase; +} + +.rst-content .section ol p, +.rst-content .section ul p { + margin-bottom: 0; +} + + +/* Adjustment to Version block */ +.rst-versions { + z-index: 1200 !important; +} + +.image-header { + display: flex; + flex-direction: row; + align-items: center; + padding-left: 16px; + padding-right:16px; + gap: 16px; +} + +.info-box { + box-shadow: 0px 4px 20px rgba(0, 0, 0, 0.05); + border-radius: 8px; + padding: 20px; +} + +.info-box:hover{ + box-shadow: 0px 4px 20px rgba(0, 0, 0, 0.1); +} + +.no-underline{ + text-decoration: none; +} +.no-underline:hover{ + text-decoration: none; +} + +.icon-hover:hover{ + height: 30px ; + width: 30px; +} + +.info-box-2 { + background-color: #F9FAFB; + border-radius: 8px; + padding-right: 16px; + padding-left: 16px; + padding-bottom: 24px; + padding-top: 4px; +} + + +.bold-link { + color: #000000 !important; + font-weight: 600; +} + +.community-box { + border: 1px solid #D2DCE6; + border-radius: 8px; + display: flex; + margin-bottom: 16px; +} + +.community-box:hover { + box-shadow: 0px 4px 20px rgba(0, 0, 0, 0.05); + text-decoration: none; +} + +.community-box p { + margin-top: 1rem !important; +} + +.tab-pane pre { + margin: 0; + padding: 0; + max-height: 252px; + overflow-y: auto; +} + +.grid-container { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(300px,1fr)); + grid-gap: 16px; +} + +.grid-item { +padding: 20px; +} + + +.nav-pills { + background-color: #F9FAFB; + color: #000000; + padding: 8px; + border-bottom:none; + border-radius: 8px; +} + +.nav-pills .nav-link.active { + background-color: #FFFFFF !important; + box-shadow: 0px 3px 14px 2px rgba(3,28,74,0.12); + border-radius: 8px; + padding: 20px; + color: #000000; + font-weight: 500; +} + +.searchDiv { + width: 100%; + position: relative; + display: block; +} + +.searchTerm { + width: 80%; + border: 2px solid var(--blue); + padding: 5px; + height: 45px; + border-radius: 5px; + outline: none; +} + +.searchButton { + width: 40px; + height: 45px; + border: 1px solid var(--blue); + background: var(--blue); + color: #fff; + border-radius: 5px; + cursor: pointer; + font-size: 20px; +} + +/*Resize the wrap to see the search bar change!*/ +.searchWrap { + width: 100%; + position: relative; + margin: 15px; + top: 50%; + left: 50%; + transform: translate(-50%, -10%); + text-align: center; +} + +.sd-card { + border: none !important; +} + +.tag { + margin-bottom: 5px; + font-size: small; +} + +/* Override float positioning of next-prev buttons so that + they take up space normally, and we can put other stuff at + the bottom of the page. */ +.prev-next-area { + display: flex; + flex-direction: row; +} +.prev-next-area a.left-prev { + margin-right: auto; + width: fit-content; + float: none; +} +.prev-next-area a.right-next { + margin-left: auto; + width: fit-content; + float: none; +} + +/* CSAT widgets */ +#csat-inputs { + display: flex; + flex-direction: row; + align-items: center; +} + +.csat-hidden { + display: none !important; +} + +#csat-feedback-label { + color: #000; + font-weight: 500; +} + +.csat-button { + margin-left: 16px; + padding: 8px 16px 8px 16px; + border-radius: 4px; + border: 1px solid #D2DCE6; + background: #FFF; + display: flex; + flex-direction: row; + align-items: center; + justify-content: center; + cursor: pointer; + width: 85px; +} + +#csat-textarea-group { + display: flex; + flex-direction: column; +} + +#csat-submit { + margin-left: auto; + font-weight: 700; + border: none; + margin-top: 12px; + cursor: pointer; +} + +#csat-feedback-received { + display: flex; + flex-direction: row; + align-items: center; + justify-content: center; +} + +.csat-button-active { + border: 1px solid #000; +} + +.csat-icon { + margin-right: 4px; +} + +footer.col.footer { + display: flex; + flex-direction: row; +} + +footer.col.footer > p { + margin-left: auto; +} + +#csat { + min-width: 60%; +} + +#csat-textarea { + resize: none; +} + + +/* Ray Assistant */ + +.container-xl.blurred { + filter: blur(5px); +} + +.chat-widget { + position: fixed; + bottom: 10px; + right: 10px; + z-index: 1000; +} + +.chat-popup { + display: none; + position: fixed; + top: 20%; + left: 50%; + transform: translate(-50%, -20%); + width: 50%; + height: 70%; + background-color: white; + border: 1px solid #ccc; + border-radius: 10px; + box-shadow: 0 5px 10px rgba(0,0,0,0.1); + z-index: 1001; + max-height: 1000px; + overflow: hidden; + padding-bottom: 40px; +} + +.chatFooter { + position: absolute; + bottom: 0; + right: 0; + width: 100%; + background-color: #f8f9fa; +} + +#openChatBtn { + background-color: #000; + color: #fff; + width: 70px; + height: 70px; + border-radius: 10px; + border: none; + display: flex; + align-items: center; + justify-content: center; +} + +#closeChatBtn { + border: none; + background-color: transparent; + color: #000; + font-size: 1.2em; +} + +#closeChatBtn:hover { + color: #888; +} + +.chatHeader { + display: flex; + justify-content: space-between; + align-items: center; +} + +.chatContentContainer { + padding: 15px; + max-height: calc(100% - 80px); + overflow-y: auto; +} + +.chatContentContainer input { + margin-top: 10px; + margin-bottom: 10px; +} + +#result{ + padding: 15px; + border-radius: 10px; + margin-top: 10px; + margin-bottom: 10px; + background-color: #f8f9fa; + max-height: calc(100% - 20px); + overflow-y: auto; +} + +.chatContentContainer textarea { + flex-grow: 1; + min-width: 50px; + max-height: 40px; + resize: none; +} + +.searchBtn { + white-space: nowrap; +} + +.input-group { + display: flex; + align-items: stretch; +} + +/* Kapa Ask AI button */ +#kapa-widget-container figure { + padding: 0 !important; + } + + .mantine-Modal-root figure { + padding: 0 !important; + } + +@font-face { + font-family: "Linux Biolinum Keyboard"; + src: url(../fonts/LinBiolinum_Kah.ttf); +} + +.keys { + font-family: "Linux Biolinum Keyboard", sans-serif; +} + +.bd-article-container h1, .bd-article-container h2, .bd-article-container h3, .bd-article-container h4, .bd-article-container h5, .bd-article-container p.caption { + color: black; +} diff --git a/docs/_static/css/examples.css b/docs/_static/css/examples.css new file mode 100644 index 000000000..d1f416d15 --- /dev/null +++ b/docs/_static/css/examples.css @@ -0,0 +1,218 @@ + +#site-navigation { + width: 330px !important; + border-right: none; + margin-left: 32px; + overflow-y: auto; + max-height: calc(100vh - var(--sidebar-top)); + position: sticky; + top: var(--sidebar-top) !important; + z-index: 1000; +} + +#site-navigation h5 { + font-size: 16px; + font-weight: 600; + color: #000; +} + +#site-navigation h6 { + font-size: 14px; + font-weight: 600; + color: #000; + text-transform: uppercase; +} + +/* Hide the default sidebar content */ +#site-navigation > div.bd-sidebar__content { + display: none; +} +#site-navigation > div.rtd-footer-container { + display: none; +} + +.searchDiv { + margin-bottom: 2em; +} + +#searchInput { + width: 100%; + color: #5F6469; + border: 1px solid #D2DCE6; + height: 50px; + border-radius: 4px; + background-color: #F9FAFB; + background-image: url("data:image/svg+xml,%3Csvg width='25' height='25' viewBox='0 0 25 25' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cg id='Systems / search-line' clip-path='url(%23clip0_1_150)'%3E%3Crect width='24' height='24' transform='translate(0.398529 0.0546875)' fill='%23F9FAFB'/%3E%3Cg id='Group'%3E%3Cpath id='Vector' d='M18.4295 16.6717L22.7125 20.9537L21.2975 22.3687L17.0155 18.0857C15.4223 19.3629 13.4405 20.0576 11.3985 20.0547C6.43053 20.0547 2.39853 16.0227 2.39853 11.0547C2.39853 6.08669 6.43053 2.05469 11.3985 2.05469C16.3665 2.05469 20.3985 6.08669 20.3985 11.0547C20.4014 13.0967 19.7068 15.0784 18.4295 16.6717ZM16.4235 15.9297C17.6926 14.6246 18.4014 12.8751 18.3985 11.0547C18.3985 7.18669 15.2655 4.05469 11.3985 4.05469C7.53053 4.05469 4.39853 7.18669 4.39853 11.0547C4.39853 14.9217 7.53053 18.0547 11.3985 18.0547C13.219 18.0576 14.9684 17.3488 16.2735 16.0797L16.4235 15.9297V15.9297Z' fill='%238C9196'/%3E%3C/g%3E%3C/g%3E%3Cdefs%3E%3CclipPath id='clip0_1_150'%3E%3Crect width='24' height='24' fill='white' transform='translate(0.398529 0.0546875)'/%3E%3C/clipPath%3E%3C/defs%3E%3C/svg%3E%0A"); + background-repeat: no-repeat; + background-position-x: 0.5em; + background-position-y: center; + background-size: 1.5em; + padding-left: 3em; +} + +#searchInput::placeholder { + color: #5F6469; + opacity: 1; +} + +.tag { + margin-bottom: 5px; + font-size: small; + color: #000000; + border: 1px solid #D2DCE6; + border-radius: 14px; + display: flex; + flex-direction: row; + align-items: center; + width: fit-content; + gap: 1em; +} + +.tag.btn-outline-primary { + color: #000000; + padding: 3px 12px 3px 12px; + line-height: 20px; +} + +.tag-btn-wrapper { + display: flex; + flex-direction: row; + flex-wrap: wrap; + gap: 1em; +} + +div.sd-container-fluid.docutils > div { + gap: var(--ray-example-gallery-gap-y) var(--ray-example-gallery-gap-x); + display: grid; + grid-template-columns: 1fr; +} + +/* Reflow to a 2-column format for normal screens */ +@media screen and (min-width: 768px) { + div.sd-container-fluid.docutils > div { + grid-template-columns: 1fr 1fr; + } +} + +div.gallery-item { + width: auto; +} + +div.gallery-item > div.sd-card { + border-radius: 8px; + box-shadow: 0px 4px 10px 0px rgba(0, 0, 0, 0.05) !important; +} + +/* Example gallery "Tutorial" title */ +div.sd-card-title > span.sd-bg-success.sd-bg-text-success { + color: #2F80ED !important; + font-weight: 500; + background: linear-gradient(180deg, rgba(25, 177, 226, 0.2) 0%, rgba(0, 109, 255, 0.2) 100%); + background-color: initial !important; +} + +/* Example gallery "Code example" title */ +div.sd-card-title > span.sd-bg-secondary.sd-bg-text-secondary { + color: #219653 !important; + font-weight: 500; + background: linear-gradient(180deg, rgba(29, 151, 108, 0.2) 0%, rgba(0, 226, 147, 0.2) 100%); + background-color: initial !important; +} + +/* Example gallery "Blog" title */ +div.sd-card-title > span.sd-bg-primary.sd-bg-text-primary { + color: #F2994A !important; + font-weight: 500; + background: linear-gradient(180deg, rgba(255, 230, 5, 0.2) 0%, rgba(255, 185, 80, 0.2) 100%); + background-color: initial !important; +} + +/* Example gallery "Video" title */ +div.sd-card-title > span.sd-bg-warning.sd-bg-text-warning { + color: #EB5757 !important; + font-weight: 500; + background: linear-gradient(180deg, rgba(150, 7, 7, 0.2) 0%, rgba(255, 115, 115, 0.2) 100%); + background-color: initial !important; +} + +/* Example gallery "Course" title */ +div.sd-card-title > span.sd-bg-info.sd-bg-text-info { + color: #7A64FF !important; + font-weight: 500; + background: linear-gradient(180deg, rgba(53, 25, 226, 0.2) 0%, rgba(183, 149, 255, 0.2) 100%); + background-color: initial !important; +} + +div.sd-card-body > p.sd-card-text > a { + text-align: initial; +} + +div.sd-card-body > p.sd-card-text > a > span { + color: rgb(81, 81, 81); +} + +#main-content { + max-width: 100%; +} + +#noMatches { + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; +} + +#noMatchesInnerContent { + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; +} + +#noMatches.hidden,.gallery-item.hidden { + display: none !important; +} + +.btn-primary { + color: #004293; + background: rgba(61, 138, 233, 0.20); + padding: 3px 12px 3px 12px; + border: 1px solid #D2DCE6; +} + +button.try-anyscale { + background-color: initial !important; + width: fit-content; + padding: 0 !important; + margin-left: auto !important; + float: initial !important; +} + +button.try-anyscale > svg { + display: none; +} + +button.try-anyscale > i { + display: none; +} + +button.try-anyscale > span { + margin: 0; + text-decoration-line: underline; + font-weight: 500; + color: #000; +} + +.top-nav-content { + justify-content: initial; +} + +/* Hide nav bar that has github, fullscreen, and print icons */ +div.header-article.row.sticky-top.noprint { + display: none !important; +} + +/* Hide the footer with 'prev article' and 'next article' buttons */ +.footer-article.hidden { + display: none !important; +} diff --git a/docs/_static/css/termynal.css b/docs/_static/css/termynal.css new file mode 100644 index 000000000..391a48078 --- /dev/null +++ b/docs/_static/css/termynal.css @@ -0,0 +1,108 @@ +/** + * termynal.js + * + * @author Ines Montani + * @version 0.0.1 + * @license MIT + */ + + :root { + --color-bg: #252a33; + --color-text: #eee; + --color-text-subtle: #a2a2a2; +} + +[data-termynal] { + width: auto; + max-width: 100%; + background: var(--color-bg); + color: var(--color-text); + font-size: 18px; + font-family: 'Fira Mono', Consolas, Menlo, Monaco, 'Courier New', Courier, monospace; + border-radius: 4px; + padding: 75px 45px 35px; + position: relative; + -webkit-box-sizing: border-box; + box-sizing: border-box; +} + +[data-termynal]:before { + content: ''; + position: absolute; + top: 15px; + left: 15px; + display: inline-block; + width: 15px; + height: 15px; + border-radius: 50%; + /* A little hack to display the window buttons in one pseudo element. */ + background: #d9515d; + -webkit-box-shadow: 25px 0 0 #f4c025, 50px 0 0 #3ec930; + box-shadow: 25px 0 0 #f4c025, 50px 0 0 #3ec930; +} + +[data-termynal]:after { + content: 'bash'; + position: absolute; + color: var(--color-text-subtle); + top: 5px; + left: 0; + width: 100%; + text-align: center; +} + +[data-ty] { + display: block; + line-height: 2; +} + +[data-ty]:before { + /* Set up defaults and ensure empty lines are displayed. */ + content: ''; + display: inline-block; + vertical-align: middle; +} + +[data-ty="input"]:before, +[data-ty-prompt]:before { + margin-right: 0.75em; + color: var(--color-text-subtle); +} + +[data-ty="input"]:before { + content: '$'; +} + +[data-ty][data-ty-prompt]:before { + content: attr(data-ty-prompt); +} + +[data-ty-cursor]:after { + content: attr(data-ty-cursor); + font-family: monospace; + margin-left: 0.5em; + -webkit-animation: blink 1s infinite; + animation: blink 1s infinite; +} + +a[data-terminal-control] { + text-align: right; + display: block; + color: #aebbff; +} + + +/* Cursor animation */ + +@-webkit-keyframes blink { + 50% { + opacity: 0; + } +} + +@keyframes blink { + 50% { + opacity: 0; + } +} + diff --git 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+30,24 @@ extensions = [ "myst_nb", "sphinx_copybutton", "sphinx_panels", + "sphinx_tabs.tabs", "IPython.sphinxext.ipython_console_highlighting", + "sphinx.ext.autosectionlabel", ] source_suffix = [".ipynb", ".html", ".md", ".rst"] + +myst_enable_extensions = [ + "dollarmath", + "amsmath", + "deflist", + "html_admonition", + "html_image", + "colon_fence", + "smartquotes", + "replacements", +] + # autodoc_pydantic_model_show_json = False # autodoc_pydantic_field_list_validators = False # autodoc_pydantic_config_members = False @@ -53,8 +67,18 @@ locales_dirs = ["./locales/"] gettext_compact = False gettext_uuid = True + +def setup(app): + app.add_css_file("css/custom.css") + app.add_css_file("css/examples.css") + app.add_css_file("css/termynal.css") + # app.add_css_file("css/use_cases.css") + + # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output -html_theme = "furo" +html_theme = "sphinx_book_theme" + + html_static_path = ["_static"] diff --git a/docs/getting_started/application/chatdb/chatdb.md b/docs/getting_started/application/chatdb/chatdb.md index 982953746..12a6aa8b6 100644 --- a/docs/getting_started/application/chatdb/chatdb.md +++ b/docs/getting_started/application/chatdb/chatdb.md @@ -4,6 +4,12 @@ ChatData generates SQL from natural language and executes it. ChatDB involves co Database, including metadata about databases, tables, and fields.![db plugins demonstration](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/d8bfeee9-e982-465e-a2b8-1164b673847e) +```{admonition} The LLM (Language Model) suitable for the ChatData scene is +* chatgpt3.5. +* chatgpt4. +* Vicuna-v1.5. +``` + ### 1.Choose Datasource If you are using DB-GPT for the first time, you need to add a data source and set the relevant connection information diff --git a/docs/getting_started/application/chatexcel/chatexcel.md b/docs/getting_started/application/chatexcel/chatexcel.md index bdd67991c..3df00c720 100644 --- a/docs/getting_started/application/chatexcel/chatexcel.md +++ b/docs/getting_started/application/chatexcel/chatexcel.md @@ -3,6 +3,11 @@ ChatExcel ![db plugins demonstration](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/4113ac15-83c2-4350-86c0-5fc795677abd) ChatExcel uses natural language to analyze and query Excel data.![db plugins demonstration](../../../../assets/chat_excel/chat_excel_1.png) +```{admonition} The LLM (Language Model) suitable for the ChatExcel scene is +* chatgpt3.5. +* chatgpt4. +``` + ### 1.Select And Upload Excel or CSV File Select your excel or csv file to upload and start the conversation. ```{tip} diff --git a/docs/getting_started/application/dashboard/dashboard.md b/docs/getting_started/application/dashboard/dashboard.md index 5cab32581..a233d0f24 100644 --- a/docs/getting_started/application/dashboard/dashboard.md +++ b/docs/getting_started/application/dashboard/dashboard.md @@ -4,6 +4,11 @@ The purpose of the DB-GPT Dashboard is to empower data analysts with efficiency. technology, allowing business analysts to perform self-service analysis directly using natural language and gain insights into their respective areas of business. +```{admonition} The LLM (Language Model) suitable for the Dashboard scene is +* chatgpt3.5. +* chatgpt4. +``` + ```{note} Dashboard now support Datasource Type * Mysql * Sqlite diff --git a/docs/getting_started/faq/deploy/deploy_faq.md b/docs/getting_started/faq/deploy/deploy_faq.md index 773e99794..b6083497c 100644 --- a/docs/getting_started/faq/deploy/deploy_faq.md +++ b/docs/getting_started/faq/deploy/deploy_faq.md @@ -100,8 +100,49 @@ pip install --use-pep517 fschat ``` ##### Q9: alembic.util.exc.CommandError: Target database is not up to date. -delete files in `DB-GPT/pilot/meta_data/alembic/versions/` and reboot. + +delete files in `DB-GPT/pilot/meta_data/alembic/versions/` and restart. ```commandline rm -rf DB-GPT/pilot/meta_data/alembic/versions/* rm -rf DB-GPT/pilot/meta_data/alembic/dbgpt.db ``` + +##### Q10: How to store DB-GPT metadata into my database + +In version 0.4.0, the metadata module of the DB-GPT application has been refactored. All metadata tables will now be automatically saved in the 'dbgpt' database, based on the database type specified in the `.env` file. If you would like to retain the existing data, it is recommended to use a data migration tool to transfer the database table information to the 'dbgpt' database. Additionally, you can change the default database name 'dbgpt' in your `.env` file. + +```commandline +### SQLite database (Current default database) +#LOCAL_DB_PATH=data/default_sqlite.db +#LOCAL_DB_TYPE=sqlite + +### Mysql database +LOCAL_DB_TYPE=mysql +LOCAL_DB_USER=root +LOCAL_DB_PASSWORD=aa12345678 +LOCAL_DB_HOST=127.0.0.1 +LOCAL_DB_PORT=3306 +# You can change it to your actual metadata database name +LOCAL_DB_NAME=dbgpt + +### This option determines the storage location of conversation records. The default is not configured to the old version of duckdb. It can be optionally db or file (if the value is db, the database configured by LOCAL_DB will be used) +CHAT_HISTORY_STORE_TYPE=db +``` + +##### Q11: pymysql.err.OperationalError: (1142, "ALTER command denied to user '{you db user}'@'{you db host}' for table '{some table name}'") + +In version 0.4.0, DB-GPT use migration tool alembic to migrate metadata. If the database user does not have DDL permissions, this error will be reported. You can solve this problem by importing the metadata information separately. + +1. Use a privileged user to execute DDL sql file +```bash +mysql -h127.0.0.1 -uroot -paa12345678 < ./assets/schema/knowledge_management.sql +``` + +2. Run DB-GPT webserver with `--disable_alembic_upgrade` +```bash +python pilot/server/dbgpt_server.py --disable_alembic_upgrade +``` +or +```bash +dbgpt start webserver --disable_alembic_upgrade +``` \ No newline at end of file diff --git a/docs/getting_started/faq/kbqa/kbqa_faq.md b/docs/getting_started/faq/kbqa/kbqa_faq.md index 61bdf2c18..7b3ea8780 100644 --- a/docs/getting_started/faq/kbqa/kbqa_faq.md +++ b/docs/getting_started/faq/kbqa/kbqa_faq.md @@ -72,7 +72,7 @@ $ mysql -h127.0.0.1 -uroot -paa12345678 < ./assets/schema/knowledge_management.s ##### Q6:when pull from 0.4.0, I found historical knowledge document disappeared -In version 0.4.0, the metadata module of the DB-GPT application has been refactored. All metadata tables will now be automatically saved in the 'dbgpt' database, based on the database type specified in the .env file. If you would like to retain the existing data, it is recommended to use a data migration tool to transfer the database table information to the 'dbgpt' database. +In version 0.4.0, the metadata module of the DB-GPT application has been refactored. All metadata tables will now be automatically saved in the 'dbgpt' database, based on the database type specified in the `.env` file. If you would like to retain the existing data, it is recommended to use a data migration tool to transfer the database table information to the 'dbgpt' database. Additionally, you can change the default database name 'dbgpt' in your `.env` file. ```{tip} old database:knowledge_management; @@ -89,5 +89,6 @@ LOCAL_DB_USER=root LOCAL_DB_PASSWORD=aa12345678 LOCAL_DB_HOST=127.0.0.1 LOCAL_DB_PORT=3306 - -``` \ No newline at end of file +# You can change it to your actual metadata database name +LOCAL_DB_NAME=dbgpt +``` diff --git a/docs/getting_started/faq/llm/llm_faq.md b/docs/getting_started/faq/llm/llm_faq.md index 7b4409d1f..53b8cf279 100644 --- a/docs/getting_started/faq/llm/llm_faq.md +++ b/docs/getting_started/faq/llm/llm_faq.md @@ -1,6 +1,6 @@ LLM USE FAQ ================================== -##### Q1:how to use openai chatgpt service +##### Q1: how to use openai chatgpt service change your LLM_MODEL in `.env` ````shell LLM_MODEL=proxyllm @@ -15,7 +15,7 @@ PROXY_SERVER_URL=https://api.openai.com/v1/chat/completions make sure your openapi API_KEY is available -##### Q2 What difference between `python dbgpt_server --light` and `python dbgpt_server` +##### Q2: What difference between `python dbgpt_server --light` and `python dbgpt_server` ```{note} * `python dbgpt_server --light` dbgpt_server does not start the llm service. Users can deploy the llm service separately by using `python llmserver`, and dbgpt_server accesses the llm service through set the LLM_SERVER environment variable in .env. The purpose is to allow for the separate deployment of dbgpt's backend service and llm service. @@ -35,7 +35,7 @@ python pilot/server/dbgpt_server.py --light ``` -##### Q3 How to use MultiGPUs +##### Q3: How to use MultiGPUs DB-GPT will use all available gpu by default. And you can modify the setting `CUDA_VISIBLE_DEVICES=0,1` in `.env` file to use the specific gpu IDs. @@ -52,7 +52,7 @@ CUDA_VISIBLE_DEVICES=3,4,5,6 python3 pilot/server/dbgpt_server.py You can modify the setting `MAX_GPU_MEMORY=xxGib` in `.env` file to configure the maximum memory used by each GPU. -##### Q4 Not Enough Memory +##### Q4: Not Enough Memory DB-GPT supported 8-bit quantization and 4-bit quantization. @@ -60,9 +60,9 @@ You can modify the setting `QUANTIZE_8bit=True` or `QUANTIZE_4bit=True` in `.env Llama-2-70b with 8-bit quantization can run with 80 GB of VRAM, and 4-bit quantization can run with 48 GB of VRAM. -Note: you need to install the latest dependencies according to [requirements.txt](https://github.com/eosphoros-ai/DB-GPT/blob/main/requirements.txt). +Note: you need to install the quantization dependencies with `pip install -e ".[quantization]"` -##### Q5 How to Add LLM Service dynamic local mode +##### Q5: How to Add LLM Service dynamic local mode Now DB-GPT through multi-llm service switch, so how to add llm service dynamic, @@ -75,7 +75,7 @@ eg: dbgpt model start --model_name chatglm2-6b --model_path /root/DB-GPT/models/ chatgpt eg: dbgpt model start --model_name chatgpt_proxyllm --model_path chatgpt_proxyllm --proxy_api_key ${OPENAI_KEY} --proxy_server_url {OPENAI_URL} ``` -##### Q6 How to Add LLM Service dynamic in remote mode +##### Q6: How to Add LLM Service dynamic in remote mode If you deploy llm service in remote machine instance, and you want to add model service to dbgpt server to manage use dbgpt start worker and set --controller_addr. @@ -88,13 +88,13 @@ eg: dbgpt start worker --model_name vicuna-13b-v1.5 \ ``` -##### Q7 dbgpt command not found +##### Q7: dbgpt command not found ```commandline pip install -e "pip install -e ".[default]" ``` -##### Q8 When starting the worker_manager on a cloud server and registering it with the controller, it is noticed that the worker's exposed IP is a private IP instead of a public IP, which leads to the inability to access the service. +##### Q8: When starting the worker_manager on a cloud server and registering it with the controller, it is noticed that the worker's exposed IP is a private IP instead of a public IP, which leads to the inability to access the service. ```commandline @@ -103,4 +103,14 @@ pip install -e "pip install -e ".[default]" automatically determined ``` +##### Q9: How to customize model path and prompt template + +DB-GPT will read the model path from `pilot.configs.model_config.LLM_MODEL_CONFIG` based on the `LLM_MODEL`. +Of course, you can use the environment variable `LLM_MODEL_PATH` to specify the model path and `LLM_PROMPT_TEMPLATE` to specify your model prompt template. + +``` +LLM_MODEL=vicuna-13b-v1.5 +LLM_MODEL_PATH=/app/models/vicuna-13b-v1.5 +# LLM_PROMPT_TEMPLATE=vicuna_v1.1 +``` diff --git a/docs/getting_started/install.rst b/docs/getting_started/install.rst index d6e6a15f8..645467e9e 100644 --- a/docs/getting_started/install.rst +++ b/docs/getting_started/install.rst @@ -18,7 +18,7 @@ DB-GPT product is a Web application that you can chat database, chat knowledge, :name: deploy :hidden: - ./install/deploy/deploy.md + ./install/deploy.rst ./install/docker/docker.md ./install/docker_compose/docker_compose.md ./install/cluster/cluster.rst diff --git a/docs/getting_started/install/cluster/cluster.rst b/docs/getting_started/install/cluster/cluster.rst index 93660d0a4..17895e7bc 100644 --- a/docs/getting_started/install/cluster/cluster.rst +++ b/docs/getting_started/install/cluster/cluster.rst @@ -77,3 +77,4 @@ By analyzing this information, we can identify performance bottlenecks in model ./vms/standalone.md ./vms/index.md + ./openai.md diff --git a/docs/getting_started/install/cluster/openai.md b/docs/getting_started/install/cluster/openai.md new file mode 100644 index 000000000..8f23ba0fa --- /dev/null +++ b/docs/getting_started/install/cluster/openai.md @@ -0,0 +1,51 @@ +OpenAI-Compatible RESTful APIs +================================== +(openai-apis-index)= + +### Install Prepare + +You must [deploy DB-GPT cluster](https://db-gpt.readthedocs.io/en/latest/getting_started/install/cluster/vms/index.html) first. + +### Launch Model API Server + +```bash +dbgpt start apiserver --controller_addr http://127.0.0.1:8000 --api_keys EMPTY +``` +By default, the Model API Server starts on port 8100. + +### Validate with cURL + +#### List models + +```bash +curl http://127.0.0.1:8100/api/v1/models \ +-H "Authorization: Bearer EMPTY" \ +-H "Content-Type: application/json" +``` + +#### Chat completions + +```bash +curl http://127.0.0.1:8100/api/v1/chat/completions \ +-H "Authorization: Bearer EMPTY" \ +-H "Content-Type: application/json" \ +-d '{"model": "vicuna-13b-v1.5", "messages": [{"role": "user", "content": "hello"}]}' +``` + +### Validate with OpenAI Official SDK + +#### Chat completions + +```python +import openai +openai.api_key = "EMPTY" +openai.api_base = "http://127.0.0.1:8100/api/v1" +model = "vicuna-13b-v1.5" + +completion = openai.ChatCompletion.create( + model=model, + messages=[{"role": "user", "content": "hello"}] +) +# print the completion +print(completion.choices[0].message.content) +``` \ No newline at end of file diff --git a/docs/getting_started/install/deploy.rst b/docs/getting_started/install/deploy.rst new file mode 100644 index 000000000..075a07403 --- /dev/null +++ b/docs/getting_started/install/deploy.rst @@ -0,0 +1,425 @@ +.. _installation: + +Installation From Source +============== + +To get started, install DB-GPT with the following steps. + + +1.Preparation +----------------- +**Download DB-GPT** + +.. code-block:: shell + + git clone https://github.com/eosphoros-ai/DB-GPT.git + +**Install Miniconda** + +We use Sqlite as default database, so there is no need for database installation. If you choose to connect to other databases, you can follow our tutorial for installation and configuration. +For the entire installation process of DB-GPT, we use the miniconda3 virtual environment. Create a virtual environment and install the Python dependencies. +`How to install Miniconda `_ + +.. code-block:: shell + + python>=3.10 + conda create -n dbgpt_env python=3.10 + conda activate dbgpt_env + # it will take some minutes + pip install -e ".[default]" + +.. code-block:: shell + + cp .env.template .env + +2.Deploy LLM Service +----------------- +DB-GPT can be deployed on servers with low hardware requirements or on servers with high hardware requirements. + +If you are low hardware requirements you can install DB-GPT by Using third-part LLM REST API Service OpenAI, Azure, tongyi. + +.. tip:: + + As our project has the ability to achieve OpenAI performance of over 85%, + + +.. note:: + + Notice make sure you have install git-lfs + + centos:yum install git-lfs + + ubuntu:apt-get install git-lfs + + macos:brew install git-lfs + +.. tabs:: + + .. tab:: OpenAI + + Installing Dependencies + + .. code-block:: + + pip install -e ".[openai]" + + Download embedding model + + .. code-block:: shell + + cd DB-GPT + mkdir models and cd models + + #### embedding model + git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese + or + git clone https://huggingface.co/moka-ai/m3e-large + + Configure LLM_MODEL, PROXY_API_URL and API_KEY in `.env` file + + .. code-block:: shell + + LLM_MODEL=chatgpt_proxyllm + PROXY_API_KEY={your-openai-sk} + PROXY_SERVER_URL=https://api.openai.com/v1/chat/completions + + .. tip:: + + Make sure your .env configuration is not overwritten + + + .. tab:: Vicuna + `Vicuna-v1.5 `_ based on llama-2 has been released, we recommend you set `LLM_MODEL=vicuna-13b-v1.5` to try this model) + + .. list-table:: vicuna-v1.5 hardware requirements + :widths: 50 50 50 + :header-rows: 1 + + * - Model + - Quantize + - VRAM Size + * - vicuna-7b-v1.5 + - 4-bit + - 8 GB + * - vicuna-7b-v1.5 + - 8-bit + - 12 GB + * - vicuna-13b-v1.5 + - 4-bit + - 12 GB + * - vicuna-13b-v1.5 + - 8-bit + - 20 GB + + + .. code-block:: shell + + cd DB-GPT + mkdir models and cd models + + #### embedding model + git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese + or + git clone https://huggingface.co/moka-ai/m3e-large + + #### llm model, if you use openai or Azure or tongyi llm api service, you don't need to download llm model + git clone https://huggingface.co/lmsys/vicuna-13b-v1.5 + + The model files are large and will take a long time to download. + + **Configure LLM_MODEL in `.env` file** + + + .. code-block:: shell + + LLM_MODEL=vicuna-13b-v1.5 + + .. tab:: Baichuan + + .. list-table:: Baichuan hardware requirements + :widths: 50 50 50 + :header-rows: 1 + + * - Model + - Quantize + - VRAM Size + * - baichuan-7b + - 4-bit + - 8 GB + * - baichuan-7b + - 8-bit + - 12 GB + * - baichuan-13b + - 4-bit + - 12 GB + * - baichuan-13b + - 8-bit + - 20 GB + + + .. code-block:: shell + + cd DB-GPT + mkdir models and cd models + + #### embedding model + git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese + or + git clone https://huggingface.co/moka-ai/m3e-large + + #### llm model + git clone https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat + or + git clone https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat + + The model files are large and will take a long time to download. + + **Configure LLM_MODEL in `.env` file** + + please rename Baichuan path to "baichuan2-13b" or "baichuan2-7b" + + .. code-block:: shell + + LLM_MODEL=baichuan2-13b + + .. tab:: ChatGLM + + + .. code-block:: shell + + cd DB-GPT + mkdir models and cd models + + #### embedding model + git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese + or + git clone https://huggingface.co/moka-ai/m3e-large + + #### llm model + git clone https://huggingface.co/THUDM/chatglm2-6b + + The model files are large and will take a long time to download. + + **Configure LLM_MODEL in `.env` file** + + please rename chatglm model path to "chatglm2-6b" + + .. code-block:: shell + + LLM_MODEL=chatglm2-6b + + .. tab:: Other LLM API + + Download embedding model + + .. code-block:: shell + + cd DB-GPT + mkdir models and cd models + + #### embedding model + git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese + or + git clone https://huggingface.co/moka-ai/m3e-large + + Now DB-GPT support LLM REST API TYPE: + + .. note:: + + * OpenAI + * Azure + * Aliyun tongyi + * Baidu wenxin + * Zhipu + * Baichuan + * Bard + + Configure LLM_MODEL and PROXY_API_URL and API_KEY in `.env` file + + .. code-block:: shell + + #OpenAI + LLM_MODEL=chatgpt_proxyllm + PROXY_API_KEY={your-openai-sk} + PROXY_SERVER_URL=https://api.openai.com/v1/chat/completions + + #Azure + LLM_MODEL=chatgpt_proxyllm + PROXY_API_KEY={your-azure-sk} + PROXY_API_BASE=https://{your domain}.openai.azure.com/ + PROXY_API_TYPE=azure + PROXY_SERVER_URL=xxxx + PROXY_API_VERSION=2023-05-15 + PROXYLLM_BACKEND=gpt-35-turbo + + #Aliyun tongyi + LLM_MODEL=tongyi_proxyllm + TONGYI_PROXY_API_KEY={your-tongyi-sk} + PROXY_SERVER_URL={your_service_url} + + ## Baidu wenxin + LLM_MODEL=wenxin_proxyllm + PROXY_SERVER_URL={your_service_url} + WEN_XIN_MODEL_VERSION={version} + WEN_XIN_API_KEY={your-wenxin-sk} + WEN_XIN_SECRET_KEY={your-wenxin-sct} + + ## Zhipu + LLM_MODEL=zhipu_proxyllm + PROXY_SERVER_URL={your_service_url} + ZHIPU_MODEL_VERSION={version} + ZHIPU_PROXY_API_KEY={your-zhipu-sk} + + ## Baichuan + LLM_MODEL=bc_proxyllm + PROXY_SERVER_URL={your_service_url} + BAICHUN_MODEL_NAME={version} + BAICHUAN_PROXY_API_KEY={your-baichuan-sk} + BAICHUAN_PROXY_API_SECRET={your-baichuan-sct} + + ## bard + LLM_MODEL=bard_proxyllm + PROXY_SERVER_URL={your_service_url} + # from https://bard.google.com/ f12-> application-> __Secure-1PSID + BARD_PROXY_API_KEY={your-bard-token} + + .. tip:: + + Make sure your .env configuration is not overwritten + + .. tab:: llama.cpp + + DB-GPT already supports `llama.cpp `_ via `llama-cpp-python `_ . + + **Preparing Model Files** + + To use llama.cpp, you need to prepare a gguf format model file, and there are two common ways to obtain it, you can choose either: + + **1. Download a pre-converted model file.** + + Suppose you want to use `Vicuna 13B v1.5 `_ , you can download the file already converted from `TheBloke/vicuna-13B-v1.5-GGUF `_ , only one file is needed. Download it to the `models` directory and rename it to `ggml-model-q4_0.gguf`. + + .. code-block:: + + wget https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF/resolve/main/vicuna-13b-v1.5.Q4_K_M.gguf -O models/ggml-model-q4_0.gguf + + **2. Convert It Yourself** + + You can convert the model file yourself according to the instructions in `llama.cpp#prepare-data--run `_ , and put the converted file in the models directory and rename it to `ggml-model-q4_0.gguf`. + + **Installing Dependencies** + + llama.cpp is an optional dependency in DB-GPT, and you can manually install it using the following command: + + .. code-block:: + + pip install -e ".[llama_cpp]" + + + **3.Modifying the Configuration File** + + Next, you can directly modify your `.env` file to enable llama.cpp. + + .. code-block:: + + LLM_MODEL=llama-cpp + llama_cpp_prompt_template=vicuna_v1.1 + + Then you can run it according to `Run `_ + + + **More Configurations** + + In DB-GPT, the model configuration can be done through `{model name}_{config key}`. + + .. list-table:: More Configurations + :widths: 50 50 50 + :header-rows: 1 + + * - Environment Variable Key + - Default + - Description + * - llama_cpp_prompt_template + - None + - Prompt template name, now support: zero_shot, vicuna_v1.1,alpaca,llama-2,baichuan-chat,internlm-chat, If None, the prompt template is automatically determined from model path。 + * - llama_cpp_model_path + - None + - Model path + * - llama_cpp_n_gpu_layers + - 1000000000 + - Number of layers to offload to the GPU, Set this to 1000000000 to offload all layers to the GPU. If your GPU VRAM is not enough, you can set a low number, eg: 10 + * - llama_cpp_n_threads + - None + - Number of threads to use. If None, the number of threads is automatically determined + * - llama_cpp_n_batch + - 512 + - Maximum number of prompt tokens to batch together when calling llama_eval + * - llama_cpp_n_gqa + - None + - Grouped-query attention. Must be 8 for llama-2 70b. + * - llama_cpp_rms_norm_eps + - 5e-06 + - 5e-6 is a good value for llama-2 models. + * - llama_cpp_cache_capacity + - None + - Maximum cache capacity. Examples: 2000MiB, 2GiB + * - llama_cpp_prefer_cpu + - False + - If a GPU is available, it will be preferred by default, unless prefer_cpu=False is configured. + + + .. tab:: vllm + + vLLM is a fast and easy-to-use library for LLM inference and serving. + + **Running vLLM** + + **1.Installing Dependencies** + + vLLM is an optional dependency in DB-GPT, and you can manually install it using the following command: + + .. code-block:: + + pip install -e ".[vllm]" + + **2.Modifying the Configuration File** + + Next, you can directly modify your .env file to enable vllm. + + .. code-block:: + + LLM_MODEL=vicuna-13b-v1.5 + MODEL_TYPE=vllm + + You can view the models supported by vLLM `here `_ + + Then you can run it according to `Run `_ + + + + + +3.Prepare sql example(Optional) +----------------- +**(Optional) load examples into SQLite** + +.. code-block:: shell + + bash ./scripts/examples/load_examples.sh + + +On windows platform: + +.. code-block:: shell + + .\scripts\examples\load_examples.bat + +4.Run db-gpt server +----------------- + +.. code-block:: shell + + python pilot/server/dbgpt_server.py + +**Open http://localhost:5000 with your browser to see the product.** + diff --git a/docs/getting_started/install/deploy/deploy.md b/docs/getting_started/install/deploy/deploy.md index b7ef6e44b..9317982d1 100644 --- a/docs/getting_started/install/deploy/deploy.md +++ b/docs/getting_started/install/deploy/deploy.md @@ -77,7 +77,7 @@ macos:brew install git-lfs ``` ##### Download LLM Model and Embedding Model -If you use OpenAI llm service, see [LLM Use FAQ](https://db-gpt.readthedocs.io/en/latest/getting_started/faq/llm/llm_faq.html) +If you use OpenAI llm service, see [How to Use LLM REST API](https://db-gpt.readthedocs.io/en/latest/getting_started/install/llm/proxyllm/proxyllm.html) ```{tip} If you use openai or Axzure or tongyi llm api service, you don't need to download llm model. diff --git a/docs/getting_started/install/environment/environment.md b/docs/getting_started/install/environment/environment.md index 4c8b23751..11aec8d40 100644 --- a/docs/getting_started/install/environment/environment.md +++ b/docs/getting_started/install/environment/environment.md @@ -6,7 +6,9 @@ LLM Model Name, see /pilot/configs/model_config.LLM_MODEL_CONFIG * LLM_MODEL=vicuna-13b MODEL_SERVER_ADDRESS + * MODEL_SERVER=http://127.0.0.1:8000 + LIMIT_MODEL_CONCURRENCY * LIMIT_MODEL_CONCURRENCY=5 @@ -59,11 +61,11 @@ Embedding Chunk size, default 500 Embedding Chunk Overlap, default 100 * KNOWLEDGE_CHUNK_OVERLAP=100 -embeding recall top k,5 +embedding recall top k,5 * KNOWLEDGE_SEARCH_TOP_SIZE=5 -embeding recall max token ,2000 +embedding recall max token ,2000 * KNOWLEDGE_SEARCH_MAX_TOKEN=5 ``` @@ -84,21 +86,6 @@ embeding recall max token ,2000 * WEAVIATE_URL=https://kt-region-m8hcy0wc.weaviate.network ``` -```{admonition} Vector Store SETTINGS -#### Chroma -* VECTOR_STORE_TYPE=Chroma -#### MILVUS -* VECTOR_STORE_TYPE=Milvus -* MILVUS_URL=127.0.0.1 -* MILVUS_PORT=19530 -* MILVUS_USERNAME -* MILVUS_PASSWORD -* MILVUS_SECURE= - -#### WEAVIATE -* WEAVIATE_URL=https://kt-region-m8hcy0wc.weaviate.network -``` - ```{admonition} Multi-GPU Setting See https://developer.nvidia.com/blog/cuda-pro-tip-control-gpu-visibility-cuda_visible_devices/ If CUDA_VISIBLE_DEVICES is not configured, all available gpus will be used diff --git a/docs/getting_started/install/llm/llm.rst b/docs/getting_started/install/llm/llm.rst index 185fd4b4f..c5f69c5fb 100644 --- a/docs/getting_started/install/llm/llm.rst +++ b/docs/getting_started/install/llm/llm.rst @@ -28,6 +28,7 @@ Multi LLMs Support, Supports multiple large language models, currently supportin :name: llama_cpp :hidden: + ./proxyllm/proxyllm.md ./llama/llama_cpp.md ./quantization/quantization.md ./vllm/vllm.md diff --git a/docs/getting_started/install/llm/proxyllm/proxyllm.md b/docs/getting_started/install/llm/proxyllm/proxyllm.md new file mode 100644 index 000000000..fae549dd3 --- /dev/null +++ b/docs/getting_started/install/llm/proxyllm/proxyllm.md @@ -0,0 +1,78 @@ +Proxy LLM API +================================== +Now DB-GPT supports connect LLM service through proxy rest api. + +LLM rest api now supports +```{note} +* OpenAI +* Azure +* Aliyun tongyi +* Baidu wenxin +* Zhipu +* Baichuan +* Bard +``` + + +### How to Integrate LLM rest API, like OpenAI, Azure, tongyi, wenxin llm api service? +update your `.env` file +```commandline +#OpenAI +LLM_MODEL=chatgpt_proxyllm +PROXY_API_KEY={your-openai-sk} +PROXY_SERVER_URL=https://api.openai.com/v1/chat/completions + +#Azure +LLM_MODEL=chatgpt_proxyllm +PROXY_API_KEY={your-azure-sk} +PROXY_API_BASE=https://{your domain}.openai.azure.com/ +PROXY_API_TYPE=azure +PROXY_SERVER_URL=xxxx +PROXY_API_VERSION=2023-05-15 +PROXYLLM_BACKEND=gpt-35-turbo + +#Aliyun tongyi +LLM_MODEL=tongyi_proxyllm +TONGYI_PROXY_API_KEY={your-tongyi-sk} +PROXY_SERVER_URL={your_service_url} + +## Baidu wenxin +LLM_MODEL=wenxin_proxyllm +PROXY_SERVER_URL={your_service_url} +WEN_XIN_MODEL_VERSION={version} +WEN_XIN_API_KEY={your-wenxin-sk} +WEN_XIN_SECRET_KEY={your-wenxin-sct} + +## Zhipu +LLM_MODEL=zhipu_proxyllm +PROXY_SERVER_URL={your_service_url} +ZHIPU_MODEL_VERSION={version} +ZHIPU_PROXY_API_KEY={your-zhipu-sk} + +## Baichuan +LLM_MODEL=bc_proxyllm +PROXY_SERVER_URL={your_service_url} +BAICHUN_MODEL_NAME={version} +BAICHUAN_PROXY_API_KEY={your-baichuan-sk} +BAICHUAN_PROXY_API_SECRET={your-baichuan-sct} + +## bard +LLM_MODEL=bard_proxyllm +PROXY_SERVER_URL={your_service_url} +# from https://bard.google.com/ f12-> application-> __Secure-1PSID +BARD_PROXY_API_KEY={your-bard-token} +``` +```{tip} +Make sure your .env configuration is not overwritten +``` + +### How to Integrate Embedding rest API, like OpenAI, Azure api service? + +```commandline +## Openai embedding model, See /pilot/model/parameter.py +EMBEDDING_MODEL=proxy_openai +proxy_openai_proxy_server_url=https://api.openai.com/v1 +proxy_openai_proxy_api_key={your-openai-sk} +proxy_openai_proxy_backend=text-embedding-ada-002 +``` + diff --git a/docs/index.rst b/docs/index.rst index 6a43c2534..56f851227 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -3,48 +3,58 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -Welcome to DB-GPT! -================================== -| As large models are released and iterated upon, they are becoming increasingly intelligent. However, in the process of using large models, we face significant challenges in data security and privacy. We need to ensure that our sensitive data and environments remain completely controlled and avoid any data privacy leaks or security risks. Based on this, we have launched the DB-GPT project to build a complete private large model solution for all database-based scenarios. This solution supports local deployment, allowing it to be applied not only in independent private environments but also to be independently deployed and isolated according to business modules, ensuring that the ability of large models is absolutely private, secure, and controllable. +Overview +------------------ -| **DB-GPT** is an experimental open-source project that uses localized GPT large models to interact with your data and environment. With this solution, you can be assured that there is no risk of data leakage, and your data is 100% private and secure. +| DB-GPT is an open-source framework for large models in the databases fields. It's purpose is to build infrastructure for the domain of large models, making it easier and more convenient to develop applications around databases. By developing various technical capabilities such as: -| **Features** -Currently, we have released multiple key features, which are listed below to demonstrate our current capabilities: +1. **SMMF(Service-oriented Multi-model Management Framework)** +2. **Text2SQL Fine-tuning** +3. **RAG(Retrieval Augmented Generation) framework and optimization** +4. **Data-Driven Agents framework collaboration** +5. **GBI(Generative Business intelligence)** -- SQL language capabilities - - SQL generation - - SQL diagnosis +etc, DB-GPT simplifies the construction of large model applications based on databases. -- Private domain Q&A and data processing - - Database knowledge Q&A - - Data processing +| In the era of Data 3.0, enterprises and developers can build their own customized applications with less code, leveraging models and databases. -- Plugins - - Support custom plugin execution tasks and natively support the Auto-GPT plugin, such as: +Features +^^^^^^^^^^^ -- Unified vector storage/indexing of knowledge base - - Support for unstructured data such as PDF, Markdown, CSV, and WebURL +| **1. Private Domain Q&A & Data Processing** +| Supports custom construction of knowledge bases through methods such as built-in, multi-file format uploads, and plugin-based web scraping. Enables unified vector storage and retrieval of massive structured and unstructured data. + +| **2.Multi-Data Source & GBI(Generative Business intelligence)** +| Supports interaction between natural language and various data sources such as Excel, databases, and data warehouses. Also supports analysis reporting. + +| **3.SMMF(Service-oriented Multi-model Management Framework)** +| Supports a wide range of models, including dozens of large language models such as open-source models and API proxies. Examples include LLaMA/LLaMA2, Baichuan, ChatGLM, Wenxin, Tongyi, Zhipu, Xinghuo, etc. + +| **4.Automated Fine-tuning** +| A lightweight framework for automated fine-tuning built around large language models, Text2SQL datasets, and methods like LoRA/QLoRA/Pturning. Makes TextSQL fine-tuning as convenient as a production line. + +| **5.Data-Driven Multi-Agents & Plugins** +| Supports executing tasks through custom plugins and natively supports the Auto-GPT plugin model. Agents protocol follows the Agent Protocol standard. + +| **6.Privacy and Security** +| Ensures data privacy and security through techniques such as privatizing large models and proxy de-identification. -- Multi LLMs Support - - Supports multiple large language models, currently supporting Vicuna (7b, 13b), ChatGLM-6b (int4, int8) - - TODO: codegen2, codet5p Getting Started ------------------ -| How to get started using DB-GPT to interact with your data and environment. -- `Quickstart Guide <./getting_started/getting_started.html>`_ +^^^^^^^^^^^^^^^^^ + +| Quickstart + +- `Quickstart Guide <./getting_started/getting_started.html>`_ | Concepts and terminology - `Concepts and Terminology <./getting_started/concepts.html>`_ -| Coming soon... - -- `Tutorials <.getting_started/tutorials.html>`_ .. toctree:: :maxdepth: 2 :caption: Getting Started + :name: getting_started :hidden: getting_started/install.rst @@ -57,10 +67,9 @@ Getting Started Modules ---------- +^^^^^^^^^ -| These modules are the core abstractions with which we can interact with data and environment smoothly. -It's very important for DB-GPT, DB-GPT also provide standard, extendable interfaces. +| These modules are the core abstractions with which we can interact with data and environment smoothly. It's very important for DB-GPT, DB-GPT also provide standard, extendable interfaces. | The docs for each module contain quickstart examples, how to guides, reference docs, and conceptual guides. @@ -78,64 +87,23 @@ It's very important for DB-GPT, DB-GPT also provide standard, extendable interfa - `Vector <./modules/vector.html>`_: Supported multi vector database. +------------- + .. toctree:: :maxdepth: 2 :caption: Modules :name: modules :hidden: - ./modules/llms.md - ./modules/prompts.md - ./modules/plugins.md - ./modules/connections.rst - ./modules/knowledge.rst - ./modules/vector.rst - -Use Cases ---------- - -| Best Practices and built-in implementations for common DB-GPT use cases: - -- `Sql generation and diagnosis <./use_cases/sql_generation_and_diagnosis.html>`_: SQL generation and diagnosis. - -- `knownledge Based QA <./use_cases/knownledge_based_qa.html>`_: A important scene for user to chat with database documents, codes, bugs and schemas. - -- `Chatbots <./use_cases/chatbots.html>`_: Language model love to chat, use multi models to chat. - -- `Querying Database Data <./use_cases/query_database_data.html>`_: Query and Analysis data from databases and give charts. - -- `Interacting with apis <./use_cases/interacting_with_api.html>`_: Interact with apis, such as create a table, deploy a database cluster, create a database and so on. - -- `Tool use with plugins <./use_cases/tool_use_with_plugin>`_: According to Plugin use tools to manage databases autonomoly. - -.. toctree:: - :maxdepth: 2 - :caption: Use Cases - :name: use_cases - :hidden: - - ./use_cases/sql_generation_and_diagnosis.md - ./use_cases/knownledge_based_qa.md - ./use_cases/chatbots.md - ./use_cases/query_database_data.md - ./use_cases/interacting_with_api.md - ./use_cases/tool_use_with_plugin.md - -Reference ------------ -| Full documentation on all methods, classes, installation methods, and integration setups for DB-GPT. - -.. toctree:: - :maxdepth: 1 - :caption: Reference - :name: reference - :hidden: - - ./reference.md - + modules/llms.md + modules/prompts.md + modules/plugins.md + modules/connections.rst + modules/knowledge.rst + modules/vector.rst Resources ----------- +----------------- | Additional resources we think may be useful as you develop your application! diff --git a/docs/locales/zh_CN/LC_MESSAGES/getting_started/application/chatdb/chatdb.po b/docs/locales/zh_CN/LC_MESSAGES/getting_started/application/chatdb/chatdb.po index 974f74f9d..d9fbb60a9 100644 --- a/docs/locales/zh_CN/LC_MESSAGES/getting_started/application/chatdb/chatdb.po +++ b/docs/locales/zh_CN/LC_MESSAGES/getting_started/application/chatdb/chatdb.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: DB-GPT 👏👏 0.3.5\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-09-26 17:51+0800\n" +"POT-Creation-Date: 2023-11-03 15:33+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -20,12 +20,12 @@ msgstr "" "Generated-By: Babel 2.12.1\n" #: ../../getting_started/application/chatdb/chatdb.md:1 -#: 0cf45852c1fd430090da81836bc961c7 +#: c1489293ce464cee9577b0aa9a3f3037 msgid "ChatData & ChatDB" msgstr "ChatData & ChatDB" #: ../../getting_started/application/chatdb/chatdb.md:3 -#: 6dc94a787ff844caa21074d71aaf351a +#: 2c421938f270427dbd0ffff892b1a5a1 #, fuzzy msgid "" "ChatData generates SQL from natural language and executes it. ChatDB " @@ -38,47 +38,68 @@ msgstr "" "plugins demonstration](../../../../assets/chat_data/chat_data.jpg)" #: ../../getting_started/application/chatdb/chatdb.md:3 -#: ../../getting_started/application/chatdb/chatdb.md:20 -#: ../../getting_started/application/chatdb/chatdb.md:24 -#: ../../getting_started/application/chatdb/chatdb.md:28 -#: ../../getting_started/application/chatdb/chatdb.md:43 -#: ../../getting_started/application/chatdb/chatdb.md:48 +#: ../../getting_started/application/chatdb/chatdb.md:26 +#: ../../getting_started/application/chatdb/chatdb.md:30 +#: ../../getting_started/application/chatdb/chatdb.md:34 +#: ../../getting_started/application/chatdb/chatdb.md:49 #: ../../getting_started/application/chatdb/chatdb.md:54 -#: 826032e82a0a40b2bd122a90a35d0161 91652ef9e3224290b0c89112bcca4474 -#: d396ffa33eef4bef8471040369414420 d7f176a7794048d3ac3573970db86d9d -#: f80e5611eca64f86baeeed6c860061f9 +#: ../../getting_started/application/chatdb/chatdb.md:60 +#: 1467691a6012498795a94a14f7eba307 32315140835948c58e1721c7e2fa88a9 +#: 3b8e3c3396ff47348105a6dec9e755ba a314854e9be945dd88ad241bfa340870 +#: d94d5f0e608f4399a0e10d593f0ab1da e0ca6ec1841040bc828ce2ef29c387b6 msgid "db plugins demonstration" msgstr "db plugins demonstration" #: ../../getting_started/application/chatdb/chatdb.md:7 -#: aa0f978d3ad34b71aacf143a4c807ba1 +#: 67cb0954cfa54e629b75cf9a241f6b9d +#, fuzzy +msgid "The LLM (Language Model) suitable for the ChatData scene is" +msgstr "ChatData场景适用的LLM * chatgpt3.5. * chatgpt4. * Vicuna-v1.5." + +#: ../../getting_started/application/chatdb/chatdb.md:8 +#: c973e19574e2405a96eb003c64063bfc +msgid "chatgpt3.5." +msgstr "" + +#: ../../getting_started/application/chatdb/chatdb.md:9 +#: 649b2382378c416591db7038a269c33b +msgid "chatgpt4." +msgstr "" + +#: ../../getting_started/application/chatdb/chatdb.md:10 +#: fac49de88fe3409f818193b953714cb9 +msgid "Vicuna-v1.5." +msgstr "" + +#: ../../getting_started/application/chatdb/chatdb.md:13 +#: 8bd004993a834b0797ebcb5b6a6b1a23 msgid "1.Choose Datasource" msgstr "1.Choose Datasource" -#: ../../getting_started/application/chatdb/chatdb.md:9 -#: 8a2338e2fbae44f1b61b2fcf062499d3 +#: ../../getting_started/application/chatdb/chatdb.md:15 +#: 34abfdced7804b51a212c0e567ffda6b msgid "" "If you are using DB-GPT for the first time, you need to add a data source" " and set the relevant connection information for the data source." msgstr "如果你是第一次使用DB-GPT, 首先需要添加数据源，设置数据源的相关连接信息" -#: ../../getting_started/application/chatdb/chatdb.md:13 -#: f1d165ab8b564445880e581a2e554434 +#: ../../getting_started/application/chatdb/chatdb.md:19 +#: 3a8d16a8a32c4ac5affbd8093677b4f8 msgid "there are some example data in DB-GPT-NEW/DB-GPT/docker/examples" msgstr "在DB-GPT-NEW/DB-GPT/docker/examples有数据示例" -#: ../../getting_started/application/chatdb/chatdb.md:15 -#: dd390cb518094c96bf5430bfa821830f +#: ../../getting_started/application/chatdb/chatdb.md:21 +#: 2c3333a2705648148f79623c220d90cd msgid "you can execute sql script to generate data." msgstr "你可以通过执行sql脚本生成测试数据" -#: ../../getting_started/application/chatdb/chatdb.md:18 -#: aebd974d23124daa80af6d74431d1ce3 +#: ../../getting_started/application/chatdb/chatdb.md:24 +#: 4994182137574d14a3eefb421ceccd8e msgid "1.1 Datasource management" msgstr "1.1 Datasource management" -#: ../../getting_started/application/chatdb/chatdb.md:20 -#: af4d12aaed5c4fc484a3e7a755a666c2 +#: ../../getting_started/application/chatdb/chatdb.md:26 +#: 94680e1487d84092abc51a7da9bf1075 msgid "" "![db plugins demonstration](https://github.com/eosphoros-ai/DB-" "GPT/assets/13723926/7678f07e-9eee-40a9-b980-5b3978a0ed52)" @@ -86,13 +107,13 @@ msgstr "" "![db plugins demonstration](https://github.com/eosphoros-ai/DB-" "GPT/assets/13723926/7678f07e-9eee-40a9-b980-5b3978a0ed52)" -#: ../../getting_started/application/chatdb/chatdb.md:22 -#: 34b7b9ce0f0142af8179a8e1763a32f8 +#: ../../getting_started/application/chatdb/chatdb.md:28 +#: 236dbd6d6cb4467593bf30597ecb215c msgid "1.2 Connection management" msgstr "1.2 Connection管理" -#: ../../getting_started/application/chatdb/chatdb.md:24 -#: 00a1af9f4e0a45b9a398f641c8198114 +#: ../../getting_started/application/chatdb/chatdb.md:30 +#: 6611193e600c4452ac8a9769c6230590 msgid "" "![db plugins demonstration](https://github.com/eosphoros-ai/DB-" "GPT/assets/13723926/25b8f5a9-d322-459e-a8b2-bfe8cb42bdd6)" @@ -100,13 +121,13 @@ msgstr "" "![db plugins demonstration](https://github.com/eosphoros-ai/DB-" "GPT/assets/13723926/25b8f5a9-d322-459e-a8b2-bfe8cb42bdd6)" -#: ../../getting_started/application/chatdb/chatdb.md:26 -#: 3b8efc25b482480b8d0f4afe5304ece0 +#: ../../getting_started/application/chatdb/chatdb.md:32 +#: 7cceb9703af54970bee4a50fb07d4509 msgid "1.3 Add Datasource" msgstr "1.3 添加Datasource" -#: ../../getting_started/application/chatdb/chatdb.md:28 -#: d36a476e1eb34a46b2d35e6c1c4c39dd +#: ../../getting_started/application/chatdb/chatdb.md:34 +#: 83c9e18cb87b4f0d9b0ce5e68b7fea77 msgid "" "![db plugins demonstration](https://github.com/eosphoros-ai/DB-" "GPT/assets/13723926/19ce31a7-4061-4da8-a9cb-efca396cc085)" @@ -114,54 +135,54 @@ msgstr "" "![db plugins demonstration](https://github.com/eosphoros-ai/DB-" "GPT/assets/13723926/19ce31a7-4061-4da8-a9cb-efca396cc085)" -#: ../../getting_started/application/chatdb/chatdb.md:31 -#: 9205388f91404099bf1add6d55f33801 +#: ../../getting_started/application/chatdb/chatdb.md:37 +#: 143fb04274cd486687c5766179f6103e msgid "now DB-GPT support Datasource Type" msgstr "DB-GPT支持数据源类型" -#: ../../getting_started/application/chatdb/chatdb.md:33 -#: 197722ccd9e54f8196e3037f0ebd4165 +#: ../../getting_started/application/chatdb/chatdb.md:39 +#: 8bcf83e66b2d4d858407fc2b21b8fe85 msgid "Mysql" msgstr "Mysql" -#: ../../getting_started/application/chatdb/chatdb.md:34 -#: e859c194648440b19941a42635f37ac5 +#: ../../getting_started/application/chatdb/chatdb.md:40 +#: cd74abd5d6f4410ca001a3de2685e768 msgid "Sqlite" msgstr "Sqlite" -#: ../../getting_started/application/chatdb/chatdb.md:35 -#: 91c695f437064f01bf1d7c85a0ecf5b4 +#: ../../getting_started/application/chatdb/chatdb.md:41 +#: fc5e01baba43449f8c3eb9b4b36a0ed8 msgid "DuckDB" msgstr "DuckDB" -#: ../../getting_started/application/chatdb/chatdb.md:36 -#: 0a8ff591969c4944890415a84aa64173 +#: ../../getting_started/application/chatdb/chatdb.md:42 +#: 10b6fe2153cd4ceba949687a54c3a68c msgid "Clickhouse" msgstr "Clickhouse" -#: ../../getting_started/application/chatdb/chatdb.md:37 -#: d52ec849653141dc95862e82ce5777e0 +#: ../../getting_started/application/chatdb/chatdb.md:43 +#: 9ce0a41784f041d39138a81099c386e9 #, fuzzy msgid "Mssql" msgstr "Mysql" -#: ../../getting_started/application/chatdb/chatdb.md:38 -#: 430a72d857114422aeecd5595df41881 +#: ../../getting_started/application/chatdb/chatdb.md:44 +#: 4af6eb835e954e0d937e98b308fb512b msgid "Spark" msgstr "Spark" -#: ../../getting_started/application/chatdb/chatdb.md:41 -#: b615a70971e7443291ba33e8bc12b437 +#: ../../getting_started/application/chatdb/chatdb.md:47 +#: 8aaa3a73090b4805b2dddf1cc355d83c msgid "2.ChatData" msgstr "2.ChatData" -#: ../../getting_started/application/chatdb/chatdb.md:42 -#: e3542c64926143958e71c7cb21d25c78 +#: ../../getting_started/application/chatdb/chatdb.md:48 +#: a34c79c99bd34233ae92d3090ff0b877 msgid "Preview Mode" msgstr "Preview Mode" -#: ../../getting_started/application/chatdb/chatdb.md:43 -#: e32f26b7c22141e181b5345a644dffd5 +#: ../../getting_started/application/chatdb/chatdb.md:49 +#: 39e31a2a01494d4191d415a2240e026d #, fuzzy msgid "" "After successfully setting up the data source, you can start conversing " @@ -173,13 +194,13 @@ msgstr "" "设置数据源成功后就可以和数据库进行对话了。你可以让它帮你生成SQL，也可以和问它数据库元数据的相关信息。 ![db plugins " "demonstration](../../../../assets/chat_data/chatdata_eg.png)" -#: ../../getting_started/application/chatdb/chatdb.md:47 -#: 4d5c0465a01b4f5a964d0e803f9cbc89 +#: ../../getting_started/application/chatdb/chatdb.md:53 +#: 999c78e8b604493a8190b0e1258d0da4 msgid "Editor Mode" msgstr "Editor Mode" -#: ../../getting_started/application/chatdb/chatdb.md:48 -#: 79b088787e8f43258bcc4292c89ad1b0 +#: ../../getting_started/application/chatdb/chatdb.md:54 +#: e4a61d1e62c743f8b13dbed92ec265ba msgid "" "In Editor Mode, you can edit your sql and execute it. ![db plugins " "demonstration](https://github.com/eosphoros-ai/DB-" @@ -189,13 +210,13 @@ msgstr "" "/eosphoros-ai/DB-" "GPT/assets/13723926/1a896dc1-7c0e-4354-8629-30357ffd8d7f)" -#: ../../getting_started/application/chatdb/chatdb.md:52 -#: 9efaf27749614cd4bea07146edddf558 +#: ../../getting_started/application/chatdb/chatdb.md:58 +#: b3a0d94083524d249f97dd426e1e1f26 msgid "3.ChatDB" msgstr "3.ChatDB" -#: ../../getting_started/application/chatdb/chatdb.md:54 -#: b2dc15f067064c60974e532c3e2f5893 +#: ../../getting_started/application/chatdb/chatdb.md:60 +#: 8f4bd453447f48019a597eb3e4a59875 msgid "" "![db plugins demonstration](https://github.com/eosphoros-ai/DB-" "GPT/assets/13723926/e04bc1b1-2c58-4b33-af62-97e89098ace7)" diff --git a/docs/locales/zh_CN/LC_MESSAGES/getting_started/application/chatexcel/chatexcel.po b/docs/locales/zh_CN/LC_MESSAGES/getting_started/application/chatexcel/chatexcel.po index 47f93237c..08f842573 100644 --- a/docs/locales/zh_CN/LC_MESSAGES/getting_started/application/chatexcel/chatexcel.po +++ b/docs/locales/zh_CN/LC_MESSAGES/getting_started/application/chatexcel/chatexcel.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: DB-GPT 👏👏 0.3.6\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-08-29 21:14+0800\n" +"POT-Creation-Date: 2023-11-03 15:33+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -20,13 +20,13 @@ msgstr "" "Generated-By: Babel 2.12.1\n" #: ../../getting_started/application/chatexcel/chatexcel.md:1 -#: ../../getting_started/application/chatexcel/chatexcel.md:9 -#: 6efcbf4652954b27beb55f600cfe75c7 eefb0c3bc131439fb2dd4045761f1ae9 +#: ../../getting_started/application/chatexcel/chatexcel.md:14 +#: 5e9c1de21de240839a510b9e05afcba1 96556d6d1d734f67ab15e548c9fdce2f msgid "ChatExcel" msgstr "" #: ../../getting_started/application/chatexcel/chatexcel.md:3 -#: 5fc4ddd2690f46658df1e09c601d81ad +#: 19590f67feea4f2580602538b79cd138 #, fuzzy msgid "" "![db plugins demonstration](https://github.com/eosphoros-ai/DB-" @@ -36,61 +36,75 @@ msgid "" msgstr "使用自然语言进行Excel数据的分析处理" #: ../../getting_started/application/chatexcel/chatexcel.md:3 -#: ../../getting_started/application/chatexcel/chatexcel.md:13 -#: ../../getting_started/application/chatexcel/chatexcel.md:17 -#: ../../getting_started/application/chatexcel/chatexcel.md:21 -#: 4c91baf5f0b244abb021f461851674cc 4eead9a4f81e4855a5c362774696999c -#: 5f309a06170946108ae70806dff157ea 790016c9c68f4a29a84b7ef8e14d6dc2 -#: 93db1eb6af69452982f6028eff626a57 e758c8b320894e2b93f8db78459b7a1f -#: ea3c99f7eafc4ae0a19706a47e4c7bf6 f18d2b88de244173ab2673f2a5e828c0 +#: ../../getting_started/application/chatexcel/chatexcel.md:18 +#: ../../getting_started/application/chatexcel/chatexcel.md:22 +#: ../../getting_started/application/chatexcel/chatexcel.md:26 +#: 286462d6d79b4189bcd9d17937a96233 7009abd113244cc5badb8e90837dc9af +#: 7eb17d02534d4f77b7602f3756da0d5e f5c1cfd0f77d4199a45988114174c67f msgid "db plugins demonstration" msgstr "" #: ../../getting_started/application/chatexcel/chatexcel.md:6 -#: 45f137031025409ba2ada9c8f7c5f1e4 +#: 338f0b19314f4026b84a670d35056e6c +#, fuzzy +msgid "The LLM (Language Model) suitable for the ChatExcel scene is" +msgstr "ChatExcel场景适用的LLM 是 scene is * chatgpt3.5. * chatgpt4." + +#: ../../getting_started/application/chatexcel/chatexcel.md:7 +#: bc09e8af60b64a8fbeecedb927a5a854 +msgid "chatgpt3.5." +msgstr "" + +#: ../../getting_started/application/chatexcel/chatexcel.md:8 +#: e840c31d671946c190e27e1b7dd28647 +msgid "chatgpt4." +msgstr "" + +#: ../../getting_started/application/chatexcel/chatexcel.md:11 +#: 2a710e2650bb44ef9d4a1ee4b8225a35 msgid "1.Select And Upload Excel or CSV File" msgstr "" -#: ../../getting_started/application/chatexcel/chatexcel.md:7 -#: cd282be2b4ef49ea8b0eaa3d53042f22 +#: ../../getting_started/application/chatexcel/chatexcel.md:12 +#: df48b1003f3640cfa621e416f0405e8d msgid "Select your excel or csv file to upload and start the conversation." msgstr "选择你的Excel或者CSV文件上传开始对话" -#: ../../getting_started/application/chatexcel/chatexcel.md:11 -#: a5ebc8643eff4b44a951b28d85488143 +#: ../../getting_started/application/chatexcel/chatexcel.md:16 +#: 7ef5d5ebb634406ea4b566bbf5e30fd7 msgid "" "The ChatExcel function supports Excel and CSV format files, select the " "corresponding file to use." msgstr "ChatExcel功能支持Excel和CSV格式的文件，选择对应格式的文件开始使用" -#: ../../getting_started/application/chatexcel/chatexcel.md:13 -#: d52927be09654c8daf29e2ef0c60a671 +#: ../../getting_started/application/chatexcel/chatexcel.md:18 +#: 40c79b71820f44439b1f541db2be9dd9 msgid "" "![db plugins " "demonstration](../../../../assets/chat_excel/chat_excel_2.png) ![db " "plugins demonstration](../../../../assets/chat_excel/chat_excel_3.png)" msgstr "" -#: ../../getting_started/application/chatexcel/chatexcel.md:16 -#: d86202165fdc4da6be06024b45f9af55 +#: ../../getting_started/application/chatexcel/chatexcel.md:21 +#: 0dd469b6f56a442485392346065e345d msgid "2.Wait for Data Processing" msgstr "等待数据处理" -#: ../../getting_started/application/chatexcel/chatexcel.md:17 -#: 3de7205fbdc741e2b79996d67264c058 +#: ../../getting_started/application/chatexcel/chatexcel.md:22 +#: 0e9213342664465187981d6fea41e7ba msgid "" "After the data is uploaded, it will first learn and process the data " "structure and field meaning. ![db plugins " "demonstration](../../../../assets/chat_excel/chat_excel_4.png)" msgstr "等待数据上传完成，会自动进行数据结构的学习和处理" -#: ../../getting_started/application/chatexcel/chatexcel.md:20 -#: fb0620dec5a24b469ceccf86e918fe54 +#: ../../getting_started/application/chatexcel/chatexcel.md:25 +#: dd2047d1199542f7abda4767b953cfac msgid "3.Use Data Analysis Calculation" msgstr "开始使用数据分析计算" -#: ../../getting_started/application/chatexcel/chatexcel.md:21 -#: 221733f01fe04e38b19f191d4001c7a7 +#: ../../getting_started/application/chatexcel/chatexcel.md:26 +#: 4e168def205743c898586e99e34d3e18 msgid "" "Now you can use natural language to analyze and query data in the dialog " "box. ![db plugins " diff --git a/docs/locales/zh_CN/LC_MESSAGES/getting_started/application/dashboard/dashboard.po b/docs/locales/zh_CN/LC_MESSAGES/getting_started/application/dashboard/dashboard.po index 32686d68c..7ec08a868 100644 --- a/docs/locales/zh_CN/LC_MESSAGES/getting_started/application/dashboard/dashboard.po +++ b/docs/locales/zh_CN/LC_MESSAGES/getting_started/application/dashboard/dashboard.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: DB-GPT 👏👏 0.3.5\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-08-29 20:50+0800\n" +"POT-Creation-Date: 2023-11-03 15:33+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -20,12 +20,12 @@ msgstr "" "Generated-By: Babel 2.12.1\n" #: ../../getting_started/application/dashboard/dashboard.md:1 -#: 2a1224e675d144269e5cc3695d4d60b4 +#: 3e4ef12c51894e5d93adcbf090a4783b msgid "Dashboard" msgstr "Dashboard" #: ../../getting_started/application/dashboard/dashboard.md:3 -#: 2b6d2f94f73d43e68806bf4c6d0d9269 +#: df9eb8d62bbe460d94e448c23fffa529 msgid "" "The purpose of the DB-GPT Dashboard is to empower data analysts with " "efficiency. DB-GPT provides intelligent reporting technology, allowing " @@ -33,38 +33,54 @@ msgid "" " language and gain insights into their respective areas of business." msgstr "DB-GPT Dashboard目的是赋能数据分析人员。DB-GPT通过提供智能报表技术，使得业务分析人员可以直接使用简单的自然语言进行自助分析。" +#: ../../getting_started/application/dashboard/dashboard.md:7 +#: 3ce90998ab9547fc95105f0000e232da +#, fuzzy +msgid "The LLM (Language Model) suitable for the Dashboard scene is" +msgstr "Dashboard场景适用的LLM是 * chatgpt3.5. * chatgpt4." + #: ../../getting_started/application/dashboard/dashboard.md:8 -#: 9612fa76c4264bab8e629ac50959faa9 +#: be8fe8f9d9344e64acf51e54f8a17d7c +msgid "chatgpt3.5." +msgstr "" + +#: ../../getting_started/application/dashboard/dashboard.md:9 +#: 82ef8f03db3f4741a92113a485a40599 +msgid "chatgpt4." +msgstr "" + +#: ../../getting_started/application/dashboard/dashboard.md:13 +#: da3aaa82126942058e72c472e2495641 msgid "Dashboard now support Datasource Type" msgstr "Dashboard目前支持的数据源类型" -#: ../../getting_started/application/dashboard/dashboard.md:9 -#: bb0b15742ebe41628fb0d1fc38caabe2 +#: ../../getting_started/application/dashboard/dashboard.md:14 +#: d21cfc9594a24b69a5040f60cba00abb msgid "Mysql" msgstr "Mysql" -#: ../../getting_started/application/dashboard/dashboard.md:10 -#: 35491581125b4bdd8422f35b11c7bc2c +#: ../../getting_started/application/dashboard/dashboard.md:15 +#: fc0e345321e84de5b9a96107fef5467e msgid "Sqlite" msgstr "Sqlite" -#: ../../getting_started/application/dashboard/dashboard.md:11 -#: 8c4389354e0344aa9a781bdfc94c2cac +#: ../../getting_started/application/dashboard/dashboard.md:16 +#: f5cc4387997441b88a9f10ba9b35b621 msgid "DuckDB" msgstr "DuckDB" -#: ../../getting_started/application/dashboard/dashboard.md:13 -#: 18e8c60f5c2f4aa698cec1e8e8b354c8 +#: ../../getting_started/application/dashboard/dashboard.md:18 +#: d8a86c3d05f84df0aa63c88f5dbfbd78 msgid "Steps to Dashboard In DB-GPT" msgstr "Dashboard使用步骤" -#: ../../getting_started/application/dashboard/dashboard.md:15 -#: 94f98e0f5c2e451ba29b9b77c4139ed9 +#: ../../getting_started/application/dashboard/dashboard.md:20 +#: dbf7b3f73677411a8e660f8d90c11e05 msgid "1 add datasource" msgstr "1.添加数据源" -#: ../../getting_started/application/dashboard/dashboard.md:17 -#: 34e1211e65b940c3beb6234bcfa423a1 +#: ../../getting_started/application/dashboard/dashboard.md:22 +#: 70a599bc716a4af2b5a07a40de6fed9d #, fuzzy msgid "" "If you are using Dashboard for the first time, you need to mock some data" @@ -74,18 +90,18 @@ msgid "" "8c36-213b1c6fb5dd)" msgstr "如果你是第一次使用Dashboard，需要构造测试数据，DB-GPT在pilot/mock_datas/提供了测试数据，只需要将数据源进行添加即可" -#: ../../getting_started/application/dashboard/dashboard.md:17 -#: f29905929b32442ba05833b6c52a11be +#: ../../getting_started/application/dashboard/dashboard.md:22 +#: e38d15f091264563840cf15f0a2fe97c msgid "add_datasource" msgstr "添加数据源" -#: ../../getting_started/application/dashboard/dashboard.md:21 -#: 367a487dd1d54681a6e83d8fdda5b793 +#: ../../getting_started/application/dashboard/dashboard.md:26 +#: c6e87a5b20bb439ab1b3dc88e764d20b msgid "2.Choose Dashboard Mode" msgstr "2.进入Dashboard" -#: ../../getting_started/application/dashboard/dashboard.md:23 -#: 1ee1e980934e4a618591b7c43921c304 +#: ../../getting_started/application/dashboard/dashboard.md:28 +#: 54c25766650546998d3aaa3655f5275b msgid "" "![create_space](https://github.com/eosphoros-ai/DB-" "GPT/assets/13723926/5e888880-0e97-4b60-8e5c-b7e7224197f0)" @@ -93,18 +109,18 @@ msgstr "" "![create_space](https://github.com/eosphoros-ai/DB-" "GPT/assets/13723926/5e888880-0e97-4b60-8e5c-b7e7224197f0)" -#: ../../getting_started/application/dashboard/dashboard.md:23 -#: 12c756afdad740a9afc9cb46cc834af8 +#: ../../getting_started/application/dashboard/dashboard.md:28 +#: ea1781528db04000ab4a72308c7be97e msgid "create_space" msgstr "create_space" -#: ../../getting_started/application/dashboard/dashboard.md:25 -#: 5a575b17408c42fbacd32d8ff792d5a8 +#: ../../getting_started/application/dashboard/dashboard.md:30 +#: 5de9b0f0853443368d90e42114e99d6e msgid "3.Select Datasource" msgstr "3.选择数据源" -#: ../../getting_started/application/dashboard/dashboard.md:27 -#: ae051f852a5a4044a147c853cc3fba60 +#: ../../getting_started/application/dashboard/dashboard.md:32 +#: 3d4c429c4660414a8d5c44dea0ea0192 msgid "" "![document](https://github.com/eosphoros-ai/DB-" "GPT/assets/13723926/da2ac8b5-eca4-48ef-938f-f9dc1ca711b3)" @@ -112,19 +128,19 @@ msgstr "" "![document](https://github.com/eosphoros-ai/DB-" "GPT/assets/13723926/da2ac8b5-eca4-48ef-938f-f9dc1ca711b3)" -#: ../../getting_started/application/dashboard/dashboard.md:27 -#: ../../getting_started/application/dashboard/dashboard.md:31 -#: 94907bb0dc694bc3a4d2ee57a84b8242 ecc0666385904fce8bb1000735482f65 +#: ../../getting_started/application/dashboard/dashboard.md:32 +#: ../../getting_started/application/dashboard/dashboard.md:36 +#: 338912391ae441328549accdb6d5522b msgid "document" msgstr "document" -#: ../../getting_started/application/dashboard/dashboard.md:29 -#: c8697e93661c48b19674e63094ba7486 +#: ../../getting_started/application/dashboard/dashboard.md:34 +#: 2c0fd7e79393417aa218908c5cc89461 msgid "4.Input your analysis goals" msgstr "4.输入分析目标" -#: ../../getting_started/application/dashboard/dashboard.md:31 -#: 473fc0d00ab54ee6bc5c21e017591cc4 +#: ../../getting_started/application/dashboard/dashboard.md:36 +#: fb0bb655581a4109a5510240e54db006 #, fuzzy msgid "" "![document](https://github.com/eosphoros-ai/DB-" @@ -136,19 +152,19 @@ msgstr "" "![document](https://github.com/eosphoros-ai/DB-GPT/assets/13723926" "/3d14a2da-165e-4b2f-a921-325c20fe5ae9)" -#: ../../getting_started/application/dashboard/dashboard.md:31 -#: ../../getting_started/application/dashboard/dashboard.md:35 -#: 00597e1268544d97a3de368b04d5dcf8 350d04e4b7204823b7a03c0a7606c951 +#: ../../getting_started/application/dashboard/dashboard.md:36 +#: ../../getting_started/application/dashboard/dashboard.md:40 +#: 44680217a9794eddb97bcb98593a1071 msgid "db plugins demonstration" msgstr "" -#: ../../getting_started/application/dashboard/dashboard.md:34 -#: b48cc911c1614def9e4738d35e8b754c +#: ../../getting_started/application/dashboard/dashboard.md:39 +#: 4a9a8eac8e77465a9519b532afdfd1b7 msgid "5.Adjust and modify your report" msgstr "5.调整" -#: ../../getting_started/application/dashboard/dashboard.md:35 -#: b0442bbc0f6c4c33914814ac92fc4b13 +#: ../../getting_started/application/dashboard/dashboard.md:40 +#: b56da5e50ced4085bb376caa26e50e78 msgid "" "![db plugins " "demonstration](../../../../assets/chat_dashboard/chat_dashboard_2.png)" diff --git a/docs/locales/zh_CN/LC_MESSAGES/getting_started/faq/deploy/deploy_faq.po b/docs/locales/zh_CN/LC_MESSAGES/getting_started/faq/deploy/deploy_faq.po index e8a085390..27b103339 100644 --- a/docs/locales/zh_CN/LC_MESSAGES/getting_started/faq/deploy/deploy_faq.po +++ b/docs/locales/zh_CN/LC_MESSAGES/getting_started/faq/deploy/deploy_faq.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: DB-GPT 👏👏 0.3.5\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-10-19 19:31+0800\n" +"POT-Creation-Date: 2023-10-27 15:57+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -20,12 +20,12 @@ msgstr "" "Generated-By: Babel 2.12.1\n" #: ../../getting_started/faq/deploy/deploy_faq.md:1 -#: fb640f7c38744cbf996dcf7f73f325f6 +#: 798fb40c5ec941fcb9d6a0795219132f msgid "Installation FAQ" -msgstr "Installation FAQ" +msgstr "安装 FAQ" #: ../../getting_started/faq/deploy/deploy_faq.md:5 -#: 79fd80e469d14d608554d53a0e0ed2e3 +#: 47d0aa43c5fe4ca3a8ceba50c18ba608 #, fuzzy msgid "" "Q1: execute `pip install -e .` error, found some package cannot find " @@ -35,18 +35,20 @@ msgstr "" "cannot find correct version." #: ../../getting_started/faq/deploy/deploy_faq.md:6 -#: f1f6e3291d1446b5bbcf744cd4c4e89a +#: 944761c1ccc543c0a6aa2fad8dc74a32 msgid "change the pip source." msgstr "替换pip源." #: ../../getting_started/faq/deploy/deploy_faq.md:13 #: ../../getting_started/faq/deploy/deploy_faq.md:20 -#: 68e1b39a08774a81b9061cc5205e4c1c dd34901f446749e998cd34ec5b6c44f4 +#: ../../getting_started/faq/deploy/deploy_faq.md:145 +#: 3cff7ea0ee7945be8d03b6b3b032515b 5ba3037287524d6384ca96ffe58798fa +#: 9635f37d34e04764855f21d2266411f6 msgid "or" msgstr "或者" #: ../../getting_started/faq/deploy/deploy_faq.md:27 -#: 0899f0e28dae443b8f912d96c797b79c +#: c1c71ca902d745b89136bb63beda3dfd msgid "" "Q2: sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) unable to" " open database file" @@ -55,80 +57,80 @@ msgstr "" " open database file" #: ../../getting_started/faq/deploy/deploy_faq.md:29 -#: 3e60d8190e49436b8c40b34a67b7bfb3 +#: 97124a4512534c63bd09f2cf5a76fd13 msgid "make sure you pull latest code or create directory with mkdir pilot/data" msgstr "make sure you pull latest code or create directory with mkdir pilot/data" #: ../../getting_started/faq/deploy/deploy_faq.md:31 -#: baeaae20238842d3b8e4ae5b337198e5 +#: 369ed2cd489d46009184036a8f8ed67a msgid "Q3: The model keeps getting killed." msgstr "Q3: The model keeps getting killed." #: ../../getting_started/faq/deploy/deploy_faq.md:33 -#: eb3936307ad64b19b73483ff9ae126f2 +#: 6d59ca711a95495d9bddf22cd804e20b msgid "" "your GPU VRAM size is not enough, try replace your hardware or replace " "other llms." msgstr "GPU显存不够, 增加显存或者换一个显存小的模型" #: ../../getting_started/faq/deploy/deploy_faq.md:35 -#: f6dba770717041699c73b4cd00d48aad +#: 7ef755bf77fa46ccb63076c3561ecc64 msgid "Q4: How to access website on the public network" msgstr "" #: ../../getting_started/faq/deploy/deploy_faq.md:37 -#: 447d9e9374de44bab6d8a03f2c936676 +#: cd3f9144525b49babb826a7447812016 msgid "" "You can try to use gradio's [network](https://github.com/gradio-" "app/gradio/blob/main/gradio/networking.py) to achieve." msgstr "" #: ../../getting_started/faq/deploy/deploy_faq.md:48 -#: 5e34dd4dfcf34feeb1815dfa974041d0 +#: 71f0174d58674b1abd3d6a02cf65abf6 msgid "Open `url` with your browser to see the website." msgstr "" #: ../../getting_started/faq/deploy/deploy_faq.md:50 -#: aaef774ce6124021a3862bc0a25d465f +#: 81b07e64feef4187beab2022f3af294d msgid "Q5: (Windows) execute `pip install -e .` error" msgstr "" #: ../../getting_started/faq/deploy/deploy_faq.md:52 -#: ec3945df451c4ec2b32ebb476f45c82b +#: f2e7cd453c10486aa9b7d90d1d771b58 msgid "The error log like the following:" msgstr "" #: ../../getting_started/faq/deploy/deploy_faq.md:71 -#: 1df09f6d9f9b4c1a8a32d6e271e5ee39 +#: 2c97bbe2f96142ec8398b376f6a21d7f msgid "" "Download and install `Microsoft C++ Build Tools` from [visual-cpp-build-" "tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/)" msgstr "" #: ../../getting_started/faq/deploy/deploy_faq.md:75 -#: 251f47bfa5694242a1c9d81a2022b7a0 +#: 95120da5a6bf4a26bf64c2dd54632e4b msgid "Q6: `Torch not compiled with CUDA enabled`" msgstr "" #: ../../getting_started/faq/deploy/deploy_faq.md:82 -#: bc9dfdfc47924a0e8d3ec535e23bf923 +#: fa936391d8bd44cebeffc92e0f893700 msgid "Install [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive)" msgstr "" #: ../../getting_started/faq/deploy/deploy_faq.md:83 -#: b5a632baa42745bdbee5d6ba516d8d8b +#: a8eb968b4b5a4f2786f7133299b8d20f msgid "" "Reinstall PyTorch [start-locally](https://pytorch.org/get-started/locally" "/#start-locally) with CUDA support." msgstr "" #: ../../getting_started/faq/deploy/deploy_faq.md:85 -#: 0092fb91642749f5a55b629017c0de6a +#: 009f8b213c9044888975f1ae8cdf7a75 msgid "Q7: ImportError: cannot import name 'PersistentClient' from 'chromadb'." msgstr "Q7: ImportError: cannot import name 'PersistentClient' from 'chromadb'." #: ../../getting_started/faq/deploy/deploy_faq.md:91 -#: 4aa87418f2a54c138bf3b7ff28a7e776 +#: 237706fe28b846dcbe77e04a3bf89a6c msgid "" "Q8: pydantic.error_wrappers.ValidationError:1 validation error for " "HuggingFaceEmbeddings.model_kwargs extra not permitted" @@ -137,14 +139,62 @@ msgstr "" "HuggingFaceEmbeddings.model_kwargs extra not permitted" #: ../../getting_started/faq/deploy/deploy_faq.md:102 -#: 6b690ab272af44f6b126cfe5ce1435ef +#: e20c5fde988b478fb7eaba0f10d7d196 msgid "Q9: alembic.util.exc.CommandError: Target database is not up to date." msgstr "" -#: ../../getting_started/faq/deploy/deploy_faq.md:103 -#: 223026d3b9124363b695937922d8f8d5 -msgid "delete files in `DB-GPT/pilot/meta_data/alembic/versions/` and reboot." -msgstr "删除`DB-GPT/pilot/meta_data/alembic/versions/`目录下文件" +#: ../../getting_started/faq/deploy/deploy_faq.md:104 +#: 6d096ff6f4754490868a0ff2b8a08f10 +msgid "delete files in `DB-GPT/pilot/meta_data/alembic/versions/` and restart." +msgstr "删除`DB-GPT/pilot/meta_data/alembic/versions/`目录下文件然后重新启动" + +#: ../../getting_started/faq/deploy/deploy_faq.md:110 +#: 2294a811682d4744b9334ee6deec4a49 +msgid "Q10: How to store DB-GPT metadata into my database" +msgstr "Q10: 如何将 DB-GPT 的元数据存储到自己的数据库中" + +#: ../../getting_started/faq/deploy/deploy_faq.md:112 +#: 034495af54f041bcb560a5366b9be912 +msgid "" +"In version 0.4.0, the metadata module of the DB-GPT application has been " +"refactored. All metadata tables will now be automatically saved in the " +"'dbgpt' database, based on the database type specified in the `.env` " +"file. If you would like to retain the existing data, it is recommended to" +" use a data migration tool to transfer the database table information to " +"the 'dbgpt' database. Additionally, you can change the default database " +"name 'dbgpt' in your `.env` file." +msgstr "" +"v0.4.0 重构了DB-" +"GPT应用的数据库元数据模块，所有的元数据库表都会自动保存在.env文件设置的数据库类型的`dbgpt`数据库中，如果想沿用以前的数据，建议使用数据迁移工具将数据库表信息挪到dbgpt数据库中。" +"另外，你可以在 `.env` 中修改默认的数据库名 'dbgpt' " + +#: ../../getting_started/faq/deploy/deploy_faq.md:132 +#: f9baf853f21b460ba5df63b84b35c040 +msgid "" +"Q11: pymysql.err.OperationalError: (1142, \"ALTER command denied to user " +"'{you db user}'@'{you db host}' for table '{some table name}'\")" +msgstr "" + +#: ../../getting_started/faq/deploy/deploy_faq.md:134 +#: a64cb2d75d6643559420c016362eb435 +msgid "" +"In version 0.4.0, DB-GPT use migration tool alembic to migrate metadata. " +"If the database user does not have DDL permissions, this error will be " +"reported. You can solve this problem by importing the metadata " +"information separately." +msgstr "" +"v0.4.0后，DB-GPT 使用 alembic 作为元数据迁移工具。" +"如果数据库用户没有 DDL 权限则会报这个错，您可以通过单独导入元数据信息来解决这个问题。" + +#: ../../getting_started/faq/deploy/deploy_faq.md:136 +#: 77c6bd4b559d457ab599b7f7730e85f1 +msgid "Use a privileged user to execute DDL sql file" +msgstr "使用一个有权限的用户来执行 DDL SQL 文件。" + +#: ../../getting_started/faq/deploy/deploy_faq.md:141 +#: 30c3fd7f7bfc4a63b5b9c4c15c64430f +msgid "Run DB-GPT webserver with `--disable_alembic_upgrade`" +msgstr "添加参数 `--disable_alembic_upgrade` 来运行 DB-GPT 的 webserver" #~ msgid "" #~ "Q2: When use Mysql, Access denied " diff --git a/docs/locales/zh_CN/LC_MESSAGES/getting_started/faq/kbqa/kbqa_faq.po b/docs/locales/zh_CN/LC_MESSAGES/getting_started/faq/kbqa/kbqa_faq.po index 73112ba21..6de5e670d 100644 --- a/docs/locales/zh_CN/LC_MESSAGES/getting_started/faq/kbqa/kbqa_faq.po +++ b/docs/locales/zh_CN/LC_MESSAGES/getting_started/faq/kbqa/kbqa_faq.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: DB-GPT 👏👏 0.3.5\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-10-20 14:49+0800\n" +"POT-Creation-Date: 2023-10-27 15:57+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -20,34 +20,34 @@ msgstr "" "Generated-By: Babel 2.12.1\n" #: ../../getting_started/faq/kbqa/kbqa_faq.md:1 -#: e95c136d802f486082c47a8c017eb725 +#: ab7d87ee62774af099fb0a8167b2d4be msgid "KBQA FAQ" msgstr "KBQA FAQ" #: ../../getting_started/faq/kbqa/kbqa_faq.md:4 -#: f19c96b7b1ec4bc7ad8c7c26582d7e59 +#: 3582be98154f4c3381765c130538d997 msgid "Q1: text2vec-large-chinese not found" msgstr "Q1: text2vec-large-chinese not found" #: ../../getting_started/faq/kbqa/kbqa_faq.md:6 -#: 24a0603be39d4418909c27f9a53b51e2 +#: 3382fb6d60b443029a04e183cb5449cf msgid "" "make sure you have download text2vec-large-chinese embedding model in " "right way" msgstr "确认下载text2vec-large-chinese模型姿势以及路径正确" #: ../../getting_started/faq/kbqa/kbqa_faq.md:18 -#: 356008dd415f4bdd9b0927d8ee073548 +#: 9fb65568d9cb4bf0be9371eae38ffe75 msgid "Q2:How to change Vector DB Type in DB-GPT." msgstr "怎么修改向量数据库类型" #: ../../getting_started/faq/kbqa/kbqa_faq.md:20 -#: 42013bfb02624010b668bd244b33c977 +#: 1904c691985044acad9d1cd84a227250 msgid "Update .env file and set VECTOR_STORE_TYPE." msgstr "怎样在.env文件设置VECTOR_STORE_TYPE" #: ../../getting_started/faq/kbqa/kbqa_faq.md:22 -#: 72b2bae15fea4e0d927bda68a8d0861d +#: a6ebd5b32fac42fbbdb7e26aaeafd781 msgid "" "DB-GPT currently support Chroma(Default), Milvus(>2.1), Weaviate vector " "database. If you want to change vector db, Update your .env, set your " @@ -61,19 +61,19 @@ msgstr "" "://db-gpt.readthedocs.io/en/latest/modules/vector.html)" #: ../../getting_started/faq/kbqa/kbqa_faq.md:38 -#: 0a12e3a5319c4a86a1db68d615d6fb8e +#: 8357695648cf4dcca7d8d2c6d0c48b0a msgid "Q3:When I use vicuna-13b, found some illegal character like this." msgstr "当使用vicuna系列模型时出现乱码。" #: ../../getting_started/faq/kbqa/kbqa_faq.md:43 -#: f89ffbf4697a4ccdb8030834c52b0473 +#: ff8cb9ecfd2d4c9fa179b872d03a97dd msgid "" "Set KNOWLEDGE_SEARCH_TOP_SIZE smaller or set KNOWLEDGE_CHUNK_SIZE " "smaller, and reboot server." msgstr "通过在.env文件将KNOWLEDGE_SEARCH_TOP_SIZE设置更小点或者在文档界面点击参数设置，将topk设置更小点" #: ../../getting_started/faq/kbqa/kbqa_faq.md:45 -#: fc837488bcc7432a92b70126e80e75d7 +#: d7a49b1b219c4cf1bcb6ba212e378a7e msgid "" "Q4:space add error (pymysql.err.OperationalError) (1054, \"Unknown column" " 'knowledge_space.context' in 'field list'\")" @@ -82,53 +82,58 @@ msgstr "" "'knowledge_space.context' in 'field list'\")" #: ../../getting_started/faq/kbqa/kbqa_faq.md:47 -#: 30c186bae2c3489eb18c18768c11c302 +#: 30d34122c9fe4d1a9870c54480c734a6 msgid "1.shutdown dbgpt_server(ctrl c)" msgstr "1.终止 dbgpt_server(ctrl c)" #: ../../getting_started/faq/kbqa/kbqa_faq.md:49 -#: 9cac5688ddb14c63905cc86e77d4567e +#: 1ccc21a493114e399007f9399f98006b msgid "2.add column context for table knowledge_space" msgstr "2.新增列 `context` for table knowledge_space" #: ../../getting_started/faq/kbqa/kbqa_faq.md:55 -#: b32219bc1c6246108f606952d8ef0132 +#: 075c48b850354ac7971bd7556b68ef52 msgid "3.execute sql ddl" msgstr "3.执行ddl" #: ../../getting_started/faq/kbqa/kbqa_faq.md:62 -#: ebfd196350994c44841d5766f776905c +#: c4484fc646324c9a976b81a4e802c435 msgid "4.restart dbgpt serve" msgstr "4.重启dbgpt server" #: ../../getting_started/faq/kbqa/kbqa_faq.md:64 -#: cfa083226efd4980a57ff15e86bb8480 +#: b3da50497f37421485d8f0e852f0b09c msgid "Q5:Use Mysql, how to use DB-GPT KBQA" msgstr "Q5:当使用 Mysql数据库时, 使用DB-GPT怎么初始化 KBQA service database schema" #: ../../getting_started/faq/kbqa/kbqa_faq.md:66 -#: 95098205d36c4ca79ad9b1b0f9b2985a +#: 97484a97b0d14d3f835efb5a0739c97e msgid "build Mysql KBQA system database schema." msgstr "构建Mysql KBQA system database schema" #: ../../getting_started/faq/kbqa/kbqa_faq.md:73 -#: efc87678042d48b38b57f700d9ff74e5 +#: bdcbaf0559a549468fab021e03ac3876 msgid "Q6:when pull from 0.4.0, I found historical knowledge document disappeared" msgstr "当从0.4.0版本拉取代码后，历史知识库问答信息没了" #: ../../getting_started/faq/kbqa/kbqa_faq.md:75 -#: 975eaff1a20a40b5b5ee18d6c6ddb9c1 +#: ea78542f6be94dfc8d97b2660bb22876 +#, fuzzy msgid "" "In version 0.4.0, the metadata module of the DB-GPT application has been " "refactored. All metadata tables will now be automatically saved in the " -"'dbgpt' database, based on the database type specified in the .env file. " -"If you would like to retain the existing data, it is recommended to use a" -" data migration tool to transfer the database table information to the " -"'dbgpt' database." -msgstr "v0.4.0 重构了DB-GPT应用的数据库元数据模块，所有的元数据库表都会自动保存在.env文件设置的数据库类型的`dbgpt`数据库中，如果想沿用以前的数据，建议使用数据迁移工具将数据库表信息挪到dbgpt数据库中。" +"'dbgpt' database, based on the database type specified in the `.env` " +"file. If you would like to retain the existing data, it is recommended to" +" use a data migration tool to transfer the database table information to " +"the 'dbgpt' database. Additionally, you can change the default database " +"name 'dbgpt' in your `.env` file." +msgstr "" +"v0.4.0 重构了DB-" +"GPT应用的数据库元数据模块，所有的元数据库表都会自动保存在.env文件设置的数据库类型的`dbgpt`数据库中，如果想沿用以前的数据，建议使用数据迁移工具将数据库表信息挪到dbgpt数据库中。" +"另外，你可以在 `.env` 中修改默认的数据库名 'dbgpt' " #: ../../getting_started/faq/kbqa/kbqa_faq.md:78 -#: 815e44fef54f4807a2cf1e8d64b73a70 +#: badc49ae6b4340be9700b92b1023e45b msgid "old database:knowledge_management; new database:dbgpt;" msgstr "" diff --git a/docs/locales/zh_CN/LC_MESSAGES/getting_started/faq/llm/llm_faq.po b/docs/locales/zh_CN/LC_MESSAGES/getting_started/faq/llm/llm_faq.po index c0791b7cb..590cccfba 100644 --- a/docs/locales/zh_CN/LC_MESSAGES/getting_started/faq/llm/llm_faq.po +++ b/docs/locales/zh_CN/LC_MESSAGES/getting_started/faq/llm/llm_faq.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: DB-GPT 👏👏 0.3.5\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-10-20 22:29+0800\n" +"POT-Creation-Date: 2023-10-30 11:37+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -19,34 +19,36 @@ msgstr "" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.12.1\n" -#: ../../getting_started/faq/llm/llm_faq.md:1 54763acec7da4deb90669195c54ec3a1 +#: ../../getting_started/faq/llm/llm_faq.md:1 98e23f85313c45169ff2ba7f80193356 msgid "LLM USE FAQ" msgstr "LLM模型使用FAQ" -#: ../../getting_started/faq/llm/llm_faq.md:3 66f73fd2ee7b462e92d3f263792a5e33 -msgid "Q1:how to use openai chatgpt service" +#: ../../getting_started/faq/llm/llm_faq.md:3 0d49acfb4af947cb969b249346b00d33 +#, fuzzy +msgid "Q1: how to use openai chatgpt service" msgstr "我怎么使用OPENAI服务" -#: ../../getting_started/faq/llm/llm_faq.md:4 9d178d8462b74cb188bbacf2ac2ac12b +#: ../../getting_started/faq/llm/llm_faq.md:4 7010fec33e264987a29de86c54da93e8 #, fuzzy msgid "change your LLM_MODEL in `.env`" msgstr "通过在.env文件设置LLM_MODEL" -#: ../../getting_started/faq/llm/llm_faq.md:9 f7ca82f257be4ac09639a7f8af5e83eb +#: ../../getting_started/faq/llm/llm_faq.md:9 0982d6d5d0b3434fb00698aaf675f3f3 msgid "set your OPENAPI KEY" msgstr "set your OPENAPI KEY" -#: ../../getting_started/faq/llm/llm_faq.md:16 d6255b20dce34a2690df7e2af3505d97 +#: ../../getting_started/faq/llm/llm_faq.md:16 63650494c1574de09c007e1d470dd53d msgid "make sure your openapi API_KEY is available" msgstr "确认openapi API_KEY是否可用" -#: ../../getting_started/faq/llm/llm_faq.md:18 6f1c6dbdb31f4210a6d21f0f3a6ae589 +#: ../../getting_started/faq/llm/llm_faq.md:18 5721ec71e344499d96c55b7e531d7c08 +#, fuzzy msgid "" -"Q2 What difference between `python dbgpt_server --light` and `python " +"Q2: What difference between `python dbgpt_server --light` and `python " "dbgpt_server`" -msgstr "Q2 `python dbgpt_server --light` 和 `python dbgpt_server`的区别是什么?" +msgstr "Q2: `python dbgpt_server --light` 和 `python dbgpt_server`的区别是什么?" -#: ../../getting_started/faq/llm/llm_faq.md:20 b839771ae9e34e998b0edf8d69deabdd +#: ../../getting_started/faq/llm/llm_faq.md:20 76a650f195dd40b6a3a3564030cdc040 msgid "" "`python dbgpt_server --light` dbgpt_server does not start the llm " "service. Users can deploy the llm service separately by using `python " @@ -58,75 +60,75 @@ msgstr "" "用户可以通过`python " "llmserver`单独部署模型服务，dbgpt_server通过LLM_SERVER环境变量来访问模型服务。目的是为了可以将dbgpt后台服务和大模型服务分离部署。" -#: ../../getting_started/faq/llm/llm_faq.md:22 aba39cef6fe84799bcd03e8f36c41296 +#: ../../getting_started/faq/llm/llm_faq.md:22 8cd87e3504784d9e891e1fb96c79e143 msgid "" "`python dbgpt_server` dbgpt_server service and the llm service are " "deployed on the same instance. when dbgpt_server starts the service, it " "also starts the llm service at the same time." msgstr "`python dbgpt_server` 是将后台服务和模型服务部署在同一台实例上.dbgpt_server在启动服务的时候同时开启模型服务." -#: ../../getting_started/faq/llm/llm_faq.md:27 c65270d479af49e28e99b35a7932adbd +#: ../../getting_started/faq/llm/llm_faq.md:27 58a6eaf57e6d425685f67058b1a642d4 msgid "" "If you want to access an external LLM service(deployed by DB-GPT), you " "need to" msgstr "如果模型服务部署(通过DB-GPT部署)在别的机器，想通过dbgpt服务访问模型服务" -#: ../../getting_started/faq/llm/llm_faq.md:29 da153e6d18c543f28e0c4e85618e3d3d +#: ../../getting_started/faq/llm/llm_faq.md:29 67ac8823ca2e49ba9c833368e2cfb53c msgid "" "1.set the variables LLM_MODEL=YOUR_MODEL_NAME, " "MODEL_SERVER=YOUR_MODEL_SERVER（eg:http://localhost:5000） in the .env " "file." msgstr "" -#: ../../getting_started/faq/llm/llm_faq.md:31 cd89b8a2075f4407b8036a74151a6377 +#: ../../getting_started/faq/llm/llm_faq.md:31 e5c066bcdf0649a1b33bbfc7fd3b1a66 msgid "2.execute dbgpt_server.py in light mode" msgstr "2.execute dbgpt_server.py light 模式" -#: ../../getting_started/faq/llm/llm_faq.md:33 8f4b9401ac4f4a25a7479bee9ef5e8c1 +#: ../../getting_started/faq/llm/llm_faq.md:33 402ff01d7ee94d97be4a0eb964e39b97 msgid "python pilot/server/dbgpt_server.py --light" msgstr "" -#: ../../getting_started/faq/llm/llm_faq.md:38 69e1064cd7554ce6b49da732f800eacc +#: ../../getting_started/faq/llm/llm_faq.md:38 86190c689d8f4d9a9b58d904e0b5867b #, fuzzy -msgid "Q3 How to use MultiGPUs" -msgstr "Q2 怎么使用 MultiGPUs" +msgid "Q3: How to use MultiGPUs" +msgstr "Q3: 怎么使用 MultiGPUs" -#: ../../getting_started/faq/llm/llm_faq.md:40 6de3f105ce96430db5756f38bbd9ca12 +#: ../../getting_started/faq/llm/llm_faq.md:40 6b08cff88750440b98956203d8b8a084 msgid "" "DB-GPT will use all available gpu by default. And you can modify the " "setting `CUDA_VISIBLE_DEVICES=0,1` in `.env` file to use the specific gpu" " IDs." msgstr "DB-GPT默认加载可利用的gpu，你也可以通过修改 在`.env`文件 `CUDA_VISIBLE_DEVICES=0,1`来指定gpu IDs" -#: ../../getting_started/faq/llm/llm_faq.md:43 87cb9bfb20af4b259d719df797c42a7d +#: ../../getting_started/faq/llm/llm_faq.md:43 93b39089e5be4475b9e90e7813f5a7d9 msgid "" "Optionally, you can also specify the gpu ID to use before the starting " "command, as shown below:" msgstr "你也可以指定gpu ID启动" -#: ../../getting_started/faq/llm/llm_faq.md:53 bcfa35cda6304ee5ab9a775a2d4eda63 +#: ../../getting_started/faq/llm/llm_faq.md:53 62e3074c109d401fa4bf1ddbdc6c7be1 msgid "" "You can modify the setting `MAX_GPU_MEMORY=xxGib` in `.env` file to " "configure the maximum memory used by each GPU." msgstr "同时你可以通过在.env文件设置`MAX_GPU_MEMORY=xxGib`修改每个GPU的最大使用内存" -#: ../../getting_started/faq/llm/llm_faq.md:55 a05c5484927844c8bb4791f0a9ccc82e +#: ../../getting_started/faq/llm/llm_faq.md:55 d235bd83545c476f8e12572658d1c723 #, fuzzy -msgid "Q4 Not Enough Memory" -msgstr "Q3 机器显存不够 " +msgid "Q4: Not Enough Memory" +msgstr "Q4: 机器显存不够 " -#: ../../getting_started/faq/llm/llm_faq.md:57 fe17a023b6eb4a92b1b927e1b94e3784 +#: ../../getting_started/faq/llm/llm_faq.md:57 b3243ed9147f42bba987d7f9b778e66f msgid "DB-GPT supported 8-bit quantization and 4-bit quantization." msgstr "DB-GPT 支持 8-bit quantization 和 4-bit quantization." -#: ../../getting_started/faq/llm/llm_faq.md:59 76c3684c10864b8e87e5c2255b6c0b7f +#: ../../getting_started/faq/llm/llm_faq.md:59 1ddb9f94ab994bfebfee46d1c19888d4 msgid "" "You can modify the setting `QUANTIZE_8bit=True` or `QUANTIZE_4bit=True` " "in `.env` file to use quantization(8-bit quantization is enabled by " "default)." msgstr "你可以通过在.env文件设置`QUANTIZE_8bit=True` or `QUANTIZE_4bit=True`" -#: ../../getting_started/faq/llm/llm_faq.md:61 c5d849a38f1a4f0687bbcffb6699dc39 +#: ../../getting_started/faq/llm/llm_faq.md:61 54b85daa3fb24b17b67a6da31d2be8b0 msgid "" "Llama-2-70b with 8-bit quantization can run with 80 GB of VRAM, and 4-bit" " quantization can run with 48 GB of VRAM." @@ -134,49 +136,77 @@ msgstr "" "Llama-2-70b with 8-bit quantization 可以运行在 80 GB VRAM机器， 4-bit " "quantization可以运行在 48 GB VRAM" -#: ../../getting_started/faq/llm/llm_faq.md:63 867329a5e3b0403083e96f72b8747fb2 +#: ../../getting_started/faq/llm/llm_faq.md:63 097d680aed184fee9eceebee55a47ac1 msgid "" -"Note: you need to install the latest dependencies according to " -"[requirements.txt](https://github.com/eosphoros-ai/DB-" -"GPT/blob/main/requirements.txt)." +"Note: you need to install the quantization dependencies with `pip install" +" -e \".[quantization]\"`" msgstr "" -#: ../../getting_started/faq/llm/llm_faq.md:65 60ceee25e9fb4ddba40c5306bfb0a82f +#: ../../getting_started/faq/llm/llm_faq.md:65 f3a51056043c49eb84471040f2b364aa #, fuzzy -msgid "Q5 How to Add LLM Service dynamic local mode" -msgstr "Q5 怎样动态新增模型服务" +msgid "Q5: How to Add LLM Service dynamic local mode" +msgstr "Q5: 怎样动态新增模型服务" -#: ../../getting_started/faq/llm/llm_faq.md:67 c99eb7f7ae844884a8f0da94238ea7e0 +#: ../../getting_started/faq/llm/llm_faq.md:67 43ee6b0f23814c94a4ddb2429801a5e1 msgid "" "Now DB-GPT through multi-llm service switch, so how to add llm service " "dynamic," msgstr "DB-GPT支持多个模型服务切换, 怎样添加一个模型服务呢" -#: ../../getting_started/faq/llm/llm_faq.md:78 cd89b8a2075f4407b8036a74151a6377 +#: ../../getting_started/faq/llm/llm_faq.md:78 c217bbf0d2b6425fa7a1c691b7704a8d #, fuzzy -msgid "Q6 How to Add LLM Service dynamic in remote mode" -msgstr "Q5 怎样动态新增模型服务" +msgid "Q6: How to Add LLM Service dynamic in remote mode" +msgstr "Q6: 怎样动态新增模型服务" -#: ../../getting_started/faq/llm/llm_faq.md:79 8833ce89465848259b08ef0a4fa68d96 +#: ../../getting_started/faq/llm/llm_faq.md:79 195bdaa937a94c7aa0d8c6e1a5430d6e msgid "" "If you deploy llm service in remote machine instance, and you want to " "add model service to dbgpt server to manage" msgstr "如果你想在远程机器实例部署大模型服务并添加到本地dbgpt_server进行管理" -#: ../../getting_started/faq/llm/llm_faq.md:81 992eb37e3cca48829636c15ba3ec2ee8 +#: ../../getting_started/faq/llm/llm_faq.md:81 c64098b838a94821963a1d16e56497ff msgid "use dbgpt start worker and set --controller_addr." msgstr "使用1`dbgpt start worker`命令并设置注册地址--controller_addr" -#: ../../getting_started/faq/llm/llm_faq.md:91 0d06d7d6dd3d4780894ecd914c89b5a2 +#: ../../getting_started/faq/llm/llm_faq.md:91 cb12d5e9d9d24f14abc3ebea877a4b24 #, fuzzy -msgid "Q7 dbgpt command not found" -msgstr "Q6 dbgpt command not found" +msgid "Q7: dbgpt command not found" +msgstr "Q7: dbgpt command not found" -#: ../../getting_started/faq/llm/llm_faq.md:97 5d9beed0d95a4503a43d0e025664273b +#: ../../getting_started/faq/llm/llm_faq.md:97 f95cdccfa82d4b3eb2a23dd297131faa +#, fuzzy msgid "" -"Q8 When starting the worker_manager on a cloud server and registering it " -"with the controller, it is noticed that the worker's exposed IP is a " +"Q8: When starting the worker_manager on a cloud server and registering it" +" with the controller, it is noticed that the worker's exposed IP is a " "private IP instead of a public IP, which leads to the inability to access" " the service." -msgstr "云服务器启动worker_manager注册到controller时，发现worker暴露的ip是私网ip, 没有以公网ip暴露，导致服务访问不到" +msgstr "" +"Q8: 云服务器启动worker_manager注册到controller时，发现worker暴露的ip是私网ip, " +"没有以公网ip暴露，导致服务访问不到" + +#: ../../getting_started/faq/llm/llm_faq.md:106 +#: 739a2983f3484acf98e877dc12f4ccda +msgid "Q9: How to customize model path and prompt template" +msgstr "Q9: 如何自定义模型路径和 prompt 模板" + +#: ../../getting_started/faq/llm/llm_faq.md:108 +#: 8b82a33a311649c7850c30c00c987c72 +#, fuzzy +msgid "" +"DB-GPT will read the model path from " +"`pilot.configs.model_config.LLM_MODEL_CONFIG` based on the `LLM_MODEL`. " +"Of course, you can use the environment variable `LLM_MODEL_PATH` to " +"specify the model path and `LLM_PROMPT_TEMPLATE` to specify your model " +"prompt template." +msgstr "" +"DB-GPT 会根据 `LLM_MODEL` 从 `pilot.configs.model_config.LLM_MODEL_CONFIG` " +"中读取模型路径。当然，你可以使用环境 `LLM_MODEL_PATH` 来指定模型路径，以及使用 `LLM_PROMPT_TEMPLATE` " +"来指定模型的 prompt 模板。" + +#~ msgid "" +#~ "Note: you need to install the " +#~ "latest dependencies according to " +#~ "[requirements.txt](https://github.com/eosphoros-ai/DB-" +#~ "GPT/blob/main/requirements.txt)." +#~ msgstr "" diff --git a/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/cluster/openai.po b/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/cluster/openai.po new file mode 100644 index 000000000..0ef41aa6d --- /dev/null +++ b/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/cluster/openai.po @@ -0,0 +1,71 @@ +# SOME DESCRIPTIVE TITLE. +# Copyright (C) 2023, csunny +# This file is distributed under the same license as the DB-GPT package. +# FIRST AUTHOR , 2023. +# +#, fuzzy +msgid "" +msgstr "" +"Project-Id-Version: DB-GPT 👏👏 0.4.0\n" +"Report-Msgid-Bugs-To: \n" +"POT-Creation-Date: 2023-11-02 21:09+0800\n" +"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" +"Last-Translator: FULL NAME \n" +"Language: zh_CN\n" +"Language-Team: zh_CN \n" +"Plural-Forms: nplurals=1; plural=0;\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=utf-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Generated-By: Babel 2.12.1\n" + +#: ../../getting_started/install/cluster/openai.md:1 +#: 01f4e2bf853341198633b367efec1522 +msgid "OpenAI-Compatible RESTful APIs" +msgstr "OpenAI RESTful 兼容接口" + +#: ../../getting_started/install/cluster/openai.md:5 +#: d8717e42335e4027bf4e76b3d28768ee +msgid "Install Prepare" +msgstr "安装准备" + +#: ../../getting_started/install/cluster/openai.md:7 +#: 9a48d8ee116942468de4c6faf9a64758 +msgid "" +"You must [deploy DB-GPT cluster](https://db-" +"gpt.readthedocs.io/en/latest/getting_started/install/cluster/vms/index.html)" +" first." +msgstr "你必须先部署 [DB-GPT 集群]" +"(https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh-cn/latest/getting_started/install/cluster/vms/index.html)。" + +#: ../../getting_started/install/cluster/openai.md:9 +#: 7673a7121f004f7ca6b1a94a7e238fa3 +msgid "Launch Model API Server" +msgstr "启动模型 API Server" + +#: ../../getting_started/install/cluster/openai.md:14 +#: 84a925c2cbcd4e4895a1d2d2fe8f720f +msgid "By default, the Model API Server starts on port 8100." +msgstr "默认情况下，模型 API Server 使用 8100 端口启动。" + +#: ../../getting_started/install/cluster/openai.md:16 +#: e53ed41977cd4721becd51eba05c6609 +msgid "Validate with cURL" +msgstr "通过 cURL 验证" + +#: ../../getting_started/install/cluster/openai.md:18 +#: 7c883b410b5c4e53a256bf17c1ded80d +msgid "List models" +msgstr "列出模型" + +#: ../../getting_started/install/cluster/openai.md:26 +#: ../../getting_started/install/cluster/openai.md:37 +#: 7cf0ed13f0754f149ec085cd6cf7a45a 990d5d5ed5d64ab49550e68495b9e7a0 +msgid "Chat completions" +msgstr "" + +#: ../../getting_started/install/cluster/openai.md:35 +#: 81583edd22df44e091d18a0832278131 +msgid "Validate with OpenAI Official SDK" +msgstr "通过 OpenAI 官方 SDK 验证" + diff --git a/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/deploy.po b/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/deploy.po new file mode 100644 index 000000000..f4c85b8b4 --- /dev/null +++ b/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/deploy.po @@ -0,0 +1,652 @@ +# SOME DESCRIPTIVE TITLE. +# Copyright (C) 2023, csunny +# This file is distributed under the same license as the DB-GPT package. +# FIRST AUTHOR , 2023. +# +#, fuzzy +msgid "" +msgstr "" +"Project-Id-Version: DB-GPT 👏👏 0.4.0\n" +"Report-Msgid-Bugs-To: \n" +"POT-Creation-Date: 2023-11-06 19:38+0800\n" +"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" +"Last-Translator: FULL NAME \n" +"Language: zh_CN\n" +"Language-Team: zh_CN \n" +"Plural-Forms: nplurals=1; plural=0;\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=utf-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Generated-By: Babel 2.12.1\n" + +#: ../../getting_started/install/deploy.rst:4 f3ea3305f122460aaa11999edc4b5de6 +msgid "Installation From Source" +msgstr "源码安装" + +#: ../../getting_started/install/deploy.rst:6 bb941f2bd56d4eb48f7c4f75ebd74176 +msgid "To get started, install DB-GPT with the following steps." +msgstr "按照以下步骤进行安装" + +#: ../../getting_started/install/deploy.rst:10 27a1e092c1f945ceb9946ebdaf89b600 +msgid "1.Preparation" +msgstr "1.准备" + +#: ../../getting_started/install/deploy.rst:11 5c5bfbdc74a14c3b9b1f1ed66617cac8 +msgid "**Download DB-GPT**" +msgstr "**下载DB-GPT项目**" + +#: ../../getting_started/install/deploy.rst:17 3065ee2f34f9417598a37fd699a4863e +msgid "**Install Miniconda**" +msgstr "**安装Miniconda**" + +#: ../../getting_started/install/deploy.rst:19 f9f3a653ffb8447284686aa37a7bb79a +msgid "" +"We use Sqlite as default database, so there is no need for database " +"installation. If you choose to connect to other databases, you can " +"follow our tutorial for installation and configuration. For the entire " +"installation process of DB-GPT, we use the miniconda3 virtual " +"environment. Create a virtual environment and install the Python " +"dependencies. `How to install Miniconda " +"`_" +msgstr "" +"目前使用Sqlite作为默认数据库，因此DB-" +"GPT快速部署不需要部署相关数据库服务。如果你想使用其他数据库，需要先部署相关数据库服务。我们目前使用Miniconda进行python环境和包依赖管理。`如何安装" +" Miniconda `_ 。" + +#: ../../getting_started/install/deploy.rst:36 a2cd2fdd1d16421f9cbe341040b153b6 +msgid "2.Deploy LLM Service" +msgstr "2.部署LLM服务" + +#: ../../getting_started/install/deploy.rst:37 180a121e3c994a92a917ace80bf12386 +msgid "" +"DB-GPT can be deployed on servers with low hardware requirements or on " +"servers with high hardware requirements." +msgstr "DB-GPT可以部署在对硬件要求不高的服务器，也可以部署在对硬件要求高的服务器" + +#: ../../getting_started/install/deploy.rst:39 395608515c0348d5849030b58da7b659 +msgid "" +"If you are low hardware requirements you can install DB-GPT by Using " +"third-part LLM REST API Service OpenAI, Azure, tongyi." +msgstr "低硬件要求模式适用于对接第三方模型服务的 API，比如 OpenAI、通义千问、 文心一言等。" + +#: ../../getting_started/install/deploy.rst:43 e29297e61e2e4d05ba88f0e1c2b1f365 +msgid "As our project has the ability to achieve OpenAI performance of over 85%," +msgstr "使用OpenAI服务可以让DB-GPT准确率达到85%" + +#: ../../getting_started/install/deploy.rst:48 d0d70d51e8684c2891c58a6da4941a52 +msgid "Notice make sure you have install git-lfs" +msgstr "确认是否已经安装git-lfs" + +#: ../../getting_started/install/deploy.rst:50 0d2781fd38eb467ebad2a3c310a344e6 +msgid "centos:yum install git-lfs" +msgstr "" + +#: ../../getting_started/install/deploy.rst:52 1574ea24ad6443409070aa3a1f7abe87 +msgid "ubuntu:apt-get install git-lfs" +msgstr "" + +#: ../../getting_started/install/deploy.rst:54 ad86473d5c87447091c713f45cbfed0e +msgid "macos:brew install git-lfs" +msgstr "" + +#: ../../getting_started/install/deploy.rst:58 +#: ../../getting_started/install/deploy.rst:229 +#: 3dd1e40f33924faab63634907a7f6511 dce32420face4ab2b99caf7f3900ede9 +msgid "OpenAI" +msgstr "OpenAI" + +#: ../../getting_started/install/deploy.rst:60 1f66400540114de2820761ef80137805 +msgid "Installing Dependencies" +msgstr "安装依赖" + +#: ../../getting_started/install/deploy.rst:66 +#: ../../getting_started/install/deploy.rst:213 +#: 31b856a6fc094334a37914c046cb1bb1 42b2f6d36ca4487f8e31d59bba123fca +msgid "Download embedding model" +msgstr "下载 embedding 模型" + +#: ../../getting_started/install/deploy.rst:78 f970fb69e47c40d7bda381ec6f045829 +msgid "Configure LLM_MODEL, PROXY_API_URL and API_KEY in `.env` file" +msgstr "在 `.env` 文件中设置 LLM_MODEL、PROXY_API_URL 和 API_KEY" + +#: ../../getting_started/install/deploy.rst:88 +#: ../../getting_started/install/deploy.rst:288 +#: 6ca04c88fc60480db2ebdc9b234a0bbb 709cfe74c45c4eff83a7d77bb30b4a2b +msgid "Make sure your .env configuration is not overwritten" +msgstr "确保你的 .env 文件不会被覆盖" + +#: ../../getting_started/install/deploy.rst:91 147aea0d753f44588f4a0c56002334ab +msgid "Vicuna" +msgstr "Vicuna" + +#: ../../getting_started/install/deploy.rst:92 6a0bd60c4ca2478cb0f3d85aff70cd3b +msgid "" +"`Vicuna-v1.5 `_ based on " +"llama-2 has been released, we recommend you set `LLM_MODEL=vicuna-" +"13b-v1.5` to try this model)" +msgstr "" +"基于 llama-2 的模型 `Vicuna-v1.5 `_ 已经发布，我们推荐你通过配置 `LLM_MODEL=vicuna-13b-v1.5` 来尝试这个模型" + +#: ../../getting_started/install/deploy.rst:94 6a111c2ef31f41d4b737cf8b6f36fb16 +msgid "vicuna-v1.5 hardware requirements" +msgstr "vicuna-v1.5 的硬件要求" + +#: ../../getting_started/install/deploy.rst:98 +#: ../../getting_started/install/deploy.rst:143 +#: dc24c0238ce141df8bdce26cc0e2ddbb e04f1ea4b36940f3a28b66cdff7b702e +msgid "Model" +msgstr "模型" + +#: ../../getting_started/install/deploy.rst:99 +#: ../../getting_started/install/deploy.rst:144 +#: b6473e65ca1a437a84226531be4da26d e0a2f7580685480aa13ca462418764d3 +msgid "Quantize" +msgstr "量化" + +#: ../../getting_started/install/deploy.rst:100 +#: ../../getting_started/install/deploy.rst:145 +#: 56471c3b174d4adf9e8cb5bebaa300a6 d82297b8b9c148c3906d8ee4ed10d8a0 +msgid "VRAM Size" +msgstr "显存" + +#: ../../getting_started/install/deploy.rst:101 +#: ../../getting_started/install/deploy.rst:104 +#: 1214432602fe47a28479ce3e21a7d88b 51838e72e42248f199653f1bf08c8155 +msgid "vicuna-7b-v1.5" +msgstr "" + +#: ../../getting_started/install/deploy.rst:102 +#: ../../getting_started/install/deploy.rst:108 +#: ../../getting_started/install/deploy.rst:147 +#: ../../getting_started/install/deploy.rst:153 +#: a64439f4e6f64c42bb76fbb819556784 ed95f498641e4a0f976318df608a1d67 +#: fc400814509048b4a1cbe1e07c539285 ff7a8cb2cce8438cb6cb0d80dabfc2b5 +msgid "4-bit" +msgstr "" + +#: ../../getting_started/install/deploy.rst:103 +#: ../../getting_started/install/deploy.rst:148 +#: 2726e8a278c34e6db59147e9f66f2436 5feab5755a41403c9d641da697de4651 +msgid "8 GB" +msgstr "" + +#: ../../getting_started/install/deploy.rst:105 +#: ../../getting_started/install/deploy.rst:111 +#: ../../getting_started/install/deploy.rst:150 +#: ../../getting_started/install/deploy.rst:156 +#: 1984406682da4da3ad7b275e44085d07 2f027d838d0c46409e54c066d7983aae +#: 5c5878fe64944872b6769f075fedca05 e2507408a9c5423988e17b7029b487e4 +msgid "8-bit" +msgstr "" + +#: ../../getting_started/install/deploy.rst:106 +#: ../../getting_started/install/deploy.rst:109 +#: ../../getting_started/install/deploy.rst:151 +#: ../../getting_started/install/deploy.rst:154 +#: 332f50702c7b46e79ea0af5cbf86c6d5 381d23253cfd40109bacefca6a179f91 +#: aafe2423c25546e789e4804e3fd91d1d cc56990a58e941d6ba023cbd4dca0357 +msgid "12 GB" +msgstr "" + +#: ../../getting_started/install/deploy.rst:107 +#: ../../getting_started/install/deploy.rst:110 +#: 1f14e2fa6d41493cb208f55eddff9773 6457f6307d8546beb5f2fb69c30922d8 +msgid "vicuna-13b-v1.5" +msgstr "" + +#: ../../getting_started/install/deploy.rst:112 +#: ../../getting_started/install/deploy.rst:157 +#: e24d3a36b5ce4cfe861dce2d1c4db592 f2e66b2da7954aaab0ee526b25a371f5 +msgid "20 GB" +msgstr "" + +#: ../../getting_started/install/deploy.rst:128 +#: ../../getting_started/install/deploy.rst:175 +#: ../../getting_started/install/deploy.rst:201 +#: 1719c11f92874c47a87c00c634b9fad8 4596fcbe415d42fdbb29b92964fae070 +#: e639ae6076a64b7b9de08527966e4550 +msgid "The model files are large and will take a long time to download." +msgstr "这个模型权重文件比较大，需要花费较长时间来下载。" + +#: ../../getting_started/install/deploy.rst:130 +#: ../../getting_started/install/deploy.rst:177 +#: ../../getting_started/install/deploy.rst:203 +#: 4ec1492d389f403ebd9dd805fcaac68e ac6c68e2bf9b47c694ea8e0506014b10 +#: e39be72282e64760903aaba45f8effb8 +msgid "**Configure LLM_MODEL in `.env` file**" +msgstr "**在 `.env` 文件中配置 LLM_MODEL**" + +#: ../../getting_started/install/deploy.rst:137 +#: ../../getting_started/install/deploy.rst:234 +#: 7ce4e2253ef24a7ea890ade04ce36682 b9d5bf4fa09649c4a098503132ce7c0c +msgid "Baichuan" +msgstr "百川" + +#: ../../getting_started/install/deploy.rst:139 +#: ffdad6a70558457fa825bad4d811100d +msgid "Baichuan hardware requirements" +msgstr "百川 的硬件要求" + +#: ../../getting_started/install/deploy.rst:146 +#: ../../getting_started/install/deploy.rst:149 +#: 59d9b64f54d34971a68e93e3101def06 a66ce354d8f143ce920303241cd8947e +msgid "baichuan-7b" +msgstr "" + +#: ../../getting_started/install/deploy.rst:152 +#: ../../getting_started/install/deploy.rst:155 +#: c530662259ca4ec5b03a18e4b690e17a fa3af65ecca54daab961f55729bbc40e +msgid "baichuan-13b" +msgstr "" + +#: ../../getting_started/install/deploy.rst:179 +#: efd73637994a4b7c97ef3557e1f3161c +msgid "please rename Baichuan path to \"baichuan2-13b\" or \"baichuan2-7b\"" +msgstr "将Baichuan模型目录修改为\"baichuan2-13b\" 或 \"baichuan2-7b\"" + +#: ../../getting_started/install/deploy.rst:185 +#: 435a3f0d0fe84b49a7305e2c0f51a5df +msgid "ChatGLM" +msgstr "" + +#: ../../getting_started/install/deploy.rst:205 +#: 165e23d3d40d4756b5a6a2580d015213 +msgid "please rename chatglm model path to \"chatglm2-6b\"" +msgstr "将 chatglm 模型目录修改为\"chatglm2-6b\"" + +#: ../../getting_started/install/deploy.rst:211 +#: b651ebb5e0424b8992bc8b49d2280bee +msgid "Other LLM API" +msgstr "其它模型 API" + +#: ../../getting_started/install/deploy.rst:225 +#: 4eabdc25f4a34676b3ece620c88d866f +msgid "Now DB-GPT support LLM REST API TYPE:" +msgstr "目前DB-GPT支持的大模型 REST API 类型:" + +#: ../../getting_started/install/deploy.rst:230 +#: d361963cc3404e5ca55a823f1f1f545c +msgid "Azure" +msgstr "" + +#: ../../getting_started/install/deploy.rst:231 +#: 3b0f17c74aaa4bbd9db935973fa1c36b +msgid "Aliyun tongyi" +msgstr "" + +#: ../../getting_started/install/deploy.rst:232 +#: 7c4c457a499943b8804e31046551006d +msgid "Baidu wenxin" +msgstr "" + +#: ../../getting_started/install/deploy.rst:233 +#: ac1880a995184295acf07fff987d7c56 +msgid "Zhipu" +msgstr "" + +#: ../../getting_started/install/deploy.rst:235 +#: 6927500d7d3445b7b1981da1df4e1666 +msgid "Bard" +msgstr "" + +#: ../../getting_started/install/deploy.rst:237 +#: 419d564de18c485780d9336b852735b6 +msgid "Configure LLM_MODEL and PROXY_API_URL and API_KEY in `.env` file" +msgstr "在`.env`文件设置 LLM_MODEL、PROXY_API_URL和 API_KEY" + +#: ../../getting_started/install/deploy.rst:290 +#: 71d5203682e24e2e896e4b9913471f78 +msgid "llama.cpp" +msgstr "" + +#: ../../getting_started/install/deploy.rst:292 +#: 36a2b82f711a4c0f9491aca9c84d3c91 +msgid "" +"DB-GPT already supports `llama.cpp " +"`_ via `llama-cpp-python " +"`_ ." +msgstr "" +"DB-GPT 已经通过 `llama-cpp-python `_ 支持了 `llama.cpp `_ 。" + +#: ../../getting_started/install/deploy.rst:294 +#: 439064115dca4ae08d8e60041f2ffe17 +msgid "**Preparing Model Files**" +msgstr "**准备模型文件**" + +#: ../../getting_started/install/deploy.rst:296 +#: 7291d6fa20b34942926e7765c01f25c9 +msgid "" +"To use llama.cpp, you need to prepare a gguf format model file, and there" +" are two common ways to obtain it, you can choose either:" +msgstr "为了使用 llama.cpp，你需要准备 gguf 格式的文件，你可以通过以下两种方法获取" + +#: ../../getting_started/install/deploy.rst:298 +#: 45752f3f5dd847469da0c5edddc530fa +msgid "**1. Download a pre-converted model file.**" +msgstr "**1.下载已转换的模型文件.**" + +#: ../../getting_started/install/deploy.rst:300 +#: c451db2157ff49b2b4992aed9907ddfa +msgid "" +"Suppose you want to use `Vicuna 13B v1.5 `_ , you can download the file already converted from " +"`TheBloke/vicuna-13B-v1.5-GGUF `_ , only one file is needed. Download it to the `models` " +"directory and rename it to `ggml-model-q4_0.gguf`." +msgstr "" +"假设您想使用 `Vicuna 13B v1.5 `_ 您可以从 `TheBloke/vicuna-" +"13B-v1.5-GGUF `_ 下载已转换的文件，只需要一个文件。将其下载到models目录并将其重命名为 `ggml-" +"model-q4_0.gguf`。" + +#: ../../getting_started/install/deploy.rst:306 +#: f5b92b51622b43d398b3dc13a5892c29 +msgid "**2. Convert It Yourself**" +msgstr "**2. 自行转换**" + +#: ../../getting_started/install/deploy.rst:308 +#: 8838ae6dcecf44ecad3fd963980c8eb3 +msgid "" +"You can convert the model file yourself according to the instructions in " +"`llama.cpp#prepare-data--run `_ , and put the converted file in the models " +"directory and rename it to `ggml-model-q4_0.gguf`." +msgstr "" +"您可以根据 `llama.cpp#prepare-data--run `_ 中的说明自行转换模型文件，并把转换后的文件放在models目录中，并重命名为`ggml-" +"model-q4_0.gguf`。" + +#: ../../getting_started/install/deploy.rst:310 +#: 3fe28d6e5eaa4bdf9c5c44a914c3577c +msgid "**Installing Dependencies**" +msgstr "**安装依赖**" + +#: ../../getting_started/install/deploy.rst:312 +#: bdc10d2e88cc4c3f84a8c4a8dc2037a9 +msgid "" +"llama.cpp is an optional dependency in DB-GPT, and you can manually " +"install it using the following command:" +msgstr "llama.cpp在DB-GPT中是可选安装项, 你可以通过以下命令进行安装" + +#: ../../getting_started/install/deploy.rst:319 +#: 9c136493448b43b5b27f66af74ff721e +msgid "**3.Modifying the Configuration File**" +msgstr "**3.修改配置文件**" + +#: ../../getting_started/install/deploy.rst:321 +#: c835a7dee1dd409fb861e7b886c6dc5b +msgid "Next, you can directly modify your `.env` file to enable llama.cpp." +msgstr "修改`.env`文件使用llama.cpp" + +#: ../../getting_started/install/deploy.rst:328 +#: ../../getting_started/install/deploy.rst:396 +#: 296e6d08409544918fee0c31b1bf195c a81e5d882faf4722b0e10d53f635f53c +msgid "" +"Then you can run it according to `Run `_" +msgstr "" +"然后你可以根据 `运行 `_ 来运行。" + +#: ../../getting_started/install/deploy.rst:331 +#: 0f7f487ee11a4e01a95f7c504f0469ba +msgid "**More Configurations**" +msgstr "**更多配置文件**" + +#: ../../getting_started/install/deploy.rst:333 +#: b0f9964497f64fb5b3740099232cd72b +msgid "" +"In DB-GPT, the model configuration can be done through `{model " +"name}_{config key}`." +msgstr "在DB-GPT中，模型配置可以通过`{模型名称}_{配置名}` 来配置。" + +#: ../../getting_started/install/deploy.rst:335 +#: 7c225de4fe9d4dd3a3c2b2a33802e656 +msgid "More Configurations" +msgstr "**更多配置文件**" + +#: ../../getting_started/install/deploy.rst:339 +#: 5cc1671910314796a9ce0b5107d3c9fe +msgid "Environment Variable Key" +msgstr "环境变量Key" + +#: ../../getting_started/install/deploy.rst:340 +#: 4359ed4e11bb47ad89a605cbf9016cd5 +msgid "Default" +msgstr "默认值" + +#: ../../getting_started/install/deploy.rst:341 +#: 5cf0efc6d1014665bb9dbdae96bf2726 +msgid "Description" +msgstr "描述" + +#: ../../getting_started/install/deploy.rst:342 +#: e7c291f80a9a40fa90d642901eca02c6 +msgid "llama_cpp_prompt_template" +msgstr "" + +#: ../../getting_started/install/deploy.rst:343 +#: ../../getting_started/install/deploy.rst:346 +#: ../../getting_started/install/deploy.rst:352 +#: ../../getting_started/install/deploy.rst:358 +#: ../../getting_started/install/deploy.rst:364 +#: 07dc7fc4e51e4d9faf8e5221bcf03ee0 549f3c57a2e9427880e457e653ce1182 +#: 7ad961957f7b49d08e4aff347749b78d c1eab368175c4fa88fe0b471919523b2 +#: e2e0bf9903484972b6d20e6837010029 +msgid "None" +msgstr "" + +#: ../../getting_started/install/deploy.rst:344 +#: 6b5044a2009f432c92fcd65db42506d8 +msgid "" +"Prompt template name, now support: zero_shot, vicuna_v1.1,alpaca,llama-2" +",baichuan-chat,internlm-chat, If None, the prompt template is " +"automatically determined from model path。" +msgstr "" +"Prompt template 现在可以支持`zero_shot, vicuna_v1.1,alpaca,llama-2,baichuan-" +"chat,internlm-chat`, 如果是None, 可以根据模型路径来自动获取模型 Prompt template" + +#: ../../getting_started/install/deploy.rst:345 +#: e01c860441ad43b88c0a8d012f97d2d8 +msgid "llama_cpp_model_path" +msgstr "" + +#: ../../getting_started/install/deploy.rst:347 +#: 1cb68d772e454812a1a0c6de4950b8ce +msgid "Model path" +msgstr "模型路径" + +#: ../../getting_started/install/deploy.rst:348 +#: 6dac03820edb4fbd8a0856405e84c5bc +msgid "llama_cpp_n_gpu_layers" +msgstr "" + +#: ../../getting_started/install/deploy.rst:349 +#: 8cd5607b7941427f9a342ca7a00e5778 +msgid "1000000000" +msgstr "" + +#: ../../getting_started/install/deploy.rst:350 +#: 61c9297656da434aa7ac2b49cf61ea9d +msgid "" +"Number of layers to offload to the GPU, Set this to 1000000000 to offload" +" all layers to the GPU. If your GPU VRAM is not enough, you can set a low" +" number, eg: 10" +msgstr "要将多少网络层转移到GPU上，将其设置为1000000000以将所有层转移到GPU上。如果您的 GPU 内存不足，可以设置较低的数字，例如：10。" + +#: ../../getting_started/install/deploy.rst:351 +#: 8c2d2182557a483aa2fda590c24faaf3 +msgid "llama_cpp_n_threads" +msgstr "" + +#: ../../getting_started/install/deploy.rst:353 +#: cc442f61ffc442ecbd98c1e7f5598e1a +msgid "" +"Number of threads to use. If None, the number of threads is automatically" +" determined" +msgstr "要使用的线程数量。如果为None，则线程数量将自动确定。" + +#: ../../getting_started/install/deploy.rst:354 +#: 8d5e917d86f048348106e6923638a0c2 +msgid "llama_cpp_n_batch" +msgstr "" + +#: ../../getting_started/install/deploy.rst:355 +#: ee2719a0a8cd4a77846cffd8e675638f +msgid "512" +msgstr "" + +#: ../../getting_started/install/deploy.rst:356 +#: 845b354315384762a611ad2daa539d57 +msgid "Maximum number of prompt tokens to batch together when calling llama_eval" +msgstr "在调用llama_eval时，批处理在一起的prompt tokens的最大数量" + +#: ../../getting_started/install/deploy.rst:357 +#: a95e788bfa5f46f3bcd6356dfd9f87eb +msgid "llama_cpp_n_gqa" +msgstr "" + +#: ../../getting_started/install/deploy.rst:359 +#: 23ad9b5f34b5440bb90b2b21bab25763 +msgid "Grouped-query attention. Must be 8 for llama-2 70b." +msgstr "对于 llama-2 70B 模型，Grouped-query attention 必须为8。" + +#: ../../getting_started/install/deploy.rst:360 +#: 9ce25b7966fc40ec8be47ecfaf5f9994 +msgid "llama_cpp_rms_norm_eps" +msgstr "" + +#: ../../getting_started/install/deploy.rst:361 +#: 58365f0d36af447ba976213646018431 +msgid "5e-06" +msgstr "" + +#: ../../getting_started/install/deploy.rst:362 +#: d00b742a759140b795ba5949f1ce9a36 +msgid "5e-6 is a good value for llama-2 models." +msgstr "对于llama-2模型来说，5e-6是一个不错的值。" + +#: ../../getting_started/install/deploy.rst:363 +#: b9972e9b19354f55a5e6d9c50513a620 +msgid "llama_cpp_cache_capacity" +msgstr "" + +#: ../../getting_started/install/deploy.rst:365 +#: 3c98c5396dd74db8b6d70fc50fa0754f +msgid "Maximum cache capacity. Examples: 2000MiB, 2GiB" +msgstr "模型缓存最大值. 例如: 2000MiB, 2GiB" + +#: ../../getting_started/install/deploy.rst:366 +#: 4277e155992c4442b69d665d6269bed6 +msgid "llama_cpp_prefer_cpu" +msgstr "" + +#: ../../getting_started/install/deploy.rst:367 +#: 049169c1210a4ecabb25702ed813ea0a +msgid "False" +msgstr "" + +#: ../../getting_started/install/deploy.rst:368 +#: 60a39e93e7874491a93893de78b7d37e +msgid "" +"If a GPU is available, it will be preferred by default, unless " +"prefer_cpu=False is configured." +msgstr "如果有可用的GPU，默认情况下会优先使用GPU，除非配置了 prefer_cpu=False。" + +#: ../../getting_started/install/deploy.rst:371 +#: 7c86780fbf634de8873afd439389cf89 +msgid "vllm" +msgstr "" + +#: ../../getting_started/install/deploy.rst:373 +#: e2827892e43d420c85b8b83c4855d197 +msgid "vLLM is a fast and easy-to-use library for LLM inference and serving." +msgstr "vLLM 是一个快速且易于使用的 LLM 推理和服务的库。" + +#: ../../getting_started/install/deploy.rst:375 +#: 81bbfa3876a74244acc82d295803fdd4 +msgid "**Running vLLM**" +msgstr "**运行vLLM**" + +#: ../../getting_started/install/deploy.rst:377 +#: 75bc518b444c417ba4d9c15246549327 +msgid "**1.Installing Dependencies**" +msgstr "**1.安装依赖**" + +#: ../../getting_started/install/deploy.rst:379 +#: 725c620b0a5045c1a64a3b2a2e9b48f3 +msgid "" +"vLLM is an optional dependency in DB-GPT, and you can manually install it" +" using the following command:" +msgstr "vLLM 在 DB-GPT 是一个可选依赖, 你可以使用下面的命令手动安装它：" + +#: ../../getting_started/install/deploy.rst:385 +#: 6f4b540107764f3592cc07cf170e4911 +msgid "**2.Modifying the Configuration File**" +msgstr "**2.修改配置文件**" + +#: ../../getting_started/install/deploy.rst:387 +#: b8576a1572674c4890e09b73e02cf0e8 +msgid "Next, you can directly modify your .env file to enable vllm." +msgstr "你可以直接修改你的 `.env` 文件" + +#: ../../getting_started/install/deploy.rst:394 +#: b006745f3aee4651aaa0cf79081b5d7f +msgid "" +"You can view the models supported by vLLM `here " +"`_" +msgstr "" +"你可以在 `这里 " +"`_ 查看 vLLM 支持的模型。" + +#: ../../getting_started/install/deploy.rst:403 +#: bc8057ee75e14737bf8fca3ceb555dac +msgid "3.Prepare sql example(Optional)" +msgstr "3.准备 sql example(可选)" + +#: ../../getting_started/install/deploy.rst:404 +#: 9b0b9112237c4b3aaa1dd5d704ea32e6 +msgid "**(Optional) load examples into SQLite**" +msgstr "**(可选) 加载样例数据到 SQLite 数据库中**" + +#: ../../getting_started/install/deploy.rst:411 +#: 0815e13b96264ffcba1526c82ba2e7c8 +msgid "On windows platform:" +msgstr "在 Windows 平台：" + +#: ../../getting_started/install/deploy.rst:418 +#: 577a4167ecac4fa88586961f225f0487 +msgid "4.Run db-gpt server" +msgstr "4.运行db-gpt server" + +#: ../../getting_started/install/deploy.rst:424 +#: a9f96b064b674f80824257b4b0a18e2a +msgid "**Open http://localhost:5000 with your browser to see the product.**" +msgstr "打开浏览器访问http://localhost:5000" + +#~ msgid "" +#~ "DB-GPT can be deployed on servers" +#~ " with low hardware requirements or on" +#~ " servers with high hardware requirements." +#~ " You can install DB-GPT by " +#~ "Using third-part LLM REST API " +#~ "Service OpenAI, Azure." +#~ msgstr "" + +#~ msgid "" +#~ "And you can also install DB-GPT" +#~ " by deploy LLM Service by download" +#~ " LLM model." +#~ msgstr "" + +#~ msgid "百川" +#~ msgstr "" + +#~ msgid "百川 硬件要求" +#~ msgstr "" + diff --git a/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/deploy/deploy.po b/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/deploy/deploy.po index 0ed8cfcd9..abb04818d 100644 --- a/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/deploy/deploy.po +++ b/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/deploy/deploy.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: DB-GPT 👏👏 0.3.5\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-10-20 22:29+0800\n" +"POT-Creation-Date: 2023-10-26 11:34+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -20,47 +20,47 @@ msgstr "" "Generated-By: Babel 2.12.1\n" #: ../../getting_started/install/deploy/deploy.md:1 -#: 7bcf028ff0884ea88f25b7e2c9608153 +#: c9d9195862204bb9b526d728b1527a98 msgid "Installation From Source" msgstr "源码安装" #: ../../getting_started/install/deploy/deploy.md:3 -#: 61f0b1135c84423bbaeb5f9f0942ad7d +#: e462f24ec27645c3afd23866fdeea761 msgid "" "This tutorial gives you a quick walkthrough about use DB-GPT with you " "environment and data." msgstr "本教程为您提供了关于如何使用DB-GPT的使用指南。" #: ../../getting_started/install/deploy/deploy.md:5 -#: d7622cd5f69f4a32b3c8e979c6b9f601 +#: 065a4cf91565437cbad46726e5aee89c msgid "Installation" msgstr "安装" #: ../../getting_started/install/deploy/deploy.md:7 -#: 4368072b6384496ebeaff3c09ca2f888 +#: 07ebe19dbb5040419c6016258d975904 msgid "To get started, install DB-GPT with the following steps." msgstr "请按照以下步骤安装DB-GPT" #: ../../getting_started/install/deploy/deploy.md:9 -#: 0dfdf8ac6e314fe7b624a685d9beebd5 +#: 1a552a8bb4fe481ba7695e1a2f8985f8 msgid "1. Hardware Requirements" msgstr "1. 硬件要求" #: ../../getting_started/install/deploy/deploy.md:10 -#: cff920f8732f4f1da3063ec2bc099271 +#: bd292acafdb74b99a570c4a8e126df5d msgid "" "DB-GPT can be deployed on servers with low hardware requirements or on " "servers with high hardware requirements." msgstr "DB-GPT可以部署在对硬件要求不高的服务器，也可以部署在对硬件要求高的服务器" #: ../../getting_started/install/deploy/deploy.md:12 -#: 8e3818824d6146c6b265731c277fbd0b +#: 913c8d0630f2460997fb856b81967903 #, fuzzy msgid "Low hardware requirements" msgstr "1. 硬件要求" #: ../../getting_started/install/deploy/deploy.md:13 -#: ca95d66526994173ac1fea20bdea5d67 +#: 70ca521385c642049789e14ab61bc46b msgid "" "The low hardware requirements mode is suitable for integrating with " "third-party LLM services' APIs, such as OpenAI, Tongyi, Wenxin, or " @@ -68,23 +68,23 @@ msgid "" msgstr "Low hardware requirements模式适用于对接第三方模型服务的api,比如OpenAI, 通义千问, 文心.cpp。" #: ../../getting_started/install/deploy/deploy.md:15 -#: 83fc53cc1b4248139f69f490b859ad8d +#: 7aacaa2505c447bfa3d0ef6418ae73d2 msgid "DB-GPT provides set proxy api to support LLM api." msgstr "DB-GPT可以通过设置proxy api来支持第三方大模型服务" #: ../../getting_started/install/deploy/deploy.md:17 -#: 418a9f24eafc4571b74d86c3f1e57a2d +#: 6b5a9f7d61d54a559363a9a5d270a580 msgid "As our project has the ability to achieve ChatGPT performance of over 85%," msgstr "由于我们的项目有能力达到85%以上的ChatGPT性能" #: ../../getting_started/install/deploy/deploy.md:19 -#: 6f85149ab0024cc99e43804206a595ed +#: cf4b1f1115c041cbafb15af61946378c #, fuzzy msgid "High hardware requirements" msgstr "1. 硬件要求" #: ../../getting_started/install/deploy/deploy.md:20 -#: 31635ffff5084814a14deb3220dd2c17 +#: dae1e9a698144a919dc3740cd676eb81 #, fuzzy msgid "" "The high hardware requirements mode is suitable for independently " @@ -98,67 +98,67 @@ msgstr "" "chatglm，vicuna等私有大模型所以对硬件有一定的要求。但总体来说，我们在消费级的显卡上即可完成项目的部署使用，具体部署的硬件说明如下:" #: ../../getting_started/install/deploy/deploy.md -#: d806b90be1614ad3b2e06c92f4b17e5c +#: a6457364eccd49c99dc6a020a9aa5185 msgid "GPU" msgstr "GPU" #: ../../getting_started/install/deploy/deploy.md -#: 4b02f41145484389ace0b547384ac269 bbba2ff3fab94482a1761264264deef9 +#: 2d737c43c9fd45efbaf1e4204227ab51 bc0aa12f56dd4d26af74d7c10187fc0c msgid "VRAM Size" msgstr "显存" #: ../../getting_started/install/deploy/deploy.md -#: 0ea63c2dcc0e43858a61e01d59ad09f9 +#: 8bfd4e58a63a42858b6be2d0ce11b2fa msgid "Performance" msgstr "Performance" #: ../../getting_started/install/deploy/deploy.md -#: 6521683eb91e450c928a72688550a63d +#: 9ff11248bfdc43e18e6c29ed95d4f807 msgid "RTX 4090" msgstr "RTX 4090" #: ../../getting_started/install/deploy/deploy.md -#: bb6340c9cdc048fbb0ed55defc1aaeb6 d991b39845ee404198e1a1e35cc416f3 +#: 229e141d0a0e4d558b11c204654f36a9 94a76fe065164a67883a79f75d10139c msgid "24 GB" msgstr "24 GB" #: ../../getting_started/install/deploy/deploy.md -#: 4134d3a89d364e33b2bdf1c7667e4755 +#: 2edf99edf84e43ea8a5dce2a5ad0056a msgid "Smooth conversation inference" msgstr "丝滑的对话体验" #: ../../getting_started/install/deploy/deploy.md -#: 096ff425ac7646a990a7133961c6e6af +#: df67002e7eb84939a1f452acc88a1fa2 msgid "RTX 3090" msgstr "RTX 3090" #: ../../getting_started/install/deploy/deploy.md -#: ecf670cdbec3493f804e6a785a83c608 +#: 333303d5e7054d2697f11bb2b53e92ec msgid "Smooth conversation inference, better than V100" msgstr "丝滑的对话体验，性能好于V100" #: ../../getting_started/install/deploy/deploy.md -#: 837b14e0a3d243bda0df7ab35b70b7e7 +#: acfb9e152ec74bacb715cd758a9be964 msgid "V100" msgstr "V100" #: ../../getting_started/install/deploy/deploy.md -#: 3b20a087c8e342c89ccb807ffc3817c2 b8b6b45253084436a5893896b35a2bd5 +#: 1127ebc45a1b4fdb828f13d55f99ce79 ed6dd464a7f640cd866712eb7f4d4b1b msgid "16 GB" msgstr "16 GB" #: ../../getting_started/install/deploy/deploy.md -#: 772e18bb0ace4f7ea68b51bfc05816ce 9351389a1fac479cbe67b1f8c2c37de5 +#: 0b6ef92756204be6984f39ee1b95d423 a5aad9380cd742c59934e5ead433a22e msgid "Conversation inference possible, noticeable stutter" msgstr "Conversation inference possible, noticeable stutter" #: ../../getting_started/install/deploy/deploy.md -#: aadb62bf48bb49d99a714bcdf3092260 +#: 7a5a394f02b04cdcb3a9785cfa0cfc7c msgid "T4" msgstr "T4" #: ../../getting_started/install/deploy/deploy.md:30 -#: 4de80d9fcf34470bae806d829836b7d7 +#: 055b00b30901485a841d958a63750341 #, fuzzy msgid "" "If your VRAM Size is not enough, DB-GPT supported 8-bit quantization and " @@ -166,109 +166,109 @@ msgid "" msgstr "如果你的显存不够，DB-GPT支持8-bit和4-bit量化版本" #: ../../getting_started/install/deploy/deploy.md:32 -#: 00d81cbf48b549f3b9128d3840d01b2e +#: 34175af61e2e4ecc9e56180b8872a30b msgid "" "Here are some of the VRAM size usage of the models we tested in some " "common scenarios." msgstr "这里是量化版本的相关说明" #: ../../getting_started/install/deploy/deploy.md -#: dc346f2bca794bb7ae34b330e82ccbcf +#: 1d1adf0f341b4ed7b47887c5923fbe08 msgid "Model" msgstr "Model" #: ../../getting_started/install/deploy/deploy.md -#: 8de6cd40de78460ba774650466f8df26 +#: 2e36f6ffdb084de3ae3d1568af60cecc msgid "Quantize" msgstr "Quantize" #: ../../getting_started/install/deploy/deploy.md -#: 3e412b8f4852482ab07a0f546e37ae7f f30054e0558b41a192cc9a2462b299ec +#: 54df9f9813274db482a683c001003e86 61380cdbc434467fbf9cb7cb1efe49b7 msgid "vicuna-7b-v1.5" msgstr "vicuna-7b-v1.5" #: ../../getting_started/install/deploy/deploy.md -#: 14358fa40cf94614acf39a803987631f 2a3f52b26b444783be04ffa795246a03 -#: 3956734b19aa44c3be08d56348b47a38 751034ca7d00447895fda1d9b8a7364f -#: a66d16e5424a42a3a1309dfb8ffc33f9 b8ebce0a9e7e481da5f16214f955665d -#: f533b3f37e6f4594aec5e0f59f241683 +#: 4390ae926c094187bf2905361a5d6cff 467d31fb940c4daf9b2afec6bb7ea7f0 +#: 525896b27182457486018a348c068c01 6788cb202dc044e59e6ae42936b1aca8 +#: 74e07aaf7fa7461e824c653129240ad1 83b491c17b434fdb910b92c4cbc007a0 +#: c14cc4d0ac384fc699a196bf62573f01 msgid "4-bit" msgstr "4-bit" #: ../../getting_started/install/deploy/deploy.md -#: 9eac7e866ebe45169c64a952c363ce43 aa56722db3014abd9022067ed5fc4f98 -#: af4df898fb47471fbb487fcf6e2d40d6 +#: 25bbb9742e604003aeb2da782e50fa46 334153c71b624064b4f50342ab79c30e +#: 6039634dfcfc4ad3a3298938534ef1e4 msgid "8 GB" msgstr "8 GB" #: ../../getting_started/install/deploy/deploy.md -#: 211aaf2e234d46108b5eee5006d5f4bb 40214b2f71ce452db3501ea9d81a0c8a -#: 72fcd5e0634e48d79813f1037e6acb45 7756b67568cc40c4b73079b26e79c85d -#: 8c21f8e90154407682c093a46b93939d ad937c14bbcd41ac92a3dbbdb8339eed -#: d1e7ee217dd64b15b934456c3a72c450 +#: 0d31a4a235b949eabd8e98c2dcb6d5ff 15bcdb7aedf1497fb0790c1bf3e5ee47 +#: 3125bc143cb0476db0a07b7788bc9928 be257ed0772d448b95873db9e044a713 +#: cf4e3ed197a84dba87a60cc5fc70f8ac eb399647bafe418c90212787e695afbb +#: ee40d24463a143ca8768da7423d25b9b msgid "8-bit" msgstr "8-bit" #: ../../getting_started/install/deploy/deploy.md -#: 4812504dfb9a4b25a5db773d9a08f34f 76ae2407ba4e4013953b9f243d9a5d92 -#: 927054919de047fd8a83df67e1400622 9773e73eb89847f8a85a2dc55b562916 -#: ce33d0c3792f43398fc7e2694653d8fc d3dc0d4cceb24d2b9dc5c7120fbed94e +#: 21efb501692440cf80fd29401e1f0afa 246e4fc9b5f44f42a36bb49fd65c08f2 +#: 9adc65ad9d9344efa83f3507fb6ed2fd b00fd0d15bf441f48f9ea75d8877d4fd +#: d92aa46491b442a69709b9b8d7322c2e f8499e166ca04bc2a84bfa2d42cee890 msgid "12 GB" msgstr "12 GB" #: ../../getting_started/install/deploy/deploy.md -#: 83e6d6ba1aa74946858f0162424752ab b6b99caeaeff44c488e3e819ed337074 +#: 39950e71d2884d2c8ce4dbc9b0cb4491 ab3349160edf447ea67b15d7a056cc6e msgid "vicuna-13b-v1.5" msgstr "vicuna-13b-v1.5" #: ../../getting_started/install/deploy/deploy.md -#: 492c5f0d560946fe879f6c339975ba37 970063dda21e4dd8be6f89a3c87832a5 -#: a66bad6054b24dd99b370312bc8b6fa6 +#: 53bd397b40e449988d9fdfd201030387 6123ea3af97f4f3dafc6be44ecfed416 +#: f493c256788e4a318cf57fa9340948a4 msgid "20 GB" msgstr "20 GB" #: ../../getting_started/install/deploy/deploy.md -#: a75f3405085441d8920db49f159588d2 cf635931c55846aea4cbccd92e4f0377 +#: 11903db1ac944b60a40c92f752c1f3dc 76831b0fad014348a081f3f64260c73e msgid "llama-2-7b" msgstr "llama-2-7b" #: ../../getting_started/install/deploy/deploy.md -#: 61d632df8c5149b393d03ac802141125 bc98c895d457495ea26e3537de83b432 +#: 2576bd18e1714557b33420e5ed56a95b 997fe3a3a46e4891b39171b81386a601 msgid "llama-2-13b" msgstr "llama-2-13b" #: ../../getting_started/install/deploy/deploy.md -#: 3ccb1f6d8a924aeeacb5373edc168103 9ecce68e159a4649a8d5e69157af17a1 +#: 8a1cf1ae302c4c2d8bfbb1a666cc9ba6 9cbe2dae8bf44181b24d9806b654b80f msgid "llama-2-70b" msgstr "llama-2-70b" #: ../../getting_started/install/deploy/deploy.md -#: ca1da6ce08674b3daa0ab9ee0330203f +#: d5e875e84f534aac8f2cac4eacde7ead msgid "48 GB" msgstr "48 GB" #: ../../getting_started/install/deploy/deploy.md -#: 34d4d20e57c1410fbdcabd09a5968cdd +#: 2e32ea59cce24ff0a96c8c152afeb09b msgid "80 GB" msgstr "80 GB" #: ../../getting_started/install/deploy/deploy.md -#: 4ec2213171054c96ac9cd46e259ce7bf 68a1752f76a54287a73e82724723ea75 +#: 176feab13e554986a1e09ce3c1d060ee edcf6676d3274fd3a578d337894467bf msgid "baichuan-7b" msgstr "baichuan-7b" #: ../../getting_started/install/deploy/deploy.md -#: 103b020a575744ad964c60a367aa1651 c659a720a1024869b09d7cc161bcd8a2 +#: 3da976a65887483abc029acb7e7640d4 837745bfb1ac41a99604f55e94fd4099 msgid "baichuan-13b" msgstr "baichuan-13b" #: ../../getting_started/install/deploy/deploy.md:51 -#: 2259a008d0e14f9e8d1e1d9234b97298 +#: 3c573a548e6a4767b4acc2e4d2dbd20c msgid "2. Install" msgstr "2. Install" #: ../../getting_started/install/deploy/deploy.md:56 -#: 875c7d8e32574552a48199577c78ccdd +#: 40d61f4ee30d4d70a45a0ffb97001cd6 msgid "" "We use Sqlite as default database, so there is no need for database " "installation. If you choose to connect to other databases, you can " @@ -283,7 +283,7 @@ msgstr "" " Miniconda](https://docs.conda.io/en/latest/miniconda.html)" #: ../../getting_started/install/deploy/deploy.md:67 -#: c03e3290e1144320a138d015171ac596 +#: 69c950c69b204b94a768d1f023cc978a msgid "" "Once the environment is installed, we have to create a new folder " "\"models\" in the DB-GPT project, and then we can put all the models " @@ -291,49 +291,48 @@ msgid "" msgstr "如果你已经安装好了环境需要创建models, 然后到huggingface官网下载模型" #: ../../getting_started/install/deploy/deploy.md:70 -#: 933401ac909741ada4acf6bcd4142ed6 +#: 2ab1a4d9de8e412f80bd04ce6b40cdf6 msgid "Notice make sure you have install git-lfs" msgstr "注意确认你已经安装了git-lfs" #: ../../getting_started/install/deploy/deploy.md:72 -#: e8e4886a83dd402c85fe3fa989322991 +#: e718db01f5404485a05857b8403df93c msgid "centos:yum install git-lfs" msgstr "" #: ../../getting_started/install/deploy/deploy.md:74 -#: 5ead7e98bddf4fa4845c3d3955f18054 +#: e98570774d28430295a094cc5f5220ae msgid "ubuntu:apt-get install git-lfs" msgstr "" #: ../../getting_started/install/deploy/deploy.md:76 -#: 08acfaaaa2544182a59df54cdf61cd84 +#: 34c33b64b8de4b00be92b525ad038f23 msgid "macos:brew install git-lfs" msgstr "" #: ../../getting_started/install/deploy/deploy.md:78 -#: 312ad44170c34531865576067c58701a +#: 34bd5fbc8a8c4898a4caa7d630137061 msgid "Download LLM Model and Embedding Model" msgstr "下载LLM模型和Embedding模型" #: ../../getting_started/install/deploy/deploy.md:80 -#: de54793643434528a417011d2919b2c4 +#: a67d0e365e684a3bbb5e8618c98884ce #, fuzzy msgid "" -"If you use OpenAI llm service, see [LLM Use FAQ](https://db-" -"gpt.readthedocs.io/en/latest/getting_started/faq/llm/llm_faq.html)" -msgstr "" -"如果想使用openai大模型服务, 可以参考[LLM Use FAQ](https://db-" -"gpt.readthedocs.io/en/latest/getting_started/faq/llm/llm_faq.html)" +"If you use OpenAI llm service, see [How to Use LLM REST API](https://db-" +"gpt.readthedocs.io/en/latest/getting_started/install/llm/proxyllm/proxyllm.html)" +msgstr "如果想使用openai大模型服务, 可以参考[如何集成LLM REST API](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh-cn/latest/getting_started/install/llm/proxyllm/proxyllm.html)" #: ../../getting_started/install/deploy/deploy.md:83 -#: 50ec1eb7c56a46ac8fbf911c7adc9b0e +#: f1a43cd2eba3458c863bfc77cf13ac1f +#, fuzzy msgid "" -"If you use openai or Azure or tongyi llm api service, you don't need to " +"If you use openai or Axzure or tongyi llm api service, you don't need to " "download llm model." msgstr "如果你想通过openai or Azure or tongyi第三方api访问模型服务，你可以不用下载llm模型" #: ../../getting_started/install/deploy/deploy.md:103 -#: 03950b2a480149388fb7b88f7d251ef5 +#: 9f46746726ec4791b6963a2e2c4376c4 msgid "" "The model files are large and will take a long time to download. During " "the download, let's configure the .env file, which needs to be copied and" @@ -341,19 +340,19 @@ msgid "" msgstr "模型文件很大，需要很长时间才能下载。在下载过程中，让我们配置.env文件，它需要从。env.template中复制和创建。" #: ../../getting_started/install/deploy/deploy.md:106 -#: 441c4333216a402a84fd52f8e56fc81b +#: 53e713c8a9664d92a6e4055789c4a7da msgid "cp .env.template .env" msgstr "cp .env.template .env" #: ../../getting_started/install/deploy/deploy.md:109 -#: 4eac3d98df6a4e788234ff0ec1ffd03e +#: 21c38ebc721242478e7ad4be4d672dc6 msgid "" "You can configure basic parameters in the .env file, for example setting " "LLM_MODEL to the model to be used" msgstr "您可以在.env文件中配置基本参数，例如将LLM_MODEL设置为要使用的模型。" #: ../../getting_started/install/deploy/deploy.md:111 -#: a36bd6d6236b4c74b161a935ae792b91 +#: 6ca180c2142b455ebcc3a8b37f3bc25a msgid "" "([Vicuna-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5) based on " "llama-2 has been released, we recommend you set `LLM_MODEL=vicuna-" @@ -364,39 +363,39 @@ msgstr "" "目前Vicuna-v1.5模型(基于llama2)已经开源了，我们推荐你使用这个模型通过设置LLM_MODEL=vicuna-13b-v1.5" #: ../../getting_started/install/deploy/deploy.md:113 -#: 78334cbf0c364eb3bc41a2a6c55ebb0d +#: 1a346658ec4b4074b3458fc806538aae msgid "3. Run" msgstr "3. Run" #: ../../getting_started/install/deploy/deploy.md:115 -#: 6d5ad6eb067d4e9fa1c574b7b706233f +#: 70f6300673834c9eb3e80145bb5bfcb8 msgid "**(Optional) load examples into SQLite**" msgstr "" #: ../../getting_started/install/deploy/deploy.md:120 -#: 07219a4ed3c44e349314ae04ebdf58e1 +#: c10f16bd300e473a950617b814d743a0 msgid "On windows platform:" msgstr "" #: ../../getting_started/install/deploy/deploy.md:125 -#: 819be2bb22044440ae00c2e7687ea249 +#: 64213616228643a7b0413805061b7a12 #, fuzzy msgid "Run db-gpt server" msgstr "1.Run db-gpt server" #: ../../getting_started/install/deploy/deploy.md:131 -#: 5ba6d7c9bf9146c797036ab4b9b4f59e +#: cb3248ebbade45bfba1366a66e4220f6 msgid "Open http://localhost:5000 with your browser to see the product." msgstr "打开浏览器访问http://localhost:5000" #: ../../getting_started/install/deploy/deploy.md:134 -#: be3a2729ef3b4742a403017b31bda7e3 +#: 0499acfd344b4d70a0cdce31f245971c #, fuzzy msgid "Multiple GPUs" msgstr "4. Multiple GPUs" #: ../../getting_started/install/deploy/deploy.md:136 -#: 00ffa1cc145e4afa830c592a629246f9 +#: d56b6c4aa795428aa64e3740401645d3 msgid "" "DB-GPT will use all available gpu by default. And you can modify the " "setting `CUDA_VISIBLE_DEVICES=0,1` in `.env` file to use the specific gpu" @@ -404,32 +403,32 @@ msgid "" msgstr "DB-GPT默认加载可利用的gpu，你也可以通过修改 在`.env`文件 `CUDA_VISIBLE_DEVICES=0,1`来指定gpu IDs" #: ../../getting_started/install/deploy/deploy.md:138 -#: bde32a5a8fea4350868be579e9ee6baa +#: ca208283904441ab802827d94b3b9590 msgid "" "Optionally, you can also specify the gpu ID to use before the starting " "command, as shown below:" msgstr "你也可以指定gpu ID启动" #: ../../getting_started/install/deploy/deploy.md:148 -#: 791ed2db2cff44c48342a7828cbd4c45 +#: 30f7c3b3d9784a4698cdd15a0c046e81 msgid "" "You can modify the setting `MAX_GPU_MEMORY=xxGib` in `.env` file to " "configure the maximum memory used by each GPU." msgstr "同时你可以通过在.env文件设置`MAX_GPU_MEMORY=xxGib`修改每个GPU的最大使用内存" #: ../../getting_started/install/deploy/deploy.md:150 -#: f86b37c8943e4f5595610706e75b4add +#: ab02884bb7f24e59b21b797501c3794b #, fuzzy msgid "Not Enough Memory" msgstr "5. Not Enough Memory" #: ../../getting_started/install/deploy/deploy.md:152 -#: 8a7bd02cbeca497aa8eecaaf1910a6ad +#: 5b246b0456f8448bb0207312a17d40c5 msgid "DB-GPT supported 8-bit quantization and 4-bit quantization." msgstr "DB-GPT 支持 8-bit quantization 和 4-bit quantization." #: ../../getting_started/install/deploy/deploy.md:154 -#: 5ad49b99fe774ba79c50de0cd694807c +#: a002dc2572d34b0f9b3ca6ac3b3b6147 msgid "" "You can modify the setting `QUANTIZE_8bit=True` or `QUANTIZE_4bit=True` " "in `.env` file to use quantization(8-bit quantization is enabled by " @@ -437,7 +436,7 @@ msgid "" msgstr "你可以通过在.env文件设置`QUANTIZE_8bit=True` or `QUANTIZE_4bit=True`" #: ../../getting_started/install/deploy/deploy.md:156 -#: b9c80e92137447da91eb944443144c69 +#: 16d429bd42a44b02875e505273d35228 msgid "" "Llama-2-70b with 8-bit quantization can run with 80 GB of VRAM, and 4-bit" " quantization can run with 48 GB of VRAM." diff --git a/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/environment/environment.po b/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/environment/environment.po index 09b3c8fa2..bd4d6fa0b 100644 --- a/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/environment/environment.po +++ b/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/environment/environment.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: DB-GPT 👏👏 0.3.5\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-08-17 13:07+0800\n" +"POT-Creation-Date: 2023-11-14 16:08+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -20,290 +20,287 @@ msgstr "" "Generated-By: Babel 2.12.1\n" #: ../../getting_started/install/environment/environment.md:1 -#: be341d16f7b24bf4ad123ab78a6d855a +#: e4787ab6eacc4362802752528bb786ec #, fuzzy msgid "Environment Parameter" msgstr "环境变量说明" #: ../../getting_started/install/environment/environment.md:4 -#: 46eddb27c90f41548ea9a724bbcebd37 +#: 4682a0734a034e0e9f2c22fa061b889e msgid "LLM MODEL Config" msgstr "模型配置" #: ../../getting_started/install/environment/environment.md:5 -#: 7deaa85df4a04fb098f5994547a8724f +#: c148f178b2964344a570bb2b3713fba3 msgid "LLM Model Name, see /pilot/configs/model_config.LLM_MODEL_CONFIG" msgstr "LLM Model Name, see /pilot/configs/model_config.LLM_MODEL_CONFIG" #: ../../getting_started/install/environment/environment.md:6 -#: 3902801c546547b3a4009df681ef7d52 +#: 9ab8d82fb338439a8c0042b92ad2f7c4 msgid "LLM_MODEL=vicuna-13b" msgstr "LLM_MODEL=vicuna-13b" #: ../../getting_started/install/environment/environment.md:8 -#: 84b0fdbfa1544ec28751e9b69b00cc02 +#: 76fb3b1299694730852f120db6fec7f9 msgid "MODEL_SERVER_ADDRESS" msgstr "MODEL_SERVER_ADDRESS" -#: ../../getting_started/install/environment/environment.md:9 -#: 0b430bfab77d405989470d00ca3f6fe0 -msgid "MODEL_SERVER=http://127.0.0.1:8000 LIMIT_MODEL_CONCURRENCY" +#: ../../getting_started/install/environment/environment.md:10 +#: 7476a0ee342f4517bbf999abecec029e +#, fuzzy +msgid "MODEL_SERVER=http://127.0.0.1:8000" msgstr "MODEL_SERVER=http://127.0.0.1:8000 LIMIT_MODEL_CONCURRENCY" #: ../../getting_started/install/environment/environment.md:12 -#: b477a25586c546729a93fb6785b7b2ec -msgid "LIMIT_MODEL_CONCURRENCY=5" +#: fb3c73990a6443e8b63c35d61175e467 +#, fuzzy +msgid "LIMIT_MODEL_CONCURRENCY" msgstr "LIMIT_MODEL_CONCURRENCY=5" #: ../../getting_started/install/environment/environment.md:14 -#: 1d6ea800af384fff9c265610f71cc94e +#: 0eb187fffa3643dbac4bbe7237d2e011 +msgid "LIMIT_MODEL_CONCURRENCY=5" +msgstr "LIMIT_MODEL_CONCURRENCY=5" + +#: ../../getting_started/install/environment/environment.md:16 +#: 1d7b8bf89c1b44e9871d9d0c382db114 msgid "MAX_POSITION_EMBEDDINGS" msgstr "MAX_POSITION_EMBEDDINGS" -#: ../../getting_started/install/environment/environment.md:16 -#: 388e758ce4ea4692a4c34294cebce7f2 +#: ../../getting_started/install/environment/environment.md:18 +#: 50d0b3f760fd4ff9829cd1ba0653fd79 msgid "MAX_POSITION_EMBEDDINGS=4096" msgstr "MAX_POSITION_EMBEDDINGS=4096" -#: ../../getting_started/install/environment/environment.md:18 -#: 16a307dce1294ceba892ff93ae4e81c0 +#: ../../getting_started/install/environment/environment.md:20 +#: d07c4bbcde214f5993d73ac2bfb1bf9e msgid "QUANTIZE_QLORA" msgstr "QUANTIZE_QLORA" -#: ../../getting_started/install/environment/environment.md:20 -#: 93ceb2b2fcd5454b82eefb0ae8c7ae77 +#: ../../getting_started/install/environment/environment.md:22 +#: 6bceef51780f45d9805270d16847ddc2 msgid "QUANTIZE_QLORA=True" msgstr "QUANTIZE_QLORA=True" -#: ../../getting_started/install/environment/environment.md:22 -#: 15ffa35d023a4530b02a85ee6168dd4b +#: ../../getting_started/install/environment/environment.md:24 +#: df9d560f69334e4aa3f6803e40a7f38d msgid "QUANTIZE_8bit" msgstr "QUANTIZE_8bit" -#: ../../getting_started/install/environment/environment.md:24 -#: 81df248ac5cb4ab0b13a711505f6a177 +#: ../../getting_started/install/environment/environment.md:26 +#: ac433b8574574432add7315558b845ea msgid "QUANTIZE_8bit=True" msgstr "QUANTIZE_8bit=True" -#: ../../getting_started/install/environment/environment.md:27 -#: 15cc7b7d41ad44f0891c1189709f00f1 +#: ../../getting_started/install/environment/environment.md:29 +#: 7b1c407517984bff9f4d509c5f45b92e msgid "LLM PROXY Settings" msgstr "LLM PROXY Settings" -#: ../../getting_started/install/environment/environment.md:28 -#: e6c1115a39404f11b193a1593bc51a22 +#: ../../getting_started/install/environment/environment.md:30 +#: ba7d52c0e95143ebb973e7eda69f0bc1 msgid "OPENAI Key" msgstr "OPENAI Key" -#: ../../getting_started/install/environment/environment.md:30 -#: 8157e0a831fe4506a426822b7565e4f6 +#: ../../getting_started/install/environment/environment.md:32 +#: 0f0bd20a7a60461e8bcfc91297cc3666 msgid "PROXY_API_KEY={your-openai-sk}" msgstr "PROXY_API_KEY={your-openai-sk}" -#: ../../getting_started/install/environment/environment.md:31 -#: 89b34d00bdb64e738bd9bc8c086b1f02 +#: ../../getting_started/install/environment/environment.md:33 +#: d9c03e0b3316415eb2ca59ad9c419b8c msgid "PROXY_SERVER_URL=https://api.openai.com/v1/chat/completions" msgstr "PROXY_SERVER_URL=https://api.openai.com/v1/chat/completions" -#: ../../getting_started/install/environment/environment.md:33 -#: 7a97df730aeb484daf19c8172e61a290 +#: ../../getting_started/install/environment/environment.md:35 +#: 45883f99c1fd494ea513f3c0f92562a3 msgid "from https://bard.google.com/ f12-> application-> __Secure-1PSID" msgstr "from https://bard.google.com/ f12-> application-> __Secure-1PSID" -#: ../../getting_started/install/environment/environment.md:35 -#: d430ddf726a049c0a9e0a9bfd5a6fe0e +#: ../../getting_started/install/environment/environment.md:37 +#: 70665dbe72c545a3b61c6efe37dfa7d5 msgid "BARD_PROXY_API_KEY={your-bard-token}" msgstr "BARD_PROXY_API_KEY={your-bard-token}" -#: ../../getting_started/install/environment/environment.md:38 -#: 23d6b0da3e7042abb55f6181c4a382d2 +#: ../../getting_started/install/environment/environment.md:40 +#: 782f8a9c9cd745a4990542ba8130c66a msgid "DATABASE SETTINGS" msgstr "DATABASE SETTINGS" -#: ../../getting_started/install/environment/environment.md:39 -#: dbae0a2d847f41f5be9396a160ef88d0 +#: ../../getting_started/install/environment/environment.md:41 +#: 50ad9eae827a407c8c77692f48b9d423 msgid "SQLite database (Current default database)" msgstr "SQLite database (Current default database)" -#: ../../getting_started/install/environment/environment.md:40 -#: bdb55b7280c341a981e9d338cce53345 +#: ../../getting_started/install/environment/environment.md:42 +#: 410041683b664cabbe7ce6cb2050c629 msgid "LOCAL_DB_PATH=data/default_sqlite.db" msgstr "LOCAL_DB_PATH=data/default_sqlite.db" -#: ../../getting_started/install/environment/environment.md:41 -#: 739d67927a9d46b28500deba1917916b +#: ../../getting_started/install/environment/environment.md:43 +#: 0fcf0f9da84d4e4a8a1503a96dd6734b msgid "LOCAL_DB_TYPE=sqlite # Database Type default:sqlite" msgstr "LOCAL_DB_TYPE=sqlite # Database Type default:sqlite" -#: ../../getting_started/install/environment/environment.md:43 -#: eb4717bce6a6483b86d9780d924c5ff1 +#: ../../getting_started/install/environment/environment.md:45 +#: 15fb9cdc51e44b71a1a375e49fb7bc6d msgid "MYSQL database" msgstr "MYSQL database" -#: ../../getting_started/install/environment/environment.md:44 -#: 0f4cdf0ff5dd4ff0b397dfa88541a2e1 +#: ../../getting_started/install/environment/environment.md:46 +#: c8cc4cb61d1c44cd9ef3546455929ef6 msgid "LOCAL_DB_TYPE=mysql" msgstr "LOCAL_DB_TYPE=mysql" -#: ../../getting_started/install/environment/environment.md:45 -#: c971ead492c34487bd766300730a9cba +#: ../../getting_started/install/environment/environment.md:47 +#: a6caf3cabc4041b5879ec3af25c85139 msgid "LOCAL_DB_USER=root" msgstr "LOCAL_DB_USER=root" -#: ../../getting_started/install/environment/environment.md:46 -#: 02828b29ad044eeab890a2f8af0e5907 +#: ../../getting_started/install/environment/environment.md:48 +#: b839bde122374e299086f120fce0144c msgid "LOCAL_DB_PASSWORD=aa12345678" msgstr "LOCAL_DB_PASSWORD=aa12345678" -#: ../../getting_started/install/environment/environment.md:47 -#: 53dc7f15b3934987b1f4c2e2d0b11299 +#: ../../getting_started/install/environment/environment.md:49 +#: 52cdbfdefda142b4a3b5cb3b060916a8 msgid "LOCAL_DB_HOST=127.0.0.1" msgstr "LOCAL_DB_HOST=127.0.0.1" -#: ../../getting_started/install/environment/environment.md:48 -#: 1ac95fc482934247a118bab8dcebeb57 +#: ../../getting_started/install/environment/environment.md:50 +#: 492db6e5c13b40898f38063980c5897c msgid "LOCAL_DB_PORT=3306" msgstr "LOCAL_DB_PORT=3306" -#: ../../getting_started/install/environment/environment.md:51 -#: 34e46aa926844be19c7196759b03af63 +#: ../../getting_started/install/environment/environment.md:53 +#: 20b101603f054c70af633439abddefec msgid "EMBEDDING SETTINGS" msgstr "EMBEDDING SETTINGS" -#: ../../getting_started/install/environment/environment.md:52 -#: 2b5aa08cc995495e85a1f7dc4f97b5d7 +#: ../../getting_started/install/environment/environment.md:54 +#: 3463a5a74cea494c8442100c0069285c msgid "EMBEDDING MODEL Name, see /pilot/configs/model_config.LLM_MODEL_CONFIG" msgstr "EMBEDDING模型, 参考see /pilot/configs/model_config.LLM_MODEL_CONFIG" -#: ../../getting_started/install/environment/environment.md:53 -#: 0de0ca551ed040248406f848feca541d +#: ../../getting_started/install/environment/environment.md:55 +#: 4c8adbf52110474bbfcd3b63cf2839f6 msgid "EMBEDDING_MODEL=text2vec" msgstr "EMBEDDING_MODEL=text2vec" -#: ../../getting_started/install/environment/environment.md:55 -#: 43019fb570904c9981eb68f33e64569c +#: ../../getting_started/install/environment/environment.md:57 +#: 8a85a75151e64827971b1a367b31ecfa msgid "Embedding Chunk size, default 500" msgstr "Embedding 切片大小, 默认500" -#: ../../getting_started/install/environment/environment.md:57 -#: 7e3f93854873461286e96887e04167aa +#: ../../getting_started/install/environment/environment.md:59 +#: 947939b0fa7e46de97d48eadf5c443d2 msgid "KNOWLEDGE_CHUNK_SIZE=500" msgstr "KNOWLEDGE_CHUNK_SIZE=500" -#: ../../getting_started/install/environment/environment.md:59 -#: 9504f4a59ae74352a524b7741113e2d6 +#: ../../getting_started/install/environment/environment.md:61 +#: 2785ad6bb0de4534a6523ac420f2c84c msgid "Embedding Chunk Overlap, default 100" msgstr "Embedding chunk Overlap, 文本块之间的最大重叠量。保留一些重叠可以保持文本块之间的连续性（例如使用滑动窗口）,默认100" -#: ../../getting_started/install/environment/environment.md:60 -#: 24e6119c2051479bbd9dba71a9c23dbe +#: ../../getting_started/install/environment/environment.md:62 +#: 40b6a8f57ee14ec1ab73143ba1516e78 msgid "KNOWLEDGE_CHUNK_OVERLAP=100" msgstr "KNOWLEDGE_CHUNK_OVERLAP=100" -#: ../../getting_started/install/environment/environment.md:62 -#: 0d180d7f2230442abee901c19526e442 -msgid "embeding recall top k,5" +#: ../../getting_started/install/environment/environment.md:64 +#: e410faa1087c45639ee210be99cf9336 +#, fuzzy +msgid "embedding recall top k,5" msgstr "embedding 召回topk, 默认5" -#: ../../getting_started/install/environment/environment.md:64 -#: a5bb9ab2ba50411cbbe87f7836bfbb6d +#: ../../getting_started/install/environment/environment.md:66 +#: abfca38fe2a04161a11259588fa4d205 msgid "KNOWLEDGE_SEARCH_TOP_SIZE=5" msgstr "KNOWLEDGE_SEARCH_TOP_SIZE=5" -#: ../../getting_started/install/environment/environment.md:66 -#: 183b8dd78cba4ae19bd2e08d69d21e0b -msgid "embeding recall max token ,2000" +#: ../../getting_started/install/environment/environment.md:68 +#: 31182c38607b4c3bbc657b5fe5b7a4f6 +#, fuzzy +msgid "embedding recall max token ,2000" msgstr "embedding向量召回最大token, 默认2000" -#: ../../getting_started/install/environment/environment.md:68 -#: ce0c711febcb44c18ae0fc858c3718d1 +#: ../../getting_started/install/environment/environment.md:70 +#: 96cd042635bc468e90c792fd9d1a7f4d msgid "KNOWLEDGE_SEARCH_MAX_TOKEN=5" msgstr "KNOWLEDGE_SEARCH_MAX_TOKEN=5" -#: ../../getting_started/install/environment/environment.md:71 -#: ../../getting_started/install/environment/environment.md:87 -#: 4cab1f399cc245b4a1a1976d2c4fc926 ec9cec667a1c4473bf9a796a26e1ce20 +#: ../../getting_started/install/environment/environment.md:73 +#: d43b408ad9bc46f2b3c97aa91627f6b3 msgid "Vector Store SETTINGS" msgstr "Vector Store SETTINGS" -#: ../../getting_started/install/environment/environment.md:72 -#: ../../getting_started/install/environment/environment.md:88 -#: 4dd04aadd46948a5b1dcf01fdb0ef074 bab7d512f33e40cf9e10f0da67e699c8 +#: ../../getting_started/install/environment/environment.md:74 +#: b1fcbf6049af4eeea91edd3de58c8512 msgid "Chroma" msgstr "Chroma" -#: ../../getting_started/install/environment/environment.md:73 -#: ../../getting_started/install/environment/environment.md:89 -#: 13eec36741b14e028e2d3859a320826e ab3ffbcf9358401993af636ba9ab2e2d +#: ../../getting_started/install/environment/environment.md:75 +#: 2fb31575b274448fb945d47ee0eb108c msgid "VECTOR_STORE_TYPE=Chroma" msgstr "VECTOR_STORE_TYPE=Chroma" -#: ../../getting_started/install/environment/environment.md:74 -#: ../../getting_started/install/environment/environment.md:90 -#: d15b91e2a2884f23a1dd2d54783b0638 d1f856d571b547098bb0c2a18f9f1979 +#: ../../getting_started/install/environment/environment.md:76 +#: 601b87cc6f1d4732b935747e907cba5a msgid "MILVUS" msgstr "MILVUS" -#: ../../getting_started/install/environment/environment.md:75 -#: ../../getting_started/install/environment/environment.md:91 -#: 1e165f6c934343c7808459cc7a65bc70 985dd60c2b7d4baaa6601a810a6522d7 +#: ../../getting_started/install/environment/environment.md:77 +#: fde6cf6982764020aa1174f7fe3a5b3e msgid "VECTOR_STORE_TYPE=Milvus" msgstr "VECTOR_STORE_TYPE=Milvus" -#: ../../getting_started/install/environment/environment.md:76 -#: ../../getting_started/install/environment/environment.md:92 -#: a1a53f051cee40ed886346a94babd75a d263e8eaee684935a58f0a4fe61c6f0e +#: ../../getting_started/install/environment/environment.md:78 +#: 40c6206c7a614edf9b0af82c2c76f518 msgid "MILVUS_URL=127.0.0.1" msgstr "MILVUS_URL=127.0.0.1" -#: ../../getting_started/install/environment/environment.md:77 -#: ../../getting_started/install/environment/environment.md:93 -#: 2741a312db1a4c6a8a1c1d62415c5fba d03bbf921ddd4f4bb715fe5610c3d0aa +#: ../../getting_started/install/environment/environment.md:79 +#: abde3c75269442cbb94a59c657d847a9 msgid "MILVUS_PORT=19530" msgstr "MILVUS_PORT=19530" -#: ../../getting_started/install/environment/environment.md:78 -#: ../../getting_started/install/environment/environment.md:94 -#: d0786490d38c4e4f971cc14f62fe1fc8 e9e0854873dc4c209861ee4eb77d25cd +#: ../../getting_started/install/environment/environment.md:80 +#: 375a837cbf6d4d65891612a7f073414a msgid "MILVUS_USERNAME" msgstr "MILVUS_USERNAME" -#: ../../getting_started/install/environment/environment.md:79 -#: ../../getting_started/install/environment/environment.md:95 -#: 9a82d07153cc432ebe754b5bc02fde0d a6485c1cfa7d4069a6894c43674c8c2b +#: ../../getting_started/install/environment/environment.md:81 +#: f785a796c8d3452c802d9a637f34cb57 msgid "MILVUS_PASSWORD" msgstr "MILVUS_PASSWORD" -#: ../../getting_started/install/environment/environment.md:80 -#: ../../getting_started/install/environment/environment.md:96 -#: 2f233f32b8ba408a9fbadb21fabb99ec 809b3219dd824485bc2cfc898530d708 +#: ../../getting_started/install/environment/environment.md:82 +#: 18cd17a50dc14add9b31f6b4c55069ef msgid "MILVUS_SECURE=" msgstr "MILVUS_SECURE=" -#: ../../getting_started/install/environment/environment.md:82 -#: ../../getting_started/install/environment/environment.md:98 -#: f00603661f2b42e1bd2bca74ad1e3c31 f378e16fdec44c559e34c6929de812e8 +#: ../../getting_started/install/environment/environment.md:84 +#: a4783d775bf2444788b758a71bd5a7e7 msgid "WEAVIATE" msgstr "WEAVIATE" -#: ../../getting_started/install/environment/environment.md:83 -#: da2049ebc6874cf0a6b562e0e2fd9ec7 +#: ../../getting_started/install/environment/environment.md:85 +#: 3cc5ca99670947e6868e27db588031e0 msgid "VECTOR_STORE_TYPE=Weaviate" msgstr "VECTOR_STORE_TYPE=Weaviate" -#: ../../getting_started/install/environment/environment.md:84 -#: ../../getting_started/install/environment/environment.md:99 -#: 25f1246629934289aad7ef01c7304097 c9fe0e413d9a4fc8abf86b3ed99e0581 +#: ../../getting_started/install/environment/environment.md:86 +#: 141a3da2e36e40ffaa0fb863081a4c07 msgid "WEAVIATE_URL=https://kt-region-m8hcy0wc.weaviate.network" msgstr "WEAVIATE_URL=https://kt-region-m8hcy0wc.weaviate.network" -#: ../../getting_started/install/environment/environment.md:102 -#: ba7c9e707f6a4cd6b99e52b58da3ab2d +#: ../../getting_started/install/environment/environment.md:89 +#: fde1941617ec4148b33c298bebeb45e4 msgid "Multi-GPU Setting" msgstr "Multi-GPU Setting" -#: ../../getting_started/install/environment/environment.md:103 -#: 5ca75fdf2c264b2c844d77f659b4f0b3 +#: ../../getting_started/install/environment/environment.md:90 +#: fe162354e15e42cda54f6c9322409321 msgid "" "See https://developer.nvidia.com/blog/cuda-pro-tip-control-gpu-" "visibility-cuda_visible_devices/ If CUDA_VISIBLE_DEVICES is not " @@ -312,50 +309,50 @@ msgstr "" "参考 https://developer.nvidia.com/blog/cuda-pro-tip-control-gpu-visibility-" "cuda_visible_devices/ 如果 CUDA_VISIBLE_DEVICES没有设置, 会使用所有可用的gpu" -#: ../../getting_started/install/environment/environment.md:106 -#: de92eb310aff43fbbbf3c5a116c3b2c6 +#: ../../getting_started/install/environment/environment.md:93 +#: c8a83b09bfc94dab8226840b275ca034 msgid "CUDA_VISIBLE_DEVICES=0" msgstr "CUDA_VISIBLE_DEVICES=0" -#: ../../getting_started/install/environment/environment.md:108 -#: d2641df6123a442b8e4444ad5f01a9aa +#: ../../getting_started/install/environment/environment.md:95 +#: a1d33bd2492a4a80bd8b679c1331280a msgid "" "Optionally, you can also specify the gpu ID to use before the starting " "command" msgstr "你也可以通过启动命令设置gpu ID" -#: ../../getting_started/install/environment/environment.md:110 -#: 76c66179d11a4e5fa369421378609aae +#: ../../getting_started/install/environment/environment.md:97 +#: 961087a5cf1b45168c7439e3a2103253 msgid "CUDA_VISIBLE_DEVICES=3,4,5,6" msgstr "CUDA_VISIBLE_DEVICES=3,4,5,6" -#: ../../getting_started/install/environment/environment.md:112 -#: 29bd0f01fdf540ad98385ea8473f7647 +#: ../../getting_started/install/environment/environment.md:99 +#: 545b438ecb9d46edacbd8b4cc95886f9 msgid "You can configure the maximum memory used by each GPU." msgstr "可以设置GPU的最大内存" -#: ../../getting_started/install/environment/environment.md:114 -#: 31e5e23838734ba7a2810e2387e6d6a0 +#: ../../getting_started/install/environment/environment.md:101 +#: a78dc8082fa04e13a7a3e43302830c26 msgid "MAX_GPU_MEMORY=16Gib" msgstr "MAX_GPU_MEMORY=16Gib" -#: ../../getting_started/install/environment/environment.md:117 -#: 99aa63ab1ae049d9b94536d6a96f3443 +#: ../../getting_started/install/environment/environment.md:104 +#: eaebcb1784be4047b739ff1b8a78faa1 msgid "Other Setting" msgstr "Other Setting" -#: ../../getting_started/install/environment/environment.md:118 -#: 3168732183874bffb59a3575d3473d62 +#: ../../getting_started/install/environment/environment.md:105 +#: 21f524662fa34bfa9cfb8855bc191cc7 msgid "Language Settings(influence prompt language)" msgstr "Language Settings(涉及prompt语言以及知识切片方式)" -#: ../../getting_started/install/environment/environment.md:119 -#: 73eb0a96f29b4739bd456faa9cb5033d +#: ../../getting_started/install/environment/environment.md:106 +#: bb5ce4a6ee794f0e910363673e54055a msgid "LANGUAGE=en" msgstr "LANGUAGE=en" -#: ../../getting_started/install/environment/environment.md:120 -#: c6646b78c6cf4d25a13108232f5b2046 +#: ../../getting_started/install/environment/environment.md:107 +#: 862f113d63b94084b89bfef29f8ab48d msgid "LANGUAGE=zh" msgstr "LANGUAGE=zh" diff --git a/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/llm/proxyllm/proxyllm.po b/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/llm/proxyllm/proxyllm.po new file mode 100644 index 000000000..f6411fd1a --- /dev/null +++ b/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/llm/proxyllm/proxyllm.po @@ -0,0 +1,97 @@ +# SOME DESCRIPTIVE TITLE. +# Copyright (C) 2023, csunny +# This file is distributed under the same license as the DB-GPT package. +# FIRST AUTHOR , 2023. +# +#, fuzzy +msgid "" +msgstr "" +"Project-Id-Version: DB-GPT 👏👏 0.4.0\n" +"Report-Msgid-Bugs-To: \n" +"POT-Creation-Date: 2023-10-26 11:26+0800\n" +"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" +"Last-Translator: FULL NAME \n" +"Language: zh_CN\n" +"Language-Team: zh_CN \n" +"Plural-Forms: nplurals=1; plural=0;\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=utf-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Generated-By: Babel 2.12.1\n" + +#: ../../getting_started/install/llm/proxyllm/proxyllm.md:1 +#: b006d689cfd2430da6a2b503a4f2fef3 +msgid "Proxy LLM API" +msgstr "Proxy LLM API" + +#: ../../getting_started/install/llm/proxyllm/proxyllm.md:3 +#: b60328fb6b074edba31c34825038bbf4 +msgid "Now DB-GPT supports connect LLM service through proxy rest api." +msgstr "DB-GPT支持对接第三方的LLM REST API 服务" + +#: ../../getting_started/install/llm/proxyllm/proxyllm.md:5 +#: 82dcc5fc9d314a6f871851c842c3b6b3 +msgid "LLM rest api now supports" +msgstr "LLM REST API服务目前支持" + +#: ../../getting_started/install/llm/proxyllm/proxyllm.md:7 +#: 2a894db1f42544b2bdc932b50050eaf4 +msgid "OpenAI" +msgstr "OpenAI" + +#: ../../getting_started/install/llm/proxyllm/proxyllm.md:8 +#: 52c288434b6b42a1a376f8d698d0aad1 +msgid "Azure" +msgstr "Azure" + +#: ../../getting_started/install/llm/proxyllm/proxyllm.md:9 +#: eeca0b58cf504586b8695e433e1a4458 +msgid "Aliyun tongyi" +msgstr "通义千问API" + +#: ../../getting_started/install/llm/proxyllm/proxyllm.md:10 +#: 7b30a85b145545f0b2d8dd3b85f98bcf +msgid "Baidu wenxin" +msgstr "百度文心API" + +#: ../../getting_started/install/llm/proxyllm/proxyllm.md:11 +#: b4cfeba632cb4f898564cf76d9c1551d +msgid "Zhipu" +msgstr "智谱API" + +#: ../../getting_started/install/llm/proxyllm/proxyllm.md:12 +#: faa92560db2b47d9b9a41bbf703fd84d +msgid "Baichuan" +msgstr "百川API" + +#: ../../getting_started/install/llm/proxyllm/proxyllm.md:13 +#: aba2dcc36b854b6193ababca772e1cf0 +#, fuzzy +msgid "Bard" +msgstr "bard API" + +#: ../../getting_started/install/llm/proxyllm/proxyllm.md:17 +#: f66f0bfcad2a4f428c953452d5f6963b +msgid "" +"How to Integrate LLM rest API, like OpenAI, Azure, tongyi, wenxin llm " +"api service?" +msgstr "如何集成这些LLM rest API呢" + +#: ../../getting_started/install/llm/proxyllm/proxyllm.md:18 +#: 01284baeb4a24bb18d48e51ad8503997 +msgid "update your `.env` file" +msgstr "更新`.env`配置文件" + +#: ../../getting_started/install/llm/proxyllm/proxyllm.md:63 +#: f602876d957446fd8056854b6b2121a1 +msgid "Make sure your .env configuration is not overwritten" +msgstr "确保文件配置不会被覆盖" + +#: ../../getting_started/install/llm/proxyllm/proxyllm.md:66 +#: 51cb501d1500440981b3b93f01ff36f4 +msgid "How to Integrate Embedding rest API, like OpenAI, Azure api service?" +msgstr "如何集成想OpenAI Embedding rest api" + +#~ msgid "Now DB-GPT support connect LLM service through proxy rest api." +#~ msgstr "" + diff --git a/docs/locales/zh_CN/LC_MESSAGES/index.po b/docs/locales/zh_CN/LC_MESSAGES/index.po index f626ea6ef..3a4c57dbc 100644 --- a/docs/locales/zh_CN/LC_MESSAGES/index.po +++ b/docs/locales/zh_CN/LC_MESSAGES/index.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: DB-GPT 0.3.0\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-08-17 21:58+0800\n" +"POT-Creation-Date: 2023-11-14 17:55+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -19,154 +19,176 @@ msgstr "" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.12.1\n" -#: ../../index.rst:34 ../../index.rst:45 3566ae9872dd4f97844f9e0680de5f5d +#: ../../index.rst:44 ../../index.rst:54 fb98707559574eb29f01bd8f6ebfac60 msgid "Getting Started" msgstr "开始" -#: ../../index.rst:59 ../../index.rst:80 6845a928056940b28be8c13d766009f8 +#: ../../index.rst:70 ../../index.rst:92 6d9603b978d44e54a257fb359c871867 msgid "Modules" msgstr "模块" -#: ../../index.rst:94 ../../index.rst:110 ef3bcf6f837345fca8539d51434e3c2c -msgid "Use Cases" -msgstr "示例" - -#: ../../index.rst:124 ../../index.rst:127 b10b8b49e4f5459f8bea881ffc9259d5 -msgid "Reference" -msgstr "参考" - -#: ../../index.rst:137 ../../index.rst:143 12e75881253a4c4383ac7364c1103348 +#: ../../index.rst:106 ../../index.rst:112 9df06739ca4446bc86ec2ff6907763ce msgid "Resources" msgstr "资源" -#: ../../index.rst:7 f1a30af655744c0b8fb7197a5fc3a45b -msgid "Welcome to DB-GPT!" -msgstr "欢迎来到DB-GPT中文文档" +#: ../../index.rst:7 7de875cfbc764937ab7f8b362d997952 +msgid "Overview" +msgstr "概览" -#: ../../index.rst:8 426686c829a342798eaae9f789260621 +#: ../../index.rst:9 770a756bd0b640ef863fd72b8d7e882a msgid "" -"As large models are released and iterated upon, they are becoming " -"increasingly intelligent. However, in the process of using large models, " -"we face significant challenges in data security and privacy. We need to " -"ensure that our sensitive data and environments remain completely " -"controlled and avoid any data privacy leaks or security risks. Based on " -"this, we have launched the DB-GPT project to build a complete private " -"large model solution for all database-based scenarios. This solution " -"supports local deployment, allowing it to be applied not only in " -"independent private environments but also to be independently deployed " -"and isolated according to business modules, ensuring that the ability of " -"large models is absolutely private, secure, and controllable." -msgstr "" -"随着大型模型的发布和迭代，它们变得越来越智能。然而，在使用大型模型的过程中，我们在数据安全和隐私方面面临着重大挑战。我们需要确保我们的敏感数据和环境得到完全控制，避免任何数据隐私泄露或安全风险。基于此" -"，我们启动了DB-" -"GPT项目，为所有基于数据库的场景构建一个完整的私有大模型解决方案。该方案“”支持本地部署，既可应用于“独立私有环境”，又可根据业务模块进行“独立部署”和“隔离”，确保“大模型”的能力绝对私有、安全、可控。" +"DB-GPT is an open-source framework for large models in the database " +"field. Its purpose is to build infrastructure for the domain of large " +"models, making it easier and more convenient to develop applications " +"around databases. By developing various technical capabilities such as:" +msgstr "DB-GPT是一个开源的数据库领域大模型框架。目的是构建大模型领域的基础设施，通过开发如" -#: ../../index.rst:10 0a7fbd17ecfd48cb8e0593e35a225e1b +#: ../../index.rst:11 8774a5ad5ce14baf9eae35fefd62e40b +msgid "**SMMF(Service-oriented Multi-model Management Framework)**" +msgstr "**服务化多模型管理**" + +#: ../../index.rst:12 b2ba120fc994436db7066486c9acd6ad +msgid "**Text2SQL Fine-tuning**" +msgstr "**Text2SQL微调**" + +#: ../../index.rst:13 d55efe86dd6b40ebbe63079edb60e421 +msgid "**RAG(Retrieval Augmented Generation) framework and optimization**" +msgstr "**检索增强**" + +#: ../../index.rst:14 3eca943c44464c9cb9bbc5724c27ad1c +msgid "**Data-Driven Agents framework collaboration**" +msgstr "**数据驱动的Agents协作框架**" + +#: ../../index.rst:15 bf41d57cbc474e2c9829f09d6b983ae1 +msgid "**GBI(Generative Business intelligence)**" +msgstr "**生成式报表分析**" + +#: ../../index.rst:17 36630469cc064317a1c196dd377c3d93 msgid "" -"**DB-GPT** is an experimental open-source project that uses localized GPT" -" large models to interact with your data and environment. With this " -"solution, you can be assured that there is no risk of data leakage, and " -"your data is 100% private and secure." -msgstr "" -"DB-GPT 是一个开源的以数据库为基础的GPT实验项目，使用本地化的GPT大模型与您的数据和环境进行交互，无数据泄露风险100% 私密，100%" -" 安全。" +"etc, DB-GPT simplifies the construction of large model applications based" +" on databases." +msgstr "等能力, 让围绕数据库构建大模型应用更简单，更方便。" -#: ../../index.rst:12 50e02964b1e24c4fa598f820796aec61 -msgid "**Features**" +#: ../../index.rst:19 82f03535c6914ebfa8b3adad34eeed2f +msgid "" +"In the era of Data 3.0, enterprises and developers can build their own " +"customized applications with less code, leveraging models and databases." +msgstr "*数据3.0 时代，基于模型、数据库，企业/开发者可以用更少的代码搭建自己的专属应用*。" + +#: ../../index.rst:22 daf64ec39c28458087d542879d106d1b +msgid "Features" msgstr "特性" -#: ../../index.rst:13 62b74478b9b046dfa7606785939ca70e +#: ../../index.rst:24 7ceb41b710f847e683479dc892baa3d5 +msgid "**1. Private Domain Q&A & Data Processing**" +msgstr "**1. 私域问答&数据处理**" + +#: ../../index.rst:25 3f480e259ee9432b934ee6474bc8de79 msgid "" -"Currently, we have released multiple key features, which are listed below" -" to demonstrate our current capabilities:" -msgstr "目前我们已经发布了多种关键的特性，这里一一列举展示一下当前发布的能力。" +"Supports custom construction of knowledge bases through methods such as " +"built-in, multi-file format uploads, and plugin-based web scraping. " +"Enables unified vector storage and retrieval of massive structured and " +"unstructured data." +msgstr "支持内置、多文件格式上传、插件自抓取等方式自定义构建知识库，对海量结构化，非结构化数据做统一向量存储与检索" -#: ../../index.rst:15 f593159ba8dd4388bd2ba189f9efd5ea -msgid "SQL language capabilities - SQL generation - SQL diagnosis" -msgstr "SQL语言能力 - SQL生成 - SQL诊断" +#: ../../index.rst:27 1f9f12be761a4a6c996788051a3fa4dd +msgid "**2.Multi-Data Source & GBI(Generative Business intelligence)**" +msgstr "**2.多数据源与可视化**" -#: ../../index.rst:19 b829655a3ef146528beb9c50538be84e +#: ../../index.rst:28 e597e6c2d4ad4d1bbcc440b3afb7c0fa msgid "" -"Private domain Q&A and data processing - Database knowledge Q&A - Data " -"processing" -msgstr "私有领域问答与数据处理 - 数据库知识问答 - 数据处理" +"Supports interaction between natural language and various data sources " +"such as Excel, databases, and data warehouses. Also supports analysis " +"reporting." +msgstr "支持自然语言与Excel、数据库、数仓等多种数据源交互,并支持分析报告。" -#: ../../index.rst:23 43e988a100a740358f1a0be1710d7960 +#: ../../index.rst:30 9c63ecf927874f9ea79f1ef5c1535e67 +msgid "**3.SMMF(Service-oriented Multi-model Management Framework)**" +msgstr "**3.多模型管理**" + +#: ../../index.rst:31 d6cfb9b69f9743d083c4644c90fd6108 msgid "" -"Plugins - Support custom plugin execution tasks and natively support the " -"Auto-GPT plugin, such as:" -msgstr "插件模型 - 支持自定义插件执行任务，并原生支持Auto-GPT插件，例如:* SQL自动执行，获取查询结果 * 自动爬取学习知识" +"Supports a wide range of models, including dozens of large language " +"models such as open-source models and API proxies. Examples include " +"LLaMA/LLaMA2, Baichuan, ChatGLM, Wenxin, Tongyi, Zhipu, Xinghuo, etc." +msgstr "海量模型支持，包括开源、API代理等几十种大语言模型。如LLaMA/LLaMA2、Baichuan、ChatGLM、文心、通义、智谱、星火等。" -#: ../../index.rst:26 888186524aef4fe5a0ba643d55783fd9 +#: ../../index.rst:33 dda6cec4316e48f2afe77005baa53a06 +msgid "**4.Automated Fine-tuning**" +msgstr "**4.自动化微调**" + +#: ../../index.rst:34 7cf1654a9779444ab3982435887d087b msgid "" -"Unified vector storage/indexing of knowledge base - Support for " -"unstructured data such as PDF, Markdown, CSV, and WebURL" -msgstr "知识库统一向量存储/索引 - 非结构化数据支持包括PDF、MarkDown、CSV、WebURL" +"A lightweight framework for automated fine-tuning built around large " +"language models, Text2SQL datasets, and methods like LoRA/QLoRA/Pturning." +" Makes TextSQL fine-tuning as convenient as a production line." +msgstr "" +"围绕大语言模型、Text2SQL数据集、LoRA/QLoRA/Pturning等微调方法构建的自动化微调轻量框架, " +"让TextSQL微调像流水线一样方便。" -#: ../../index.rst:29 fab4f961fe1746dca3d5e369de714108 +#: ../../index.rst:36 f58f114546f04b658aaa67fd895fba2b +msgid "**5.Data-Driven Multi-Agents & Plugins**" +msgstr "**5.数据驱动的插件模型**" + +#: ../../index.rst:37 a93fdca3de054cb0812d7f5ca3d12375 msgid "" -"Milti LLMs Support - Supports multiple large language models, currently " -"supporting Vicuna (7b, 13b), ChatGLM-6b (int4, int8) - TODO: codegen2, " -"codet5p" -msgstr "多模型支持 - 支持多种大语言模型, 当前已支持Vicuna(7b,13b), ChatGLM-6b(int4, int8)" +"Supports executing tasks through custom plugins and natively supports the" +" Auto-GPT plugin model. Agents protocol follows the Agent Protocol " +"standard." +msgstr "支持自定义插件执行任务，原生支持Auto-GPT插件模型，Agents协议采用Agent Protocol标准" -#: ../../index.rst:35 7bc21f280364448da0edc046378be622 +#: ../../index.rst:39 3a0e89b151694e4b8e87646efe313568 +msgid "**6.Privacy and Security**" +msgstr "**6.隐私安全**" + +#: ../../index.rst:40 aa50fc40f22f4fae8225a0a0a97c17dc msgid "" -"How to get started using DB-GPT to interact with your data and " -"environment." -msgstr "开始使用DB-GPT与您的数据环境进行交互。" +"Ensures data privacy and security through techniques such as privatizing " +"large models and proxy de-identification." +msgstr "通过私有化大模型、代理脱敏等多种技术保障数据的隐私安全" -#: ../../index.rst:36 5362221ecdf5427faa51df83d4a939ee +#: ../../index.rst:46 d8bf21a7abd749608cddcdb2e358f3be +msgid "Quickstart" +msgstr "快速开始" + +#: ../../index.rst:48 d1f117a7cbb94c80afc0660e899d8154 #, fuzzy msgid "`Quickstart Guide <./getting_started/getting_started.html>`_" msgstr "`使用指南 <./getting_started/getting_started.html>`_" -#: ../../index.rst:38 e028ed5afce842fbb76a6ce825d5a8e2 +#: ../../index.rst:50 5fd56979f31b4a0b93082004f1cb90c7 msgid "Concepts and terminology" msgstr "相关概念" -#: ../../index.rst:40 f773ac5e50054c308920c0b95a44b0cb +#: ../../index.rst:52 09c6889d02fa417c9ffde312211726f0 #, fuzzy msgid "`Concepts and Terminology <./getting_started/concepts.html>`_" msgstr "`相关概念 <./getting_started/concepts.html>`_" -#: ../../index.rst:42 afeee818ec45454da12b80161b5f1de0 -msgid "Coming soon..." -msgstr "" - -#: ../../index.rst:44 cb4f911316234b86aad88b83d2784ad3 -msgid "`Tutorials <.getting_started/tutorials.html>`_" -msgstr "`教程 <.getting_started/tutorials.html>`_" - -#: ../../index.rst:61 de365722d61442b99f44fcde8a8c9efb +#: ../../index.rst:72 5bd727134fc94cfb88abb755ccceac03 msgid "" "These modules are the core abstractions with which we can interact with " -"data and environment smoothly." -msgstr "这些模块是我们可以与数据和环境顺利地进行交互的核心组成。" +"data and environment smoothly. It's very important for DB-GPT, DB-GPT " +"also provide standard, extendable interfaces." +msgstr "这些模块是我们能够与数据和环境顺利交互的核心抽象。这对于DB-GPT来说非常重要，DB-GPT还提供了标准的、可扩展的接口。" -#: ../../index.rst:62 8fbb6303d3cd4fcc956815f44ef1fa8d -msgid "" -"It's very important for DB-GPT, DB-GPT also provide standard, extendable " -"interfaces." -msgstr "DB-GPT还提供了标准的、可扩展的接口。" - -#: ../../index.rst:64 797ac43f459b4662a5097c8cb783c4ba +#: ../../index.rst:74 1a5eb0b7cb884309be3431112c8f38e5 msgid "" "The docs for each module contain quickstart examples, how to guides, " "reference docs, and conceptual guides." msgstr "每个模块的文档都包含快速入门的例子、操作指南、参考文档和相关概念等内容。" -#: ../../index.rst:66 70d7b89c1c154c65aabedbb3c94c8771 +#: ../../index.rst:76 24aa8c08d1dc460ab23d69a5bb9c8fc3 msgid "The modules are as follows" msgstr "组成模块如下：" -#: ../../index.rst:68 794d8a939e274894910fcdbb3ee52429 +#: ../../index.rst:78 9f4280cca1f743cb9b868cc67e3f3ce7 msgid "" "`LLMs <./modules/llms.html>`_: Supported multi models management and " "integrations." msgstr "`LLMs <./modules/llms.html>`_:基于FastChat提供大模型的运行环境。支持多模型管理和集成。 " -#: ../../index.rst:70 58b0d2fcac2c471494fe2b6b5b3f1b49 +#: ../../index.rst:80 d357811f110f40e79f0c20ef9cb60d0c msgid "" "`Prompts <./modules/prompts.html>`_: Prompt management, optimization, and" " serialization for multi database." @@ -174,86 +196,35 @@ msgstr "" "`Prompt自动生成与优化 <./modules/prompts.html>`_: 自动化生成高质量的Prompt " "，并进行优化，提高系统的响应效率" -#: ../../index.rst:72 d433b62e11f64e18995bd334f93992a6 +#: ../../index.rst:82 3cb9acc9f11a46638e6687f743d6b7f3 msgid "`Plugins <./modules/plugins.html>`_: Plugins management, scheduler." msgstr "`Agent与插件： <./modules/plugins.html>`_:提供Agent和插件机制，使得用户可以自定义并增强系统的行为。" -#: ../../index.rst:74 3e7eb10f64274c07ace90b84ffc904b4 +#: ../../index.rst:84 b24c462cb5364890a6ca990f09f48cfc #, fuzzy msgid "" "`Knowledge <./modules/knowledge.html>`_: Knowledge management, embedding," " and search." msgstr "`知识库能力： <./modules/knowledge.html>`_: 支持私域知识库问答能力, " -#: ../../index.rst:76 3af796f287f54de0869a086cfa24b568 +#: ../../index.rst:86 7448b231fe8745f1965a1f48ffc5444a msgid "" "`Connections <./modules/connections.html>`_: Supported multi databases " "connection. management connections and interact with this." msgstr "`连接模块 <./modules/connections.html>`_: 用于连接不同的模块和数据源，实现数据的流转和交互 " -#: ../../index.rst:78 27273a4020a540f4b28e7c54ea9c9232 +#: ../../index.rst:88 c677fb24869347ff907f1529ef333b6b #, fuzzy msgid "`Vector <./modules/vector.html>`_: Supported multi vector database." msgstr "`LLMs <./modules/llms.html>`_:基于FastChat提供大模型的运行环境。支持多模型管理和集成。 " -#: ../../index.rst:96 d887747669d1429f950c56131cd35a62 -msgid "Best Practices and built-in implementations for common DB-GPT use cases:" -msgstr "DB-GPT用例的最佳实践和内置方法:" - -#: ../../index.rst:98 63ea6a449012432baeeef975db5c3ac1 -msgid "" -"`Sql generation and diagnosis " -"<./use_cases/sql_generation_and_diagnosis.html>`_: SQL generation and " -"diagnosis." -msgstr "`Sql生成和诊断 <./use_cases/sql_generation_and_diagnosis.html>`_: Sql生成和诊断。" - -#: ../../index.rst:100 c7388a2147af48d1a6619492a3b926db -msgid "" -"`knownledge Based QA <./use_cases/knownledge_based_qa.html>`_: A " -"important scene for user to chat with database documents, codes, bugs and" -" schemas." -msgstr "`知识库问答 <./use_cases/knownledge_based_qa.html>`_: 用户与数据库文档、代码和bug聊天的重要场景\"" - -#: ../../index.rst:102 f5a459f5a84241648a6a05f7ba3026c0 -msgid "" -"`Chatbots <./use_cases/chatbots.html>`_: Language model love to chat, use" -" multi models to chat." -msgstr "`聊天机器人 <./use_cases/chatbots.html>`_: 使用多模型进行对话" - -#: ../../index.rst:104 d566174db6eb4834854c00ce7295c297 -msgid "" -"`Querying Database Data <./use_cases/query_database_data.html>`_: Query " -"and Analysis data from databases and give charts." -msgstr "`查询数据库数据 <./use_cases/query_database_data.html>`_:从数据库中查询和分析数据并给出图表。" - -#: ../../index.rst:106 89aac5a738ae4aeb84bc324803ada354 -msgid "" -"`Interacting with apis <./use_cases/interacting_with_api.html>`_: " -"Interact with apis, such as create a table, deploy a database cluster, " -"create a database and so on." -msgstr "" -"`API交互 <./use_cases/interacting_with_api.html>`_: " -"与API交互，例如创建表、部署数据库集群、创建数据库等。" - -#: ../../index.rst:108 f100fb0cdd264cf186bf554771488aa1 -msgid "" -"`Tool use with plugins <./use_cases/tool_use_with_plugin>`_: According to" -" Plugin use tools to manage databases autonomoly." -msgstr "`插件工具 <./use_cases/tool_use_with_plugin>`_: 根据插件使用工具自主管理数据库。" - -#: ../../index.rst:125 93809b95cc4a41249d7ab6b264981167 -msgid "" -"Full documentation on all methods, classes, installation methods, and " -"integration setups for DB-GPT." -msgstr "关于DB-GPT的所有方法、类、安装方法和集成设置的完整文档。" - -#: ../../index.rst:139 4a95e65e128e4fc0907a3f51f1f2611b +#: ../../index.rst:108 2e56f2cb1a8b40dda9465c0a1af94196 msgid "" "Additional resources we think may be useful as you develop your " "application!" -msgstr "“我们认为在您开发应用程序时可能有用的其他资源!”" +msgstr "我们认为在您开发应用程序时可能有用的其他资源!" -#: ../../index.rst:141 4934fbae909644769dd83c7f99c0fcd0 +#: ../../index.rst:110 590362cb3b7442d49eafa58cb323e127 msgid "" "`Discord `_: if your have some problem or " "ideas, you can talk from discord." @@ -271,3 +242,325 @@ msgstr "`Discord `_:如果您有任何问题，可 #~ msgid "Guides for how other companies/products can be used with DB-GPT" #~ msgstr "其他公司/产品如何与DB-GPT一起使用的方法指南" +#~ msgid "Use Cases" +#~ msgstr "示例" + +#~ msgid "" +#~ "Best Practices and built-in " +#~ "implementations for common DB-GPT use" +#~ " cases:" +#~ msgstr "DB-GPT用例的最佳实践和内置方法:" + +#~ msgid "" +#~ "`Sql generation and diagnosis " +#~ "<./use_cases/sql_generation_and_diagnosis.html>`_: SQL " +#~ "generation and diagnosis." +#~ msgstr "`Sql生成和诊断 <./use_cases/sql_generation_and_diagnosis.html>`_: Sql生成和诊断。" + +#~ msgid "" +#~ "`knownledge Based QA " +#~ "<./use_cases/knownledge_based_qa.html>`_: A important " +#~ "scene for user to chat with " +#~ "database documents, codes, bugs and " +#~ "schemas." +#~ msgstr "" +#~ "`知识库问答 <./use_cases/knownledge_based_qa.html>`_: " +#~ "用户与数据库文档、代码和bug聊天的重要场景\"" + +#~ msgid "" +#~ "`Chatbots <./use_cases/chatbots.html>`_: Language " +#~ "model love to chat, use multi " +#~ "models to chat." +#~ msgstr "`聊天机器人 <./use_cases/chatbots.html>`_: 使用多模型进行对话" + +#~ msgid "" +#~ "`Querying Database Data " +#~ "<./use_cases/query_database_data.html>`_: Query and " +#~ "Analysis data from databases and give" +#~ " charts." +#~ msgstr "`查询数据库数据 <./use_cases/query_database_data.html>`_:从数据库中查询和分析数据并给出图表。" + +#~ msgid "" +#~ "`Interacting with apis " +#~ "<./use_cases/interacting_with_api.html>`_: Interact with" +#~ " apis, such as create a table, " +#~ "deploy a database cluster, create a " +#~ "database and so on." +#~ msgstr "" +#~ "`API交互 <./use_cases/interacting_with_api.html>`_: " +#~ "与API交互，例如创建表、部署数据库集群、创建数据库等。" + +#~ msgid "" +#~ "`Tool use with plugins " +#~ "<./use_cases/tool_use_with_plugin>`_: According to " +#~ "Plugin use tools to manage databases " +#~ "autonomoly." +#~ msgstr "`插件工具 <./use_cases/tool_use_with_plugin>`_: 根据插件使用工具自主管理数据库。" + +#~ msgid "Reference" +#~ msgstr "参考" + +#~ msgid "Welcome to DB-GPT!" +#~ msgstr "欢迎来到DB-GPT中文文档" + +#~ msgid "" +#~ "As large models are released and " +#~ "iterated upon, they are becoming " +#~ "increasingly intelligent. However, in the " +#~ "process of using large models, we " +#~ "face significant challenges in data " +#~ "security and privacy. We need to " +#~ "ensure that our sensitive data and " +#~ "environments remain completely controlled and" +#~ " avoid any data privacy leaks or " +#~ "security risks. Based on this, we " +#~ "have launched the DB-GPT project " +#~ "to build a complete private large " +#~ "model solution for all database-based" +#~ " scenarios. This solution supports local" +#~ " deployment, allowing it to be " +#~ "applied not only in independent private" +#~ " environments but also to be " +#~ "independently deployed and isolated according" +#~ " to business modules, ensuring that " +#~ "the ability of large models is " +#~ "absolutely private, secure, and controllable." +#~ msgstr "" +#~ "随着大型模型的发布和迭代，它们变得越来越智能。然而，在使用大型模型的过程中，我们在数据安全和隐私方面面临着重大挑战。我们需要确保我们的敏感数据和环境得到完全控制，避免任何数据隐私泄露或安全风险。基于此" +#~ "，我们启动了DB-" +#~ "GPT项目，为所有基于数据库的场景构建一个完整的私有大模型解决方案。该方案“”支持本地部署，既可应用于“独立私有环境”，又可根据业务模块进行“独立部署”和“隔离”，确保“大模型”的能力绝对私有、安全、可控。" + +#~ msgid "" +#~ "**DB-GPT** is an experimental open-" +#~ "source project that uses localized GPT" +#~ " large models to interact with your" +#~ " data and environment. With this " +#~ "solution, you can be assured that " +#~ "there is no risk of data leakage," +#~ " and your data is 100% private " +#~ "and secure." +#~ msgstr "" +#~ "DB-GPT " +#~ "是一个开源的以数据库为基础的GPT实验项目，使用本地化的GPT大模型与您的数据和环境进行交互，无数据泄露风险100% " +#~ "私密，100% 安全。" + +#~ msgid "" +#~ "Currently, we have released multiple key" +#~ " features, which are listed below to" +#~ " demonstrate our current capabilities:" +#~ msgstr "目前我们已经发布了多种关键的特性，这里一一列举展示一下当前发布的能力。" + +#~ msgid "SQL language capabilities - SQL generation - SQL diagnosis" +#~ msgstr "SQL语言能力 - SQL生成 - SQL诊断" + +#~ msgid "" +#~ "Private domain Q&A and data processing" +#~ " - Database knowledge Q&A - Data " +#~ "processing" +#~ msgstr "私有领域问答与数据处理 - 数据库知识问答 - 数据处理" + +#~ msgid "" +#~ "Plugins - Support custom plugin " +#~ "execution tasks and natively support the" +#~ " Auto-GPT plugin, such as:" +#~ msgstr "插件模型 - 支持自定义插件执行任务，并原生支持Auto-GPT插件，例如:* SQL自动执行，获取查询结果 * 自动爬取学习知识" + +#~ msgid "" +#~ "Unified vector storage/indexing of knowledge" +#~ " base - Support for unstructured data" +#~ " such as PDF, Markdown, CSV, and " +#~ "WebURL" +#~ msgstr "知识库统一向量存储/索引 - 非结构化数据支持包括PDF、MarkDown、CSV、WebURL" + +#~ msgid "" +#~ "Multi LLMs Support - Supports multiple" +#~ " large language models, currently " +#~ "supporting Vicuna (7b, 13b), ChatGLM-6b" +#~ " (int4, int8) - TODO: codegen2, " +#~ "codet5p" +#~ msgstr "多模型支持 - 支持多种大语言模型, 当前已支持Vicuna(7b,13b), ChatGLM-6b(int4, int8)" + +#~ msgid "" +#~ "Full documentation on all methods, " +#~ "classes, installation methods, and integration" +#~ " setups for DB-GPT." +#~ msgstr "关于DB-GPT的所有方法、类、安装方法和集成设置的完整文档。" + +#~ msgid "" +#~ "**DB-GPT** is an open-source " +#~ "framework for large models in the " +#~ "database field. Its purpose is to " +#~ "build infrastructure for the domain of" +#~ " large models, making it easier and" +#~ " more convenient to develop applications" +#~ " around databases." +#~ msgstr "" + +#~ msgid "By developing various technical capabilities such as" +#~ msgstr "" + +#~ msgid "SMMF(Service-oriented Multi-model Management Framework)" +#~ msgstr "" + +#~ msgid "Text2SQL Fine-tuning" +#~ msgstr "" + +#~ msgid "RAG(Retrieval Augmented Generation) framework and optimization" +#~ msgstr "" + +#~ msgid "Data-Driven Agents framework collaboration" +#~ msgstr "" + +#~ msgid "" +#~ "5. GBI(Generative Business intelligence) etc," +#~ " DB-GPT simplifies the construction " +#~ "of large model applications based on " +#~ "databases." +#~ msgstr "" + +#~ msgid "" +#~ "**1. Private Domain Q&A & Data " +#~ "Processing** Supports custom construction of" +#~ " knowledge bases through methods such " +#~ "as built-in, multi-file format " +#~ "uploads, and plugin-based web scraping." +#~ " Enables unified vector storage and " +#~ "retrieval of massive structured and " +#~ "unstructured data." +#~ msgstr "" + +#~ msgid "" +#~ "**2.Multi-Data Source & GBI(Generative " +#~ "Business intelligence)** Supports interaction " +#~ "between natural language and various " +#~ "data sources such as Excel, databases," +#~ " and data warehouses. Also supports " +#~ "analysis reporting." +#~ msgstr "" + +#~ msgid "" +#~ "**3.SMMF(Service-oriented Multi-model " +#~ "Management Framework)** Supports a wide " +#~ "range of models, including dozens of " +#~ "large language models such as open-" +#~ "source models and API proxies. Examples" +#~ " include LLaMA/LLaMA2, Baichuan, ChatGLM, " +#~ "Wenxin, Tongyi, Zhipu, Xinghuo, etc." +#~ msgstr "" + +#~ msgid "" +#~ "**4.Automated Fine-tuning** A lightweight " +#~ "framework for automated fine-tuning " +#~ "built around large language models, " +#~ "Text2SQL datasets, and methods like " +#~ "LoRA/QLoRA/Pturning. Makes TextSQL fine-tuning" +#~ " as convenient as a production line." +#~ msgstr "" + +#~ msgid "" +#~ "**5.Data-Driven Multi-Agents & Plugins**" +#~ " Supports executing tasks through custom" +#~ " plugins and natively supports the " +#~ "Auto-GPT plugin model. Agents protocol " +#~ "follows the Agent Protocol standard." +#~ msgstr "" + +#~ msgid "" +#~ "**6.Privacy and Security** Ensures data " +#~ "privacy and security through techniques " +#~ "such as privatizing large models and " +#~ "proxy de-identification." +#~ msgstr "" + +#~ msgid "Coming soon..." +#~ msgstr "" + +#~ msgid "`Tutorials <.getting_started/tutorials.html>`_" +#~ msgstr "`教程 <.getting_started/tutorials.html>`_" + +#~ msgid "" +#~ "DB-GPT is an open-source framework" +#~ " for large models in the database " +#~ "field. Its purpose is to build " +#~ "infrastructure for the domain of large" +#~ " models, making it easier and more" +#~ " convenient to develop applications around" +#~ " databases. By developing various technical" +#~ " capabilities such as **1. SMMF(Service-" +#~ "oriented Multi-model Management Framework)**" +#~ " **2. Text2SQL Fine-tuning** **3. " +#~ "RAG(Retrieval Augmented Generation) framework " +#~ "and optimization** **4. Data-Driven " +#~ "Agents framework collaboration** **5. " +#~ "GBI(Generative Business intelligence)** etc, " +#~ "DB-GPT simplifies the construction of " +#~ "large model applications based on " +#~ "databases." +#~ msgstr "" + +#~ msgid "" +#~ "**1. Private Domain Q&A & Data " +#~ "Processing** ::Supports custom construction of" +#~ " knowledge bases through methods such " +#~ "as built-in, multi-file format " +#~ "uploads, and plugin-based web scraping." +#~ " Enables unified vector storage and " +#~ "retrieval of massive structured and " +#~ "unstructured data." +#~ msgstr "" + +#~ msgid "" +#~ "**2.Multi-Data Source & GBI(Generative " +#~ "Business intelligence)** ::Supports interaction " +#~ "between natural language and various " +#~ "data sources such as Excel, databases," +#~ " and data warehouses. Also supports " +#~ "analysis reporting." +#~ msgstr "" + +#~ msgid "" +#~ "**3.SMMF(Service-oriented Multi-model " +#~ "Management Framework)** ::Supports a wide " +#~ "range of models, including dozens of " +#~ "large language models such as open-" +#~ "source models and API proxies. Examples" +#~ " include LLaMA/LLaMA2, Baichuan, ChatGLM, " +#~ "Wenxin, Tongyi, Zhipu, Xinghuo, etc." +#~ msgstr "" + +#~ msgid "" +#~ "**4.Automated Fine-tuning** ::A lightweight" +#~ " framework for automated fine-tuning " +#~ "built around large language models, " +#~ "Text2SQL datasets, and methods like " +#~ "LoRA/QLoRA/Pturning. Makes TextSQL fine-tuning" +#~ " as convenient as a production line." +#~ msgstr "" + +#~ msgid "" +#~ "**5.Data-Driven Multi-Agents & Plugins**" +#~ " ::Supports executing tasks through custom" +#~ " plugins and natively supports the " +#~ "Auto-GPT plugin model. Agents protocol " +#~ "follows the Agent Protocol standard." +#~ msgstr "" + +#~ msgid "" +#~ "**6.Privacy and Security** ::Ensures data " +#~ "privacy and security through techniques " +#~ "such as privatizing large models and " +#~ "proxy de-identification." +#~ msgstr "" + +#~ msgid "" +#~ "How to get started using DB-GPT" +#~ " to interact with your data and " +#~ "environment." +#~ msgstr "开始使用DB-GPT与您的数据环境进行交互。" + +#~ msgid "" +#~ "It's very important for DB-GPT, " +#~ "DB-GPT also provide standard, extendable" +#~ " interfaces." +#~ msgstr "DB-GPT还提供了标准的、可扩展的接口。" + diff --git a/docs/locales/zh_CN/LC_MESSAGES/modules/knowledge.po b/docs/locales/zh_CN/LC_MESSAGES/modules/knowledge.po index 0b3eac094..bb2dae7af 100644 --- a/docs/locales/zh_CN/LC_MESSAGES/modules/knowledge.po +++ b/docs/locales/zh_CN/LC_MESSAGES/modules/knowledge.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: DB-GPT 0.3.0\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-07-13 15:39+0800\n" +"POT-Creation-Date: 2023-11-02 21:04+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -19,103 +19,84 @@ msgstr "" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.12.1\n" -#: ../../modules/knowledge.rst:2 ../../modules/knowledge.rst:136 -#: 3cc8fa6e9fbd4d889603d99424e9529a +#: ../../modules/knowledge.md:1 b94b3b15cb2441ed9d78abd222a717b7 msgid "Knowledge" msgstr "知识" -#: ../../modules/knowledge.rst:4 0465a393d9d541958c39c1d07c885d1f +#: ../../modules/knowledge.md:3 c6d6e308a6ce42948d29e928136ef561 #, fuzzy msgid "" "As the knowledge base is currently the most significant user demand " "scenario, we natively support the construction and processing of " "knowledge bases. At the same time, we also provide multiple knowledge " -"base management strategies in this project, such as pdf knowledge,md " -"knowledge, txt knowledge, word knowledge, ppt knowledge:" +"base management strategies in this project, such as:" msgstr "" "由于知识库是当前用户需求最显著的场景，我们原生支持知识库的构建和处理。同时，我们还在本项目中提供了多种知识库管理策略，如:pdf,md , " "txt, word, ppt" -#: ../../modules/knowledge.rst:6 e670cbe14d8e4da88ba935e4120c31e0 -msgid "" -"We currently support many document formats: raw text, txt, pdf, md, html," -" doc, ppt, and url. In the future, we will continue to support more types" -" of knowledge, including audio, video, various databases, and big data " -"sources. Of course, we look forward to your active participation in " -"contributing code." +#: ../../modules/knowledge.md:4 268abc408d40410ba90cf5f121dc5270 +msgid "Default built-in knowledge base" msgstr "" -#: ../../modules/knowledge.rst:9 e0bf601a1a0c458297306db6ff79f931 -msgid "**Create your own knowledge repository**" +#: ../../modules/knowledge.md:5 558c3364c38b458a8ebf81030efc2a48 +msgid "Custom addition of knowledge bases" +msgstr "" + +#: ../../modules/knowledge.md:6 9cb3ce62da1440579c095848c7aef88c +msgid "" +"Various usage scenarios such as constructing knowledge bases through " +"plugin capabilities and web crawling. Users only need to organize the " +"knowledge documents, and they can use our existing capabilities to build " +"the knowledge base required for the large model." +msgstr "" + +#: ../../modules/knowledge.md:9 b8ca6bc4dd9845baa56e36eea7fac2a2 +#, fuzzy +msgid "Create your own knowledge repository" msgstr "创建你自己的知识库" -#: ../../modules/knowledge.rst:11 bb26708135d44615be3c1824668010f6 -msgid "1.prepare" -msgstr "准备" +#: ../../modules/knowledge.md:11 17d7178a67924f43aa5b6293707ef041 +msgid "" +"1.Place personal knowledge files or folders in the pilot/datasets " +"directory." +msgstr "" -#: ../../modules/knowledge.rst:13 c150a0378f3e4625908fa0d8a25860e9 +#: ../../modules/knowledge.md:13 31c31f14bf444981939689f9a9fb038a #, fuzzy msgid "" -"We currently support many document formats: TEXT(raw text), " -"DOCUMENT(.txt, .pdf, .md, .doc, .ppt, .html), and URL." +"We currently support many document formats: txt, pdf, md, html, doc, ppt," +" and url." msgstr "当前支持txt, pdf, md, html, doc, ppt, url文档格式" -#: ../../modules/knowledge.rst:15 7f9f02a93d5d4325b3d2d976f4bb28a0 +#: ../../modules/knowledge.md:15 9ad2f2e05f8842a9b9d8469a3704df23 msgid "before execution:" msgstr "开始前" -#: ../../modules/knowledge.rst:24 59699a8385e04982a992cf0d71f6dcd5 -#, fuzzy +#: ../../modules/knowledge.md:22 6fd2775914b641c4b8e486417b558ea6 msgid "" -"2.prepare embedding model, you can download from https://huggingface.co/." -" Notice you have installed git-lfs." +"2.Update your .env, set your vector store type, VECTOR_STORE_TYPE=Chroma " +"(now only support Chroma and Milvus, if you set Milvus, please set " +"MILVUS_URL and MILVUS_PORT)" msgstr "" -"提前准备Embedding Model, 你可以在https://huggingface.co/进行下载，注意：你需要先安装git-lfs.eg:" -" git clone https://huggingface.co/THUDM/chatglm2-6b" -#: ../../modules/knowledge.rst:27 2be1a17d0b54476b9dea080d244fd747 -msgid "" -"eg: git clone https://huggingface.co/sentence-transformers/all-" -"MiniLM-L6-v2" -msgstr "eg: git clone https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2" - -#: ../../modules/knowledge.rst:33 d328f6e243624c9488ebd27c9324621b -msgid "" -"3.prepare vector_store instance and vector store config, now we support " -"Chroma, Milvus and Weaviate." -msgstr "提前准备向量数据库环境，目前支持Chroma, Milvus and Weaviate向量数据库" - -#: ../../modules/knowledge.rst:63 44f97154eff647d399fd30b6f9e3b867 -msgid "" -"3.init Url Type EmbeddingEngine api and embedding your document into " -"vector store in your code." -msgstr "初始化 Url类型 EmbeddingEngine api， 将url文档embedding向量化到向量数据库 " - -#: ../../modules/knowledge.rst:75 e2581b414f0148bca88253c7af9cd591 -msgid "If you want to add your source_reader or text_splitter, do this:" -msgstr "如果你想手动添加你自定义的source_reader和text_splitter, 请参考:" - -#: ../../modules/knowledge.rst:95 74c110414f924bbfa3d512e45ba2f30f -#, fuzzy -msgid "" -"4.init Document Type EmbeddingEngine api and embedding your document into" -" vector store in your code. Document type can be .txt, .pdf, .md, .doc, " -".ppt." +#: ../../modules/knowledge.md:25 131c5f58898a4682940910980edb2043 +msgid "2.Run the knowledge repository initialization command" msgstr "" -"初始化 文档型类型 EmbeddingEngine api， 将文档embedding向量化到向量数据库(文档可以是.txt, .pdf, " -".md, .html, .doc, .ppt)" -#: ../../modules/knowledge.rst:108 0afd40098d5f4dfd9e44fe1d8004da25 +#: ../../modules/knowledge.md:31 2cf550f17881497bb881b19efcc18c23 msgid "" -"5.init TEXT Type EmbeddingEngine api and embedding your document into " -"vector store in your code." -msgstr "初始化TEXT类型 EmbeddingEngine api， 将文档embedding向量化到向量数据库" +"Optionally, you can run `dbgpt knowledge load --help` command to see more" +" usage." +msgstr "" -#: ../../modules/knowledge.rst:120 a66961bf3efd41fa8ea938129446f5a5 -msgid "4.similar search based on your knowledge base. ::" -msgstr "在知识库进行相似性搜索" +#: ../../modules/knowledge.md:33 c8a2ea571b944bdfbcad48fa8b54fcc9 +msgid "" +"3.Add the knowledge repository in the interface by entering the name of " +"your knowledge repository (if not specified, enter \"default\") so you " +"can use it for Q&A based on your knowledge base." +msgstr "" -#: ../../modules/knowledge.rst:126 b7066f408378450db26770f83fbd2716 +#: ../../modules/knowledge.md:35 b701170ad75e49dea7d7734c15681e0f msgid "" "Note that the default vector model used is text2vec-large-chinese (which " "is a large model, so if your personal computer configuration is not " @@ -125,48 +106,6 @@ msgstr "" "注意，这里默认向量模型是text2vec-large-chinese(模型比较大，如果个人电脑配置不够建议采用text2vec-base-" "chinese),因此确保需要将模型download下来放到models目录中。" -#: ../../modules/knowledge.rst:128 58481d55cab74936b6e84b24c39b1674 -#, fuzzy -msgid "" -"`pdf_embedding <./knowledge/pdf/pdf_embedding.html>`_: supported pdf " -"embedding." -msgstr "pdf_embedding <./knowledge/pdf_embedding.html>`_: supported pdf embedding." - -#: ../../modules/knowledge.rst:129 fbb013c4f1bc46af910c91292f6690cf -#, fuzzy -msgid "" -"`markdown_embedding <./knowledge/markdown/markdown_embedding.html>`_: " -"supported markdown embedding." -msgstr "pdf_embedding <./knowledge/pdf_embedding.html>`_: supported pdf embedding." - -#: ../../modules/knowledge.rst:130 59d45732f4914d16b4e01aee0992edf7 -#, fuzzy -msgid "" -"`word_embedding <./knowledge/word/word_embedding.html>`_: supported word " -"embedding." -msgstr "pdf_embedding <./knowledge/pdf_embedding.html>`_: supported pdf embedding." - -#: ../../modules/knowledge.rst:131 df0e6f311861423e885b38e020a7c0f0 -#, fuzzy -msgid "" -"`url_embedding <./knowledge/url/url_embedding.html>`_: supported url " -"embedding." -msgstr "pdf_embedding <./knowledge/pdf_embedding.html>`_: supported pdf embedding." - -#: ../../modules/knowledge.rst:132 7c550c1f5bc34fe9986731fb465e12cd -#, fuzzy -msgid "" -"`ppt_embedding <./knowledge/ppt/ppt_embedding.html>`_: supported ppt " -"embedding." -msgstr "pdf_embedding <./knowledge/pdf_embedding.html>`_: supported pdf embedding." - -#: ../../modules/knowledge.rst:133 8648684cb191476faeeb548389f79050 -#, fuzzy -msgid "" -"`string_embedding <./knowledge/string/string_embedding.html>`_: supported" -" raw text embedding." -msgstr "pdf_embedding <./knowledge/pdf_embedding.html>`_: supported pdf embedding." - #~ msgid "before execution: python -m spacy download zh_core_web_sm" #~ msgstr "在执行之前请先执行python -m spacy download zh_core_web_sm" @@ -201,3 +140,112 @@ msgstr "pdf_embedding <./knowledge/pdf_embedding.html>`_: supported pdf embeddin #~ "and MILVUS_PORT)" #~ msgstr "2.更新你的.env，设置你的向量存储类型，VECTOR_STORE_TYPE=Chroma(现在只支持Chroma和Milvus，如果你设置了Milvus，请设置MILVUS_URL和MILVUS_PORT)" +#~ msgid "" +#~ "We currently support many document " +#~ "formats: raw text, txt, pdf, md, " +#~ "html, doc, ppt, and url. In the" +#~ " future, we will continue to support" +#~ " more types of knowledge, including " +#~ "audio, video, various databases, and big" +#~ " data sources. Of course, we look " +#~ "forward to your active participation in" +#~ " contributing code." +#~ msgstr "" + +#~ msgid "1.prepare" +#~ msgstr "准备" + +#~ msgid "" +#~ "2.prepare embedding model, you can " +#~ "download from https://huggingface.co/. Notice " +#~ "you have installed git-lfs." +#~ msgstr "" +#~ "提前准备Embedding Model, 你可以在https://huggingface.co/进行下载，注意" +#~ "：你需要先安装git-lfs.eg: git clone " +#~ "https://huggingface.co/THUDM/chatglm2-6b" + +#~ msgid "" +#~ "eg: git clone https://huggingface.co/sentence-" +#~ "transformers/all-MiniLM-L6-v2" +#~ msgstr "" +#~ "eg: git clone https://huggingface.co/sentence-" +#~ "transformers/all-MiniLM-L6-v2" + +#~ msgid "" +#~ "3.prepare vector_store instance and vector " +#~ "store config, now we support Chroma, " +#~ "Milvus and Weaviate." +#~ msgstr "提前准备向量数据库环境，目前支持Chroma, Milvus and Weaviate向量数据库" + +#~ msgid "" +#~ "3.init Url Type EmbeddingEngine api and" +#~ " embedding your document into vector " +#~ "store in your code." +#~ msgstr "初始化 Url类型 EmbeddingEngine api， 将url文档embedding向量化到向量数据库 " + +#~ msgid "If you want to add your source_reader or text_splitter, do this:" +#~ msgstr "如果你想手动添加你自定义的source_reader和text_splitter, 请参考:" + +#~ msgid "" +#~ "4.init Document Type EmbeddingEngine api " +#~ "and embedding your document into vector" +#~ " store in your code. Document type" +#~ " can be .txt, .pdf, .md, .doc, " +#~ ".ppt." +#~ msgstr "" +#~ "初始化 文档型类型 EmbeddingEngine api， " +#~ "将文档embedding向量化到向量数据库(文档可以是.txt, .pdf, .md, .html," +#~ " .doc, .ppt)" + +#~ msgid "" +#~ "5.init TEXT Type EmbeddingEngine api and" +#~ " embedding your document into vector " +#~ "store in your code." +#~ msgstr "初始化TEXT类型 EmbeddingEngine api， 将文档embedding向量化到向量数据库" + +#~ msgid "4.similar search based on your knowledge base. ::" +#~ msgstr "在知识库进行相似性搜索" + +#~ msgid "" +#~ "`pdf_embedding <./knowledge/pdf/pdf_embedding.html>`_: " +#~ "supported pdf embedding." +#~ msgstr "" +#~ "pdf_embedding <./knowledge/pdf_embedding.html>`_: " +#~ "supported pdf embedding." + +#~ msgid "" +#~ "`markdown_embedding " +#~ "<./knowledge/markdown/markdown_embedding.html>`_: supported " +#~ "markdown embedding." +#~ msgstr "" +#~ "pdf_embedding <./knowledge/pdf_embedding.html>`_: " +#~ "supported pdf embedding." + +#~ msgid "" +#~ "`word_embedding <./knowledge/word/word_embedding.html>`_: " +#~ "supported word embedding." +#~ msgstr "" +#~ "pdf_embedding <./knowledge/pdf_embedding.html>`_: " +#~ "supported pdf embedding." + +#~ msgid "" +#~ "`url_embedding <./knowledge/url/url_embedding.html>`_: " +#~ "supported url embedding." +#~ msgstr "" +#~ "pdf_embedding <./knowledge/pdf_embedding.html>`_: " +#~ "supported pdf embedding." + +#~ msgid "" +#~ "`ppt_embedding <./knowledge/ppt/ppt_embedding.html>`_: " +#~ "supported ppt embedding." +#~ msgstr "" +#~ "pdf_embedding <./knowledge/pdf_embedding.html>`_: " +#~ "supported pdf embedding." + +#~ msgid "" +#~ "`string_embedding <./knowledge/string/string_embedding.html>`_:" +#~ " supported raw text embedding." +#~ msgstr "" +#~ "pdf_embedding <./knowledge/pdf_embedding.html>`_: " +#~ "supported pdf embedding." + diff --git a/docs/locales/zh_CN/LC_MESSAGES/modules/plugins.po b/docs/locales/zh_CN/LC_MESSAGES/modules/plugins.po index d42bc3d55..662d7051d 100644 --- a/docs/locales/zh_CN/LC_MESSAGES/modules/plugins.po +++ b/docs/locales/zh_CN/LC_MESSAGES/modules/plugins.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: DB-GPT 0.3.0\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-06-14 21:47+0800\n" +"POT-Creation-Date: 2023-11-03 15:33+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -19,11 +19,11 @@ msgstr "" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.12.1\n" -#: ../../modules/plugins.md:1 8e0200134cca45b6aead6d05b60ca95a +#: ../../modules/plugins.md:1 e8c539b65ccd459793e8ed3812903578 msgid "Plugins" msgstr "插件" -#: ../../modules/plugins.md:3 d0d532cfe9b44fa0916d7d5b912a744a +#: ../../modules/plugins.md:3 0d6f6bdcf843416fb35d9f51df52bead msgid "" "The ability of Agent and Plugin is the core of whether large models can " "be automated. In this project, we natively support the plugin mode, and " @@ -35,49 +35,62 @@ msgstr "" "Agent与插件能力是大模型能否自动化的核心，在本的项目中，原生支持插件模式，大模型可以自动化完成目标。 同时为了充分发挥社区的优势" "，本项目中所用的插件原生支持Auto-GPT插件生态，即Auto-GPT的插件可以直接在我们的项目中运行。" -#: ../../modules/plugins.md:5 2f78a6b397a24f34b0d5771ca93efb0b +#: ../../modules/plugins.md:5 625763bc41fe417c8e4ea03ab2f8fdfc +#, fuzzy +msgid "The LLM (Language Model) suitable for the Plugin scene is" +msgstr "Plugin场景适用的LLM是 * chatgpt3.5. * chatgpt4." + +#: ../../modules/plugins.md:6 b3bd64693a4f4bf8b64b9224d3e1532e +msgid "chatgpt3.5." +msgstr "" + +#: ../../modules/plugins.md:7 46d9220e63384594b54c2c176077d962 +msgid "chatgpt4." +msgstr "" + +#: ../../modules/plugins.md:10 8c539e139f6648b2bef5dc683b8e093c #, fuzzy msgid "Local Plugins" msgstr "插件" -#: ../../modules/plugins.md:7 54a817a638c3440989191b3bffaca6de +#: ../../modules/plugins.md:12 2cc7ba992d524913b3377cad3bf747d3 msgid "1.1 How to write local plugins." msgstr "" -#: ../../modules/plugins.md:9 fbdc0a9d327f432aa6a380117dfb2f11 +#: ../../modules/plugins.md:14 eddffc1d2c434e45890a9befa1bb5160 msgid "" "Local plugins use the Auto-GPT plugin template. A simple example is as " "follows: first write a plugin file called \"sql_executor.py\"." msgstr "" -#: ../../modules/plugins.md:39 dc398ab427bd4d15b3b7c8cb1ff032b3 +#: ../../modules/plugins.md:44 06efbea552bb4dc7828d842b779e41d4 msgid "" "Then set the \"can_handle_post_prompt\" method of the plugin template to " "True. In the \"post_prompt\" method, write the prompt information and the" " mapped plugin function." msgstr "" -#: ../../modules/plugins.md:81 c9d4019392bf452e906057cbe9271005 +#: ../../modules/plugins.md:86 afd3cfb379bb463e97e515ae65790830 msgid "1.2 How to use local plugins" msgstr "" -#: ../../modules/plugins.md:83 9beaed4a71124ecf9544a1dba0d1e722 +#: ../../modules/plugins.md:88 f43a70e4cb5c4846a5bb8df3853021ba msgid "" "Pack your plugin project into `your-plugin.zip` and place it in the " "`/plugins/` directory of the DB-GPT project. After starting the " "webserver, you can select and use it in the `Plugin Model` section." msgstr "" -#: ../../modules/plugins.md:86 9a1439c883a947d7acac3fd1196b3c1e +#: ../../modules/plugins.md:91 8269458bd7f5480dbc56100865eb1eb0 #, fuzzy msgid "Public Plugins" msgstr "插件" -#: ../../modules/plugins.md:88 2ed4c509bf5848adb3fa163752a1cfa1 +#: ../../modules/plugins.md:93 ec5bb7b6b2cf464d8b8400f3dfd9a50e msgid "1.1 How to use public plugins" msgstr "" -#: ../../modules/plugins.md:90 dd5ba8d582204b2f89ce802a1232b11d +#: ../../modules/plugins.md:95 3025a85c905c49b6b2ac3f5c39c84c93 msgid "" "By default, after launching the webserver, plugins from the public plugin" " library `DB-GPT-Plugins` will be automatically loaded. For more details," @@ -85,17 +98,17 @@ msgid "" "Plugins)" msgstr "" -#: ../../modules/plugins.md:92 244f0591bc5045eab175754521b414c4 +#: ../../modules/plugins.md:97 e73d7779df254ba49fe7123ce06353aa msgid "1.2 Contribute to the DB-GPT-Plugins repository" msgstr "" -#: ../../modules/plugins.md:94 e00bac1a299b46caa19b9cf16709d6ba +#: ../../modules/plugins.md:99 3297fb00dfc940e8a614c3858640cfe5 msgid "" "Please refer to the plugin development process in the public plugin " "library, and put the configuration parameters in `.plugin_env`" msgstr "" -#: ../../modules/plugins.md:96 315fbf576ea24158adc7b564f53940e0 +#: ../../modules/plugins.md:101 13280b270b304e139ed67e5b0dafa5b4 msgid "" "We warmly welcome everyone to contribute plugins to the public plugin " "library!" diff --git a/docs/locales/zh_CN/LC_MESSAGES/modules/prompts.po b/docs/locales/zh_CN/LC_MESSAGES/modules/prompts.po index 138b308b7..810e20f04 100644 --- a/docs/locales/zh_CN/LC_MESSAGES/modules/prompts.po +++ b/docs/locales/zh_CN/LC_MESSAGES/modules/prompts.po @@ -6,9 +6,9 @@ #, fuzzy msgid "" msgstr "" -"Project-Id-Version: DB-GPT 0.3.0\n" +"Project-Id-Version: DB-GPT 👏👏 0.4.0\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-06-14 22:33+0800\n" +"POT-Creation-Date: 2023-11-03 11:47+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -19,21 +19,130 @@ msgstr "" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.12.1\n" -#: ../../modules/prompts.md:1 b9279c238c014a74aecbc75b5d3dc202 +#: ../../modules/prompts.md:1 3c5bdc61dc4a4301acdc9775c854a896 msgid "Prompts" -msgstr "" +msgstr "Prompts" -#: ../../modules/prompts.md:3 f0c720c1c85b401cbc26ed0eb3f6e70e +#: ../../modules/prompts.md:3 118fc2b85e8b4e02a6868b3bc2a7892c msgid "" -"Prompt is a very important part of the interaction between the large " +"**Prompt** is a very important part of the interaction between the large " "model and the user, and to a certain extent, it determines the quality " "and accuracy of the answer generated by the large model. In this project," -" we will automatically optimize the corresponding prompt according to " +" we will automatically optimize the corresponding Prompt according to " "user input and usage scenarios, making it easier and more efficient for " "users to use large language models." -msgstr "Prompt是与大模型交互过程中非常重要的部分，一定程度上Prompt决定了大模型生成答案的质量与准确性，在本的项目中，我们会根据用户输入与使用场景，自动优化对应的Prompt，让用户使用大语言模型变得更简单、更高效。" +msgstr "**Prompt**是大模型与用户交互中非常重要的一环，在一定程度上决定了大模型生成答案的质量和准确性。在这个项目中，我们会根据用户输入和使用场景自动地优化相应提示，让用户更轻松、更高效地使用大语言模型。" -#: ../../modules/prompts.md:5 6576d32e28a14be6a5d8180eed000aa7 -msgid "1.DB-GPT Prompt" +#: ../../modules/prompts.md:5 41614effa0a445b7b5a119311b902305 +msgid "Prompt Management" +msgstr "Prompt 管理" + +#: ../../modules/prompts.md:7 a8ed0a7b3d1243ffa1ed80c24d1ab518 +msgid "" +"Here, you can choose to create a Prompt in **Public Prompts** space or " +"**Private Prompts** space." +msgstr "该页面允许用户选择**公共Prompts**或者**私有Prompts**空间来创建相应的 Prompt。" + +#: ../../modules/prompts.md:9 ../../modules/prompts.md:17 +#: ../../modules/prompts.md:31 ../../modules/prompts.md:45 +#: 68db272acc6b4572aa275940da4b788b 92d46d647bbb4035add92f750511a840 +#: af1789fae8cb47b8a81e68520086f35e d7c2f6f43b5c406d82b7dc5bd92d183c +#: e2f91ca11e784fe5943d0738671f68bf +msgid "image" msgstr "" +#: ../../modules/prompts.md:11 102220bf95f04f81acc9a0093458f297 +msgid "" +"The difference between **Public Prompts** and **Private Prompts** is that" +" Prompts in **Public Prompts** space can be viewed and used by all users," +" while prompts in **Private Prompts** space can only be viewed and used " +"by the owner." +msgstr "" +"**公共 Prompts**和**私有 Prompts**空间的区别在于，**公共 Prompts**空间下的 Prompt " +"可供所有的用户查看和使用，而**私有 Prompts**空间下的 Prompt 只能被所有者查看和使用。" + +#: ../../modules/prompts.md:13 2e0d2f6b335a4aacbdc83b7b7042a701 +msgid "Create Prompt" +msgstr "创建 Prompt" + +#: ../../modules/prompts.md:15 c9f8c3d1698941e08b90a35fffb2fce1 +msgid "Click the \"Add Prompts\" button to pop up the following subpage:" +msgstr "点击 \"新增Prompts\"按钮可以弹出如下的子页面：" + +#: ../../modules/prompts.md:19 23ed81a83ab2458f826f2b5d9c55a89a +msgid "" +"**Scene**: It is assumed here that when we have a lot of Prompts, we " +"often classify the Prompts according to scene, such as Prompts in the " +"chat knowledge scene, Prompts in the chat data scene, Prompts in the chat" +" normal scene, etc." +msgstr "" +"**场景**：这里假设，当我们有很多 Prompts 时，往往会根据场景对 Prompts 进行分类，比如在 DB-GPT 项目中，chat " +"knowledge 场景的 Prompts、chat data 场景的 Prompts、chat normal 场景的 Prompts 等等。" + +#: ../../modules/prompts.md:21 11299da493e741869fe67237f1cb1794 +msgid "" +"**Sub Scene**: Continuing with the above, assuming that we have a lot of " +"Prompts, scene classification alone is not enough. For example, in the " +"chat data scenario, there can be many types of sub-scene: anomaly " +"recognition sub scene, attribution analysis sub scene, etc. sub scene is " +"used to distinguish subcategories under each scene." +msgstr "" +"**次级场景**：接着上面的内容，如果我们的 Prompt 很多时，仅使用场景一级分类是不够的。例如，在 chat data " +"场景中，还可以细分为很多的次级场景：异常识别次级场景、归因分析次级场景等等。次级场景是用于区分每个场景下的子类别。" + +#: ../../modules/prompts.md:23 c15d62af27094d14acb6428c0e3e1a1d +msgid "" +"**Name**: Considering that a Prompt generally contains a lot of content, " +"for ease of use and easy search, we need to name the Prompt. Note: The " +"name of the Prompt is not allowed to be repeated. Name is the unique key " +"that identifies a Prompt." +msgstr "" +"**名称**：考虑到每个 Prompt 的内容会非常多，为了方便用户使用和搜索，我们需要给每个 Prompt 命名。注意：Prompt " +"的名称不允许重复，名称是一个 Prompt 的唯一键。" + +#: ../../modules/prompts.md:25 621fe9c729c94e9bbde637b5a1856284 +msgid "**Content**: Here is the actual Prompt content that will be input to LLM." +msgstr "**内容**：这里是实际要输入 LLM 的提示内容。" + +#: ../../modules/prompts.md:27 ac2f153f704c4841a044daaf6548262b +msgid "Edit Prompt" +msgstr "编辑 Prompt" + +#: ../../modules/prompts.md:29 3d6238ea482842e0968f691f3fd0c947 +msgid "" +"Existing Prompts can be edited. Note that except **name**, other items " +"can be modified." +msgstr "已有的 Prompts 可以被编辑，除了名称不可修改，其余的内容均可修改。" + +#: ../../modules/prompts.md:33 7cbe985fd9534471bce5f93a93da82fd +msgid "Delete Prompt" +msgstr "删除 Prompt" + +#: ../../modules/prompts.md:35 849ab9ef2a2c4a29bb827eb373f37b7d +msgid "" +"Ordinary users can only delete Prompts created by themselves in the " +"private Prompts space. Administrator users can delete Prompts in public " +"Prompts spaces and private Prompts spaces." +msgstr "" +"普通用户只能删除他们自己在私有 Prompts 空间中创建的 Prompts，管理员可以删除 公共 Prompts 空间下的 " +"Prompts，也可以删除私有 Prompts 空间下的 Prompts（即使 Prompts 的创建者不是管理员）。" + +#: ../../modules/prompts.md:38 191921e5664d4326b01f0c45dc88a1e5 +msgid "Use Prompt" +msgstr "使用 Prompt" + +#: ../../modules/prompts.md:40 87ad58641f834f30bce178e748d75284 +msgid "" +"Users can find and use Prompts next to the input boxes in each scene. " +"Click to view all contents of Prompts library." +msgstr "用户可以在每个场景中的输入框旁边找到并使用 Prompts。 点击悬浮图标可以查看当前用户能使用的全部 Prompts。" + +#: ../../modules/prompts.md:42 60458c7980174c73bc0d56e9e27cd2b3 +msgid "" +"✓ Hover the mouse over each Prompt to preview the Prompt content. ✓ " +"Click Prompt to automatically fill in the Prompt content in the input " +"box." +msgstr "" +"✓ 将鼠标悬停在每个 Prompt 上，可预览 Prompt 的内容。 ✓ 单击对应的 Prompt，可自动将 Prompt " +"的内容填充到输入框中。" + diff --git a/docs/modules/knownledge.md b/docs/modules/knowledge.md similarity index 97% rename from docs/modules/knownledge.md rename to docs/modules/knowledge.md index 03fb45eec..7c8b2758f 100644 --- a/docs/modules/knownledge.md +++ b/docs/modules/knowledge.md @@ -1,4 +1,4 @@ -# Knownledge +# Knowledge As the knowledge base is currently the most significant user demand scenario, we natively support the construction and processing of knowledge bases. At the same time, we also provide multiple knowledge base management strategies in this project, such as: 1. Default built-in knowledge base @@ -32,4 +32,4 @@ Optionally, you can run `dbgpt knowledge load --help` command to see more usage. 3.Add the knowledge repository in the interface by entering the name of your knowledge repository (if not specified, enter "default") so you can use it for Q&A based on your knowledge base. -Note that the default vector model used is text2vec-large-chinese (which is a large model, so if your personal computer configuration is not enough, it is recommended to use text2vec-base-chinese). Therefore, ensure that you download the model and place it in the models directory. \ No newline at end of file +Note that the default vector model used is text2vec-large-chinese (which is a large model, so if your personal computer configuration is not enough, it is recommended to use text2vec-base-chinese). Therefore, ensure that you download the model and place it in the models directory. diff --git a/docs/modules/plugins.md b/docs/modules/plugins.md index 6f2617146..b78578066 100644 --- a/docs/modules/plugins.md +++ b/docs/modules/plugins.md @@ -2,6 +2,11 @@ The ability of Agent and Plugin is the core of whether large models can be automated. In this project, we natively support the plugin mode, and large models can automatically achieve their goals. At the same time, in order to give full play to the advantages of the community, the plugins used in this project natively support the Auto-GPT plugin ecology, that is, Auto-GPT plugins can directly run in our project. +```{admonition} The LLM (Language Model) suitable for the Plugin scene is +* chatgpt3.5. +* chatgpt4. +``` + ## Local Plugins ### 1.1 How to write local plugins. diff --git a/docs/modules/prompts.md b/docs/modules/prompts.md index 2e8f5181d..177475618 100644 --- a/docs/modules/prompts.md +++ b/docs/modules/prompts.md @@ -1,5 +1,46 @@ # Prompts -Prompt is a very important part of the interaction between the large model and the user, and to a certain extent, it determines the quality and accuracy of the answer generated by the large model. In this project, we will automatically optimize the corresponding prompt according to user input and usage scenarios, making it easier and more efficient for users to use large language models. +**Prompt** is a very important part of the interaction between the large model and the user, and to a certain extent, it determines the quality and accuracy of the answer generated by the large model. In this project, we will automatically optimize the corresponding Prompt according to user input and usage scenarios, making it easier and more efficient for users to use large language models. -### 1.DB-GPT Prompt \ No newline at end of file +## Prompt Management + +Here, you can choose to create a Prompt in **Public Prompts** space or **Private Prompts** space. + +image + +The difference between **Public Prompts** and **Private Prompts** is that Prompts in **Public Prompts** space can be viewed and used by all users, while prompts in **Private Prompts** space can only be viewed and used by the owner. + +### Create Prompt + +Click the "Add Prompts" button to pop up the following subpage: + +image + +**Scene**: It is assumed here that when we have a lot of Prompts, we often classify the Prompts according to scene, such as Prompts in the chat knowledge scene, Prompts in the chat data scene, Prompts in the chat normal scene, etc. + +**Sub Scene**: Continuing with the above, assuming that we have a lot of Prompts, scene classification alone is not enough. For example, in the chat data scenario, there can be many types of sub-scene: anomaly recognition sub scene, attribution analysis sub scene, etc. sub scene is used to distinguish subcategories under each scene. + +**Name**: Considering that a Prompt generally contains a lot of content, for ease of use and easy search, we need to name the Prompt. Note: The name of the Prompt is not allowed to be repeated. Name is the unique key that identifies a Prompt. + +**Content**: Here is the actual Prompt content that will be input to LLM. + +### Edit Prompt + +Existing Prompts can be edited. Note that except **name**, other items can be modified. + +image + +### Delete Prompt + +Ordinary users can only delete Prompts created by themselves in the private Prompts space. Administrator users can delete Prompts in public Prompts spaces and private Prompts spaces. + + +## Use Prompt + +Users can find and use Prompts next to the input boxes in each scene. Click to view all contents of Prompts library. + +✓ Hover the mouse over each Prompt to preview the Prompt content. +✓ Click Prompt to automatically fill in the Prompt content in the input box. + +image +image \ No newline at end of file diff --git a/docs/reference.md b/docs/reference.md deleted file mode 100644 index 4a938e09d..000000000 --- a/docs/reference.md +++ /dev/null @@ -1 +0,0 @@ -# Reference \ No newline at end of file diff --git a/docs/requirements.txt b/docs/requirements.txt index 8cee184af..d0a125868 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -9,9 +9,9 @@ sphinx_book_theme sphinx_rtd_theme==1.0.0 sphinx-typlog-theme==0.8.0 sphinx-panels +sphinx-tabs==3.4.0 toml myst_nb sphinx_copybutton pydata-sphinx-theme==0.13.1 -pydantic-settings furo \ No newline at end of file diff --git a/examples/app.py b/examples/app.py deleted file mode 100644 index 07f8a5a51..000000000 --- a/examples/app.py +++ /dev/null @@ -1,74 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- - -import gradio as gr -from langchain.agents import AgentType, initialize_agent, load_tools -from llama_index import ( - Document, - GPTVectorStoreIndex, - LangchainEmbedding, - LLMPredictor, - ServiceContext, -) - -from pilot.model.llm_out.vicuna_llm import VicunaEmbeddingLLM, VicunaRequestLLM - - -def agent_demo(): - llm = VicunaRequestLLM() - - tools = load_tools(["python_repl"], llm=llm) - agent = initialize_agent( - tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True - ) - agent.run("Write a SQL script that Query 'select count(1)!'") - - -def knowledged_qa_demo(text_list): - llm_predictor = LLMPredictor(llm=VicunaRequestLLM()) - hfemb = VicunaEmbeddingLLM() - embed_model = LangchainEmbedding(hfemb) - documents = [Document(t) for t in text_list] - - service_context = ServiceContext.from_defaults( - llm_predictor=llm_predictor, embed_model=embed_model - ) - index = GPTVectorStoreIndex.from_documents( - documents, service_context=service_context - ) - return index - - -def get_answer(q): - base_knowledge = """ """ - text_list = [base_knowledge] - index = knowledged_qa_demo(text_list) - response = index.query(q) - return response.response - - -def get_similar(q): - from pilot.vector_store.extract_tovec import knownledge_tovec_st - - docsearch = knownledge_tovec_st("./datasets/plan.md") - docs = docsearch.similarity_search_with_score(q, k=1) - - for doc in docs: - dc, s = doc - print(s) - yield dc.page_content - - -if __name__ == "__main__": - # agent_demo() - - with gr.Blocks() as demo: - gr.Markdown("数据库智能助手") - with gr.Tab("知识问答"): - text_input = gr.TextArea() - text_output = gr.TextArea() - text_button = gr.Button() - - text_button.click(get_similar, inputs=text_input, outputs=text_output) - - demo.queue(concurrency_count=3).launch(server_name="0.0.0.0") diff --git a/examples/awel/simple_chat_dag_example.py b/examples/awel/simple_chat_dag_example.py new file mode 100644 index 000000000..b53c2415e --- /dev/null +++ b/examples/awel/simple_chat_dag_example.py @@ -0,0 +1,56 @@ +"""AWEL: Simple chat dag example + + DB-GPT will automatically load and execute the current file after startup. + + Example: + + .. code-block:: shell + + curl -X POST http://127.0.0.1:5000/api/v1/awel/trigger/examples/simple_chat \ + -H "Content-Type: application/json" -d '{ + "model": "proxyllm", + "user_input": "hello" + }' +""" +from typing import Dict +from pydantic import BaseModel, Field + +from pilot.awel import DAG, HttpTrigger, MapOperator +from pilot.scene.base_message import ModelMessage +from pilot.model.base import ModelOutput +from pilot.model.operator.model_operator import ModelOperator + + +class TriggerReqBody(BaseModel): + model: str = Field(..., description="Model name") + user_input: str = Field(..., description="User input") + + +class RequestHandleOperator(MapOperator[TriggerReqBody, Dict]): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + async def map(self, input_value: TriggerReqBody) -> Dict: + hist = [] + hist.append(ModelMessage.build_human_message(input_value.user_input)) + hist = list(h.dict() for h in hist) + params = { + "prompt": input_value.user_input, + "messages": hist, + "model": input_value.model, + "echo": False, + } + print(f"Receive input value: {input_value}") + return params + + +with DAG("dbgpt_awel_simple_dag_example") as dag: + # Receive http request and trigger dag to run. + trigger = HttpTrigger( + "/examples/simple_chat", methods="POST", request_body=TriggerReqBody + ) + request_handle_task = RequestHandleOperator() + model_task = ModelOperator() + # type(out) == ModelOutput + model_parse_task = MapOperator(lambda out: out.to_dict()) + trigger >> request_handle_task >> model_task >> model_parse_task diff --git a/examples/awel/simple_dag_example.py b/examples/awel/simple_dag_example.py new file mode 100644 index 000000000..bfc9b45b5 --- /dev/null +++ b/examples/awel/simple_dag_example.py @@ -0,0 +1,34 @@ +"""AWEL: Simple dag example + + DB-GPT will automatically load and execute the current file after startup. + + Example: + + .. code-block:: shell + + curl -X GET http://127.0.0.1:5000/api/v1/awel/trigger/examples/hello\?name\=zhangsan + +""" +from pydantic import BaseModel, Field + +from pilot.awel import DAG, HttpTrigger, MapOperator + + +class TriggerReqBody(BaseModel): + name: str = Field(..., description="User name") + age: int = Field(18, description="User age") + + +class RequestHandleOperator(MapOperator[TriggerReqBody, str]): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + async def map(self, input_value: TriggerReqBody) -> str: + print(f"Receive input value: {input_value}") + return f"Hello, {input_value.name}, your age is {input_value.age}" + + +with DAG("simple_dag_example") as dag: + trigger = HttpTrigger("/examples/hello", request_body=TriggerReqBody) + map_node = RequestHandleOperator() + trigger >> map_node diff --git a/examples/awel/simple_rag_example.py b/examples/awel/simple_rag_example.py new file mode 100644 index 000000000..c7cd934dc --- /dev/null +++ b/examples/awel/simple_rag_example.py @@ -0,0 +1,73 @@ +"""AWEL: Simple rag example + + DB-GPT will automatically load and execute the current file after startup. + + Example: + + .. code-block:: shell + + curl -X POST http://127.0.0.1:5000/api/v1/awel/trigger/examples/simple_rag \ + -H "Content-Type: application/json" -d '{ + "conv_uid": "36f0e992-8825-11ee-8638-0242ac150003", + "model_name": "proxyllm", + "chat_mode": "chat_knowledge", + "user_input": "What is DB-GPT?", + "select_param": "default" + }' + +""" + +from pilot.awel import HttpTrigger, DAG, MapOperator +from pilot.scene.operator._experimental import ( + ChatContext, + PromptManagerOperator, + ChatHistoryStorageOperator, + ChatHistoryOperator, + EmbeddingEngingOperator, + BaseChatOperator, +) +from pilot.scene.base import ChatScene +from pilot.openapi.api_view_model import ConversationVo +from pilot.model.base import ModelOutput +from pilot.model.operator.model_operator import ModelOperator + + +class RequestParseOperator(MapOperator[ConversationVo, ChatContext]): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + async def map(self, input_value: ConversationVo) -> ChatContext: + return ChatContext( + current_user_input=input_value.user_input, + model_name=input_value.model_name, + chat_session_id=input_value.conv_uid, + select_param=input_value.select_param, + chat_scene=ChatScene.ChatKnowledge, + ) + + +with DAG("simple_rag_example") as dag: + trigger_task = HttpTrigger( + "/examples/simple_rag", methods="POST", request_body=ConversationVo + ) + req_parse_task = RequestParseOperator() + # TODO should register prompt template first + prompt_task = PromptManagerOperator() + history_storage_task = ChatHistoryStorageOperator() + history_task = ChatHistoryOperator() + embedding_task = EmbeddingEngingOperator() + chat_task = BaseChatOperator() + model_task = ModelOperator() + output_parser_task = MapOperator(lambda out: out.to_dict()["text"]) + + ( + trigger_task + >> req_parse_task + >> prompt_task + >> history_storage_task + >> history_task + >> embedding_task + >> chat_task + >> model_task + >> output_parser_task + ) diff --git a/examples/embdserver.py b/examples/embdserver.py deleted file mode 100644 index ae0dfcae8..000000000 --- a/examples/embdserver.py +++ /dev/null @@ -1,82 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- - -import json -import os -import sys -from urllib.parse import urljoin - -import gradio as gr -import requests - -ROOT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -sys.path.append(ROOT_PATH) - - -from langchain.prompts import PromptTemplate - -from pilot.configs.config import Config -from pilot.conversation import conv_qa_prompt_template, conv_templates - -llmstream_stream_path = "generate_stream" - -CFG = Config() - - -def generate(query): - template_name = "conv_one_shot" - state = conv_templates[template_name].copy() - - # pt = PromptTemplate( - # template=conv_qa_prompt_template, - # input_variables=["context", "question"] - # ) - - # result = pt.format(context="This page covers how to use the Chroma ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Chroma wrappers.", - # question=query) - - # print(result) - - state.append_message(state.roles[0], query) - state.append_message(state.roles[1], None) - - prompt = state.get_prompt() - params = { - "model": "chatglm-6b", - "prompt": prompt, - "temperature": 1.0, - "max_new_tokens": 1024, - "stop": "###", - } - - response = requests.post( - url=urljoin(CFG.MODEL_SERVER, llmstream_stream_path), data=json.dumps(params) - ) - - skip_echo_len = len(params["prompt"]) + 1 - params["prompt"].count("") * 3 - - for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"): - if chunk: - data = json.loads(chunk.decode()) - if data["error_code"] == 0: - if "vicuna" in CFG.LLM_MODEL: - output = data["text"][skip_echo_len:].strip() - else: - output = data["text"].strip() - - state.messages[-1][-1] = output + "▌" - yield (output) - - -if __name__ == "__main__": - print(CFG.LLM_MODEL) - with gr.Blocks() as demo: - gr.Markdown("数据库SQL生成助手") - with gr.Tab("SQL生成"): - text_input = gr.TextArea() - text_output = gr.TextArea() - text_button = gr.Button("提交") - - text_button.click(generate, inputs=text_input, outputs=text_output) - - demo.queue(concurrency_count=3).launch(server_name="0.0.0.0") diff --git a/examples/gpt_index.py b/examples/gpt_index.py deleted file mode 100644 index a4683af1a..000000000 --- a/examples/gpt_index.py +++ /dev/null @@ -1,19 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -import logging -import sys - -from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader - -logging.basicConfig(stream=sys.stdout, level=logging.INFO) -logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) - -# read the document of data dir -documents = SimpleDirectoryReader("data").load_data() -# split the document to chunk, max token size=500, convert chunk to vector - -index = GPTVectorStoreIndex(documents) - -# save index -index.save_to_disk("index.json") diff --git a/examples/gradio_test.py b/examples/gradio_test.py deleted file mode 100644 index 593c6c1f4..000000000 --- a/examples/gradio_test.py +++ /dev/null @@ -1,21 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- - -import gradio as gr - - -def change_tab(): - return gr.Tabs.update(selected=1) - - -with gr.Blocks() as demo: - with gr.Tabs() as tabs: - with gr.TabItem("Train", id=0): - t = gr.Textbox() - with gr.TabItem("Inference", id=1): - i = gr.Image() - - btn = gr.Button() - btn.click(change_tab, None, tabs) - -demo.launch() diff --git a/examples/knowledge_embedding/csv_embedding_test.py b/examples/knowledge_embedding/csv_embedding_test.py deleted file mode 100644 index dcf4873b2..000000000 --- a/examples/knowledge_embedding/csv_embedding_test.py +++ /dev/null @@ -1,18 +0,0 @@ -from pilot.embedding_engine.csv_embedding import CSVEmbedding - -# path = "/Users/chenketing/Downloads/share_ireserve双写数据异常2.xlsx" -path = "xx.csv" -model_name = "your_path/all-MiniLM-L6-v2" -vector_store_path = "your_path/" - - -pdf_embedding = CSVEmbedding( - file_path=path, - model_name=model_name, - vector_store_config={ - "vector_store_name": "url", - "vector_store_path": "vector_store_path", - }, -) -pdf_embedding.source_embedding() -print("success") diff --git a/examples/knowledge_embedding/pdf_embedding_test.py b/examples/knowledge_embedding/pdf_embedding_test.py deleted file mode 100644 index ef0e1d87e..000000000 --- a/examples/knowledge_embedding/pdf_embedding_test.py +++ /dev/null @@ -1,18 +0,0 @@ -from pilot.embedding_engine.pdf_embedding import PDFEmbedding - -path = "xxx.pdf" -path = "your_path/OceanBase-数据库-V4.1.0-应用开发.pdf" -model_name = "your_path/all-MiniLM-L6-v2" -vector_store_path = "your_path/" - - -pdf_embedding = PDFEmbedding( - file_path=path, - model_name=model_name, - vector_store_config={ - "vector_store_name": "ob-pdf", - "vector_store_path": vector_store_path, - }, -) -pdf_embedding.source_embedding() -print("success") diff --git a/examples/knowledge_embedding/url_embedding_test.py b/examples/knowledge_embedding/url_embedding_test.py deleted file mode 100644 index c702fd1f7..000000000 --- a/examples/knowledge_embedding/url_embedding_test.py +++ /dev/null @@ -1,17 +0,0 @@ -from pilot.embedding_engine.url_embedding import URLEmbedding - -path = "https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-8-2023" -model_name = "your_path/all-MiniLM-L6-v2" -vector_store_path = "your_path" - - -pdf_embedding = URLEmbedding( - file_path=path, - model_name=model_name, - vector_store_config={ - "vector_store_name": "url", - "vector_store_path": "vector_store_path", - }, -) -pdf_embedding.source_embedding() -print("success") diff --git a/examples/proxy_example.py b/examples/proxy_example.py deleted file mode 100644 index a3d2f3bc4..000000000 --- a/examples/proxy_example.py +++ /dev/null @@ -1,67 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -import dashscope -import requests -import hashlib -from http import HTTPStatus -from dashscope import Generation - - -def call_with_messages(): - messages = [ - {"role": "system", "content": "你是生活助手机器人。"}, - {"role": "user", "content": "如何做西红柿鸡蛋？"}, - ] - gen = Generation() - response = gen.call( - Generation.Models.qwen_turbo, - messages=messages, - stream=True, - top_p=0.8, - result_format="message", # set the result to be "message" format. - ) - - for response in response: - # The response status_code is HTTPStatus.OK indicate success, - # otherwise indicate request is failed, you can get error code - # and message from code and message. - if response.status_code == HTTPStatus.OK: - print(response.output) # The output text - print(response.usage) # The usage information - else: - print(response.code) # The error code. - print(response.message) # The error message. - - -def build_access_token(api_key: str, secret_key: str) -> str: - """ - Generate Access token according AK, SK - """ - - url = "https://aip.baidubce.com/oauth/2.0/token" - params = { - "grant_type": "client_credentials", - "client_id": api_key, - "client_secret": secret_key, - } - - res = requests.get(url=url, params=params) - - if res.status_code == 200: - return res.json().get("access_token") - - -def _calculate_md5(text: str) -> str: - md5 = hashlib.md5() - md5.update(text.encode("utf-8")) - encrypted = md5.hexdigest() - return encrypted - - -def baichuan_call(): - url = "https://api.baichuan-ai.com/v1/stream/chat" - - -if __name__ == "__main__": - call_with_messages() diff --git a/examples/t5_example.py b/examples/t5_example.py deleted file mode 100644 index b49e79d4e..000000000 --- a/examples/t5_example.py +++ /dev/null @@ -1,257 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -import torch -from langchain.embeddings.huggingface import HuggingFaceEmbeddings -from langchain.llms.base import LLM -from llama_index import ( - GPTListIndex, - GPTVectorStoreIndex, - LangchainEmbedding, - LLMPredictor, - PromptHelper, - SimpleDirectoryReader, -) -from transformers import pipeline - - -class FlanLLM(LLM): - model_name = "google/flan-t5-large" - pipeline = pipeline( - "text2text-generation", - model=model_name, - device=0, - model_kwargs={"torch_dtype": torch.bfloat16}, - ) - - def _call(self, prompt, stop=None): - return self.pipeline(prompt, max_length=9999)[0]["generated_text"] - - def _identifying_params(self): - return {"name_of_model": self.model_name} - - def _llm_type(self): - return "custome" - - -llm_predictor = LLMPredictor(llm=FlanLLM()) -hfemb = HuggingFaceEmbeddings() -embed_model = LangchainEmbedding(hfemb) - -text1 = """ - 执行计划是对一条 SQL 查询语句在数据库中执行过程的描述。用户可以通过 EXPLAIN 命令查看优化器针对指定 SQL 生成的逻辑执行计划。 - -如果要分析某条 SQL 的性能问题，通常需要先查看 SQL 的执行计划，排查每一步 SQL 执行是否存在问题。所以读懂执行计划是 SQL 优化的先决条件，而了解执行计划的算子是理解 EXPLAIN 命令的关键。 - -OceanBase 数据库的执行计划命令有三种模式：EXPLAIN BASIC、EXPLAIN 和 EXPLAIN EXTENDED。这三种模式对执行计划展现不同粒度的细节信息: - -EXPLAIN BASIC 命令用于最基本的计划展示。 - -EXPLAIN EXTENDED 命令用于最详细的计划展示（通常在排查问题时使用这种展示模式）。 - -EXPLAIN 命令所展示的信息可以帮助普通用户了解整个计划的执行方式。 - -EXPLAIN 命令格式如下： -EXPLAIN [BASIC | EXTENDED | PARTITIONS | FORMAT = format_name] [PRETTY | PRETTY_COLOR] explainable_stmt -format_name: - { TRADITIONAL | JSON } -explainable_stmt: - { SELECT statement - | DELETE statement - | INSERT statement - | REPLACE statement - | UPDATE statement } - - -EXPLAIN 命令适用于 SELECT、DELETE、INSERT、REPLACE 和 UPDATE 语句，显示优化器所提供的有关语句执行计划的信息，包括如何处理该语句，如何联接表以及以何种顺序联接表等信息。 - -一般来说，可以使用 EXPLAIN EXTENDED 命令，将表扫描的范围段展示出来。使用 EXPLAIN OUTLINE 命令可以显示 Outline 信息。 - -FORMAT 选项可用于选择输出格式。TRADITIONAL 表示以表格格式显示输出，这也是默认设置。JSON 表示以 JSON 格式显示信息。 - -使用 EXPLAIN PARTITITIONS 也可用于检查涉及分区表的查询。如果检查针对非分区表的查询，则不会产生错误，但 PARTIONS 列的值始终为 NULL。 - -对于复杂的执行计划，可以使用 PRETTY 或者 PRETTY_COLOR 选项将计划树中的父节点和子节点使用树线或彩色树线连接起来，使得执行计划展示更方便阅读。示例如下： -obclient> CREATE TABLE p1table(c1 INT ,c2 INT) PARTITION BY HASH(c1) PARTITIONS 2; -Query OK, 0 rows affected - -obclient> CREATE TABLE p2table(c1 INT ,c2 INT) PARTITION BY HASH(c1) PARTITIONS 4; -Query OK, 0 rows affected - -obclient> EXPLAIN EXTENDED PRETTY_COLOR SELECT * FROM p1table p1 JOIN p2table p2 ON p1.c1=p2.c2\G -*************************** 1. row *************************** -Query Plan: ========================================================== -|ID|OPERATOR |NAME |EST. ROWS|COST| ----------------------------------------------------------- -|0 |PX COORDINATOR | |1 |278 | -|1 | EXCHANGE OUT DISTR |:EX10001|1 |277 | -|2 | HASH JOIN | |1 |276 | -|3 | ├PX PARTITION ITERATOR | |1 |92 | -|4 | │ TABLE SCAN |P1 |1 |92 | -|5 | └EXCHANGE IN DISTR | |1 |184 | -|6 | EXCHANGE OUT DISTR (PKEY)|:EX10000|1 |184 | -|7 | PX PARTITION ITERATOR | |1 |183 | -|8 | TABLE SCAN |P2 |1 |183 | -========================================================== - -Outputs & filters: -------------------------------------- - 0 - output([INTERNAL_FUNCTION(P1.C1, P1.C2, P2.C1, P2.C2)]), filter(nil) - 1 - output([INTERNAL_FUNCTION(P1.C1, P1.C2, P2.C1, P2.C2)]), filter(nil), dop=1 - 2 - output([P1.C1], [P2.C2], [P1.C2], [P2.C1]), filter(nil), - equal_conds([P1.C1 = P2.C2]), other_conds(nil) - 3 - output([P1.C1], [P1.C2]), filter(nil) - 4 - output([P1.C1], [P1.C2]), filter(nil), - access([P1.C1], [P1.C2]), partitions(p[0-1]) - 5 - output([P2.C2], [P2.C1]), filter(nil) - 6 - (#keys=1, [P2.C2]), output([P2.C2], [P2.C1]), filter(nil), dop=1 - 7 - output([P2.C1], [P2.C2]), filter(nil) - 8 - output([P2.C1], [P2.C2]), filter(nil), - access([P2.C1], [P2.C2]), partitions(p[0-3]) - -1 row in set - - - - -## 执行计划形状与算子信息 - -在数据库系统中，执行计划在内部通常是以树的形式来表示的，但是不同的数据库会选择不同的方式展示给用户。 - -如下示例分别为 PostgreSQL 数据库、Oracle 数据库和 OceanBase 数据库对于 TPCDS Q3 的计划展示。 - -```sql -obclient> SELECT /*TPC-DS Q3*/ * - FROM (SELECT dt.d_year, - item.i_brand_id brand_id, - item.i_brand brand, - Sum(ss_net_profit) sum_agg - FROM date_dim dt, - store_sales, - item - WHERE dt.d_date_sk = store_sales.ss_sold_date_sk - AND store_sales.ss_item_sk = item.i_item_sk - AND item.i_manufact_id = 914 - AND dt.d_moy = 11 - GROUP BY dt.d_year, - item.i_brand, - item.i_brand_id - ORDER BY dt.d_year, - sum_agg DESC, - brand_id) - WHERE ROWNUM <= 100; - -PostgreSQL 数据库执行计划展示如下： -Limit (cost=13986.86..13987.20 rows=27 width=91) - Sort (cost=13986.86..13986.93 rows=27 width=65) - Sort Key: dt.d_year, (sum(store_sales.ss_net_profit)), item.i_brand_id - HashAggregate (cost=13985.95..13986.22 rows=27 width=65) - Merge Join (cost=13884.21..13983.91 rows=204 width=65) - Merge Cond: (dt.d_date_sk = store_sales.ss_sold_date_sk) - Index Scan using date_dim_pkey on date_dim dt (cost=0.00..3494.62 rows=6080 width=8) - Filter: (d_moy = 11) - Sort (cost=12170.87..12177.27 rows=2560 width=65) - Sort Key: store_sales.ss_sold_date_sk - Nested Loop (cost=6.02..12025.94 rows=2560 width=65) - Seq Scan on item (cost=0.00..1455.00 rows=16 width=59) - Filter: (i_manufact_id = 914) - Bitmap Heap Scan on store_sales (cost=6.02..658.94 rows=174 width=14) - Recheck Cond: (ss_item_sk = item.i_item_sk) - Bitmap Index Scan on store_sales_pkey (cost=0.00..5.97 rows=174 width=0) - Index Cond: (ss_item_sk = item.i_item_sk) - - - -Oracle 数据库执行计划展示如下： -Plan hash value: 2331821367 --------------------------------------------------------------------------------------------------- -| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | --------------------------------------------------------------------------------------------------- -| 0 | SELECT STATEMENT | | 100 | 9100 | 3688 (1)| 00:00:01 | -|* 1 | COUNT STOPKEY | | | | | | -| 2 | VIEW | | 2736 | 243K| 3688 (1)| 00:00:01 | -|* 3 | SORT ORDER BY STOPKEY | | 2736 | 256K| 3688 (1)| 00:00:01 | -| 4 | HASH GROUP BY | | 2736 | 256K| 3688 (1)| 00:00:01 | -|* 5 | HASH JOIN | | 2736 | 256K| 3686 (1)| 00:00:01 | -|* 6 | TABLE ACCESS FULL | DATE_DIM | 6087 | 79131 | 376 (1)| 00:00:01 | -| 7 | NESTED LOOPS | | 2865 | 232K| 3310 (1)| 00:00:01 | -| 8 | NESTED LOOPS | | 2865 | 232K| 3310 (1)| 00:00:01 | -|* 9 | TABLE ACCESS FULL | ITEM | 18 | 1188 | 375 (0)| 00:00:01 | -|* 10 | INDEX RANGE SCAN | SYS_C0010069 | 159 | | 2 (0)| 00:00:01 | -| 11 | TABLE ACCESS BY INDEX ROWID| STORE_SALES | 159 | 2703 | 163 (0)| 00:00:01 | --------------------------------------------------------------------------------------------------- - -OceanBase 数据库执行计划展示如下： -|ID|OPERATOR |NAME |EST. ROWS|COST | -------------------------------------------------------- -|0 |LIMIT | |100 |81141| -|1 | TOP-N SORT | |100 |81127| -|2 | HASH GROUP BY | |2924 |68551| -|3 | HASH JOIN | |2924 |65004| -|4 | SUBPLAN SCAN |VIEW1 |2953 |19070| -|5 | HASH GROUP BY | |2953 |18662| -|6 | NESTED-LOOP JOIN| |2953 |15080| -|7 | TABLE SCAN |ITEM |19 |11841| -|8 | TABLE SCAN |STORE_SALES|161 |73 | -|9 | TABLE SCAN |DT |6088 |29401| -======================================================= - -由示例可见，OceanBase 数据库的计划展示与 Oracle 数据库类似。 - -OceanBase 数据库执行计划中的各列的含义如下： -列名 含义 -ID 执行树按照前序遍历的方式得到的编号（从 0 开始）。 -OPERATOR 操作算子的名称。 -NAME 对应表操作的表名（索引名）。 -EST. ROWS 估算该操作算子的输出行数。 -COST 该操作算子的执行代价（微秒）。 - - -OceanBase 数据库 EXPLAIN 命令输出的第一部分是执行计划的树形结构展示。其中每一个操作在树中的层次通过其在 operator 中的缩进予以展示，层次最深的优先执行，层次相同的以特定算子的执行顺序为标准来执行。 - -问题: update a not exists (b…) -我一开始以为 B是驱动表，B的数据挺多的 后来看到NLAJ，是说左边的表关联右边的表 -所以这个的驱动表是不是实际是A，用A的匹配B的，这个理解有问题吗 - -回答: 没错 A 驱动 B的 - -问题: 光知道最下最右的是驱动表了 所以一开始搞得有点懵 :sweat_smile: - -回答: nlj应该原理应该都是左表(驱动表)的记录探测右表(被驱动表)， 选哪张成为左表或右表就基于一些其他考量了，比如数据量， 而anti join/semi join只是对 not exist/exist的一种优化，相关的原理和资料网上可以查阅一下 - -问题: 也就是nlj 就是按照之前理解的谁先执行 谁就是驱动表 也就是执行计划中的最右的表 -而anti join/semi join，谁在not exist左面，谁就是驱动表。这么理解对吧 - -回答: nlj也是左表的表是驱动表，这个要了解下计划执行方面的基本原理，取左表的一行数据，再遍历右表，一旦满足连接条件，就可以返回数据 -anti/semi只是因为not exists/exist的语义只是返回左表数据，改成anti join是一种计划优化，连接的方式比子查询更优 -""" - -from llama_index import Document - -text_list = [text1] -documents = [Document(t) for t in text_list] - -num_output = 250 -max_input_size = 512 - -max_chunk_overlap = 20 -prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap) - -index = GPTListIndex( - documents, - embed_model=embed_model, - llm_predictor=llm_predictor, - prompt_helper=prompt_helper, -) -index.save_to_disk("index.json") - - -if __name__ == "__main__": - import logging - - logging.getLogger().setLevel(logging.CRITICAL) - for d in documents: - print(d) - - response = index.query("数据库的执行计划命令有多少?") - print(response) diff --git a/pilot/awel/__init__.py b/pilot/awel/__init__.py new file mode 100644 index 000000000..3cfc3c2bc --- /dev/null +++ b/pilot/awel/__init__.py @@ -0,0 +1,87 @@ +"""Agentic Workflow Expression Language (AWEL) + +Note: + +AWEL is still an experimental feature and only opens the lowest level API. +The stability of this API cannot be guaranteed at present. + +""" + +from pilot.component import SystemApp + +from .dag.base import DAGContext, DAG + +from .operator.base import BaseOperator, WorkflowRunner +from .operator.common_operator import ( + JoinOperator, + ReduceStreamOperator, + MapOperator, + BranchOperator, + InputOperator, + BranchFunc, +) + +from .operator.stream_operator import ( + StreamifyAbsOperator, + UnstreamifyAbsOperator, + TransformStreamAbsOperator, +) + +from .task.base import TaskState, TaskOutput, TaskContext, InputContext, InputSource +from .task.task_impl import ( + SimpleInputSource, + SimpleCallDataInputSource, + DefaultTaskContext, + DefaultInputContext, + SimpleTaskOutput, + SimpleStreamTaskOutput, + _is_async_iterator, +) +from .trigger.http_trigger import HttpTrigger +from .runner.local_runner import DefaultWorkflowRunner + +__all__ = [ + "initialize_awel", + "DAGContext", + "DAG", + "BaseOperator", + "JoinOperator", + "ReduceStreamOperator", + "MapOperator", + "BranchOperator", + "InputOperator", + "BranchFunc", + "WorkflowRunner", + "TaskState", + "TaskOutput", + "TaskContext", + "InputContext", + "InputSource", + "DefaultWorkflowRunner", + "SimpleInputSource", + "SimpleCallDataInputSource", + "DefaultTaskContext", + "DefaultInputContext", + "SimpleTaskOutput", + "SimpleStreamTaskOutput", + "StreamifyAbsOperator", + "UnstreamifyAbsOperator", + "TransformStreamAbsOperator", + "HttpTrigger", +] + + +def initialize_awel(system_app: SystemApp, dag_filepath: str): + from .dag.dag_manager import DAGManager + from .dag.base import DAGVar + from .trigger.trigger_manager import DefaultTriggerManager + from .operator.base import initialize_runner + + DAGVar.set_current_system_app(system_app) + + system_app.register(DefaultTriggerManager) + dag_manager = DAGManager(system_app, dag_filepath) + system_app.register_instance(dag_manager) + initialize_runner(DefaultWorkflowRunner()) + # Load all dags + dag_manager.load_dags() diff --git a/pilot/awel/base.py b/pilot/awel/base.py new file mode 100644 index 000000000..97cb8ad05 --- /dev/null +++ b/pilot/awel/base.py @@ -0,0 +1,7 @@ +from abc import ABC, abstractmethod + + +class Trigger(ABC): + @abstractmethod + async def trigger(self) -> None: + """Trigger the workflow or a specific operation in the workflow.""" diff --git a/pilot/server/componet_configs.py b/pilot/awel/dag/__init__.py similarity index 100% rename from pilot/server/componet_configs.py rename to pilot/awel/dag/__init__.py diff --git a/pilot/awel/dag/base.py b/pilot/awel/dag/base.py new file mode 100644 index 000000000..ceb13c8ad --- /dev/null +++ b/pilot/awel/dag/base.py @@ -0,0 +1,364 @@ +from abc import ABC, abstractmethod +from typing import Optional, Dict, List, Sequence, Union, Any, Set +import uuid +import contextvars +import threading +import asyncio +import logging +from collections import deque +from functools import cache +from concurrent.futures import Executor + +from pilot.component import SystemApp +from ..resource.base import ResourceGroup +from ..task.base import TaskContext + +logger = logging.getLogger(__name__) + +DependencyType = Union["DependencyMixin", Sequence["DependencyMixin"]] + + +def _is_async_context(): + try: + loop = asyncio.get_running_loop() + return asyncio.current_task(loop=loop) is not None + except RuntimeError: + return False + + +class DependencyMixin(ABC): + @abstractmethod + def set_upstream(self, nodes: DependencyType) -> "DependencyMixin": + """Set one or more upstream nodes for this node. + + Args: + nodes (DependencyType): Upstream nodes to be set to current node. + + Returns: + DependencyMixin: Returns self to allow method chaining. + + Raises: + ValueError: If no upstream nodes are provided or if an argument is not a DependencyMixin. + """ + + @abstractmethod + def set_downstream(self, nodes: DependencyType) -> "DependencyMixin": + """Set one or more downstream nodes for this node. + + Args: + nodes (DependencyType): Downstream nodes to be set to current node. + + Returns: + DependencyMixin: Returns self to allow method chaining. + + Raises: + ValueError: If no downstream nodes are provided or if an argument is not a DependencyMixin. + """ + + def __lshift__(self, nodes: DependencyType) -> DependencyType: + """Implements self << nodes + + Example: + + .. code-block:: python + + # means node.set_upstream(input_node) + node << input_node + + # means node2.set_upstream([input_node]) + node2 << [input_node] + """ + self.set_upstream(nodes) + return nodes + + def __rshift__(self, nodes: DependencyType) -> DependencyType: + """Implements self >> nodes + + Example: + + .. code-block:: python + + # means node.set_downstream(next_node) + node >> next_node + + # means node2.set_downstream([next_node]) + node2 >> [next_node] + + """ + self.set_downstream(nodes) + return nodes + + def __rrshift__(self, nodes: DependencyType) -> "DependencyMixin": + """Implements [node] >> self""" + self.__lshift__(nodes) + return self + + def __rlshift__(self, nodes: DependencyType) -> "DependencyMixin": + """Implements [node] << self""" + self.__rshift__(nodes) + return self + + +class DAGVar: + _thread_local = threading.local() + _async_local = contextvars.ContextVar("current_dag_stack", default=deque()) + _system_app: SystemApp = None + _executor: Executor = None + + @classmethod + def enter_dag(cls, dag) -> None: + is_async = _is_async_context() + if is_async: + stack = cls._async_local.get() + stack.append(dag) + cls._async_local.set(stack) + else: + if not hasattr(cls._thread_local, "current_dag_stack"): + cls._thread_local.current_dag_stack = deque() + cls._thread_local.current_dag_stack.append(dag) + + @classmethod + def exit_dag(cls) -> None: + is_async = _is_async_context() + if is_async: + stack = cls._async_local.get() + if stack: + stack.pop() + cls._async_local.set(stack) + else: + if ( + hasattr(cls._thread_local, "current_dag_stack") + and cls._thread_local.current_dag_stack + ): + cls._thread_local.current_dag_stack.pop() + + @classmethod + def get_current_dag(cls) -> Optional["DAG"]: + is_async = _is_async_context() + if is_async: + stack = cls._async_local.get() + return stack[-1] if stack else None + else: + if ( + hasattr(cls._thread_local, "current_dag_stack") + and cls._thread_local.current_dag_stack + ): + return cls._thread_local.current_dag_stack[-1] + return None + + @classmethod + def get_current_system_app(cls) -> SystemApp: + if not cls._system_app: + raise RuntimeError("System APP not set for DAGVar") + return cls._system_app + + @classmethod + def set_current_system_app(cls, system_app: SystemApp) -> None: + if cls._system_app: + logger.warn("System APP has already set, nothing to do") + else: + cls._system_app = system_app + + @classmethod + def get_executor(cls) -> Executor: + return cls._executor + + @classmethod + def set_executor(cls, executor: Executor) -> None: + cls._executor = executor + + +class DAGNode(DependencyMixin, ABC): + resource_group: Optional[ResourceGroup] = None + """The resource group of current DAGNode""" + + def __init__( + self, + dag: Optional["DAG"] = None, + node_id: Optional[str] = None, + node_name: Optional[str] = None, + system_app: Optional[SystemApp] = None, + executor: Optional[Executor] = None, + ) -> None: + super().__init__() + self._upstream: List["DAGNode"] = [] + self._downstream: List["DAGNode"] = [] + self._dag: Optional["DAG"] = dag or DAGVar.get_current_dag() + self._system_app: Optional[SystemApp] = ( + system_app or DAGVar.get_current_system_app() + ) + self._executor: Optional[Executor] = executor or DAGVar.get_executor() + if not node_id and self._dag: + node_id = self._dag._new_node_id() + self._node_id: str = node_id + self._node_name: str = node_name + + @property + def node_id(self) -> str: + return self._node_id + + @property + def system_app(self) -> SystemApp: + return self._system_app + + def set_node_id(self, node_id: str) -> None: + self._node_id = node_id + + def __hash__(self) -> int: + if self.node_id: + return hash(self.node_id) + else: + return super().__hash__() + + def __eq__(self, other: Any) -> bool: + if not isinstance(other, DAGNode): + return False + return self.node_id == other.node_id + + @property + def node_name(self) -> str: + return self._node_name + + @property + def dag(self) -> "DAG": + return self._dag + + def set_upstream(self, nodes: DependencyType) -> "DAGNode": + self.set_dependency(nodes) + + def set_downstream(self, nodes: DependencyType) -> "DAGNode": + self.set_dependency(nodes, is_upstream=False) + + @property + def upstream(self) -> List["DAGNode"]: + return self._upstream + + @property + def downstream(self) -> List["DAGNode"]: + return self._downstream + + def set_dependency(self, nodes: DependencyType, is_upstream: bool = True) -> None: + if not isinstance(nodes, Sequence): + nodes = [nodes] + if not all(isinstance(node, DAGNode) for node in nodes): + raise ValueError( + "all nodes to set dependency to current node must be instance of 'DAGNode'" + ) + nodes: Sequence[DAGNode] = nodes + dags = set([node.dag for node in nodes if node.dag]) + if self.dag: + dags.add(self.dag) + if not dags: + raise ValueError("set dependency to current node must in a DAG context") + if len(dags) != 1: + raise ValueError( + "set dependency to current node just support in one DAG context" + ) + dag = dags.pop() + self._dag = dag + + dag._append_node(self) + for node in nodes: + if is_upstream and node not in self.upstream: + node._dag = dag + dag._append_node(node) + + self._upstream.append(node) + node._downstream.append(self) + elif node not in self._downstream: + node._dag = dag + dag._append_node(node) + + self._downstream.append(node) + node._upstream.append(self) + + +class DAGContext: + def __init__(self) -> None: + self._curr_task_ctx = None + self._share_data: Dict[str, Any] = {} + + @property + def current_task_context(self) -> TaskContext: + return self._curr_task_ctx + + def set_current_task_context(self, _curr_task_ctx: TaskContext) -> None: + self._curr_task_ctx = _curr_task_ctx + + async def get_share_data(self, key: str) -> Any: + return self._share_data.get(key) + + async def save_to_share_data(self, key: str, data: Any) -> None: + self._share_data[key] = data + + +class DAG: + def __init__( + self, dag_id: str, resource_group: Optional[ResourceGroup] = None + ) -> None: + self._dag_id = dag_id + self.node_map: Dict[str, DAGNode] = {} + self._root_nodes: Set[DAGNode] = None + self._leaf_nodes: Set[DAGNode] = None + self._trigger_nodes: Set[DAGNode] = None + + def _append_node(self, node: DAGNode) -> None: + self.node_map[node.node_id] = node + # clear cached nodes + self._root_nodes = None + self._leaf_nodes = None + + def _new_node_id(self) -> str: + return str(uuid.uuid4()) + + @property + def dag_id(self) -> str: + return self._dag_id + + def _build(self) -> None: + from ..operator.common_operator import TriggerOperator + + nodes = set() + for _, node in self.node_map.items(): + nodes = nodes.union(_get_nodes(node)) + self._root_nodes = list(set(filter(lambda x: not x.upstream, nodes))) + self._leaf_nodes = list(set(filter(lambda x: not x.downstream, nodes))) + self._trigger_nodes = list( + set(filter(lambda x: isinstance(x, TriggerOperator), nodes)) + ) + + @property + def root_nodes(self) -> List[DAGNode]: + if not self._root_nodes: + self._build() + return self._root_nodes + + @property + def leaf_nodes(self) -> List[DAGNode]: + if not self._leaf_nodes: + self._build() + return self._leaf_nodes + + @property + def trigger_nodes(self): + if not self._trigger_nodes: + self._build() + return self._trigger_nodes + + def __enter__(self): + DAGVar.enter_dag(self) + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + DAGVar.exit_dag() + + +def _get_nodes(node: DAGNode, is_upstream: Optional[bool] = True) -> set[DAGNode]: + nodes = set() + if not node: + return nodes + nodes.add(node) + stream_nodes = node.upstream if is_upstream else node.downstream + for node in stream_nodes: + nodes = nodes.union(_get_nodes(node, is_upstream)) + return nodes diff --git a/pilot/awel/dag/dag_manager.py b/pilot/awel/dag/dag_manager.py new file mode 100644 index 000000000..58830e121 --- /dev/null +++ b/pilot/awel/dag/dag_manager.py @@ -0,0 +1,42 @@ +from typing import Dict, Optional +import logging +from pilot.component import BaseComponent, ComponentType, SystemApp +from .loader import DAGLoader, LocalFileDAGLoader +from .base import DAG + +logger = logging.getLogger(__name__) + + +class DAGManager(BaseComponent): + name = ComponentType.AWEL_DAG_MANAGER + + def __init__(self, system_app: SystemApp, dag_filepath: str): + super().__init__(system_app) + self.dag_loader = LocalFileDAGLoader(dag_filepath) + self.system_app = system_app + self.dag_map: Dict[str, DAG] = {} + + def init_app(self, system_app: SystemApp): + self.system_app = system_app + + def load_dags(self): + dags = self.dag_loader.load_dags() + triggers = [] + for dag in dags: + dag_id = dag.dag_id + if dag_id in self.dag_map: + raise ValueError(f"Load DAG error, DAG ID {dag_id} has already exist") + triggers += dag.trigger_nodes + from ..trigger.trigger_manager import DefaultTriggerManager + + trigger_manager: DefaultTriggerManager = self.system_app.get_component( + ComponentType.AWEL_TRIGGER_MANAGER, + DefaultTriggerManager, + default_component=None, + ) + if trigger_manager: + for trigger in triggers: + trigger_manager.register_trigger(trigger) + trigger_manager.after_register() + else: + logger.warn("No trigger manager, not register dag trigger") diff --git a/pilot/awel/dag/loader.py b/pilot/awel/dag/loader.py new file mode 100644 index 000000000..2eb89f8bc --- /dev/null +++ b/pilot/awel/dag/loader.py @@ -0,0 +1,93 @@ +from abc import ABC, abstractmethod +from typing import List +import os +import hashlib +import sys +import logging +import traceback + +from .base import DAG + +logger = logging.getLogger(__name__) + + +class DAGLoader(ABC): + @abstractmethod + def load_dags(self) -> List[DAG]: + """Load dags""" + + +class LocalFileDAGLoader(DAGLoader): + def __init__(self, filepath: str) -> None: + super().__init__() + self._filepath = filepath + + def load_dags(self) -> List[DAG]: + if not os.path.exists(self._filepath): + return [] + if os.path.isdir(self._filepath): + return _process_directory(self._filepath) + else: + return _process_file(self._filepath) + + +def _process_directory(directory: str) -> List[DAG]: + dags = [] + for file in os.listdir(directory): + if file.endswith(".py"): + filepath = os.path.join(directory, file) + dags += _process_file(filepath) + return dags + + +def _process_file(filepath) -> List[DAG]: + mods = _load_modules_from_file(filepath) + results = _process_modules(mods) + return results + + +def _load_modules_from_file(filepath: str): + import importlib + import importlib.machinery + import importlib.util + + logger.info(f"Importing {filepath}") + + org_mod_name, _ = os.path.splitext(os.path.split(filepath)[-1]) + path_hash = hashlib.sha1(filepath.encode("utf-8")).hexdigest() + mod_name = f"unusual_prefix_{path_hash}_{org_mod_name}" + + if mod_name in sys.modules: + del sys.modules[mod_name] + + def parse(mod_name, filepath): + try: + loader = importlib.machinery.SourceFileLoader(mod_name, filepath) + spec = importlib.util.spec_from_loader(mod_name, loader) + new_module = importlib.util.module_from_spec(spec) + sys.modules[spec.name] = new_module + loader.exec_module(new_module) + return [new_module] + except Exception as e: + msg = traceback.format_exc() + logger.error(f"Failed to import: {filepath}, error message: {msg}") + # TODO save error message + return [] + + return parse(mod_name, filepath) + + +def _process_modules(mods) -> List[DAG]: + top_level_dags = ( + (o, m) for m in mods for o in m.__dict__.values() if isinstance(o, DAG) + ) + found_dags = [] + for dag, mod in top_level_dags: + try: + # TODO validate dag params + logger.info(f"Found dag {dag} from mod {mod} and model file {mod.__file__}") + found_dags.append(dag) + except Exception: + msg = traceback.format_exc() + logger.error(f"Failed to dag file, error message: {msg}") + return found_dags diff --git a/pilot/awel/dag/tests/__init__.py b/pilot/awel/dag/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/awel/dag/tests/test_dag.py b/pilot/awel/dag/tests/test_dag.py new file mode 100644 index 000000000..c30530dc8 --- /dev/null +++ b/pilot/awel/dag/tests/test_dag.py @@ -0,0 +1,51 @@ +import pytest +import threading +import asyncio +from ..dag import DAG, DAGContext + + +def test_dag_context_sync(): + dag1 = DAG("dag1") + dag2 = DAG("dag2") + + with dag1: + assert DAGContext.get_current_dag() == dag1 + with dag2: + assert DAGContext.get_current_dag() == dag2 + assert DAGContext.get_current_dag() == dag1 + assert DAGContext.get_current_dag() is None + + +def test_dag_context_threading(): + def thread_function(dag): + DAGContext.enter_dag(dag) + assert DAGContext.get_current_dag() == dag + DAGContext.exit_dag() + + dag1 = DAG("dag1") + dag2 = DAG("dag2") + + thread1 = threading.Thread(target=thread_function, args=(dag1,)) + thread2 = threading.Thread(target=thread_function, args=(dag2,)) + + thread1.start() + thread2.start() + thread1.join() + thread2.join() + + assert DAGContext.get_current_dag() is None + + +@pytest.mark.asyncio +async def test_dag_context_async(): + async def async_function(dag): + DAGContext.enter_dag(dag) + assert DAGContext.get_current_dag() == dag + DAGContext.exit_dag() + + dag1 = DAG("dag1") + dag2 = DAG("dag2") + + await asyncio.gather(async_function(dag1), async_function(dag2)) + + assert DAGContext.get_current_dag() is None diff --git a/pilot/awel/operator/__init__.py b/pilot/awel/operator/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/awel/operator/base.py b/pilot/awel/operator/base.py new file mode 100644 index 000000000..09aa87141 --- /dev/null +++ b/pilot/awel/operator/base.py @@ -0,0 +1,206 @@ +from abc import ABC, abstractmethod, ABCMeta + +from types import FunctionType +from typing import ( + List, + Generic, + TypeVar, + AsyncIterator, + Union, + Any, + Dict, + Optional, + cast, +) +import functools +from inspect import signature +from pilot.component import SystemApp, ComponentType +from pilot.utils.executor_utils import ( + ExecutorFactory, + DefaultExecutorFactory, + blocking_func_to_async, + BlockingFunction, +) + +from ..dag.base import DAGNode, DAGContext, DAGVar, DAG +from ..task.base import ( + TaskContext, + TaskOutput, + TaskState, + OUT, + T, + InputContext, + InputSource, +) + +F = TypeVar("F", bound=FunctionType) + +CALL_DATA = Union[Dict, Dict[str, Dict]] + + +class WorkflowRunner(ABC, Generic[T]): + """Abstract base class representing a runner for executing workflows in a DAG. + + This class defines the interface for executing workflows within the DAG, + handling the flow from one DAG node to another. + """ + + @abstractmethod + async def execute_workflow( + self, node: "BaseOperator", call_data: Optional[CALL_DATA] = None + ) -> DAGContext: + """Execute the workflow starting from a given operator. + + Args: + node (RunnableDAGNode): The starting node of the workflow to be executed. + call_data (CALL_DATA): The data pass to root operator node. + + Returns: + DAGContext: The context after executing the workflow, containing the final state and data. + """ + + +default_runner: WorkflowRunner = None + + +class BaseOperatorMeta(ABCMeta): + """Metaclass of BaseOperator.""" + + @classmethod + def _apply_defaults(cls, func: F) -> F: + sig_cache = signature(func) + + @functools.wraps(func) + def apply_defaults(self: "BaseOperator", *args: Any, **kwargs: Any) -> Any: + dag: Optional[DAG] = kwargs.get("dag") or DAGVar.get_current_dag() + task_id: Optional[str] = kwargs.get("task_id") + system_app: Optional[SystemApp] = ( + kwargs.get("system_app") or DAGVar.get_current_system_app() + ) + executor = kwargs.get("executor") or DAGVar.get_executor() + if not executor: + if system_app: + executor = system_app.get_component( + ComponentType.EXECUTOR_DEFAULT, ExecutorFactory + ).create() + else: + executor = DefaultExecutorFactory().create() + DAGVar.set_executor(executor) + + if not task_id and dag: + task_id = dag._new_node_id() + runner: Optional[WorkflowRunner] = kwargs.get("runner") or default_runner + # print(f"self: {self}, kwargs dag: {kwargs.get('dag')}, kwargs: {kwargs}") + # for arg in sig_cache.parameters: + # if arg not in kwargs: + # kwargs[arg] = default_args[arg] + if not kwargs.get("dag"): + kwargs["dag"] = dag + if not kwargs.get("task_id"): + kwargs["task_id"] = task_id + if not kwargs.get("runner"): + kwargs["runner"] = runner + if not kwargs.get("system_app"): + kwargs["system_app"] = system_app + if not kwargs.get("executor"): + kwargs["executor"] = executor + real_obj = func(self, *args, **kwargs) + return real_obj + + return cast(T, apply_defaults) + + def __new__(cls, name, bases, namespace, **kwargs): + new_cls = super().__new__(cls, name, bases, namespace, **kwargs) + new_cls.__init__ = cls._apply_defaults(new_cls.__init__) + return new_cls + + +class BaseOperator(DAGNode, ABC, Generic[OUT], metaclass=BaseOperatorMeta): + """Abstract base class for operator nodes that can be executed within a workflow. + + This class extends DAGNode by adding execution capabilities. + """ + + def __init__( + self, + task_id: Optional[str] = None, + task_name: Optional[str] = None, + dag: Optional[DAG] = None, + runner: WorkflowRunner = None, + **kwargs, + ) -> None: + """Initializes a BaseOperator with an optional workflow runner. + + Args: + runner (WorkflowRunner, optional): The runner used to execute the workflow. Defaults to None. + """ + super().__init__(node_id=task_id, node_name=task_name, dag=dag, **kwargs) + if not runner: + from pilot.awel import DefaultWorkflowRunner + + runner = DefaultWorkflowRunner() + + self._runner: WorkflowRunner = runner + self._dag_ctx: DAGContext = None + + @property + def current_dag_context(self) -> DAGContext: + return self._dag_ctx + + async def _run(self, dag_ctx: DAGContext) -> TaskOutput[OUT]: + if not self.node_id: + raise ValueError(f"The DAG Node ID can't be empty, current node {self}") + self._dag_ctx = dag_ctx + return await self._do_run(dag_ctx) + + @abstractmethod + async def _do_run(self, dag_ctx: DAGContext) -> TaskOutput[OUT]: + """ + Abstract method to run the task within the DAG node. + + Args: + dag_ctx (DAGContext): The context of the DAG when this node is run. + + Returns: + TaskOutput[OUT]: The task output after this node has been run. + """ + + async def call(self, call_data: Optional[CALL_DATA] = None) -> OUT: + """Execute the node and return the output. + + This method is a high-level wrapper for executing the node. + + Args: + call_data (CALL_DATA): The data pass to root operator node. + + Returns: + OUT: The output of the node after execution. + """ + out_ctx = await self._runner.execute_workflow(self, call_data) + return out_ctx.current_task_context.task_output.output + + async def call_stream( + self, call_data: Optional[CALL_DATA] = None + ) -> AsyncIterator[OUT]: + """Execute the node and return the output as a stream. + + This method is used for nodes where the output is a stream. + + Args: + call_data (CALL_DATA): The data pass to root operator node. + + Returns: + AsyncIterator[OUT]: An asynchronous iterator over the output stream. + """ + out_ctx = await self._runner.execute_workflow(self, call_data) + return out_ctx.current_task_context.task_output.output_stream + + async def blocking_func_to_async( + self, func: BlockingFunction, *args, **kwargs + ) -> Any: + return await blocking_func_to_async(self._executor, func, *args, **kwargs) + + +def initialize_runner(runner: WorkflowRunner): + global default_runner + default_runner = runner diff --git a/pilot/awel/operator/common_operator.py b/pilot/awel/operator/common_operator.py new file mode 100644 index 000000000..2c0d41dde --- /dev/null +++ b/pilot/awel/operator/common_operator.py @@ -0,0 +1,246 @@ +from typing import Generic, Dict, List, Union, Callable, Any, AsyncIterator, Awaitable +import asyncio +import logging + +from ..dag.base import DAGContext +from ..task.base import ( + TaskContext, + TaskOutput, + IN, + OUT, + InputContext, + InputSource, +) + +from .base import BaseOperator + + +logger = logging.getLogger(__name__) + + +class JoinOperator(BaseOperator, Generic[OUT]): + """Operator that joins inputs using a custom combine function. + + This node type is useful for combining the outputs of upstream nodes. + """ + + def __init__(self, combine_function, **kwargs): + super().__init__(**kwargs) + if not callable(combine_function): + raise ValueError("combine_function must be callable") + self.combine_function = combine_function + + async def _do_run(self, dag_ctx: DAGContext) -> TaskOutput[OUT]: + """Run the join operation on the DAG context's inputs. + Args: + dag_ctx (DAGContext): The current context of the DAG. + + Returns: + TaskOutput[OUT]: The task output after this node has been run. + """ + curr_task_ctx: TaskContext[OUT] = dag_ctx.current_task_context + input_ctx: InputContext = await curr_task_ctx.task_input.map_all( + self.combine_function + ) + # All join result store in the first parent output + join_output = input_ctx.parent_outputs[0].task_output + curr_task_ctx.set_task_output(join_output) + return join_output + + +class ReduceStreamOperator(BaseOperator, Generic[IN, OUT]): + def __init__(self, reduce_function=None, **kwargs): + """Initializes a ReduceStreamOperator with a combine function. + + Args: + combine_function: A function that defines how to combine inputs. + + Raises: + ValueError: If the combine_function is not callable. + """ + super().__init__(**kwargs) + if reduce_function and not callable(reduce_function): + raise ValueError("reduce_function must be callable") + self.reduce_function = reduce_function + + async def _do_run(self, dag_ctx: DAGContext) -> TaskOutput[OUT]: + """Run the join operation on the DAG context's inputs. + + Args: + dag_ctx (DAGContext): The current context of the DAG. + + Returns: + TaskOutput[OUT]: The task output after this node has been run. + """ + curr_task_ctx: TaskContext[OUT] = dag_ctx.current_task_context + task_input = curr_task_ctx.task_input + if not task_input.check_stream(): + raise ValueError("ReduceStreamOperator expects stream data") + if not task_input.check_single_parent(): + raise ValueError("ReduceStreamOperator expects single parent") + + reduce_function = self.reduce_function or self.reduce + + input_ctx: InputContext = await task_input.reduce(reduce_function) + # All join result store in the first parent output + reduce_output = input_ctx.parent_outputs[0].task_output + curr_task_ctx.set_task_output(reduce_output) + return reduce_output + + async def reduce(self, input_value: AsyncIterator[IN]) -> OUT: + raise NotImplementedError + + +class MapOperator(BaseOperator, Generic[IN, OUT]): + """Map operator that applies a mapping function to its inputs. + + This operator transforms its input data using a provided mapping function and + passes the transformed data downstream. + """ + + def __init__(self, map_function=None, **kwargs): + """Initializes a MapDAGNode with a mapping function. + + Args: + map_function: A function that defines how to map the input data. + + Raises: + ValueError: If the map_function is not callable. + """ + super().__init__(**kwargs) + if map_function and not callable(map_function): + raise ValueError("map_function must be callable") + self.map_function = map_function + + async def _do_run(self, dag_ctx: DAGContext) -> TaskOutput[OUT]: + """Run the mapping operation on the DAG context's inputs. + + This method applies the mapping function to the input context and updates + the DAG context with the new data. + + Args: + dag_ctx (DAGContext[IN]): The current context of the DAG. + + Returns: + TaskOutput[OUT]: The task output after this node has been run. + + Raises: + ValueError: If not a single parent or the map_function is not callable + """ + curr_task_ctx: TaskContext[OUT] = dag_ctx.current_task_context + if not curr_task_ctx.task_input.check_single_parent(): + num_parents = len(curr_task_ctx.task_input.parent_outputs) + raise ValueError( + f"task {curr_task_ctx.task_id} MapDAGNode expects single parent, now number of parents: {num_parents}" + ) + map_function = self.map_function or self.map + + input_ctx: InputContext = await curr_task_ctx.task_input.map(map_function) + # All join result store in the first parent output + reduce_output = input_ctx.parent_outputs[0].task_output + curr_task_ctx.set_task_output(reduce_output) + return reduce_output + + async def map(self, input_value: IN) -> OUT: + raise NotImplementedError + + +BranchFunc = Union[Callable[[IN], bool], Callable[[IN], Awaitable[bool]]] + + +class BranchOperator(BaseOperator, Generic[IN, OUT]): + """Operator node that branches the workflow based on a provided function. + + This node filters its input data using a branching function and + allows for conditional paths in the workflow. + """ + + def __init__( + self, branches: Dict[BranchFunc[IN], Union[BaseOperator, str]], **kwargs + ): + """ + Initializes a BranchDAGNode with a branching function. + + Args: + branches (Dict[BranchFunc[IN], Union[BaseOperator, str]]): Dict of function that defines the branching condition. + + Raises: + ValueError: If the branch_function is not callable. + """ + super().__init__(**kwargs) + if branches: + for branch_function, value in branches.items(): + if not callable(branch_function): + raise ValueError("branch_function must be callable") + if isinstance(value, BaseOperator): + branches[branch_function] = value.node_name or value.node_name + self._branches = branches + + async def _do_run(self, dag_ctx: DAGContext) -> TaskOutput[OUT]: + """Run the branching operation on the DAG context's inputs. + + This method applies the branching function to the input context to determine + the path of execution in the workflow. + + Args: + dag_ctx (DAGContext[IN]): The current context of the DAG. + + Returns: + TaskOutput[OUT]: The task output after this node has been run. + """ + curr_task_ctx: TaskContext[OUT] = dag_ctx.current_task_context + task_input = curr_task_ctx.task_input + if task_input.check_stream(): + raise ValueError("BranchDAGNode expects no stream data") + if not task_input.check_single_parent(): + raise ValueError("BranchDAGNode expects single parent") + + branches = self._branches + if not branches: + branches = await self.branchs() + + branch_func_tasks = [] + branch_nodes: List[str] = [] + for func, node_name in branches.items(): + branch_nodes.append(node_name) + branch_func_tasks.append( + curr_task_ctx.task_input.predicate_map(func, failed_value=None) + ) + + branch_input_ctxs: List[InputContext] = await asyncio.gather(*branch_func_tasks) + parent_output = task_input.parent_outputs[0].task_output + curr_task_ctx.set_task_output(parent_output) + skip_node_names = [] + for i, ctx in enumerate(branch_input_ctxs): + node_name = branch_nodes[i] + branch_out = ctx.parent_outputs[0].task_output + logger.info( + f"branch_input_ctxs {i} result {branch_out.output}, is_empty: {branch_out.is_empty}" + ) + if ctx.parent_outputs[0].task_output.is_empty: + logger.info(f"Skip node name {node_name}") + skip_node_names.append(node_name) + curr_task_ctx.update_metadata("skip_node_names", skip_node_names) + return parent_output + + async def branchs(self) -> Dict[BranchFunc[IN], Union[BaseOperator, str]]: + raise NotImplementedError + + +class InputOperator(BaseOperator, Generic[OUT]): + def __init__(self, input_source: InputSource[OUT], **kwargs) -> None: + super().__init__(**kwargs) + self._input_source = input_source + + async def _do_run(self, dag_ctx: DAGContext) -> TaskOutput[OUT]: + curr_task_ctx: TaskContext[OUT] = dag_ctx.current_task_context + task_output = await self._input_source.read(curr_task_ctx) + curr_task_ctx.set_task_output(task_output) + return task_output + + +class TriggerOperator(InputOperator, Generic[OUT]): + def __init__(self, **kwargs) -> None: + from ..task.task_impl import SimpleCallDataInputSource + + super().__init__(input_source=SimpleCallDataInputSource(), **kwargs) diff --git a/pilot/awel/operator/stream_operator.py b/pilot/awel/operator/stream_operator.py new file mode 100644 index 000000000..7de916a83 --- /dev/null +++ b/pilot/awel/operator/stream_operator.py @@ -0,0 +1,90 @@ +from abc import ABC, abstractmethod +from typing import Generic, AsyncIterator +from ..task.base import OUT, IN, TaskOutput, TaskContext +from ..dag.base import DAGContext +from .base import BaseOperator + + +class StreamifyAbsOperator(BaseOperator[OUT], ABC, Generic[IN, OUT]): + async def _do_run(self, dag_ctx: DAGContext) -> TaskOutput[OUT]: + curr_task_ctx: TaskContext[OUT] = dag_ctx.current_task_context + output = await curr_task_ctx.task_input.parent_outputs[0].task_output.streamify( + self.streamify + ) + curr_task_ctx.set_task_output(output) + return output + + @abstractmethod + async def streamify(self, input_value: IN) -> AsyncIterator[OUT]: + """Convert a value of IN to an AsyncIterator[OUT] + + Args: + input_value (IN): The data of parent operator's output + + Example: + + .. code-block:: python + + class MyStreamOperator(StreamifyAbsOperator[int, int]): + async def streamify(self, input_value: int) -> AsyncIterator[int] + for i in range(input_value): + yield i + """ + + +class UnstreamifyAbsOperator(BaseOperator[OUT], Generic[IN, OUT]): + async def _do_run(self, dag_ctx: DAGContext) -> TaskOutput[OUT]: + curr_task_ctx: TaskContext[OUT] = dag_ctx.current_task_context + output = await curr_task_ctx.task_input.parent_outputs[ + 0 + ].task_output.unstreamify(self.unstreamify) + curr_task_ctx.set_task_output(output) + return output + + @abstractmethod + async def unstreamify(self, input_value: AsyncIterator[IN]) -> OUT: + """Convert a value of AsyncIterator[IN] to an OUT. + + Args: + input_value (AsyncIterator[IN])): The data of parent operator's output + + Example: + + .. code-block:: python + + class MyUnstreamOperator(UnstreamifyAbsOperator[int, int]): + async def unstreamify(self, input_value: AsyncIterator[int]) -> int + value_cnt = 0 + async for v in input_value: + value_cnt += 1 + return value_cnt + """ + + +class TransformStreamAbsOperator(BaseOperator[OUT], Generic[IN, OUT]): + async def _do_run(self, dag_ctx: DAGContext) -> TaskOutput[OUT]: + curr_task_ctx: TaskContext[OUT] = dag_ctx.current_task_context + output = await curr_task_ctx.task_input.parent_outputs[ + 0 + ].task_output.transform_stream(self.transform_stream) + curr_task_ctx.set_task_output(output) + return output + + @abstractmethod + async def transform_stream( + self, input_value: AsyncIterator[IN] + ) -> AsyncIterator[OUT]: + """Transform an AsyncIterator[IN] to another AsyncIterator[OUT] using a given function. + + Args: + input_value (AsyncIterator[IN])): The data of parent operator's output + + Example: + + .. code-block:: python + + class MyTransformStreamOperator(TransformStreamAbsOperator[int, int]): + async def unstreamify(self, input_value: AsyncIterator[int]) -> AsyncIterator[int] + async for v in input_value: + yield v + 1 + """ diff --git a/pilot/awel/resource/__init__.py b/pilot/awel/resource/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/awel/resource/base.py b/pilot/awel/resource/base.py new file mode 100644 index 000000000..97fefbbc3 --- /dev/null +++ b/pilot/awel/resource/base.py @@ -0,0 +1,8 @@ +from abc import ABC, abstractmethod + + +class ResourceGroup(ABC): + @property + @abstractmethod + def name(self) -> str: + """The name of current resource group""" diff --git a/pilot/awel/runner/__init__.py b/pilot/awel/runner/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/awel/runner/job_manager.py b/pilot/awel/runner/job_manager.py new file mode 100644 index 000000000..7a1d12ead --- /dev/null +++ b/pilot/awel/runner/job_manager.py @@ -0,0 +1,82 @@ +from typing import List, Set, Optional, Dict +import uuid +import logging +from ..dag.base import DAG + +from ..operator.base import BaseOperator, CALL_DATA + +logger = logging.getLogger(__name__) + + +class DAGNodeInstance: + def __init__(self, node_instance: DAG) -> None: + pass + + +class DAGInstance: + def __init__(self, dag: DAG) -> None: + self._dag = dag + + +class JobManager: + def __init__( + self, + root_nodes: List[BaseOperator], + all_nodes: List[BaseOperator], + end_node: BaseOperator, + id2call_data: Dict[str, Dict], + ) -> None: + self._root_nodes = root_nodes + self._all_nodes = all_nodes + self._end_node = end_node + self._id2node_data = id2call_data + + @staticmethod + def build_from_end_node( + end_node: BaseOperator, call_data: Optional[CALL_DATA] = None + ) -> "JobManager": + nodes = _build_from_end_node(end_node) + root_nodes = _get_root_nodes(nodes) + id2call_data = _save_call_data(root_nodes, call_data) + return JobManager(root_nodes, nodes, end_node, id2call_data) + + def get_call_data_by_id(self, node_id: str) -> Optional[Dict]: + return self._id2node_data.get(node_id) + + +def _save_call_data( + root_nodes: List[BaseOperator], call_data: CALL_DATA +) -> Dict[str, Dict]: + id2call_data = {} + logger.debug(f"_save_call_data: {call_data}, root_nodes: {root_nodes}") + if not call_data: + return id2call_data + if len(root_nodes) == 1: + node = root_nodes[0] + logger.info(f"Save call data to node {node.node_id}, call_data: {call_data}") + id2call_data[node.node_id] = call_data + else: + for node in root_nodes: + node_id = node.node_id + logger.info( + f"Save call data to node {node.node_id}, call_data: {call_data.get(node_id)}" + ) + id2call_data[node_id] = call_data.get(node_id) + return id2call_data + + +def _build_from_end_node(end_node: BaseOperator) -> List[BaseOperator]: + nodes = [] + if isinstance(end_node, BaseOperator): + task_id = end_node.node_id + if not task_id: + task_id = str(uuid.uuid4()) + end_node.set_node_id(task_id) + nodes.append(end_node) + for node in end_node.upstream: + nodes += _build_from_end_node(node) + return nodes + + +def _get_root_nodes(nodes: List[BaseOperator]) -> List[BaseOperator]: + return list(set(filter(lambda x: not x.upstream, nodes))) diff --git a/pilot/awel/runner/local_runner.py b/pilot/awel/runner/local_runner.py new file mode 100644 index 000000000..6f8a0a484 --- /dev/null +++ b/pilot/awel/runner/local_runner.py @@ -0,0 +1,106 @@ +from typing import Dict, Optional, Set, List +import logging + +from ..dag.base import DAGContext +from ..operator.base import WorkflowRunner, BaseOperator, CALL_DATA +from ..operator.common_operator import BranchOperator, JoinOperator, TriggerOperator +from ..task.base import TaskContext, TaskState +from ..task.task_impl import DefaultInputContext, DefaultTaskContext, SimpleTaskOutput +from .job_manager import JobManager + +logger = logging.getLogger(__name__) + + +class DefaultWorkflowRunner(WorkflowRunner): + async def execute_workflow( + self, node: BaseOperator, call_data: Optional[CALL_DATA] = None + ) -> DAGContext: + # Create DAG context + dag_ctx = DAGContext() + job_manager = JobManager.build_from_end_node(node, call_data) + logger.info( + f"Begin run workflow from end operator, id: {node.node_id}, call_data: {call_data}" + ) + dag = node.dag + # Save node output + node_outputs: Dict[str, TaskContext] = {} + skip_node_ids = set() + await self._execute_node( + job_manager, node, dag_ctx, node_outputs, skip_node_ids + ) + + return dag_ctx + + async def _execute_node( + self, + job_manager: JobManager, + node: BaseOperator, + dag_ctx: DAGContext, + node_outputs: Dict[str, TaskContext], + skip_node_ids: Set[str], + ): + # Skip run node + if node.node_id in node_outputs: + return + + # Run all upstream node + for upstream_node in node.upstream: + if isinstance(upstream_node, BaseOperator): + await self._execute_node( + job_manager, upstream_node, dag_ctx, node_outputs, skip_node_ids + ) + + inputs = [ + node_outputs[upstream_node.node_id] for upstream_node in node.upstream + ] + input_ctx = DefaultInputContext(inputs) + task_ctx = DefaultTaskContext(node.node_id, TaskState.INIT, task_output=None) + task_ctx.set_call_data(job_manager.get_call_data_by_id(node.node_id)) + + task_ctx.set_task_input(input_ctx) + dag_ctx.set_current_task_context(task_ctx) + task_ctx.set_current_state(TaskState.RUNNING) + + if node.node_id in skip_node_ids: + task_ctx.set_current_state(TaskState.SKIP) + task_ctx.set_task_output(SimpleTaskOutput(None)) + node_outputs[node.node_id] = task_ctx + return + try: + logger.debug( + f"Begin run operator, node id: {node.node_id}, node name: {node.node_name}, cls: {node}" + ) + await node._run(dag_ctx) + node_outputs[node.node_id] = dag_ctx.current_task_context + task_ctx.set_current_state(TaskState.SUCCESS) + + if isinstance(node, BranchOperator): + skip_nodes = task_ctx.metadata.get("skip_node_names", []) + logger.debug( + f"Current is branch operator, skip node names: {skip_nodes}" + ) + _skip_current_downstream_by_node_name(node, skip_nodes, skip_node_ids) + except Exception as e: + logger.info(f"Run operator {node.node_id} error, error message: {str(e)}") + task_ctx.set_current_state(TaskState.FAILED) + raise e + + +def _skip_current_downstream_by_node_name( + branch_node: BranchOperator, skip_nodes: List[str], skip_node_ids: Set[str] +): + if not skip_nodes: + return + for child in branch_node.downstream: + if child.node_name in skip_nodes: + logger.info(f"Skip node name {child.node_name}, node id {child.node_id}") + _skip_downstream_by_id(child, skip_node_ids) + + +def _skip_downstream_by_id(node: BaseOperator, skip_node_ids: Set[str]): + if isinstance(node, JoinOperator): + # Not skip join node + return + skip_node_ids.add(node.node_id) + for child in node.downstream: + _skip_downstream_by_id(child, skip_node_ids) diff --git a/pilot/awel/task/__init__.py b/pilot/awel/task/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/awel/task/base.py b/pilot/awel/task/base.py new file mode 100644 index 000000000..88b0df343 --- /dev/null +++ b/pilot/awel/task/base.py @@ -0,0 +1,367 @@ +from abc import ABC, abstractmethod +from enum import Enum +from typing import ( + TypeVar, + Generic, + Optional, + AsyncIterator, + Union, + Callable, + Any, + Dict, + List, +) + +IN = TypeVar("IN") +OUT = TypeVar("OUT") +T = TypeVar("T") + + +class TaskState(str, Enum): + """Enumeration representing the state of a task in the workflow. + + This Enum defines various states a task can be in during its lifecycle in the DAG. + """ + + INIT = "init" # Initial state of the task, not yet started + SKIP = "skip" # State indicating the task was skipped + RUNNING = "running" # State indicating the task is currently running + SUCCESS = "success" # State indicating the task completed successfully + FAILED = "failed" # State indicating the task failed during execution + + +class TaskOutput(ABC, Generic[T]): + """Abstract base class representing the output of a task. + + This class encapsulates the output of a task and provides methods to access the output data. + It can be subclassed to implement specific output behaviors. + """ + + @property + def is_stream(self) -> bool: + """Check if the output is a stream. + + Returns: + bool: True if the output is a stream, False otherwise. + """ + return False + + @property + def is_empty(self) -> bool: + """Check if the output is empty. + + Returns: + bool: True if the output is empty, False otherwise. + """ + return False + + @property + def output(self) -> Optional[T]: + """Return the output of the task. + + Returns: + T: The output of the task. None if the output is empty. + """ + raise NotImplementedError + + @property + def output_stream(self) -> Optional[AsyncIterator[T]]: + """Return the output of the task as an asynchronous stream. + + Returns: + AsyncIterator[T]: An asynchronous iterator over the output. None if the output is empty. + """ + raise NotImplementedError + + @abstractmethod + def set_output(self, output_data: Union[T, AsyncIterator[T]]) -> None: + """Set the output data to current object. + + Args: + output_data (Union[T, AsyncIterator[T]]): Output data. + """ + + @abstractmethod + def new_output(self) -> "TaskOutput[T]": + """Create new output object""" + + async def map(self, map_func) -> "TaskOutput[T]": + """Apply a mapping function to the task's output. + + Args: + map_func: A function to apply to the task's output. + + Returns: + TaskOutput[T]: The result of applying the mapping function. + """ + raise NotImplementedError + + async def reduce(self, reduce_func) -> "TaskOutput[T]": + """Apply a reducing function to the task's output. + + Stream TaskOutput to Nonstream TaskOutput. + + Args: + reduce_func: A reducing function to apply to the task's output. + + Returns: + TaskOutput[T]: The result of applying the reducing function. + """ + raise NotImplementedError + + async def streamify( + self, transform_func: Callable[[T], AsyncIterator[T]] + ) -> "TaskOutput[T]": + """Convert a value of type T to an AsyncIterator[T] using a transform function. + + Args: + transform_func (Callable[[T], AsyncIterator[T]]): Function to transform a T value into an AsyncIterator[T]. + + Returns: + TaskOutput[T]: The result of applying the reducing function. + """ + raise NotImplementedError + + async def transform_stream( + self, transform_func: Callable[[AsyncIterator[T]], AsyncIterator[T]] + ) -> "TaskOutput[T]": + """Transform an AsyncIterator[T] to another AsyncIterator[T] using a given function. + + Args: + transform_func (Callable[[AsyncIterator[T]], AsyncIterator[T]]): Function to apply to the AsyncIterator[T]. + + Returns: + TaskOutput[T]: The result of applying the reducing function. + """ + raise NotImplementedError + + async def unstreamify( + self, transform_func: Callable[[AsyncIterator[T]], T] + ) -> "TaskOutput[T]": + """Convert an AsyncIterator[T] to a value of type T using a transform function. + + Args: + transform_func (Callable[[AsyncIterator[T]], T]): Function to transform an AsyncIterator[T] into a T value. + + Returns: + TaskOutput[T]: The result of applying the reducing function. + """ + raise NotImplementedError + + async def check_condition(self, condition_func) -> bool: + """Check if current output meets a given condition. + + Args: + condition_func: A function to determine if the condition is met. + Returns: + bool: True if current output meet the condition, False otherwise. + """ + raise NotImplementedError + + +class TaskContext(ABC, Generic[T]): + """Abstract base class representing the context of a task within a DAG. + + This class provides the interface for accessing task-related information + and manipulating task output. + """ + + @property + @abstractmethod + def task_id(self) -> str: + """Return the unique identifier of the task. + + Returns: + str: The unique identifier of the task. + """ + + @property + @abstractmethod + def task_input(self) -> "InputContext": + """Return the InputContext of current task. + + Returns: + InputContext: The InputContext of current task. + """ + + @abstractmethod + def set_task_input(self, input_ctx: "InputContext") -> None: + """Set the InputContext object to current task. + + Args: + input_ctx (InputContext): The InputContext of current task + """ + + @property + @abstractmethod + def task_output(self) -> TaskOutput[T]: + """Return the output object of the task. + + Returns: + TaskOutput[T]: The output object of the task. + """ + + @abstractmethod + def set_task_output(self, task_output: TaskOutput[T]) -> None: + """Set the output object to current task.""" + + @property + @abstractmethod + def current_state(self) -> TaskState: + """Get the current state of the task. + + Returns: + TaskState: The current state of the task. + """ + + @abstractmethod + def set_current_state(self, task_state: TaskState) -> None: + """Set current task state + + Args: + task_state (TaskState): The task state to be set. + """ + + @abstractmethod + def new_ctx(self) -> "TaskContext": + """Create new task context + + Returns: + TaskContext: A new instance of a TaskContext. + """ + + @property + @abstractmethod + def metadata(self) -> Dict[str, Any]: + """Get the metadata of current task + + Returns: + Dict[str, Any]: The metadata + """ + + def update_metadata(self, key: str, value: Any) -> None: + """Update metadata with key and value + + Args: + key (str): The key of metadata + value (str): The value to be add to metadata + """ + self.metadata[key] = value + + @property + def call_data(self) -> Optional[Dict]: + """Get the call data for current data""" + return self.metadata.get("call_data") + + def set_call_data(self, call_data: Dict) -> None: + """Set call data for current task""" + self.update_metadata("call_data", call_data) + + +class InputContext(ABC): + """Abstract base class representing the context of inputs to a operator node. + + This class defines methods to manipulate and access the inputs for a operator node. + """ + + @property + @abstractmethod + def parent_outputs(self) -> List[TaskContext]: + """Get the outputs from the parent nodes. + + Returns: + List[TaskContext]: A list of contexts of the parent nodes' outputs. + """ + + @abstractmethod + async def map(self, map_func: Callable[[Any], Any]) -> "InputContext": + """Apply a mapping function to the inputs. + + Args: + map_func (Callable[[Any], Any]): A function to be applied to the inputs. + + Returns: + InputContext: A new InputContext instance with the mapped inputs. + """ + + @abstractmethod + async def map_all(self, map_func: Callable[..., Any]) -> "InputContext": + """Apply a mapping function to all inputs. + + Args: + map_func (Callable[..., Any]): A function to be applied to all inputs. + + Returns: + InputContext: A new InputContext instance with the mapped inputs. + """ + + @abstractmethod + async def reduce(self, reduce_func: Callable[[Any], Any]) -> "InputContext": + """Apply a reducing function to the inputs. + + Args: + reduce_func (Callable[[Any], Any]): A function that reduces the inputs. + + Returns: + InputContext: A new InputContext instance with the reduced inputs. + """ + + @abstractmethod + async def filter(self, filter_func: Callable[[Any], bool]) -> "InputContext": + """Filter the inputs based on a provided function. + + Args: + filter_func (Callable[[Any], bool]): A function that returns True for inputs to keep. + + Returns: + InputContext: A new InputContext instance with the filtered inputs. + """ + + @abstractmethod + async def predicate_map( + self, predicate_func: Callable[[Any], bool], failed_value: Any = None + ) -> "InputContext": + """Predicate the inputs based on a provided function. + + Args: + predicate_func (Callable[[Any], bool]): A function that returns True for inputs is predicate True. + failed_value (Any): The value to be set if the return value of predicate function is False + Returns: + InputContext: A new InputContext instance with the predicate inputs. + """ + + def check_single_parent(self) -> bool: + """Check if there is only a single parent output. + + Returns: + bool: True if there is only one parent output, False otherwise. + """ + return len(self.parent_outputs) == 1 + + def check_stream(self, skip_empty: bool = False) -> bool: + """Check if all parent outputs are streams. + + Args: + skip_empty (bool): Skip empty output or not. + + Returns: + bool: True if all parent outputs are streams, False otherwise. + """ + for out in self.parent_outputs: + if out.task_output.is_empty and skip_empty: + continue + if not (out.task_output and out.task_output.is_stream): + return False + return True + + +class InputSource(ABC, Generic[T]): + """Abstract base class representing the source of inputs to a DAG node.""" + + @abstractmethod + async def read(self, task_ctx: TaskContext) -> TaskOutput[T]: + """Read the data from current input source. + + Returns: + TaskOutput[T]: The output object read from current source + """ diff --git a/pilot/awel/task/task_impl.py b/pilot/awel/task/task_impl.py new file mode 100644 index 000000000..f969c135c --- /dev/null +++ b/pilot/awel/task/task_impl.py @@ -0,0 +1,339 @@ +from abc import ABC, abstractmethod +from typing import ( + Callable, + Coroutine, + Iterator, + AsyncIterator, + List, + Generic, + TypeVar, + Any, + Tuple, + Dict, + Union, +) +import asyncio +import logging +from .base import TaskOutput, TaskContext, TaskState, InputContext, InputSource, T + + +logger = logging.getLogger(__name__) + + +async def _reduce_stream(stream: AsyncIterator, reduce_function) -> Any: + # Init accumulator + try: + accumulator = await stream.__anext__() + except StopAsyncIteration: + raise ValueError("Stream is empty") + is_async = asyncio.iscoroutinefunction(reduce_function) + async for element in stream: + if is_async: + accumulator = await reduce_function(accumulator, element) + else: + accumulator = reduce_function(accumulator, element) + return accumulator + + +class SimpleTaskOutput(TaskOutput[T], Generic[T]): + def __init__(self, data: T) -> None: + super().__init__() + self._data = data + + @property + def output(self) -> T: + return self._data + + def set_output(self, output_data: T | AsyncIterator[T]) -> None: + self._data = output_data + + def new_output(self) -> TaskOutput[T]: + return SimpleTaskOutput(None) + + @property + def is_empty(self) -> bool: + return not self._data + + async def _apply_func(self, func) -> Any: + if asyncio.iscoroutinefunction(func): + out = await func(self._data) + else: + out = func(self._data) + return out + + async def map(self, map_func) -> TaskOutput[T]: + out = await self._apply_func(map_func) + return SimpleTaskOutput(out) + + async def check_condition(self, condition_func) -> bool: + return await self._apply_func(condition_func) + + async def streamify( + self, transform_func: Callable[[T], AsyncIterator[T]] + ) -> TaskOutput[T]: + out = await self._apply_func(transform_func) + return SimpleStreamTaskOutput(out) + + +class SimpleStreamTaskOutput(TaskOutput[T], Generic[T]): + def __init__(self, data: AsyncIterator[T]) -> None: + super().__init__() + self._data = data + + @property + def is_stream(self) -> bool: + return True + + @property + def is_empty(self) -> bool: + return not self._data + + @property + def output_stream(self) -> AsyncIterator[T]: + return self._data + + def set_output(self, output_data: T | AsyncIterator[T]) -> None: + self._data = output_data + + def new_output(self) -> TaskOutput[T]: + return SimpleStreamTaskOutput(None) + + async def map(self, map_func) -> TaskOutput[T]: + is_async = asyncio.iscoroutinefunction(map_func) + + async def new_iter() -> AsyncIterator[T]: + async for out in self._data: + if is_async: + out = await map_func(out) + else: + out = map_func(out) + yield out + + return SimpleStreamTaskOutput(new_iter()) + + async def reduce(self, reduce_func) -> TaskOutput[T]: + out = await _reduce_stream(self._data, reduce_func) + return SimpleTaskOutput(out) + + async def unstreamify( + self, transform_func: Callable[[AsyncIterator[T]], T] + ) -> TaskOutput[T]: + if asyncio.iscoroutinefunction(transform_func): + out = await transform_func(self._data) + else: + out = transform_func(self._data) + return SimpleTaskOutput(out) + + async def transform_stream( + self, transform_func: Callable[[AsyncIterator[T]], AsyncIterator[T]] + ) -> TaskOutput[T]: + if asyncio.iscoroutinefunction(transform_func): + out = await transform_func(self._data) + else: + out = transform_func(self._data) + return SimpleStreamTaskOutput(out) + + +def _is_async_iterator(obj): + return ( + hasattr(obj, "__anext__") + and callable(getattr(obj, "__anext__", None)) + and hasattr(obj, "__aiter__") + and callable(getattr(obj, "__aiter__", None)) + ) + + +class BaseInputSource(InputSource, ABC): + def __init__(self) -> None: + super().__init__() + self._is_read = False + + @abstractmethod + def _read_data(self, task_ctx: TaskContext) -> Any: + """Read data with task context""" + + async def read(self, task_ctx: TaskContext) -> Coroutine[Any, Any, TaskOutput]: + data = self._read_data(task_ctx) + if _is_async_iterator(data): + if self._is_read: + raise ValueError(f"Input iterator {data} has been read!") + output = SimpleStreamTaskOutput(data) + else: + output = SimpleTaskOutput(data) + self._is_read = True + return output + + +class SimpleInputSource(BaseInputSource): + def __init__(self, data: Any) -> None: + super().__init__() + self._data = data + + def _read_data(self, task_ctx: TaskContext) -> Any: + return self._data + + +class SimpleCallDataInputSource(BaseInputSource): + def __init__(self) -> None: + super().__init__() + + def _read_data(self, task_ctx: TaskContext) -> Any: + call_data = task_ctx.call_data + data = call_data.get("data") if call_data else None + if not (call_data and data): + raise ValueError("No call data for current SimpleCallDataInputSource") + return data + + +class DefaultTaskContext(TaskContext, Generic[T]): + def __init__( + self, task_id: str, task_state: TaskState, task_output: TaskOutput[T] + ) -> None: + super().__init__() + self._task_id = task_id + self._task_state = task_state + self._output = task_output + self._task_input = None + self._metadata = {} + + @property + def task_id(self) -> str: + return self._task_id + + @property + def task_input(self) -> InputContext: + return self._task_input + + def set_task_input(self, input_ctx: "InputContext") -> None: + self._task_input = input_ctx + + @property + def task_output(self) -> TaskOutput: + return self._output + + def set_task_output(self, task_output: TaskOutput) -> None: + self._output = task_output + + @property + def current_state(self) -> TaskState: + return self._task_state + + def set_current_state(self, task_state: TaskState) -> None: + self._task_state = task_state + + def new_ctx(self) -> TaskContext: + new_output = self._output.new_output() + return DefaultTaskContext(self._task_id, self._task_state, new_output) + + @property + def metadata(self) -> Dict[str, Any]: + return self._metadata + + +class DefaultInputContext(InputContext): + def __init__(self, outputs: List[TaskContext]) -> None: + super().__init__() + self._outputs = outputs + + @property + def parent_outputs(self) -> List[TaskContext]: + return self._outputs + + async def _apply_func( + self, func: Callable[[Any], Any], apply_type: str = "map" + ) -> Tuple[List[TaskContext], List[TaskOutput]]: + new_outputs: List[TaskContext] = [] + map_tasks = [] + for out in self._outputs: + new_outputs.append(out.new_ctx()) + result = None + if apply_type == "map": + result = out.task_output.map(func) + elif apply_type == "reduce": + result = out.task_output.reduce(func) + elif apply_type == "check_condition": + result = out.task_output.check_condition(func) + else: + raise ValueError(f"Unsupport apply type {apply_type}") + map_tasks.append(result) + results = await asyncio.gather(*map_tasks) + return new_outputs, results + + async def map(self, map_func: Callable[[Any], Any]) -> InputContext: + new_outputs, results = await self._apply_func(map_func) + for i, task_ctx in enumerate(new_outputs): + task_ctx: TaskContext = task_ctx + task_ctx.set_task_output(results[i]) + return DefaultInputContext(new_outputs) + + async def map_all(self, map_func: Callable[..., Any]) -> InputContext: + if not self._outputs: + return DefaultInputContext([]) + # Some parent may be empty + not_empty_idx = 0 + for i, p in enumerate(self._outputs): + if p.task_output.is_empty: + continue + not_empty_idx = i + break + # All output is empty? + is_steam = self._outputs[not_empty_idx].task_output.is_stream + if is_steam: + if not self.check_stream(skip_empty=True): + raise ValueError( + "The output in all tasks must has same output format to map_all" + ) + outputs = [] + for out in self._outputs: + if out.task_output.is_stream: + outputs.append(out.task_output.output_stream) + else: + outputs.append(out.task_output.output) + if asyncio.iscoroutinefunction(map_func): + map_res = await map_func(*outputs) + else: + map_res = map_func(*outputs) + single_output: TaskContext = self._outputs[not_empty_idx].new_ctx() + single_output.task_output.set_output(map_res) + logger.debug( + f"Current map_all map_res: {map_res}, is steam: {single_output.task_output.is_stream}" + ) + return DefaultInputContext([single_output]) + + async def reduce(self, reduce_func: Callable[[Any], Any]) -> InputContext: + if not self.check_stream(): + raise ValueError( + "The output in all tasks must has same output format of stream to apply reduce function" + ) + new_outputs, results = await self._apply_func(reduce_func, apply_type="reduce") + for i, task_ctx in enumerate(new_outputs): + task_ctx: TaskContext = task_ctx + task_ctx.set_task_output(results[i]) + return DefaultInputContext(new_outputs) + + async def filter(self, filter_func: Callable[[Any], bool]) -> InputContext: + new_outputs, results = await self._apply_func( + filter_func, apply_type="check_condition" + ) + result_outputs = [] + for i, task_ctx in enumerate(new_outputs): + if results[i]: + result_outputs.append(task_ctx) + return DefaultInputContext(result_outputs) + + async def predicate_map( + self, predicate_func: Callable[[Any], bool], failed_value: Any = None + ) -> "InputContext": + new_outputs, results = await self._apply_func( + predicate_func, apply_type="check_condition" + ) + result_outputs = [] + for i, task_ctx in enumerate(new_outputs): + task_ctx: TaskContext = task_ctx + if results[i]: + task_ctx.task_output.set_output(True) + result_outputs.append(task_ctx) + else: + task_ctx.task_output.set_output(failed_value) + result_outputs.append(task_ctx) + return DefaultInputContext(result_outputs) diff --git a/pilot/awel/tests/__init__.py b/pilot/awel/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/awel/tests/conftest.py b/pilot/awel/tests/conftest.py new file mode 100644 index 000000000..2279cceba --- /dev/null +++ b/pilot/awel/tests/conftest.py @@ -0,0 +1,102 @@ +import pytest +import pytest_asyncio +from typing import AsyncIterator, List +from contextlib import contextmanager, asynccontextmanager +from .. import ( + WorkflowRunner, + InputOperator, + DAGContext, + TaskState, + DefaultWorkflowRunner, + SimpleInputSource, +) +from ..task.task_impl import _is_async_iterator + + +@pytest.fixture +def runner(): + return DefaultWorkflowRunner() + + +def _create_stream(num_nodes) -> List[AsyncIterator[int]]: + iters = [] + for _ in range(num_nodes): + + async def stream_iter(): + for i in range(10): + yield i + + stream_iter = stream_iter() + assert _is_async_iterator(stream_iter) + iters.append(stream_iter) + return iters + + +def _create_stream_from(output_streams: List[List[int]]) -> List[AsyncIterator[int]]: + iters = [] + for single_stream in output_streams: + + async def stream_iter(): + for i in single_stream: + yield i + + stream_iter = stream_iter() + assert _is_async_iterator(stream_iter) + iters.append(stream_iter) + return iters + + +@asynccontextmanager +async def _create_input_node(**kwargs): + num_nodes = kwargs.get("num_nodes") + is_stream = kwargs.get("is_stream", False) + if is_stream: + outputs = kwargs.get("output_streams") + if outputs: + if num_nodes and num_nodes != len(outputs): + raise ValueError( + f"num_nodes {num_nodes} != the length of output_streams {len(outputs)}" + ) + outputs = _create_stream_from(outputs) + else: + num_nodes = num_nodes or 1 + outputs = _create_stream(num_nodes) + else: + outputs = kwargs.get("outputs", ["Hello."]) + nodes = [] + for output in outputs: + print(f"output: {output}") + input_source = SimpleInputSource(output) + input_node = InputOperator(input_source) + nodes.append(input_node) + yield nodes + + +@pytest_asyncio.fixture +async def input_node(request): + param = getattr(request, "param", {}) + async with _create_input_node(**param) as input_nodes: + yield input_nodes[0] + + +@pytest_asyncio.fixture +async def stream_input_node(request): + param = getattr(request, "param", {}) + param["is_stream"] = True + async with _create_input_node(**param) as input_nodes: + yield input_nodes[0] + + +@pytest_asyncio.fixture +async def input_nodes(request): + param = getattr(request, "param", {}) + async with _create_input_node(**param) as input_nodes: + yield input_nodes + + +@pytest_asyncio.fixture +async def stream_input_nodes(request): + param = getattr(request, "param", {}) + param["is_stream"] = True + async with _create_input_node(**param) as input_nodes: + yield input_nodes diff --git a/pilot/awel/tests/test_http_operator.py b/pilot/awel/tests/test_http_operator.py new file mode 100644 index 000000000..c57e70fe1 --- /dev/null +++ b/pilot/awel/tests/test_http_operator.py @@ -0,0 +1,51 @@ +import pytest +from typing import List +from .. import ( + DAG, + WorkflowRunner, + DAGContext, + TaskState, + InputOperator, + MapOperator, + JoinOperator, + BranchOperator, + ReduceStreamOperator, + SimpleInputSource, +) +from .conftest import ( + runner, + input_node, + input_nodes, + stream_input_node, + stream_input_nodes, + _is_async_iterator, +) + + +def _register_dag_to_fastapi_app(dag): + # TODO + pass + + +@pytest.mark.asyncio +async def test_http_operator(runner: WorkflowRunner, stream_input_node: InputOperator): + with DAG("test_map") as dag: + pass + # http_req_task = HttpRequestOperator(endpoint="/api/completions") + # db_task = DBQueryOperator(table_name="user_info") + # prompt_task = PromptTemplateOperator( + # system_prompt="You are an AI designed to solve the user's goals with given commands, please follow the constraints of the system's input for your answers." + # ) + # llm_task = ChatGPTLLMOperator(model="chagpt-3.5") + # output_parser_task = CommonOutputParserOperator() + # http_res_task = HttpResponseOperator() + # ( + # http_req_task + # >> db_task + # >> prompt_task + # >> llm_task + # >> output_parser_task + # >> http_res_task + # ) + + _register_dag_to_fastapi_app(dag) diff --git a/pilot/awel/tests/test_run_dag.py b/pilot/awel/tests/test_run_dag.py new file mode 100644 index 000000000..c0ea8e7ad --- /dev/null +++ b/pilot/awel/tests/test_run_dag.py @@ -0,0 +1,141 @@ +import pytest +from typing import List +from .. import ( + DAG, + WorkflowRunner, + DAGContext, + TaskState, + InputOperator, + MapOperator, + JoinOperator, + BranchOperator, + ReduceStreamOperator, + SimpleInputSource, +) +from .conftest import ( + runner, + input_node, + input_nodes, + stream_input_node, + stream_input_nodes, + _is_async_iterator, +) + + +@pytest.mark.asyncio +async def test_input_node(runner: WorkflowRunner): + input_node = InputOperator(SimpleInputSource("hello")) + res: DAGContext[str] = await runner.execute_workflow(input_node) + assert res.current_task_context.current_state == TaskState.SUCCESS + assert res.current_task_context.task_output.output == "hello" + + async def new_steam_iter(n: int): + for i in range(n): + yield i + + num_iter = 10 + steam_input_node = InputOperator(SimpleInputSource(new_steam_iter(num_iter))) + res: DAGContext[str] = await runner.execute_workflow(steam_input_node) + assert res.current_task_context.current_state == TaskState.SUCCESS + output_steam = res.current_task_context.task_output.output_stream + assert output_steam + assert _is_async_iterator(output_steam) + i = 0 + async for x in output_steam: + assert x == i + i += 1 + + +@pytest.mark.asyncio +async def test_map_node(runner: WorkflowRunner, stream_input_node: InputOperator): + with DAG("test_map") as dag: + map_node = MapOperator(lambda x: x * 2) + stream_input_node >> map_node + res: DAGContext[int] = await runner.execute_workflow(map_node) + output_steam = res.current_task_context.task_output.output_stream + assert output_steam + i = 0 + async for x in output_steam: + assert x == i * 2 + i += 1 + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "stream_input_node, expect_sum", + [ + ({"output_streams": [[0, 1, 2, 3]]}, 6), + ({"output_streams": [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]}, 55), + ], + indirect=["stream_input_node"], +) +async def test_reduce_node( + runner: WorkflowRunner, stream_input_node: InputOperator, expect_sum: int +): + with DAG("test_reduce_node") as dag: + reduce_node = ReduceStreamOperator(lambda x, y: x + y) + stream_input_node >> reduce_node + res: DAGContext[int] = await runner.execute_workflow(reduce_node) + assert res.current_task_context.current_state == TaskState.SUCCESS + assert not res.current_task_context.task_output.is_stream + assert res.current_task_context.task_output.output == expect_sum + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "input_nodes", + [ + ({"outputs": [0, 1, 2]}), + ], + indirect=["input_nodes"], +) +async def test_join_node(runner: WorkflowRunner, input_nodes: List[InputOperator]): + def join_func(p1, p2, p3) -> int: + return p1 + p2 + p3 + + with DAG("test_join_node") as dag: + join_node = JoinOperator(join_func) + for input_node in input_nodes: + input_node >> join_node + res: DAGContext[int] = await runner.execute_workflow(join_node) + assert res.current_task_context.current_state == TaskState.SUCCESS + assert not res.current_task_context.task_output.is_stream + assert res.current_task_context.task_output.output == 3 + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "input_node, is_odd", + [ + ({"outputs": [0]}, False), + ({"outputs": [1]}, True), + ], + indirect=["input_node"], +) +async def test_branch_node( + runner: WorkflowRunner, input_node: InputOperator, is_odd: bool +): + def join_func(o1, o2) -> int: + print(f"join func result, o1: {o1}, o2: {o2}") + return o1 or o2 + + with DAG("test_join_node") as dag: + odd_node = MapOperator( + lambda x: 999, task_id="odd_node", task_name="odd_node_name" + ) + even_node = MapOperator( + lambda x: 888, task_id="even_node", task_name="even_node_name" + ) + join_node = JoinOperator(join_func) + branch_node = BranchOperator( + {lambda x: x % 2 == 1: odd_node, lambda x: x % 2 == 0: even_node} + ) + branch_node >> odd_node >> join_node + branch_node >> even_node >> join_node + + input_node >> branch_node + + res: DAGContext[int] = await runner.execute_workflow(join_node) + assert res.current_task_context.current_state == TaskState.SUCCESS + expect_res = 999 if is_odd else 888 + assert res.current_task_context.task_output.output == expect_res diff --git a/pilot/awel/trigger/__init__.py b/pilot/awel/trigger/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/awel/trigger/base.py b/pilot/awel/trigger/base.py new file mode 100644 index 000000000..28662498f --- /dev/null +++ b/pilot/awel/trigger/base.py @@ -0,0 +1,11 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod + +from ..operator.common_operator import TriggerOperator + + +class Trigger(TriggerOperator, ABC): + @abstractmethod + async def trigger(self) -> None: + """Trigger the workflow or a specific operation in the workflow.""" diff --git a/pilot/awel/trigger/http_trigger.py b/pilot/awel/trigger/http_trigger.py new file mode 100644 index 000000000..175d4b63f --- /dev/null +++ b/pilot/awel/trigger/http_trigger.py @@ -0,0 +1,137 @@ +from __future__ import annotations + +from typing import Union, Type, List, TYPE_CHECKING, Optional, Any, Dict +from starlette.requests import Request +from starlette.responses import Response +from pydantic import BaseModel +import logging + +from .base import Trigger +from ..dag.base import DAG +from ..operator.base import BaseOperator + +if TYPE_CHECKING: + from fastapi import APIRouter, FastAPI + +RequestBody = Union[Request, Type[BaseModel], str] + +logger = logging.getLogger(__name__) + + +class HttpTrigger(Trigger): + def __init__( + self, + endpoint: str, + methods: Optional[Union[str, List[str]]] = "GET", + request_body: Optional[RequestBody] = None, + streaming_response: Optional[bool] = False, + response_model: Optional[Type] = None, + response_headers: Optional[Dict[str, str]] = None, + response_media_type: Optional[str] = None, + status_code: Optional[int] = 200, + router_tags: Optional[List[str]] = None, + **kwargs, + ) -> None: + super().__init__(**kwargs) + if not endpoint.startswith("/"): + endpoint = "/" + endpoint + self._endpoint = endpoint + self._methods = methods + self._req_body = request_body + self._streaming_response = streaming_response + self._response_model = response_model + self._status_code = status_code + self._router_tags = router_tags + self._response_headers = response_headers + self._response_media_type = response_media_type + self._end_node: BaseOperator = None + + async def trigger(self) -> None: + pass + + def mount_to_router(self, router: "APIRouter") -> None: + from fastapi import Depends + + methods = self._methods if isinstance(self._methods, list) else [self._methods] + + def create_route_function(name, req_body_cls: Optional[Type[BaseModel]]): + async def _request_body_dependency(request: Request): + return await _parse_request_body(request, self._req_body) + + async def route_function(body=Depends(_request_body_dependency)): + return await _trigger_dag( + body, + self.dag, + self._streaming_response, + self._response_headers, + self._response_media_type, + ) + + route_function.__name__ = name + return route_function + + function_name = f"AWEL_trigger_route_{self._endpoint.replace('/', '_')}" + request_model = ( + self._req_body + if isinstance(self._req_body, type) + and issubclass(self._req_body, BaseModel) + else None + ) + dynamic_route_function = create_route_function(function_name, request_model) + logger.info( + f"mount router function {dynamic_route_function}({function_name}), endpoint: {self._endpoint}, methods: {methods}" + ) + + router.api_route( + self._endpoint, + methods=methods, + response_model=self._response_model, + status_code=self._status_code, + tags=self._router_tags, + )(dynamic_route_function) + + +async def _parse_request_body( + request: Request, request_body_cls: Optional[Type[BaseModel]] +): + if not request_body_cls: + return None + if request.method == "POST": + json_data = await request.json() + return request_body_cls(**json_data) + elif request.method == "GET": + return request_body_cls(**request.query_params) + else: + return request + + +async def _trigger_dag( + body: Any, + dag: DAG, + streaming_response: Optional[bool] = False, + response_headers: Optional[Dict[str, str]] = None, + response_media_type: Optional[str] = None, +) -> Any: + from fastapi.responses import StreamingResponse + + end_node = dag.leaf_nodes + if len(end_node) != 1: + raise ValueError("HttpTrigger just support one leaf node in dag") + end_node = end_node[0] + if not streaming_response: + return await end_node.call(call_data={"data": body}) + else: + headers = response_headers + media_type = response_media_type if response_media_type else "text/event-stream" + if not headers: + headers = { + "Content-Type": "text/event-stream", + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "Transfer-Encoding": "chunked", + } + return StreamingResponse( + end_node.call_stream(call_data={"data": body}), + headers=headers, + media_type=media_type, + ) diff --git a/pilot/awel/trigger/trigger_manager.py b/pilot/awel/trigger/trigger_manager.py new file mode 100644 index 000000000..feb674ffb --- /dev/null +++ b/pilot/awel/trigger/trigger_manager.py @@ -0,0 +1,74 @@ +from abc import ABC, abstractmethod +from typing import Any, TYPE_CHECKING, Optional +import logging + +if TYPE_CHECKING: + from fastapi import APIRouter + +from pilot.component import SystemApp, BaseComponent, ComponentType + +logger = logging.getLogger(__name__) + + +class TriggerManager(ABC): + @abstractmethod + def register_trigger(self, trigger: Any) -> None: + """ "Register a trigger to current manager""" + + +class HttpTriggerManager(TriggerManager): + def __init__( + self, + router: Optional["APIRouter"] = None, + router_prefix: Optional[str] = "/api/v1/awel/trigger", + ) -> None: + if not router: + from fastapi import APIRouter + + router = APIRouter() + self._router_prefix = router_prefix + self._router = router + self._trigger_map = {} + + def register_trigger(self, trigger: Any) -> None: + from .http_trigger import HttpTrigger + + if not isinstance(trigger, HttpTrigger): + raise ValueError(f"Current trigger {trigger} not an object of HttpTrigger") + trigger: HttpTrigger = trigger + trigger_id = trigger.node_id + if trigger_id not in self._trigger_map: + trigger.mount_to_router(self._router) + self._trigger_map[trigger_id] = trigger + + def _init_app(self, system_app: SystemApp): + logger.info( + f"Include router {self._router} to prefix path {self._router_prefix}" + ) + system_app.app.include_router( + self._router, prefix=self._router_prefix, tags=["AWEL"] + ) + + +class DefaultTriggerManager(TriggerManager, BaseComponent): + name = ComponentType.AWEL_TRIGGER_MANAGER + + def __init__(self, system_app: SystemApp | None = None): + self.system_app = system_app + self.http_trigger = HttpTriggerManager() + super().__init__(None) + + def init_app(self, system_app: SystemApp): + self.system_app = system_app + + def register_trigger(self, trigger: Any) -> None: + from .http_trigger import HttpTrigger + + if isinstance(trigger, HttpTrigger): + logger.info(f"Register trigger {trigger}") + self.http_trigger.register_trigger(trigger) + else: + raise ValueError(f"Unsupport trigger: {trigger}") + + def after_register(self) -> None: + self.http_trigger._init_app(self.system_app) diff --git a/pilot/base_modules/agent/commands/command_mange.py b/pilot/base_modules/agent/commands/command_mange.py index be9e02811..89aa2f5f4 100644 --- a/pilot/base_modules/agent/commands/command_mange.py +++ b/pilot/base_modules/agent/commands/command_mange.py @@ -5,7 +5,9 @@ import time import json import logging import xml.etree.ElementTree as ET +import pandas as pd +from pilot.common.json_utils import serialize from datetime import datetime from typing import Any, Callable, Optional, List from pydantic import BaseModel @@ -184,6 +186,8 @@ class PluginStatus(BaseModel): start_time = datetime.now().timestamp() * 1000 end_time: int = None + df: Any = None + class ApiCall: agent_prefix = "" @@ -191,7 +195,12 @@ class ApiCall: name_prefix = "" name_end = "" - def __init__(self, plugin_generator: Any = None, display_registry: Any = None): + def __init__( + self, + plugin_generator: Any = None, + display_registry: Any = None, + backend_rendering: bool = False, + ): # self.name: str = "" # self.status: Status = Status.TODO.value # self.logo_url: str = None @@ -204,6 +213,7 @@ class ApiCall: self.plugin_generator = plugin_generator self.display_registry = display_registry self.start_time = datetime.now().timestamp() * 1000 + self.backend_rendering: bool = False def __repr__(self): return f"ApiCall(name={self.name}, status={self.status}, args={self.args})" @@ -227,7 +237,7 @@ class ApiCall: i += 1 return False - def __check_last_plugin_call_ready(self, all_context): + def check_last_plugin_call_ready(self, all_context): start_agent_count = all_context.count(self.agent_prefix) end_agent_count = all_context.count(self.agent_end) @@ -236,7 +246,14 @@ class ApiCall: return False def __deal_error_md_tags(self, all_context, api_context, include_end: bool = True): - error_md_tags = ["```", "```python", "```xml", "```json", "```markdown"] + error_md_tags = [ + "```", + "```python", + "```xml", + "```json", + "```markdown", + "```sql", + ] if include_end == False: md_tag_end = "" else: @@ -255,7 +272,6 @@ class ApiCall: return all_context def api_view_context(self, all_context: str, display_mode: bool = False): - error_mk_tags = ["```", "```python", "```xml"] call_context_map = extract_content_open_ending( all_context, self.agent_prefix, self.agent_end, True ) @@ -263,32 +279,18 @@ class ApiCall: api_status = self.plugin_status_map.get(api_context) if api_status is not None: if display_mode: - if api_status.api_result: - all_context = self.__deal_error_md_tags( - all_context, api_context - ) + all_context = self.__deal_error_md_tags(all_context, api_context) + if Status.FAILED.value == api_status.status: all_context = all_context.replace( - api_context, api_status.api_result + api_context, + f'\nError:{api_status.err_msg}\n' + + self.to_view_antv_vis(api_status), ) else: - if api_status.status == Status.FAILED.value: - all_context = self.__deal_error_md_tags( - all_context, api_context - ) - all_context = all_context.replace( - api_context, - f"""\nERROR!{api_status.err_msg}\n """, - ) - else: - cost = (api_status.end_time - self.start_time) / 1000 - cost_str = "{:.2f}".format(cost) - all_context = self.__deal_error_md_tags( - all_context, api_context - ) - all_context = all_context.replace( - api_context, - f'\nWaiting...{cost_str}S\n', - ) + all_context = all_context.replace( + api_context, self.to_view_antv_vis(api_status) + ) + else: all_context = self.__deal_error_md_tags( all_context, api_context, False @@ -302,8 +304,8 @@ class ApiCall: now_time = datetime.now().timestamp() * 1000 cost = (now_time - self.start_time) / 1000 cost_str = "{:.2f}".format(cost) - for tag in error_mk_tags: - all_context = all_context.replace(tag + api_context, api_context) + all_context = self.__deal_error_md_tags(all_context, api_context) + all_context = all_context.replace( api_context, f'\nWaiting...{cost_str}S\n', @@ -348,7 +350,8 @@ class ApiCall: if api_status.api_result: param["result"] = api_status.api_result - return json.dumps(param) + + return json.dumps(param, default=serialize, ensure_ascii=False) def to_view_text(self, api_status: PluginStatus): api_call_element = ET.Element("dbgpt-view") @@ -356,10 +359,45 @@ class ApiCall: result = ET.tostring(api_call_element, encoding="utf-8") return result.decode("utf-8") + def to_view_antv_vis(self, api_status: PluginStatus): + if self.backend_rendering: + html_table = api_status.df.to_html( + index=False, escape=False, sparsify=False + ) + table_str = "".join(html_table.split()) + table_str = table_str.replace("\n", " ") + html = f""" \n

[SQL]{api_status.args["sql"]}
{table_str}
\n """ + return html + else: + api_call_element = ET.Element("chart-view") + api_call_element.attrib["content"] = self.__to_antv_vis_param(api_status) + api_call_element.text = "\n" + # api_call_element.set("content", self.__to_antv_vis_param(api_status)) + # api_call_element.text = self.__to_antv_vis_param(api_status) + result = ET.tostring(api_call_element, encoding="utf-8") + return result.decode("utf-8") + + # return f'' + + def __to_antv_vis_param(self, api_status: PluginStatus): + param = {} + if api_status.name: + param["type"] = api_status.name + if api_status.args: + param["sql"] = api_status.args["sql"] + # if api_status.err_msg: + # param["err_msg"] = api_status.err_msg + + if api_status.api_result: + param["data"] = api_status.api_result + else: + param["data"] = [] + return json.dumps(param, ensure_ascii=False) + def run(self, llm_text): if self.__is_need_wait_plugin_call(llm_text): # wait api call generate complete - if self.__check_last_plugin_call_ready(llm_text): + if self.check_last_plugin_call_ready(llm_text): self.update_from_context(llm_text) for key, value in self.plugin_status_map.items(): if value.status == Status.TODO.value: @@ -379,7 +417,7 @@ class ApiCall: def run_display_sql(self, llm_text, sql_run_func): if self.__is_need_wait_plugin_call(llm_text): # wait api call generate complete - if self.__check_last_plugin_call_ready(llm_text): + if self.check_last_plugin_call_ready(llm_text): self.update_from_context(llm_text) for key, value in self.plugin_status_map.items(): if value.status == Status.TODO.value: @@ -391,6 +429,7 @@ class ApiCall: param = { "df": sql_run_func(sql), } + value.df = param["df"] if self.display_registry.is_valid_command(value.name): value.api_result = self.display_registry.call( value.name, **param @@ -406,3 +445,49 @@ class ApiCall: value.err_msg = str(e) value.end_time = datetime.now().timestamp() * 1000 return self.api_view_context(llm_text, True) + + def display_sql_llmvis(self, llm_text, sql_run_func): + """ + Render charts using the Antv standard protocol + Args: + llm_text: LLM response text + sql_run_func: sql run function + + Returns: + ChartView protocol text + """ + try: + if self.__is_need_wait_plugin_call(llm_text): + # wait api call generate complete + if self.check_last_plugin_call_ready(llm_text): + self.update_from_context(llm_text) + for key, value in self.plugin_status_map.items(): + if value.status == Status.TODO.value: + value.status = Status.RUNNING.value + logging.info(f"sql展示执行:{value.name},{value.args}") + try: + sql = value.args["sql"] + if sql is not None and len(sql) > 0: + data_df = sql_run_func(sql) + value.df = data_df + value.api_result = json.loads( + data_df.to_json( + orient="records", + date_format="iso", + date_unit="s", + ) + ) + value.status = Status.COMPLETED.value + else: + value.status = Status.FAILED.value + value.err_msg = "No executable sql！" + + except Exception as e: + value.status = Status.FAILED.value + value.err_msg = str(e) + value.end_time = datetime.now().timestamp() * 1000 + except Exception as e: + logging.error("Api parsing exception", e) + raise ValueError("Api parsing exception," + str(e)) + + return self.api_view_context(llm_text, True) diff --git a/pilot/base_modules/agent/commands/disply_type/show_chart_gen.py b/pilot/base_modules/agent/commands/disply_type/show_chart_gen.py index 166992822..956a064c2 100644 --- a/pilot/base_modules/agent/commands/disply_type/show_chart_gen.py +++ b/pilot/base_modules/agent/commands/disply_type/show_chart_gen.py @@ -1,7 +1,6 @@ from pandas import DataFrame from pilot.base_modules.agent.commands.command_mange import command -from pilot.configs.config import Config import pandas as pd import uuid import os diff --git a/pilot/base_modules/agent/controller.py b/pilot/base_modules/agent/controller.py index ef699a047..e0c0ffaf8 100644 --- a/pilot/base_modules/agent/controller.py +++ b/pilot/base_modules/agent/controller.py @@ -83,7 +83,7 @@ async def agent_hub_update(update_param: PluginHubParam = Body()): return Result.succ(None) except Exception as e: logger.error("Agent Hub Update Error!", e) - return Result.faild(code="E0020", msg=f"Agent Hub Update Error! {e}") + return Result.failed(code="E0020", msg=f"Agent Hub Update Error! {e}") @router.post("/v1/agent/query", response_model=Result[str]) @@ -133,7 +133,7 @@ async def agent_install(plugin_name: str, user: str = None): return Result.succ(None) except Exception as e: logger.error("Plugin Install Error!", e) - return Result.faild(code="E0021", msg=f"Plugin Install Error {e}") + return Result.failed(code="E0021", msg=f"Plugin Install Error {e}") @router.post("/v1/agent/uninstall", response_model=Result[str]) @@ -147,7 +147,7 @@ async def agent_uninstall(plugin_name: str, user: str = None): return Result.succ(None) except Exception as e: logger.error("Plugin Uninstall Error!", e) - return Result.faild(code="E0022", msg=f"Plugin Uninstall Error {e}") + return Result.failed(code="E0022", msg=f"Plugin Uninstall Error {e}") @router.post("/v1/personal/agent/upload", response_model=Result[str]) @@ -160,4 +160,4 @@ async def personal_agent_upload(doc_file: UploadFile = File(...), user: str = No return Result.succ(None) except Exception as e: logger.error("Upload Personal Plugin Error!", e) - return Result.faild(code="E0023", msg=f"Upload Personal Plugin Error {e}") + return Result.failed(code="E0023", msg=f"Upload Personal Plugin Error {e}") diff --git a/pilot/base_modules/agent/db/__init__.py b/pilot/base_modules/agent/db/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/base_modules/agent/db/my_plugin_db.py b/pilot/base_modules/agent/db/my_plugin_db.py index acfb70e23..fb4fe25ee 100644 --- a/pilot/base_modules/agent/db/my_plugin_db.py +++ b/pilot/base_modules/agent/db/my_plugin_db.py @@ -4,7 +4,12 @@ from sqlalchemy import Column, Integer, String, Index, DateTime, func from sqlalchemy import UniqueConstraint from pilot.base_modules.meta_data.base_dao import BaseDao -from pilot.base_modules.meta_data.meta_data import Base, engine, session +from pilot.base_modules.meta_data.meta_data import ( + Base, + engine, + session, + META_DATA_DATABASE, +) class MyPluginEntity(Base): @@ -27,7 +32,7 @@ class MyPluginEntity(Base): succ_count = Column( Integer, nullable=True, default=0, comment="plugin total success count" ) - created_at = Column( + gmt_created = Column( DateTime, default=datetime.utcnow, comment="plugin install time" ) UniqueConstraint("user_code", "name", name="uk_name") @@ -36,7 +41,10 @@ class MyPluginEntity(Base): class MyPluginDao(BaseDao[MyPluginEntity]): def __init__(self): super().__init__( - database="dbgpt", orm_base=Base, db_engine=engine, session=session + database=META_DATA_DATABASE, + orm_base=Base, + db_engine=engine, + session=session, ) def add(self, engity: MyPluginEntity): @@ -50,7 +58,7 @@ class MyPluginDao(BaseDao[MyPluginEntity]): version=engity.version, use_count=engity.use_count or 0, succ_count=engity.succ_count or 0, - created_at=datetime.now(), + gmt_created=datetime.now(), ) session.add(my_plugin) session.commit() diff --git a/pilot/base_modules/agent/db/plugin_hub_db.py b/pilot/base_modules/agent/db/plugin_hub_db.py index 89ec314c7..70cf16930 100644 --- a/pilot/base_modules/agent/db/plugin_hub_db.py +++ b/pilot/base_modules/agent/db/plugin_hub_db.py @@ -6,9 +6,14 @@ from sqlalchemy import UniqueConstraint from pilot.base_modules.meta_data.meta_data import Base from pilot.base_modules.meta_data.base_dao import BaseDao -from pilot.base_modules.meta_data.meta_data import Base, engine, session - +from pilot.base_modules.meta_data.meta_data import ( + Base, + engine, + session, + META_DATA_DATABASE, +) +# TODO We should consider that the production environment does not have permission to execute the DDL char_set_sql = DDL("ALTER TABLE plugin_hub CONVERT TO CHARACTER SET utf8mb4") @@ -30,7 +35,9 @@ class PluginHubEntity(Base): storage_channel = Column(String(255), comment="plugin storage channel") storage_url = Column(String(255), comment="plugin download url") download_param = Column(String(255), comment="plugin download param") - created_at = Column(DateTime, default=datetime.utcnow, comment="plugin upload time") + gmt_created = Column( + DateTime, default=datetime.utcnow, comment="plugin upload time" + ) installed = Column(Integer, default=False, comment="plugin already installed count") UniqueConstraint("name", name="uk_name") @@ -40,7 +47,10 @@ class PluginHubEntity(Base): class PluginHubDao(BaseDao[PluginHubEntity]): def __init__(self): super().__init__( - database="dbgpt", orm_base=Base, db_engine=engine, session=session + database=META_DATA_DATABASE, + orm_base=Base, + db_engine=engine, + session=session, ) def add(self, engity: PluginHubEntity): @@ -54,7 +64,7 @@ class PluginHubDao(BaseDao[PluginHubEntity]): version=engity.version, storage_channel=engity.storage_channel, storage_url=engity.storage_url, - created_at=timezone.localize(datetime.now()), + gmt_created=timezone.localize(datetime.now()), ) session.add(plugin_hub) session.commit() diff --git a/pilot/base_modules/agent/hub/agent_hub.py b/pilot/base_modules/agent/hub/agent_hub.py index 063a2e03a..470b4ab71 100644 --- a/pilot/base_modules/agent/hub/agent_hub.py +++ b/pilot/base_modules/agent/hub/agent_hub.py @@ -12,7 +12,7 @@ from ..db.my_plugin_db import MyPluginDao, MyPluginEntity from ..common.schema import PluginStorageType from ..plugins_util import scan_plugins, update_from_git -logger = logging.getLogger("agent_hub") +logger = logging.getLogger(__name__) Default_User = "default" DEFAULT_PLUGIN_REPO = "https://github.com/eosphoros-ai/DB-GPT-Plugins.git" TEMP_PLUGIN_PATH = "" diff --git a/pilot/base_modules/agent/plugins_util.py b/pilot/base_modules/agent/plugins_util.py index facc1472d..b5f12c5eb 100644 --- a/pilot/base_modules/agent/plugins_util.py +++ b/pilot/base_modules/agent/plugins_util.py @@ -9,6 +9,7 @@ import requests import git import threading import datetime +import logging from pathlib import Path from typing import List from urllib.parse import urlparse @@ -19,7 +20,8 @@ from auto_gpt_plugin_template import AutoGPTPluginTemplate from pilot.configs.config import Config from pilot.configs.model_config import PLUGINS_DIR -from pilot.logs import logger + +logger = logging.getLogger(__name__) def inspect_zip_for_modules(zip_path: str, debug: bool = False) -> list[str]: @@ -109,7 +111,7 @@ def load_native_plugins(cfg: Config): print("save file") cfg.set_plugins(scan_plugins(cfg.debug_mode)) else: - print("get file faild，response code：", response.status_code) + print("get file failed，response code：", response.status_code) except Exception as e: print("load plugin from git exception!" + str(e)) diff --git a/pilot/base_modules/meta_data/meta_data.py b/pilot/base_modules/meta_data/meta_data.py index 11cd29e38..5b79a38a5 100644 --- a/pilot/base_modules/meta_data/meta_data.py +++ b/pilot/base_modules/meta_data/meta_data.py @@ -1,26 +1,19 @@ -import uuid import os -import duckdb import sqlite3 import logging -import fnmatch -from datetime import datetime -from typing import Optional, Type, TypeVar -from sqlalchemy import create_engine, DateTime, String, func, MetaData, DDL +from sqlalchemy import create_engine, DDL from sqlalchemy.exc import OperationalError -from sqlalchemy.ext.asyncio import AsyncSession -from sqlalchemy.orm import Mapped from sqlalchemy.orm import sessionmaker from sqlalchemy.ext.declarative import declarative_base -from alembic import context, command +from alembic import command from alembic.config import Config as AlembicConfig from urllib.parse import quote from pilot.configs.config import Config -logger = logging.getLogger("meta_data") +logger = logging.getLogger(__name__) CFG = Config() default_db_path = os.path.join(os.getcwd(), "meta_data") @@ -28,7 +21,8 @@ default_db_path = os.path.join(os.getcwd(), "meta_data") os.makedirs(default_db_path, exist_ok=True) # Meta Info -db_name = "dbgpt" +META_DATA_DATABASE = CFG.LOCAL_DB_NAME +db_name = META_DATA_DATABASE db_path = default_db_path + f"/{db_name}.db" connection = sqlite3.connect(db_path) @@ -47,6 +41,7 @@ if CFG.LOCAL_DB_TYPE == "mysql": try: # try to connect with engine_temp.connect() as conn: + # TODO We should consider that the production environment does not have permission to execute the DDL conn.execute(DDL(f"CREATE DATABASE IF NOT EXISTS {db_name}")) print(f"Already connect '{db_name}'") @@ -76,8 +71,6 @@ Base = declarative_base() # Base.metadata.create_all() -# 创建Alembic配置对象 - alembic_ini_path = default_db_path + "/alembic.ini" alembic_cfg = AlembicConfig(alembic_ini_path) @@ -100,7 +93,18 @@ alembic_cfg.attributes["session"] = session # Base.metadata.drop_all(engine) -def ddl_init_and_upgrade(): +def ddl_init_and_upgrade(disable_alembic_upgrade: bool): + """Initialize and upgrade database metadata + + Args: + disable_alembic_upgrade (bool): Whether to enable alembic to initialize and upgrade database metadata + """ + if disable_alembic_upgrade: + logger.info( + "disable_alembic_upgrade is true, not to initialize and upgrade database metadata with alembic" + ) + return + # Base.metadata.create_all(bind=engine) # 生成并应用迁移脚本 # command.upgrade(alembic_cfg, 'head') diff --git a/pilot/base_modules/meta_data/requirement.txt b/pilot/base_modules/meta_data/requirement.txt deleted file mode 100644 index de93d8a90..000000000 --- a/pilot/base_modules/meta_data/requirement.txt +++ /dev/null @@ -1 +0,0 @@ -alembic==1.12.0 \ No newline at end of file diff --git a/pilot/cache/__init__.py b/pilot/cache/__init__.py new file mode 100644 index 000000000..65f768a7e --- /dev/null +++ b/pilot/cache/__init__.py @@ -0,0 +1,10 @@ +from pilot.cache.llm_cache import LLMCacheClient, LLMCacheKey, LLMCacheValue +from pilot.cache.manager import CacheManager, initialize_cache + +__all__ = [ + "LLMCacheKey", + "LLMCacheValue", + "LLMCacheClient", + "CacheManager", + "initialize_cache", +] diff --git a/pilot/cache/base.py b/pilot/cache/base.py new file mode 100644 index 000000000..feb135288 --- /dev/null +++ b/pilot/cache/base.py @@ -0,0 +1,161 @@ +from abc import ABC, abstractmethod, abstractclassmethod +from typing import Any, TypeVar, Generic, Optional, Type, Dict +from dataclasses import dataclass +from enum import Enum + +T = TypeVar("T", bound="Serializable") + +K = TypeVar("K") +V = TypeVar("V") + + +class Serializable(ABC): + @abstractmethod + def serialize(self) -> bytes: + """Convert the object into bytes for storage or transmission. + + Returns: + bytes: The byte array after serialization + """ + + @abstractmethod + def to_dict(self) -> Dict: + """Convert the object's state to a dictionary.""" + + # @staticmethod + # @abstractclassmethod + # def from_dict(cls: Type["Serializable"], obj_dict: Dict) -> "Serializable": + # """Deserialize a dictionary to an Serializable object. + # """ + + +class RetrievalPolicy(str, Enum): + EXACT_MATCH = "exact_match" + SIMILARITY_MATCH = "similarity_match" + + +class CachePolicy(str, Enum): + LRU = "lru" + FIFO = "fifo" + + +@dataclass +class CacheConfig: + retrieval_policy: Optional[RetrievalPolicy] = RetrievalPolicy.EXACT_MATCH + cache_policy: Optional[CachePolicy] = CachePolicy.LRU + + +class CacheKey(Serializable, ABC, Generic[K]): + """The key of the cache. Must be hashable and comparable. + + Supported cache keys: + - The LLM cache key: Include user prompt and the parameters to LLM. + - The embedding model cache key: Include the texts to embedding and the parameters to embedding model. + """ + + @abstractmethod + def __hash__(self) -> int: + """Return the hash value of the key.""" + + @abstractmethod + def __eq__(self, other: Any) -> bool: + """Check equality with another key.""" + + @abstractmethod + def get_hash_bytes(self) -> bytes: + """Return the byte array of hash value.""" + + @abstractmethod + def get_value(self) -> K: + """Get the underlying value of the cache key. + + Returns: + K: The real object of current cache key + """ + + +class CacheValue(Serializable, ABC, Generic[V]): + """Cache value abstract class.""" + + @abstractmethod + def get_value(self) -> V: + """Get the underlying real value.""" + + +class Serializer(ABC): + """The serializer abstract class for serializing cache keys and values.""" + + @abstractmethod + def serialize(self, obj: Serializable) -> bytes: + """Serialize a cache object. + + Args: + obj (Serializable): The object to serialize + """ + + @abstractmethod + def deserialize(self, data: bytes, cls: Type[Serializable]) -> Serializable: + """Deserialize data back into a cache object of the specified type. + + Args: + data (bytes): The byte array to deserialize + cls (Type[Serializable]): The type of current object + + Returns: + Serializable: The serializable object + """ + + +class CacheClient(ABC, Generic[K, V]): + """The cache client interface.""" + + @abstractmethod + async def get( + self, key: CacheKey[K], cache_config: Optional[CacheConfig] = None + ) -> Optional[CacheValue[V]]: + """Retrieve a value from the cache using the provided key. + + Args: + key (CacheKey[K]): The key to get cache + cache_config (Optional[CacheConfig]): Cache config + + Returns: + Optional[CacheValue[V]]: The value retrieved according to key. If cache key not exist, return None. + """ + + @abstractmethod + async def set( + self, + key: CacheKey[K], + value: CacheValue[V], + cache_config: Optional[CacheConfig] = None, + ) -> None: + """Set a value in the cache for the provided key. + + Args: + key (CacheKey[K]): The key to set to cache + value (CacheValue[V]): The value to set to cache + cache_config (Optional[CacheConfig]): Cache config + """ + + @abstractmethod + async def exists( + self, key: CacheKey[K], cache_config: Optional[CacheConfig] = None + ) -> bool: + """Check if a key exists in the cache. + + Args: + key (CacheKey[K]): The key to set to cache + cache_config (Optional[CacheConfig]): Cache config + + Return: + bool: True if the key in the cache, otherwise is False + """ + + @abstractmethod + def new_key(self, **kwargs) -> CacheKey[K]: + """Create a cache key with params""" + + @abstractmethod + def new_value(self, **kwargs) -> CacheValue[K]: + """Create a cache key with params""" diff --git a/pilot/cache/embedding_cache.py b/pilot/cache/embedding_cache.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/cache/llm_cache.py b/pilot/cache/llm_cache.py new file mode 100644 index 000000000..ad559df03 --- /dev/null +++ b/pilot/cache/llm_cache.py @@ -0,0 +1,148 @@ +from typing import Optional, Dict, Any, Union, List +from dataclasses import dataclass, asdict +import json +import hashlib + +from pilot.cache.base import CacheKey, CacheValue, Serializer, CacheClient, CacheConfig +from pilot.cache.manager import CacheManager +from pilot.cache.storage.base import CacheStorage +from pilot.model.base import ModelType, ModelOutput + + +@dataclass +class LLMCacheKeyData: + prompt: str + model_name: str + temperature: Optional[float] = 0.7 + max_new_tokens: Optional[int] = None + top_p: Optional[float] = 1.0 + model_type: Optional[str] = ModelType.HF + + +CacheOutputType = Union[ModelOutput, List[ModelOutput]] + + +@dataclass +class LLMCacheValueData: + output: CacheOutputType + user: Optional[str] = None + _is_list: Optional[bool] = False + + @staticmethod + def from_dict(**kwargs) -> "LLMCacheValueData": + output = kwargs.get("output") + if not output: + raise ValueError("Can't new LLMCacheValueData object, output is None") + if isinstance(output, dict): + output = ModelOutput(**output) + elif isinstance(output, list): + kwargs["_is_list"] = True + output_list = [] + for out in output: + if isinstance(out, dict): + out = ModelOutput(**out) + output_list.append(out) + output = output_list + kwargs["output"] = output + return LLMCacheValueData(**kwargs) + + def to_dict(self) -> Dict: + output = self.output + is_list = False + if isinstance(output, list): + output_list = [] + is_list = True + for out in output: + output_list.append(out.to_dict()) + output = output_list + else: + output = output.to_dict() + return {"output": output, "_is_list": is_list, "user": self.user} + + @property + def is_list(self) -> bool: + return self._is_list + + def __str__(self) -> str: + if not isinstance(self.output, list): + return f"user: {self.user}, output: {self.output}" + else: + return f"user: {self.user}, output(last two item): {self.output[-2:]}" + + +class LLMCacheKey(CacheKey[LLMCacheKeyData]): + def __init__(self, serializer: Serializer = None, **kwargs) -> None: + super().__init__() + self._serializer = serializer + self.config = LLMCacheKeyData(**kwargs) + + def __hash__(self) -> int: + serialize_bytes = self.serialize() + return int(hashlib.sha256(serialize_bytes).hexdigest(), 16) + + def __eq__(self, other: Any) -> bool: + if not isinstance(other, LLMCacheKey): + return False + return self.config == other.config + + def get_hash_bytes(self) -> bytes: + serialize_bytes = self.serialize() + return hashlib.sha256(serialize_bytes).digest() + + def to_dict(self) -> Dict: + return asdict(self.config) + + def serialize(self) -> bytes: + return self._serializer.serialize(self) + + def get_value(self) -> LLMCacheKeyData: + return self.config + + +class LLMCacheValue(CacheValue[LLMCacheValueData]): + def __init__(self, serializer: Serializer = None, **kwargs) -> None: + super().__init__() + self._serializer = serializer + self.value = LLMCacheValueData.from_dict(**kwargs) + + def to_dict(self) -> Dict: + return self.value.to_dict() + + def serialize(self) -> bytes: + return self._serializer.serialize(self) + + def get_value(self) -> LLMCacheValueData: + return self.value + + def __str__(self) -> str: + return f"vaue: {str(self.value)}" + + +class LLMCacheClient(CacheClient[LLMCacheKeyData, LLMCacheValueData]): + def __init__(self, cache_manager: CacheManager) -> None: + super().__init__() + self._cache_manager: CacheManager = cache_manager + + async def get( + self, key: LLMCacheKey, cache_config: Optional[CacheConfig] = None + ) -> Optional[LLMCacheValue]: + return await self._cache_manager.get(key, LLMCacheValue, cache_config) + + async def set( + self, + key: LLMCacheKey, + value: LLMCacheValue, + cache_config: Optional[CacheConfig] = None, + ) -> None: + return await self._cache_manager.set(key, value, cache_config) + + async def exists( + self, key: LLMCacheKey, cache_config: Optional[CacheConfig] = None + ) -> bool: + return await self.get(key, cache_config) is not None + + def new_key(self, **kwargs) -> LLMCacheKey: + return LLMCacheKey(serializer=self._cache_manager.serializer, **kwargs) + + def new_value(self, **kwargs) -> LLMCacheValue: + return LLMCacheValue(serializer=self._cache_manager.serializer, **kwargs) diff --git a/pilot/cache/manager.py b/pilot/cache/manager.py new file mode 100644 index 000000000..0e76df0b3 --- /dev/null +++ b/pilot/cache/manager.py @@ -0,0 +1,126 @@ +from abc import ABC, abstractmethod +from typing import Optional, Type +import logging +from concurrent.futures import Executor +from pilot.cache.storage.base import CacheStorage, StorageItem +from pilot.cache.base import ( + K, + V, + CacheKey, + CacheValue, + CacheConfig, + Serializer, + Serializable, +) +from pilot.component import BaseComponent, ComponentType, SystemApp +from pilot.utils.executor_utils import ExecutorFactory, blocking_func_to_async + +logger = logging.getLogger(__name__) + + +class CacheManager(BaseComponent, ABC): + name = ComponentType.MODEL_CACHE_MANAGER + + def __init__(self, system_app: SystemApp | None = None): + super().__init__(system_app) + + def init_app(self, system_app: SystemApp): + self.system_app = system_app + + @abstractmethod + async def set( + self, + key: CacheKey[K], + value: CacheValue[V], + cache_config: Optional[CacheConfig] = None, + ): + """Set cache""" + + @abstractmethod + async def get( + self, + key: CacheKey[K], + cls: Type[Serializable], + cache_config: Optional[CacheConfig] = None, + ) -> CacheValue[V]: + """Get cache with key""" + + @property + @abstractmethod + def serializer(self) -> Serializer: + """Get cache serializer""" + + +class LocalCacheManager(CacheManager): + def __init__( + self, system_app: SystemApp, serializer: Serializer, storage: CacheStorage + ) -> None: + super().__init__(system_app) + self._serializer = serializer + self._storage = storage + + @property + def executor(self) -> Executor: + """Return executor to submit task""" + self._executor = self.system_app.get_component( + ComponentType.EXECUTOR_DEFAULT, ExecutorFactory + ).create() + + async def set( + self, + key: CacheKey[K], + value: CacheValue[V], + cache_config: Optional[CacheConfig] = None, + ): + if self._storage.support_async(): + await self._storage.aset(key, value, cache_config) + else: + await blocking_func_to_async( + self.executor, self._storage.set, key, value, cache_config + ) + + async def get( + self, + key: CacheKey[K], + cls: Type[Serializable], + cache_config: Optional[CacheConfig] = None, + ) -> CacheValue[V]: + if self._storage.support_async(): + item_bytes = await self._storage.aget(key, cache_config) + else: + item_bytes = await blocking_func_to_async( + self.executor, self._storage.get, key, cache_config + ) + if not item_bytes: + return None + return self._serializer.deserialize(item_bytes.value_data, cls) + + @property + def serializer(self) -> Serializer: + return self._serializer + + +def initialize_cache( + system_app: SystemApp, storage_type: str, max_memory_mb: int, persist_dir: str +): + from pilot.cache.protocal.json_protocal import JsonSerializer + from pilot.cache.storage.base import MemoryCacheStorage + + cache_storage = None + if storage_type == "disk": + try: + from pilot.cache.storage.disk.disk_storage import DiskCacheStorage + + cache_storage = DiskCacheStorage( + persist_dir, mem_table_buffer_mb=max_memory_mb + ) + except ImportError as e: + logger.warn( + f"Can't import DiskCacheStorage, use MemoryCacheStorage, import error message: {str(e)}" + ) + cache_storage = MemoryCacheStorage(max_memory_mb=max_memory_mb) + else: + cache_storage = MemoryCacheStorage(max_memory_mb=max_memory_mb) + system_app.register( + LocalCacheManager, serializer=JsonSerializer(), storage=cache_storage + ) diff --git a/pilot/cache/protocal/__init__.py b/pilot/cache/protocal/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/cache/protocal/json_protocal.py b/pilot/cache/protocal/json_protocal.py new file mode 100644 index 000000000..6f73fef3f --- /dev/null +++ b/pilot/cache/protocal/json_protocal.py @@ -0,0 +1,44 @@ +from abc import ABC, abstractmethod +from typing import Dict, Type +import json + +from pilot.cache.base import Serializable, Serializer + +JSON_ENCODING = "utf-8" + + +class JsonSerializable(Serializable, ABC): + @abstractmethod + def to_dict(self) -> Dict: + """Return the dict of current serializable object""" + + def serialize(self) -> bytes: + """Convert the object into bytes for storage or transmission.""" + return json.dumps(self.to_dict(), ensure_ascii=False).encode(JSON_ENCODING) + + +class JsonSerializer(Serializer): + """The serializer abstract class for serializing cache keys and values.""" + + def serialize(self, obj: Serializable) -> bytes: + """Serialize a cache object. + + Args: + obj (Serializable): The object to serialize + """ + return json.dumps(obj.to_dict(), ensure_ascii=False).encode(JSON_ENCODING) + + def deserialize(self, data: bytes, cls: Type[Serializable]) -> Serializable: + """Deserialize data back into a cache object of the specified type. + + Args: + data (bytes): The byte array to deserialize + cls (Type[Serializable]): The type of current object + + Returns: + Serializable: The serializable object + """ + # Convert bytes back to JSON and then to the specified class + json_data = json.loads(data.decode(JSON_ENCODING)) + # Assume that the cls has an __init__ that accepts a dictionary + return cls(**json_data) diff --git a/pilot/cache/storage/__init__.py b/pilot/cache/storage/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/cache/storage/base.py b/pilot/cache/storage/base.py new file mode 100644 index 000000000..ea07bfacf --- /dev/null +++ b/pilot/cache/storage/base.py @@ -0,0 +1,252 @@ +from abc import ABC, abstractmethod +from typing import Optional +from dataclasses import dataclass +from collections import OrderedDict +import msgpack +import logging + +from pilot.cache.base import ( + K, + V, + CacheKey, + CacheValue, + CacheClient, + CacheConfig, + RetrievalPolicy, + CachePolicy, +) +from pilot.utils.memory_utils import _get_object_bytes + +logger = logging.getLogger(__name__) + + +@dataclass +class StorageItem: + """ + A class representing a storage item. + + This class encapsulates data related to a storage item, such as its length, + the hash of the key, and the data for both the key and value. + + Parameters: + length (int): The bytes length of the storage item. + key_hash (bytes): The hash value of the storage item's key. + key_data (bytes): The data of the storage item's key, represented in bytes. + value_data (bytes): The data of the storage item's value, also in bytes. + """ + + length: int # The bytes length of the storage item + key_hash: bytes # The hash value of the storage item's key + key_data: bytes # The data of the storage item's key + value_data: bytes # The data of the storage item's value + + @staticmethod + def build_from( + key_hash: bytes, key_data: bytes, value_data: bytes + ) -> "StorageItem": + length = ( + 32 + + _get_object_bytes(key_hash) + + _get_object_bytes(key_data) + + _get_object_bytes(value_data) + ) + return StorageItem( + length=length, key_hash=key_hash, key_data=key_data, value_data=value_data + ) + + @staticmethod + def build_from_kv(key: CacheKey[K], value: CacheValue[V]) -> "StorageItem": + key_hash = key.get_hash_bytes() + key_data = key.serialize() + value_data = value.serialize() + return StorageItem.build_from(key_hash, key_data, value_data) + + def serialize(self) -> bytes: + """Serialize the StorageItem into a byte stream using MessagePack. + + This method packs the object data into a dictionary, marking the + key_data and value_data fields as raw binary data to avoid re-serialization. + + Returns: + bytes: The serialized bytes. + """ + obj = { + "length": self.length, + "key_hash": msgpack.ExtType(1, self.key_hash), + "key_data": msgpack.ExtType(2, self.key_data), + "value_data": msgpack.ExtType(3, self.value_data), + } + return msgpack.packb(obj) + + @staticmethod + def deserialize(data: bytes) -> "StorageItem": + """Deserialize bytes back into a StorageItem using MessagePack. + + This extracts the fields from the MessagePack dict back into + a StorageItem object. + + Args: + data (bytes): Serialized bytes + + Returns: + StorageItem: Deserialized StorageItem object. + """ + obj = msgpack.unpackb(data) + key_hash = obj["key_hash"].data + key_data = obj["key_data"].data + value_data = obj["value_data"].data + + return StorageItem( + length=obj["length"], + key_hash=key_hash, + key_data=key_data, + value_data=value_data, + ) + + +class CacheStorage(ABC): + @abstractmethod + def check_config( + self, + cache_config: Optional[CacheConfig] = None, + raise_error: Optional[bool] = True, + ) -> bool: + """Check whether the CacheConfig is legal. + + Args: + cache_config (Optional[CacheConfig]): Cache config. + raise_error (Optional[bool]): Whether raise error if illegal. + + Returns: + ValueError: Error when raise_error is True and config is illegal. + """ + + def support_async(self) -> bool: + return False + + @abstractmethod + def get( + self, key: CacheKey[K], cache_config: Optional[CacheConfig] = None + ) -> Optional[StorageItem]: + """Retrieve a storage item from the cache using the provided key. + + Args: + key (CacheKey[K]): The key to get cache + cache_config (Optional[CacheConfig]): Cache config + + Returns: + Optional[StorageItem]: The storage item retrieved according to key. If cache key not exist, return None. + """ + + async def aget( + self, key: CacheKey[K], cache_config: Optional[CacheConfig] = None + ) -> Optional[StorageItem]: + """Retrieve a storage item from the cache using the provided key asynchronously. + + Args: + key (CacheKey[K]): The key to get cache + cache_config (Optional[CacheConfig]): Cache config + + Returns: + Optional[StorageItem]: The storage item of bytes retrieved according to key. If cache key not exist, return None. + """ + raise NotImplementedError + + @abstractmethod + def set( + self, + key: CacheKey[K], + value: CacheValue[V], + cache_config: Optional[CacheConfig] = None, + ) -> None: + """Set a value in the cache for the provided key asynchronously. + + Args: + key (CacheKey[K]): The key to set to cache + value (CacheValue[V]): The value to set to cache + cache_config (Optional[CacheConfig]): Cache config + """ + + async def aset( + self, + key: CacheKey[K], + value: CacheValue[V], + cache_config: Optional[CacheConfig] = None, + ) -> None: + """Set a value in the cache for the provided key asynchronously. + + Args: + key (CacheKey[K]): The key to set to cache + value (CacheValue[V]): The value to set to cache + cache_config (Optional[CacheConfig]): Cache config + """ + raise NotImplementedError + + +class MemoryCacheStorage(CacheStorage): + def __init__(self, max_memory_mb: int = 256): + self.cache = OrderedDict() + self.max_memory = max_memory_mb * 1024 * 1024 + self.current_memory_usage = 0 + + def check_config( + self, + cache_config: Optional[CacheConfig] = None, + raise_error: Optional[bool] = True, + ) -> bool: + if ( + cache_config + and cache_config.retrieval_policy != RetrievalPolicy.EXACT_MATCH + ): + if raise_error: + raise ValueError( + "MemoryCacheStorage only supports 'EXACT_MATCH' retrieval policy" + ) + return False + return True + + def get( + self, key: CacheKey[K], cache_config: Optional[CacheConfig] = None + ) -> Optional[StorageItem]: + self.check_config(cache_config, raise_error=True) + # Exact match retrieval + key_hash = hash(key) + item: StorageItem = self.cache.get(key_hash) + logger.debug(f"MemoryCacheStorage get key {key}, hash {key_hash}, item: {item}") + + if not item: + return None + # Move the item to the end of the OrderedDict to signify recent use. + self.cache.move_to_end(key_hash) + return item + + def set( + self, + key: CacheKey[K], + value: CacheValue[V], + cache_config: Optional[CacheConfig] = None, + ) -> None: + key_hash = hash(key) + item = StorageItem.build_from_kv(key, value) + # Calculate memory size of the new entry + new_entry_size = _get_object_bytes(item) + # Evict entries if necessary + while self.current_memory_usage + new_entry_size > self.max_memory: + self._apply_cache_policy(cache_config) + + # Store the item in the cache. + self.cache[key_hash] = item + self.current_memory_usage += new_entry_size + logger.debug(f"MemoryCacheStorage set key {key}, hash {key_hash}, item: {item}") + + def exists( + self, key: CacheKey[K], cache_config: Optional[CacheConfig] = None + ) -> bool: + return self.get(key, cache_config) is not None + + def _apply_cache_policy(self, cache_config: Optional[CacheConfig] = None): + # Remove the oldest/newest item based on the cache policy. + if cache_config and cache_config.cache_policy == CachePolicy.FIFO: + self.cache.popitem(last=False) + else: # Default is LRU + self.cache.popitem(last=True) diff --git a/pilot/cache/storage/disk/__init__.py b/pilot/cache/storage/disk/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/cache/storage/disk/disk_storage.py b/pilot/cache/storage/disk/disk_storage.py new file mode 100644 index 000000000..04fb19c6e --- /dev/null +++ b/pilot/cache/storage/disk/disk_storage.py @@ -0,0 +1,93 @@ +from typing import Optional +import logging +from pilot.cache.base import ( + K, + V, + CacheKey, + CacheValue, + CacheConfig, + RetrievalPolicy, + CachePolicy, +) +from pilot.cache.storage.base import StorageItem, CacheStorage +from rocksdict import Rdict +from rocksdict import Rdict, Options, SliceTransform, PlainTableFactoryOptions + + +logger = logging.getLogger(__name__) + + +def db_options( + mem_table_buffer_mb: Optional[int] = 256, background_threads: Optional[int] = 2 +): + opt = Options() + # create table + opt.create_if_missing(True) + # config to more jobs, default 2 + opt.set_max_background_jobs(background_threads) + # configure mem-table to a large value + opt.set_write_buffer_size(mem_table_buffer_mb * 1024 * 1024) + # opt.set_write_buffer_size(1024) + # opt.set_level_zero_file_num_compaction_trigger(4) + # configure l0 and l1 size, let them have the same size (1 GB) + # opt.set_max_bytes_for_level_base(0x40000000) + # 256 MB file size + # opt.set_target_file_size_base(0x10000000) + # use a smaller compaction multiplier + # opt.set_max_bytes_for_level_multiplier(4.0) + # use 8-byte prefix (2 ^ 64 is far enough for transaction counts) + # opt.set_prefix_extractor(SliceTransform.create_max_len_prefix(8)) + # set to plain-table + # opt.set_plain_table_factory(PlainTableFactoryOptions()) + return opt + + +class DiskCacheStorage(CacheStorage): + def __init__( + self, persist_dir: str, mem_table_buffer_mb: Optional[int] = 256 + ) -> None: + super().__init__() + self.db: Rdict = Rdict( + persist_dir, db_options(mem_table_buffer_mb=mem_table_buffer_mb) + ) + + def check_config( + self, + cache_config: Optional[CacheConfig] = None, + raise_error: Optional[bool] = True, + ) -> bool: + if ( + cache_config + and cache_config.retrieval_policy != RetrievalPolicy.EXACT_MATCH + ): + if raise_error: + raise ValueError( + "DiskCacheStorage only supports 'EXACT_MATCH' retrieval policy" + ) + return False + return True + + def get( + self, key: CacheKey[K], cache_config: Optional[CacheConfig] = None + ) -> Optional[StorageItem]: + self.check_config(cache_config, raise_error=True) + + # Exact match retrieval + key_hash = key.get_hash_bytes() + item_bytes = self.db.get(key_hash) + if not item_bytes: + return None + item = StorageItem.deserialize(item_bytes) + logger.debug(f"Read file cache, key: {key}, storage item: {item}") + return item + + def set( + self, + key: CacheKey[K], + value: CacheValue[V], + cache_config: Optional[CacheConfig] = None, + ) -> None: + item = StorageItem.build_from_kv(key, value) + key_hash = item.key_hash + self.db[key_hash] = item.serialize() + logger.debug(f"Save file cache, key: {key}, value: {value}") diff --git a/pilot/cache/storage/tests/__init__.py b/pilot/cache/storage/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/cache/storage/tests/test_storage.py b/pilot/cache/storage/tests/test_storage.py new file mode 100644 index 000000000..489873d08 --- /dev/null +++ b/pilot/cache/storage/tests/test_storage.py @@ -0,0 +1,53 @@ +import pytest +from ..base import StorageItem +from pilot.utils.memory_utils import _get_object_bytes + + +def test_build_from(): + key_hash = b"key_hash" + key_data = b"key_data" + value_data = b"value_data" + item = StorageItem.build_from(key_hash, key_data, value_data) + + assert item.key_hash == key_hash + assert item.key_data == key_data + assert item.value_data == value_data + assert item.length == 32 + _get_object_bytes(key_hash) + _get_object_bytes( + key_data + ) + _get_object_bytes(value_data) + + +def test_build_from_kv(): + class MockCacheKey: + def get_hash_bytes(self): + return b"key_hash" + + def serialize(self): + return b"key_data" + + class MockCacheValue: + def serialize(self): + return b"value_data" + + key = MockCacheKey() + value = MockCacheValue() + item = StorageItem.build_from_kv(key, value) + + assert item.key_hash == key.get_hash_bytes() + assert item.key_data == key.serialize() + assert item.value_data == value.serialize() + + +def test_serialize_deserialize(): + key_hash = b"key_hash" + key_data = b"key_data" + value_data = b"value_data" + item = StorageItem.build_from(key_hash, key_data, value_data) + + serialized = item.serialize() + deserialized = StorageItem.deserialize(serialized) + + assert deserialized.key_hash == item.key_hash + assert deserialized.key_data == item.key_data + assert deserialized.value_data == item.value_data + assert deserialized.length == item.length diff --git a/pilot/common/chat_util.py b/pilot/common/chat_util.py new file mode 100644 index 000000000..6cb8f1b53 --- /dev/null +++ b/pilot/common/chat_util.py @@ -0,0 +1,47 @@ +import asyncio +from typing import Coroutine, List, Any + +from starlette.responses import StreamingResponse + +from pilot.scene.base_chat import BaseChat +from pilot.scene.chat_factory import ChatFactory + +chat_factory = ChatFactory() + + +async def llm_chat_response_nostream(chat_scene: str, **chat_param): + """llm_chat_response_nostream""" + chat: BaseChat = chat_factory.get_implementation(chat_scene, **chat_param) + res = await chat.get_llm_response() + return res + + +async def llm_chat_response(chat_scene: str, **chat_param): + chat: BaseChat = chat_factory.get_implementation(chat_scene, **chat_param) + return chat.stream_call() + + +async def run_async_tasks( + tasks: List[Coroutine], + concurrency_limit: int = None, +) -> List[Any]: + """Run a list of async tasks.""" + tasks_to_execute: List[Any] = tasks + + async def _gather() -> List[Any]: + if concurrency_limit: + semaphore = asyncio.Semaphore(concurrency_limit) + + async def _execute_task(task): + async with semaphore: + return await task + + # Execute tasks with semaphore limit + return await asyncio.gather( + *[_execute_task(task) for task in tasks_to_execute] + ) + else: + return await asyncio.gather(*tasks_to_execute) + + # outputs: List[Any] = asyncio.run(_gather()) + return await _gather() diff --git a/pilot/common/global_helper.py b/pilot/common/global_helper.py new file mode 100644 index 000000000..d189d1356 --- /dev/null +++ b/pilot/common/global_helper.py @@ -0,0 +1,448 @@ +"""General utils functions.""" + +import asyncio +import os +import random +import sys +import time +import traceback +import uuid +from contextlib import contextmanager +from dataclasses import dataclass +from functools import partial, wraps +from itertools import islice +from pathlib import Path +from typing import ( + Any, + AsyncGenerator, + Callable, + Dict, + Generator, + Iterable, + List, + Optional, + Set, + Type, + Union, + cast, +) + + +class GlobalsHelper: + """Helper to retrieve globals. + + Helpful for global caching of certain variables that can be expensive to load. + (e.g. tokenization) + + """ + + _tokenizer: Optional[Callable[[str], List]] = None + _stopwords: Optional[List[str]] = None + + @property + def tokenizer(self) -> Callable[[str], List]: + """Get tokenizer.""" + if self._tokenizer is None: + tiktoken_import_err = ( + "`tiktoken` package not found, please run `pip install tiktoken`" + ) + try: + import tiktoken + except ImportError: + raise ImportError(tiktoken_import_err) + enc = tiktoken.get_encoding("gpt2") + self._tokenizer = cast(Callable[[str], List], enc.encode) + self._tokenizer = partial(self._tokenizer, allowed_special="all") + return self._tokenizer # type: ignore + + @property + def stopwords(self) -> List[str]: + """Get stopwords.""" + if self._stopwords is None: + try: + import nltk + from nltk.corpus import stopwords + except ImportError: + raise ImportError( + "`nltk` package not found, please run `pip install nltk`" + ) + + from llama_index.utils import get_cache_dir + + cache_dir = get_cache_dir() + nltk_data_dir = os.environ.get("NLTK_DATA", cache_dir) + + # update nltk path for nltk so that it finds the data + if nltk_data_dir not in nltk.data.path: + nltk.data.path.append(nltk_data_dir) + + try: + nltk.data.find("corpora/stopwords") + except LookupError: + nltk.download("stopwords", download_dir=nltk_data_dir) + self._stopwords = stopwords.words("english") + return self._stopwords + + +globals_helper = GlobalsHelper() + + +def get_new_id(d: Set) -> str: + """Get a new ID.""" + while True: + new_id = str(uuid.uuid4()) + if new_id not in d: + break + return new_id + + +def get_new_int_id(d: Set) -> int: + """Get a new integer ID.""" + while True: + new_id = random.randint(0, sys.maxsize) + if new_id not in d: + break + return new_id + + +@contextmanager +def temp_set_attrs(obj: Any, **kwargs: Any) -> Generator: + """Temporary setter. + + Utility class for setting a temporary value for an attribute on a class. + Taken from: https://tinyurl.com/2p89xymh + + """ + prev_values = {k: getattr(obj, k) for k in kwargs} + for k, v in kwargs.items(): + setattr(obj, k, v) + try: + yield + finally: + for k, v in prev_values.items(): + setattr(obj, k, v) + + +@dataclass +class ErrorToRetry: + """Exception types that should be retried. + + Args: + exception_cls (Type[Exception]): Class of exception. + check_fn (Optional[Callable[[Any]], bool]]): + A function that takes an exception instance as input and returns + whether to retry. + + """ + + exception_cls: Type[Exception] + check_fn: Optional[Callable[[Any], bool]] = None + + +def retry_on_exceptions_with_backoff( + lambda_fn: Callable, + errors_to_retry: List[ErrorToRetry], + max_tries: int = 10, + min_backoff_secs: float = 0.5, + max_backoff_secs: float = 60.0, +) -> Any: + """Execute lambda function with retries and exponential backoff. + + Args: + lambda_fn (Callable): Function to be called and output we want. + errors_to_retry (List[ErrorToRetry]): List of errors to retry. + At least one needs to be provided. + max_tries (int): Maximum number of tries, including the first. Defaults to 10. + min_backoff_secs (float): Minimum amount of backoff time between attempts. + Defaults to 0.5. + max_backoff_secs (float): Maximum amount of backoff time between attempts. + Defaults to 60. + + """ + if not errors_to_retry: + raise ValueError("At least one error to retry needs to be provided") + + error_checks = { + error_to_retry.exception_cls: error_to_retry.check_fn + for error_to_retry in errors_to_retry + } + exception_class_tuples = tuple(error_checks.keys()) + + backoff_secs = min_backoff_secs + tries = 0 + + while True: + try: + return lambda_fn() + except exception_class_tuples as e: + traceback.print_exc() + tries += 1 + if tries >= max_tries: + raise + check_fn = error_checks.get(e.__class__) + if check_fn and not check_fn(e): + raise + time.sleep(backoff_secs) + backoff_secs = min(backoff_secs * 2, max_backoff_secs) + + +def truncate_text(text: str, max_length: int) -> str: + """Truncate text to a maximum length.""" + if len(text) <= max_length: + return text + return text[: max_length - 3] + "..." + + +def iter_batch(iterable: Union[Iterable, Generator], size: int) -> Iterable: + """Iterate over an iterable in batches. + + >>> list(iter_batch([1,2,3,4,5], 3)) + [[1, 2, 3], [4, 5]] + """ + source_iter = iter(iterable) + while source_iter: + b = list(islice(source_iter, size)) + if len(b) == 0: + break + yield b + + +def concat_dirs(dirname: str, basename: str) -> str: + """ + Append basename to dirname, avoiding backslashes when running on windows. + + os.path.join(dirname, basename) will add a backslash before dirname if + basename does not end with a slash, so we make sure it does. + """ + dirname += "/" if dirname[-1] != "/" else "" + return os.path.join(dirname, basename) + + +def get_tqdm_iterable(items: Iterable, show_progress: bool, desc: str) -> Iterable: + """ + Optionally get a tqdm iterable. Ensures tqdm.auto is used. + """ + _iterator = items + if show_progress: + try: + from tqdm.auto import tqdm + + return tqdm(items, desc=desc) + except ImportError: + pass + return _iterator + + +def count_tokens(text: str) -> int: + tokens = globals_helper.tokenizer(text) + return len(tokens) + + +def get_transformer_tokenizer_fn(model_name: str) -> Callable[[str], List[str]]: + """ + Args: + model_name(str): the model name of the tokenizer. + For instance, fxmarty/tiny-llama-fast-tokenizer. + """ + try: + from transformers import AutoTokenizer + except ImportError: + raise ValueError( + "`transformers` package not found, please run `pip install transformers`" + ) + tokenizer = AutoTokenizer.from_pretrained(model_name) + return tokenizer.tokenize + + +def get_cache_dir() -> str: + """Locate a platform-appropriate cache directory for llama_index, + and create it if it doesn't yet exist. + """ + # User override + if "LLAMA_INDEX_CACHE_DIR" in os.environ: + path = Path(os.environ["LLAMA_INDEX_CACHE_DIR"]) + + # Linux, Unix, AIX, etc. + elif os.name == "posix" and sys.platform != "darwin": + path = Path("/tmp/llama_index") + + # Mac OS + elif sys.platform == "darwin": + path = Path(os.path.expanduser("~"), "Library/Caches/llama_index") + + # Windows (hopefully) + else: + local = os.environ.get("LOCALAPPDATA", None) or os.path.expanduser( + "~\\AppData\\Local" + ) + path = Path(local, "llama_index") + + if not os.path.exists(path): + os.makedirs( + path, exist_ok=True + ) # prevents https://github.com/jerryjliu/llama_index/issues/7362 + return str(path) + + +def add_sync_version(func: Any) -> Any: + """Decorator for adding sync version of an async function. The sync version + is added as a function attribute to the original function, func. + + Args: + func(Any): the async function for which a sync variant will be built. + """ + assert asyncio.iscoroutinefunction(func) + + @wraps(func) + def _wrapper(*args: Any, **kwds: Any) -> Any: + return asyncio.get_event_loop().run_until_complete(func(*args, **kwds)) + + func.sync = _wrapper + return func + + +# Sample text from llama_index's readme +SAMPLE_TEXT = """ +Context +LLMs are a phenomenal piece of technology for knowledge generation and reasoning. +They are pre-trained on large amounts of publicly available data. +How do we best augment LLMs with our own private data? +We need a comprehensive toolkit to help perform this data augmentation for LLMs. + +Proposed Solution +That's where LlamaIndex comes in. LlamaIndex is a "data framework" to help +you build LLM apps. It provides the following tools: + +Offers data connectors to ingest your existing data sources and data formats +(APIs, PDFs, docs, SQL, etc.) +Provides ways to structure your data (indices, graphs) so that this data can be +easily used with LLMs. +Provides an advanced retrieval/query interface over your data: +Feed in any LLM input prompt, get back retrieved context and knowledge-augmented output. +Allows easy integrations with your outer application framework +(e.g. with LangChain, Flask, Docker, ChatGPT, anything else). +LlamaIndex provides tools for both beginner users and advanced users. +Our high-level API allows beginner users to use LlamaIndex to ingest and +query their data in 5 lines of code. Our lower-level APIs allow advanced users to +customize and extend any module (data connectors, indices, retrievers, query engines, +reranking modules), to fit their needs. +""" + +_LLAMA_INDEX_COLORS = { + "llama_pink": "38;2;237;90;200", + "llama_blue": "38;2;90;149;237", + "llama_turquoise": "38;2;11;159;203", + "llama_lavender": "38;2;155;135;227", +} + +_ANSI_COLORS = { + "red": "31", + "green": "32", + "yellow": "33", + "blue": "34", + "magenta": "35", + "cyan": "36", + "pink": "38;5;200", +} + + +def get_color_mapping( + items: List[str], use_llama_index_colors: bool = True +) -> Dict[str, str]: + """ + Get a mapping of items to colors. + + Args: + items (List[str]): List of items to be mapped to colors. + use_llama_index_colors (bool, optional): Flag to indicate + whether to use LlamaIndex colors or ANSI colors. + Defaults to True. + + Returns: + Dict[str, str]: Mapping of items to colors. + """ + if use_llama_index_colors: + color_palette = _LLAMA_INDEX_COLORS + else: + color_palette = _ANSI_COLORS + + colors = list(color_palette.keys()) + return {item: colors[i % len(colors)] for i, item in enumerate(items)} + + +def _get_colored_text(text: str, color: str) -> str: + """ + Get the colored version of the input text. + + Args: + text (str): Input text. + color (str): Color to be applied to the text. + + Returns: + str: Colored version of the input text. + """ + all_colors = {**_LLAMA_INDEX_COLORS, **_ANSI_COLORS} + + if color not in all_colors: + return f"\033[1;3m{text}\033[0m" # just bolded and italicized + + color = all_colors[color] + + return f"\033[1;3;{color}m{text}\033[0m" + + +def print_text(text: str, color: Optional[str] = None, end: str = "") -> None: + """ + Print the text with the specified color. + + Args: + text (str): Text to be printed. + color (str, optional): Color to be applied to the text. Supported colors are: + llama_pink, llama_blue, llama_turquoise, llama_lavender, + red, green, yellow, blue, magenta, cyan, pink. + end (str, optional): String appended after the last character of the text. + + Returns: + None + """ + text_to_print = _get_colored_text(text, color) if color is not None else text + print(text_to_print, end=end) + + +def infer_torch_device() -> str: + """Infer the input to torch.device.""" + try: + has_cuda = torch.cuda.is_available() + except NameError: + import torch + + has_cuda = torch.cuda.is_available() + if has_cuda: + return "cuda" + if torch.backends.mps.is_available(): + return "mps" + return "cpu" + + +def unit_generator(x: Any) -> Generator[Any, None, None]: + """A function that returns a generator of a single element. + + Args: + x (Any): the element to build yield + + Yields: + Any: the single element + """ + yield x + + +async def async_unit_generator(x: Any) -> AsyncGenerator[Any, None]: + """A function that returns a generator of a single element. + + Args: + x (Any): the element to build yield + + Yields: + Any: the single element + """ + yield x diff --git a/pilot/common/json_utils.py b/pilot/common/json_utils.py new file mode 100644 index 000000000..324d20867 --- /dev/null +++ b/pilot/common/json_utils.py @@ -0,0 +1,7 @@ +import json +from datetime import date + + +def serialize(obj): + if isinstance(obj, date): + return obj.isoformat() diff --git a/pilot/common/llm_metadata.py b/pilot/common/llm_metadata.py new file mode 100644 index 000000000..73d5bc0fa --- /dev/null +++ b/pilot/common/llm_metadata.py @@ -0,0 +1,43 @@ +from pydantic import Field, BaseModel + +DEFAULT_CONTEXT_WINDOW = 3900 +DEFAULT_NUM_OUTPUTS = 256 + + +class LLMMetadata(BaseModel): + context_window: int = Field( + default=DEFAULT_CONTEXT_WINDOW, + description=( + "Total number of tokens the model can be input and output for one response." + ), + ) + num_output: int = Field( + default=DEFAULT_NUM_OUTPUTS, + description="Number of tokens the model can output when generating a response.", + ) + is_chat_model: bool = Field( + default=False, + description=( + "Set True if the model exposes a chat interface (i.e. can be passed a" + " sequence of messages, rather than text), like OpenAI's" + " /v1/chat/completions endpoint." + ), + ) + is_function_calling_model: bool = Field( + default=False, + # SEE: https://openai.com/blog/function-calling-and-other-api-updates + description=( + "Set True if the model supports function calling messages, similar to" + " OpenAI's function calling API. For example, converting 'Email Anya to" + " see if she wants to get coffee next Friday' to a function call like" + " `send_email(to: string, body: string)`." + ), + ) + model_name: str = Field( + default="unknown", + description=( + "The model's name used for logging, testing, and sanity checking. For some" + " models this can be automatically discerned. For other models, like" + " locally loaded models, this must be manually specified." + ), + ) diff --git a/pilot/common/prompt_util.py b/pilot/common/prompt_util.py new file mode 100644 index 000000000..3854d130b --- /dev/null +++ b/pilot/common/prompt_util.py @@ -0,0 +1,239 @@ +"""General prompt helper that can help deal with LLM context window token limitations. + +At its core, it calculates available context size by starting with the context window +size of an LLM and reserve token space for the prompt template, and the output. + +It provides utility for "repacking" text chunks (retrieved from index) to maximally +make use of the available context window (and thereby reducing the number of LLM calls +needed), or truncating them so that they fit in a single LLM call. +""" + +import logging +from string import Formatter +from typing import Callable, List, Optional, Sequence + +from pydantic import Field, PrivateAttr, BaseModel + +from pilot.common.global_helper import globals_helper +from pilot.common.llm_metadata import LLMMetadata +from pilot.embedding_engine.loader.token_splitter import TokenTextSplitter + +DEFAULT_PADDING = 5 +DEFAULT_CHUNK_OVERLAP_RATIO = 0.1 + +DEFAULT_CONTEXT_WINDOW = 3000 # tokens +DEFAULT_NUM_OUTPUTS = 256 # tokens + +logger = logging.getLogger(__name__) + + +class PromptHelper(BaseModel): + """Prompt helper. + + General prompt helper that can help deal with LLM context window token limitations. + + At its core, it calculates available context size by starting with the context + window size of an LLM and reserve token space for the prompt template, and the + output. + + It provides utility for "repacking" text chunks (retrieved from index) to maximally + make use of the available context window (and thereby reducing the number of LLM + calls needed), or truncating them so that they fit in a single LLM call. + + Args: + context_window (int): Context window for the LLM. + num_output (int): Number of outputs for the LLM. + chunk_overlap_ratio (float): Chunk overlap as a ratio of chunk size + chunk_size_limit (Optional[int]): Maximum chunk size to use. + tokenizer (Optional[Callable[[str], List]]): Tokenizer to use. + separator (str): Separator for text splitter + + """ + + context_window: int = Field( + default=DEFAULT_CONTEXT_WINDOW, + description="The maximum context size that will get sent to the LLM.", + ) + num_output: int = Field( + default=DEFAULT_NUM_OUTPUTS, + description="The amount of token-space to leave in input for generation.", + ) + chunk_overlap_ratio: float = Field( + default=DEFAULT_CHUNK_OVERLAP_RATIO, + description="The percentage token amount that each chunk should overlap.", + ) + chunk_size_limit: Optional[int] = Field(description="The maximum size of a chunk.") + separator: str = Field( + default=" ", description="The separator when chunking tokens." + ) + + _tokenizer: Callable[[str], List] = PrivateAttr() + + def __init__( + self, + context_window: int = DEFAULT_CONTEXT_WINDOW, + num_output: int = DEFAULT_NUM_OUTPUTS, + chunk_overlap_ratio: float = DEFAULT_CHUNK_OVERLAP_RATIO, + chunk_size_limit: Optional[int] = None, + tokenizer: Optional[Callable[[str], List]] = None, + separator: str = " ", + ) -> None: + """Init params.""" + if chunk_overlap_ratio > 1.0 or chunk_overlap_ratio < 0.0: + raise ValueError("chunk_overlap_ratio must be a float between 0. and 1.") + + # TODO: make configurable + self._tokenizer = tokenizer or globals_helper.tokenizer + + super().__init__( + context_window=context_window, + num_output=num_output, + chunk_overlap_ratio=chunk_overlap_ratio, + chunk_size_limit=chunk_size_limit, + separator=separator, + ) + + @classmethod + def from_llm_metadata( + cls, + llm_metadata: LLMMetadata, + chunk_overlap_ratio: float = DEFAULT_CHUNK_OVERLAP_RATIO, + chunk_size_limit: Optional[int] = None, + tokenizer: Optional[Callable[[str], List]] = None, + separator: str = " ", + ) -> "PromptHelper": + """Create from llm predictor. + + This will autofill values like context_window and num_output. + + """ + context_window = llm_metadata.context_window + if llm_metadata.num_output == -1: + num_output = DEFAULT_NUM_OUTPUTS + else: + num_output = llm_metadata.num_output + + return cls( + context_window=context_window, + num_output=num_output, + chunk_overlap_ratio=chunk_overlap_ratio, + chunk_size_limit=chunk_size_limit, + tokenizer=tokenizer, + separator=separator, + ) + + @classmethod + def class_name(cls) -> str: + return "PromptHelper" + + def _get_available_context_size(self, template: str) -> int: + """Get available context size. + + This is calculated as: + available context window = total context window + - input (partially filled prompt) + - output (room reserved for response) + + Notes: + - Available context size is further clamped to be non-negative. + """ + empty_prompt_txt = get_empty_prompt_txt(template) + num_empty_prompt_tokens = len(self._tokenizer(empty_prompt_txt)) + context_size_tokens = ( + self.context_window - num_empty_prompt_tokens - self.num_output + ) + if context_size_tokens < 0: + raise ValueError( + f"Calculated available context size {context_size_tokens} was" + " not non-negative." + ) + return context_size_tokens + + def _get_available_chunk_size( + self, prompt_template: str, num_chunks: int = 1, padding: int = 5 + ) -> int: + """Get available chunk size. + + This is calculated as: + available chunk size = available context window // number_chunks + - padding + + Notes: + - By default, we use padding of 5 (to save space for formatting needs). + - Available chunk size is further clamped to chunk_size_limit if specified. + """ + available_context_size = self._get_available_context_size(prompt_template) + result = available_context_size // num_chunks - padding + if self.chunk_size_limit is not None: + result = min(result, self.chunk_size_limit) + return result + + def get_text_splitter_given_prompt( + self, + prompt_template: str, + num_chunks: int = 1, + padding: int = DEFAULT_PADDING, + ) -> TokenTextSplitter: + """Get text splitter configured to maximally pack available context window, + taking into account of given prompt, and desired number of chunks. + """ + chunk_size = self._get_available_chunk_size( + prompt_template, num_chunks, padding=padding + ) + if chunk_size <= 0: + raise ValueError(f"Chunk size {chunk_size} is not positive.") + chunk_overlap = int(self.chunk_overlap_ratio * chunk_size) + return TokenTextSplitter( + separator=self.separator, + chunk_size=chunk_size, + chunk_overlap=chunk_overlap, + tokenizer=self._tokenizer, + ) + + def repack( + self, + prompt_template: str, + text_chunks: Sequence[str], + padding: int = DEFAULT_PADDING, + ) -> List[str]: + """Repack text chunks to fit available context window. + + This will combine text chunks into consolidated chunks + that more fully "pack" the prompt template given the context_window. + + """ + text_splitter = self.get_text_splitter_given_prompt( + prompt_template, padding=padding + ) + combined_str = "\n\n".join([c.strip() for c in text_chunks if c.strip()]) + return text_splitter.split_text(combined_str) + + +def get_empty_prompt_txt(template: str) -> str: + """Get empty prompt text. + + Substitute empty strings in parts of the prompt that have + not yet been filled out. Skip variables that have already + been partially formatted. This is used to compute the initial tokens. + + """ + # partial_kargs = prompt.kwargs + + partial_kargs = {} + template_vars = get_template_vars(template) + empty_kwargs = {v: "" for v in template_vars if v not in partial_kargs} + all_kwargs = {**partial_kargs, **empty_kwargs} + prompt = template.format(**all_kwargs) + return prompt + + +def get_template_vars(template_str: str) -> List[str]: + """Get template variables from a template string.""" + variables = [] + formatter = Formatter() + + for _, variable_name, _, _ in formatter.parse(template_str): + if variable_name: + variables.append(variable_name) + + return variables diff --git a/pilot/component.py b/pilot/component.py index 8f8c8c5a4..891ba7ad9 100644 --- a/pilot/component.py +++ b/pilot/component.py @@ -46,10 +46,16 @@ class ComponentType(str, Enum): WORKER_MANAGER = "dbgpt_worker_manager" WORKER_MANAGER_FACTORY = "dbgpt_worker_manager_factory" MODEL_CONTROLLER = "dbgpt_model_controller" + MODEL_REGISTRY = "dbgpt_model_registry" + MODEL_API_SERVER = "dbgpt_model_api_server" + MODEL_CACHE_MANAGER = "dbgpt_model_cache_manager" AGENT_HUB = "dbgpt_agent_hub" EXECUTOR_DEFAULT = "dbgpt_thread_pool_default" TRACER = "dbgpt_tracer" TRACER_SPAN_STORAGE = "dbgpt_tracer_span_storage" + RAG_GRAPH_DEFAULT = "dbgpt_rag_engine_default" + AWEL_TRIGGER_MANAGER = "dbgpt_awel_trigger_manager" + AWEL_DAG_MANAGER = "dbgpt_awel_dag_manager" class BaseComponent(LifeCycle, ABC): @@ -68,7 +74,6 @@ class BaseComponent(LifeCycle, ABC): This method needs to be implemented by every component to define how it integrates with the main system app. """ - pass T = TypeVar("T", bound=BaseComponent) @@ -90,13 +95,28 @@ class SystemApp(LifeCycle): """Returns the internal ASGI app.""" return self._asgi_app - def register(self, component: Type[BaseComponent], *args, **kwargs): - """Register a new component by its type.""" + def register(self, component: Type[BaseComponent], *args, **kwargs) -> T: + """Register a new component by its type. + + Args: + component (Type[BaseComponent]): The component class to register + + Returns: + T: The instance of registered component + """ instance = component(self, *args, **kwargs) self.register_instance(instance) + return instance - def register_instance(self, instance: T): - """Register an already initialized component.""" + def register_instance(self, instance: T) -> T: + """Register an already initialized component. + + Args: + instance (T): The component instance to register + + Returns: + T: The instance of registered component + """ name = instance.name if isinstance(name, ComponentType): name = name.value @@ -107,18 +127,34 @@ class SystemApp(LifeCycle): logger.info(f"Register component with name {name} and instance: {instance}") self.components[name] = instance instance.init_app(self) + return instance def get_component( self, name: Union[str, ComponentType], component_type: Type[T], default_component=_EMPTY_DEFAULT_COMPONENT, + or_register_component: Type[BaseComponent] = None, + *args, + **kwargs, ) -> T: - """Retrieve a registered component by its name and type.""" + """Retrieve a registered component by its name and type. + + Args: + name (Union[str, ComponentType]): Component name + component_type (Type[T]): The type of current retrieve component + default_component : The default component instance if not retrieve by name + or_register_component (Type[BaseComponent]): The new component to register if not retrieve by name + + Returns: + T: The instance retrieved by component name + """ if isinstance(name, ComponentType): name = name.value component = self.components.get(name) if not component: + if or_register_component: + return self.register(or_register_component, *args, **kwargs) if default_component != _EMPTY_DEFAULT_COMPONENT: return default_component raise ValueError(f"No component found with name {name}") diff --git a/pilot/configs/config.py b/pilot/configs/config.py index 680f9f856..f93cd7b83 100644 --- a/pilot/configs/config.py +++ b/pilot/configs/config.py @@ -184,7 +184,7 @@ class Config(metaclass=Singleton): if self.LOCAL_DB_HOST is None and self.LOCAL_DB_PATH == "": self.LOCAL_DB_HOST = "127.0.0.1" - self.LOCAL_DB_NAME = os.getenv("LOCAL_DB_NAME") + self.LOCAL_DB_NAME = os.getenv("LOCAL_DB_NAME", "dbgpt") self.LOCAL_DB_PORT = int(os.getenv("LOCAL_DB_PORT", 3306)) self.LOCAL_DB_USER = os.getenv("LOCAL_DB_USER", "root") self.LOCAL_DB_PASSWORD = os.getenv("LOCAL_DB_PASSWORD", "aa123456") @@ -194,6 +194,8 @@ class Config(metaclass=Singleton): ### LLM Model Service Configuration self.LLM_MODEL = os.getenv("LLM_MODEL", "vicuna-13b-v1.5") + self.LLM_MODEL_PATH = os.getenv("LLM_MODEL_PATH") + ### Proxy llm backend, this configuration is only valid when "LLM_MODEL=proxyllm" ### When we use the rest API provided by deployment frameworks like fastchat as a proxyllm, "PROXYLLM_BACKEND" is the model they actually deploy. ### We need to use "PROXYLLM_BACKEND" to load the prompt of the corresponding scene. @@ -251,6 +253,19 @@ class Config(metaclass=Singleton): ### Temporary configuration self.USE_FASTCHAT: bool = os.getenv("USE_FASTCHAT", "True").lower() == "true" + self.MODEL_CACHE_ENABLE: bool = ( + os.getenv("MODEL_CACHE_ENABLE", "True").lower() == "true" + ) + self.MODEL_CACHE_STORAGE_TYPE: str = os.getenv( + "MODEL_CACHE_STORAGE_TYPE", "disk" + ) + self.MODEL_CACHE_MAX_MEMORY_MB: int = int( + os.getenv("MODEL_CACHE_MAX_MEMORY_MB", 256) + ) + self.MODEL_CACHE_STORAGE_DISK_DIR: str = os.getenv( + "MODEL_CACHE_STORAGE_DISK_DIR" + ) + def set_debug_mode(self, value: bool) -> None: """Set the debug mode value""" self.debug_mode = value diff --git a/pilot/configs/model_config.py b/pilot/configs/model_config.py index f05d62f05..890d380f9 100644 --- a/pilot/configs/model_config.py +++ b/pilot/configs/model_config.py @@ -2,6 +2,7 @@ # -*- coding:utf-8 -*- import os +from functools import cache ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) MODEL_PATH = os.path.join(ROOT_PATH, "models") @@ -14,6 +15,8 @@ DATA_DIR = os.path.join(PILOT_PATH, "data") # nltk.data.path = [os.path.join(PILOT_PATH, "nltk_data")] + nltk.data.path PLUGINS_DIR = os.path.join(ROOT_PATH, "plugins") FONT_DIR = os.path.join(PILOT_PATH, "fonts") +MODEL_DISK_CACHE_DIR = os.path.join(DATA_DIR, "model_cache") +_DAG_DEFINITION_DIR = os.path.join(ROOT_PATH, "examples/awel") current_directory = os.getcwd() @@ -21,6 +24,7 @@ new_directory = PILOT_PATH os.chdir(new_directory) +@cache def get_device() -> str: try: import torch @@ -49,6 +53,8 @@ LLM_MODEL_CONFIG = { "chatglm-6b": os.path.join(MODEL_PATH, "chatglm-6b"), "chatglm2-6b": os.path.join(MODEL_PATH, "chatglm2-6b"), "chatglm2-6b-int4": os.path.join(MODEL_PATH, "chatglm2-6b-int4"), + # https://huggingface.co/THUDM/chatglm3-6b + "chatglm3-6b": os.path.join(MODEL_PATH, "chatglm3-6b"), "guanaco-33b-merged": os.path.join(MODEL_PATH, "guanaco-33b-merged"), "falcon-40b": os.path.join(MODEL_PATH, "falcon-40b"), "gorilla-7b": os.path.join(MODEL_PATH, "gorilla-7b"), @@ -70,6 +76,18 @@ LLM_MODEL_CONFIG = { "baichuan-7b": os.path.join(MODEL_PATH, "baichuan-7b"), "baichuan2-7b": os.path.join(MODEL_PATH, "Baichuan2-7B-Chat"), "baichuan2-13b": os.path.join(MODEL_PATH, "Baichuan2-13B-Chat"), + # https://huggingface.co/Qwen/Qwen-7B-Chat + "qwen-7b-chat": os.path.join(MODEL_PATH, "Qwen-7B-Chat"), + # https://huggingface.co/Qwen/Qwen-7B-Chat-Int8 + "qwen-7b-chat-int8": os.path.join(MODEL_PATH, "Qwen-7B-Chat-Int8"), + # https://huggingface.co/Qwen/Qwen-7B-Chat-Int4 + "qwen-7b-chat-int4": os.path.join(MODEL_PATH, "Qwen-7B-Chat-Int4"), + # https://huggingface.co/Qwen/Qwen-14B-Chat + "qwen-14b-chat": os.path.join(MODEL_PATH, "Qwen-14B-Chat"), + # https://huggingface.co/Qwen/Qwen-14B-Chat-Int8 + "qwen-14b-chat-int8": os.path.join(MODEL_PATH, "Qwen-14B-Chat-Int8"), + # https://huggingface.co/Qwen/Qwen-14B-Chat-Int4 + "qwen-14b-chat-int4": os.path.join(MODEL_PATH, "Qwen-14B-Chat-Int4"), # (Llama2 based) We only support WizardLM-13B-V1.2 for now, which is trained from Llama-2 13b, see https://huggingface.co/WizardLM/WizardLM-13B-V1.2 "wizardlm-13b": os.path.join(MODEL_PATH, "WizardLM-13B-V1.2"), # wget https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF/resolve/main/vicuna-13b-v1.5.Q4_K_M.gguf -O models/ggml-model-q4_0.gguf @@ -77,9 +95,40 @@ LLM_MODEL_CONFIG = { # https://huggingface.co/internlm/internlm-chat-7b-v1_1, 7b vs 7b-v1.1: https://github.com/InternLM/InternLM/issues/288 "internlm-7b": os.path.join(MODEL_PATH, "internlm-chat-7b"), "internlm-7b-8k": os.path.join(MODEL_PATH, "internlm-chat-7b-8k"), - "internlm-20b": os.path.join(MODEL_PATH, "internlm-20b-chat"), + "internlm-20b": os.path.join(MODEL_PATH, "internlm-chat-20b"), + "codellama-7b": os.path.join(MODEL_PATH, "CodeLlama-7b-Instruct-hf"), + "codellama-7b-sql-sft": os.path.join(MODEL_PATH, "codellama-7b-sql-sft"), + "codellama-13b": os.path.join(MODEL_PATH, "CodeLlama-13b-Instruct-hf"), + "codellama-13b-sql-sft": os.path.join(MODEL_PATH, "codellama-13b-sql-sft"), # For test now "opt-125m": os.path.join(MODEL_PATH, "opt-125m"), + # https://huggingface.co/microsoft/Orca-2-7b + "orca-2-7b": os.path.join(MODEL_PATH, "Orca-2-7b"), + # https://huggingface.co/microsoft/Orca-2-13b + "orca-2-13b": os.path.join(MODEL_PATH, "Orca-2-13b"), + # https://huggingface.co/openchat/openchat_3.5 + "openchat_3.5": os.path.join(MODEL_PATH, "openchat_3.5"), + # https://huggingface.co/hfl/chinese-alpaca-2-7b + "chinese-alpaca-2-7b": os.path.join(MODEL_PATH, "chinese-alpaca-2-7b"), + # https://huggingface.co/hfl/chinese-alpaca-2-13b + "chinese-alpaca-2-13b": os.path.join(MODEL_PATH, "chinese-alpaca-2-13b"), + # https://huggingface.co/THUDM/codegeex2-6b + "codegeex2-6b": os.path.join(MODEL_PATH, "codegeex2-6b"), + # https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha + "zephyr-7b-alpha": os.path.join(MODEL_PATH, "zephyr-7b-alpha"), + # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 + "mistral-7b-instruct-v0.1": os.path.join(MODEL_PATH, "Mistral-7B-Instruct-v0.1"), + # https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca + "mistral-7b-openorca": os.path.join(MODEL_PATH, "Mistral-7B-OpenOrca"), + # https://huggingface.co/Xwin-LM/Xwin-LM-7B-V0.1 + "xwin-lm-7b-v0.1": os.path.join(MODEL_PATH, "Xwin-LM-7B-V0.1"), + # https://huggingface.co/Xwin-LM/Xwin-LM-13B-V0.1 + "xwin-lm-13b-v0.1": os.path.join(MODEL_PATH, "Xwin-LM-13B-V0.1"), + # https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1 + "xwin-lm-70b-v0.1": os.path.join(MODEL_PATH, "Xwin-LM-70B-V0.1"), + # https://huggingface.co/01-ai/Yi-34B-Chat + "yi-34b-chat": os.path.join(MODEL_PATH, "Yi-34B-Chat"), + "yi-6b-chat": os.path.join(MODEL_PATH, "Yi-6B-Chat"), } EMBEDDING_MODEL_CONFIG = { diff --git a/pilot/connections/manages/connect_config_db.py b/pilot/connections/manages/connect_config_db.py index f3c7cc3ae..d4a9bf2a5 100644 --- a/pilot/connections/manages/connect_config_db.py +++ b/pilot/connections/manages/connect_config_db.py @@ -1,9 +1,15 @@ -from pilot.base_modules.meta_data.base_dao import BaseDao -from pilot.base_modules.meta_data.meta_data import Base, engine, session from typing import List from sqlalchemy import Column, Integer, String, Index, DateTime, func, Boolean, Text from sqlalchemy import UniqueConstraint +from pilot.base_modules.meta_data.base_dao import BaseDao +from pilot.base_modules.meta_data.meta_data import ( + Base, + engine, + session, + META_DATA_DATABASE, +) + class ConnectConfigEntity(Base): __tablename__ = "connect_config" @@ -29,7 +35,10 @@ class ConnectConfigEntity(Base): class ConnectConfigDao(BaseDao[ConnectConfigEntity]): def __init__(self): super().__init__( - database="dbgpt", orm_base=Base, db_engine=engine, session=session + database=META_DATA_DATABASE, + orm_base=Base, + db_engine=engine, + session=session, ) def update(self, entity: ConnectConfigEntity): diff --git a/pilot/connections/rdbms/conn_clickhouse.py b/pilot/connections/rdbms/conn_clickhouse.py index 0433b4b76..b2762556a 100644 --- a/pilot/connections/rdbms/conn_clickhouse.py +++ b/pilot/connections/rdbms/conn_clickhouse.py @@ -106,3 +106,13 @@ class ClickhouseConnect(RDBMSDatabase): return [ (table_comment[0], table_comment[1]) for table_comment in table_comments ] + + def table_simple_info(self): + # group_concat() not supported in clickhouse, use arrayStringConcat+groupArray instead; and quotes need to be escaped + _sql = f""" + select concat(TABLE_NAME, \'(\' , arrayStringConcat(groupArray(column_name),\'-\'), \')\') as schema_info + from information_schema.COLUMNS where table_schema=\'{self.get_current_db_name()}\' group by TABLE_NAME; """ + + cursor = self.session.execute(text(_sql)) + results = cursor.fetchall() + return results diff --git a/pilot/datasets/mysql/url.md b/pilot/datasets/mysql/url.md deleted file mode 100644 index 20592cb72..000000000 --- a/pilot/datasets/mysql/url.md +++ /dev/null @@ -1 +0,0 @@ -LlamaIndex是一个数据框架，旨在帮助您构建LLM应用程序。它包括一个向量存储索引和一个简单的目录阅读器，可以帮助您处理和操作数据。此外，LlamaIndex还提供了一个GPT Index，可以用于数据增强和生成更好的LM模型。 \ No newline at end of file diff --git a/pilot/embedding_engine/knowledge_type.py b/pilot/embedding_engine/knowledge_type.py index 77fb98666..c4c278be4 100644 --- a/pilot/embedding_engine/knowledge_type.py +++ b/pilot/embedding_engine/knowledge_type.py @@ -41,7 +41,11 @@ class KnowledgeType(Enum): def get_knowledge_embedding( - knowledge_type, knowledge_source, vector_store_config, source_reader, text_splitter + knowledge_type, + knowledge_source, + vector_store_config=None, + source_reader=None, + text_splitter=None, ): match knowledge_type: case KnowledgeType.DOCUMENT.value: diff --git a/pilot/embedding_engine/loader/splitter_utils.py b/pilot/embedding_engine/loader/splitter_utils.py new file mode 100644 index 000000000..9c57f2111 --- /dev/null +++ b/pilot/embedding_engine/loader/splitter_utils.py @@ -0,0 +1,81 @@ +from typing import Callable, List + + +def split_text_keep_separator(text: str, separator: str) -> List[str]: + """Split text with separator and keep the separator at the end of each split.""" + parts = text.split(separator) + result = [separator + s if i > 0 else s for i, s in enumerate(parts)] + return [s for s in result if s] + + +def split_by_sep(sep: str, keep_sep: bool = True) -> Callable[[str], List[str]]: + """Split text by separator.""" + if keep_sep: + return lambda text: split_text_keep_separator(text, sep) + else: + return lambda text: text.split(sep) + + +def split_by_char() -> Callable[[str], List[str]]: + """Split text by character.""" + return lambda text: list(text) + + +def split_by_sentence_tokenizer() -> Callable[[str], List[str]]: + import os + + import nltk + + from llama_index.utils import get_cache_dir + + cache_dir = get_cache_dir() + nltk_data_dir = os.environ.get("NLTK_DATA", cache_dir) + + # update nltk path for nltk so that it finds the data + if nltk_data_dir not in nltk.data.path: + nltk.data.path.append(nltk_data_dir) + + try: + nltk.data.find("tokenizers/punkt") + except LookupError: + nltk.download("punkt", download_dir=nltk_data_dir) + + tokenizer = nltk.tokenize.PunktSentenceTokenizer() + + # get the spans and then return the sentences + # using the start index of each span + # instead of using end, use the start of the next span if available + def split(text: str) -> List[str]: + spans = list(tokenizer.span_tokenize(text)) + sentences = [] + for i, span in enumerate(spans): + start = span[0] + if i < len(spans) - 1: + end = spans[i + 1][0] + else: + end = len(text) + sentences.append(text[start:end]) + + return sentences + + return split + + +def split_by_regex(regex: str) -> Callable[[str], List[str]]: + """Split text by regex.""" + import re + + return lambda text: re.findall(regex, text) + + +def split_by_phrase_regex() -> Callable[[str], List[str]]: + """Split text by phrase regex. + + This regular expression will split the sentences into phrases, + where each phrase is a sequence of one or more non-comma, + non-period, and non-semicolon characters, followed by an optional comma, + period, or semicolon. The regular expression will also capture the + delimiters themselves as separate items in the list of phrases. + """ + regex = "[^,.;。]+[,.;。]?" + return split_by_regex(regex) diff --git a/pilot/embedding_engine/loader/token_splitter.py b/pilot/embedding_engine/loader/token_splitter.py new file mode 100644 index 000000000..358d51790 --- /dev/null +++ b/pilot/embedding_engine/loader/token_splitter.py @@ -0,0 +1,184 @@ +"""Token splitter.""" +from typing import Callable, List, Optional + +from pydantic import Field, PrivateAttr, BaseModel + + +from pilot.common.global_helper import globals_helper +from pilot.embedding_engine.loader.splitter_utils import split_by_sep, split_by_char + +DEFAULT_METADATA_FORMAT_LEN = 2 +DEFAULT_CHUNK_OVERLAP = 20 +DEFAULT_CHUNK_SIZE = 1024 + + +class TokenTextSplitter(BaseModel): + """Implementation of splitting text that looks at word tokens.""" + + chunk_size: int = Field( + default=DEFAULT_CHUNK_SIZE, description="The token chunk size for each chunk." + ) + chunk_overlap: int = Field( + default=DEFAULT_CHUNK_OVERLAP, + description="The token overlap of each chunk when splitting.", + ) + separator: str = Field( + default=" ", description="Default separator for splitting into words" + ) + backup_separators: List = Field( + default_factory=list, description="Additional separators for splitting." + ) + # callback_manager: CallbackManager = Field( + # default_factory=CallbackManager, exclude=True + # ) + tokenizer: Callable = Field( + default_factory=globals_helper.tokenizer, # type: ignore + description="Tokenizer for splitting words into tokens.", + exclude=True, + ) + + _split_fns: List[Callable] = PrivateAttr() + + def __init__( + self, + chunk_size: int = DEFAULT_CHUNK_SIZE, + chunk_overlap: int = DEFAULT_CHUNK_OVERLAP, + tokenizer: Optional[Callable] = None, + # callback_manager: Optional[CallbackManager] = None, + separator: str = " ", + backup_separators: Optional[List[str]] = ["\n"], + ): + """Initialize with parameters.""" + if chunk_overlap > chunk_size: + raise ValueError( + f"Got a larger chunk overlap ({chunk_overlap}) than chunk size " + f"({chunk_size}), should be smaller." + ) + # callback_manager = callback_manager or CallbackManager([]) + tokenizer = tokenizer or globals_helper.tokenizer + + all_seps = [separator] + (backup_separators or []) + self._split_fns = [split_by_sep(sep) for sep in all_seps] + [split_by_char()] + + super().__init__( + chunk_size=chunk_size, + chunk_overlap=chunk_overlap, + separator=separator, + backup_separators=backup_separators, + # callback_manager=callback_manager, + tokenizer=tokenizer, + ) + + @classmethod + def class_name(cls) -> str: + return "TokenTextSplitter" + + def split_text_metadata_aware(self, text: str, metadata_str: str) -> List[str]: + """Split text into chunks, reserving space required for metadata str.""" + metadata_len = len(self.tokenizer(metadata_str)) + DEFAULT_METADATA_FORMAT_LEN + effective_chunk_size = self.chunk_size - metadata_len + if effective_chunk_size <= 0: + raise ValueError( + f"Metadata length ({metadata_len}) is longer than chunk size " + f"({self.chunk_size}). Consider increasing the chunk size or " + "decreasing the size of your metadata to avoid this." + ) + elif effective_chunk_size < 50: + print( + f"Metadata length ({metadata_len}) is close to chunk size " + f"({self.chunk_size}). Resulting chunks are less than 50 tokens. " + "Consider increasing the chunk size or decreasing the size of " + "your metadata to avoid this.", + flush=True, + ) + + return self._split_text(text, chunk_size=effective_chunk_size) + + def split_text(self, text: str) -> List[str]: + """Split text into chunks.""" + return self._split_text(text, chunk_size=self.chunk_size) + + def _split_text(self, text: str, chunk_size: int) -> List[str]: + """Split text into chunks up to chunk_size.""" + if text == "": + return [] + + splits = self._split(text, chunk_size) + chunks = self._merge(splits, chunk_size) + return chunks + + def _split(self, text: str, chunk_size: int) -> List[str]: + """Break text into splits that are smaller than chunk size. + + The order of splitting is: + 1. split by separator + 2. split by backup separators (if any) + 3. split by characters + + NOTE: the splits contain the separators. + """ + if len(self.tokenizer(text)) <= chunk_size: + return [text] + + for split_fn in self._split_fns: + splits = split_fn(text) + if len(splits) > 1: + break + + new_splits = [] + for split in splits: + split_len = len(self.tokenizer(split)) + if split_len <= chunk_size: + new_splits.append(split) + else: + # recursively split + new_splits.extend(self._split(split, chunk_size=chunk_size)) + return new_splits + + def _merge(self, splits: List[str], chunk_size: int) -> List[str]: + """Merge splits into chunks. + + The high-level idea is to keep adding splits to a chunk until we + exceed the chunk size, then we start a new chunk with overlap. + + When we start a new chunk, we pop off the first element of the previous + chunk until the total length is less than the chunk size. + """ + chunks: List[str] = [] + + cur_chunk: List[str] = [] + cur_len = 0 + for split in splits: + split_len = len(self.tokenizer(split)) + if split_len > chunk_size: + print( + f"Got a split of size {split_len}, ", + f"larger than chunk size {chunk_size}.", + ) + + # if we exceed the chunk size after adding the new split, then + # we need to end the current chunk and start a new one + if cur_len + split_len > chunk_size: + # end the previous chunk + chunk = "".join(cur_chunk).strip() + if chunk: + chunks.append(chunk) + + # start a new chunk with overlap + # keep popping off the first element of the previous chunk until: + # 1. the current chunk length is less than chunk overlap + # 2. the total length is less than chunk size + while cur_len > self.chunk_overlap or cur_len + split_len > chunk_size: + # pop off the first element + first_chunk = cur_chunk.pop(0) + cur_len -= len(self.tokenizer(first_chunk)) + + cur_chunk.append(split) + cur_len += split_len + + # handle the last chunk + chunk = "".join(cur_chunk).strip() + if chunk: + chunks.append(chunk) + + return chunks diff --git a/pilot/embedding_engine/source_embedding.py b/pilot/embedding_engine/source_embedding.py index 950c84ded..311ddd8b4 100644 --- a/pilot/embedding_engine/source_embedding.py +++ b/pilot/embedding_engine/source_embedding.py @@ -38,11 +38,11 @@ class SourceEmbedding(ABC): - embedding_args: Optional """ self.file_path = file_path - self.vector_store_config = vector_store_config + self.vector_store_config = vector_store_config or {} self.source_reader = source_reader or None self.text_splitter = text_splitter or None self.embedding_args = embedding_args - self.embeddings = vector_store_config["embeddings"] + self.embeddings = self.vector_store_config.get("embeddings", None) @abstractmethod @register diff --git a/pilot/embedding_engine/url_embedding.py b/pilot/embedding_engine/url_embedding.py index 71842c2f1..2269e28ea 100644 --- a/pilot/embedding_engine/url_embedding.py +++ b/pilot/embedding_engine/url_embedding.py @@ -33,7 +33,7 @@ class URLEmbedding(SourceEmbedding): file_path, vector_store_config, source_reader=None, text_splitter=None ) self.file_path = file_path - self.vector_store_config = vector_store_config + self.vector_store_config = vector_store_config or None self.source_reader = source_reader or None self.text_splitter = text_splitter or None diff --git a/pilot/graph_engine/__init__.py b/pilot/graph_engine/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/graph_engine/graph_engine.py b/pilot/graph_engine/graph_engine.py new file mode 100644 index 000000000..bea5f3123 --- /dev/null +++ b/pilot/graph_engine/graph_engine.py @@ -0,0 +1,207 @@ +import logging +from typing import Any, Optional, Callable, Tuple, List + +from langchain.schema import Document +from langchain.text_splitter import RecursiveCharacterTextSplitter + +from pilot.embedding_engine import KnowledgeType +from pilot.embedding_engine.knowledge_type import get_knowledge_embedding +from pilot.graph_engine.index_struct import KG +from pilot.graph_engine.node import TextNode +from pilot.utils import utils + +logger = logging.getLogger(__name__) + + +class RAGGraphEngine: + """Knowledge RAG Graph Engine. + Build a RAG Graph Client can extract triplets and insert into graph store. + Args: + knowledge_type (Optional[str]): Default: KnowledgeType.DOCUMENT.value + extracting triplets. + knowledge_source (Optional[str]): + model_name (Optional[str]): llm model name + graph_store (Optional[GraphStore]): The graph store to use.refrence:llama-index + include_embeddings (bool): Whether to include embeddings in the index. + Defaults to False. + max_object_length (int): The maximum length of the object in a triplet. + Defaults to 128. + extract_triplet_fn (Optional[Callable]): The function to use for + extracting triplets. Defaults to None. + """ + + index_struct_cls = KG + + def __init__( + self, + knowledge_type: Optional[str] = KnowledgeType.DOCUMENT.value, + knowledge_source: Optional[str] = None, + text_splitter=None, + graph_store=None, + index_struct: Optional[KG] = None, + model_name: Optional[str] = None, + max_triplets_per_chunk: int = 10, + include_embeddings: bool = False, + max_object_length: int = 128, + extract_triplet_fn: Optional[Callable] = None, + **kwargs: Any, + ) -> None: + """Initialize params.""" + from llama_index.graph_stores import SimpleGraphStore + + # need to set parameters before building index in base class. + self.knowledge_source = knowledge_source + self.knowledge_type = knowledge_type + self.model_name = model_name + self.text_splitter = text_splitter + self.index_struct = index_struct + self.include_embeddings = include_embeddings + self.graph_store = graph_store or SimpleGraphStore() + # self.graph_store = graph_store + self.max_triplets_per_chunk = max_triplets_per_chunk + self._max_object_length = max_object_length + self._extract_triplet_fn = extract_triplet_fn + + def knowledge_graph(self, docs=None): + """knowledge docs into graph store""" + if not docs: + if self.text_splitter: + self.text_splitter = RecursiveCharacterTextSplitter( + chunk_size=2000, chunk_overlap=100 + ) + knowledge_source = get_knowledge_embedding( + knowledge_type=self.knowledge_type, + knowledge_source=self.knowledge_source, + text_splitter=self.text_splitter, + ) + docs = knowledge_source.read() + if self.index_struct is None: + self.index_struct = self._build_index_from_docs(docs) + + def _extract_triplets(self, text: str) -> List[Tuple[str, str, str]]: + """Extract triplets from text by function or llm""" + if self._extract_triplet_fn is not None: + return self._extract_triplet_fn(text) + else: + return self._llm_extract_triplets(text) + + def _llm_extract_triplets(self, text: str) -> List[Tuple[str, str, str]]: + """Extract triplets from text by llm""" + from pilot.scene.base import ChatScene + from pilot.common.chat_util import llm_chat_response_nostream + import uuid + + chat_param = { + "chat_session_id": uuid.uuid1(), + "current_user_input": text, + "select_param": "triplet", + "model_name": self.model_name, + } + loop = utils.get_or_create_event_loop() + triplets = loop.run_until_complete( + llm_chat_response_nostream( + ChatScene.ExtractTriplet.value(), **{"chat_param": chat_param} + ) + ) + return triplets + + def _build_index_from_docs(self, documents: List[Document]) -> KG: + """Build the index from nodes. + Args:documents:List[Document] + """ + index_struct = self.index_struct_cls() + triplets = [] + for doc in documents: + trips = self._extract_triplets_task([doc], index_struct) + triplets.extend(trips) + print(triplets) + text_node = TextNode(text=doc.page_content, metadata=doc.metadata) + for triplet in triplets: + subj, _, obj = triplet + self.graph_store.upsert_triplet(*triplet) + index_struct.add_node([subj, obj], text_node) + return index_struct + # num_threads = 5 + # chunk_size = ( + # len(documents) + # if (len(documents) < num_threads) + # else len(documents) // num_threads + # ) + # + # import concurrent + # triples = [] + # future_tasks = [] + # with concurrent.futures.ThreadPoolExecutor() as executor: + # for i in range(num_threads): + # start = i * chunk_size + # end = start + chunk_size if i < num_threads - 1 else None + # # doc = documents[start:end] + # future_tasks.append( + # executor.submit( + # self._extract_triplets_task, + # documents[start:end], + # index_struct, + # ) + # ) + # # for doc in documents[start:end]: + # # future_tasks.append( + # # executor.submit( + # # self._extract_triplets_task, + # # doc, + # # index_struct, + # # ) + # # ) + # + # # result = [future.result() for future in future_tasks] + # completed_futures, _ = concurrent.futures.wait(future_tasks, return_when=concurrent.futures.ALL_COMPLETED) + # for future in completed_futures: + # # 获取已完成的future的结果并添加到results列表中 + # result = future.result() + # triplets.extend(result) + # print(f"total triplets-{triples}") + # for triplet in triplets: + # subj, _, obj = triplet + # self.graph_store.upsert_triplet(*triplet) + # # index_struct.add_node([subj, obj], text_node) + # return index_struct + # for doc in documents: + # triplets = self._extract_triplets(doc.page_content) + # if len(triplets) == 0: + # continue + # text_node = TextNode(text=doc.page_content, metadata=doc.metadata) + # logger.info(f"extracted knowledge triplets: {triplets}") + # for triplet in triplets: + # subj, _, obj = triplet + # self.graph_store.upsert_triplet(*triplet) + # index_struct.add_node([subj, obj], text_node) + # + # return index_struct + + def search(self, query): + from pilot.graph_engine.graph_search import RAGGraphSearch + + graph_search = RAGGraphSearch(graph_engine=self) + return graph_search.search(query) + + def _extract_triplets_task(self, docs, index_struct): + triple_results = [] + for doc in docs: + import threading + + thread_id = threading.get_ident() + print(f"current thread-{thread_id} begin extract triplets task") + triplets = self._extract_triplets(doc.page_content) + if len(triplets) == 0: + triplets = [] + text_node = TextNode(text=doc.page_content, metadata=doc.metadata) + logger.info(f"extracted knowledge triplets: {triplets}") + print( + f"current thread-{thread_id} end extract triplets tasks, triplets-{triplets}" + ) + triple_results.extend(triplets) + return triple_results + # for triplet in triplets: + # subj, _, obj = triplet + # self.graph_store.upsert_triplet(*triplet) + # self.graph_store.upsert_triplet(*triplet) + # index_struct.add_node([subj, obj], text_node) diff --git a/pilot/graph_engine/graph_factory.py b/pilot/graph_engine/graph_factory.py new file mode 100644 index 000000000..3a8b99c17 --- /dev/null +++ b/pilot/graph_engine/graph_factory.py @@ -0,0 +1,34 @@ +from __future__ import annotations +from abc import ABC, abstractmethod +from typing import Any, Type + +from pilot.component import BaseComponent, ComponentType + + +class RAGGraphFactory(BaseComponent, ABC): + name = ComponentType.RAG_GRAPH_DEFAULT.value + + @abstractmethod + def create(self, model_name: str = None, embedding_cls: Type = None): + """Create RAG Graph Engine""" + + +class DefaultRAGGraphFactory(RAGGraphFactory): + def __init__( + self, system_app=None, default_model_name: str = None, **kwargs: Any + ) -> None: + super().__init__(system_app=system_app) + self._default_model_name = default_model_name + self.kwargs = kwargs + from pilot.graph_engine.graph_engine import RAGGraphEngine + + self.rag_engine = RAGGraphEngine(model_name="proxyllm") + + def init_app(self, system_app): + pass + + def create(self, model_name: str = None, rag_cls: Type = None): + if not model_name: + model_name = self._default_model_name + + return self.rag_engine diff --git a/pilot/graph_engine/graph_search.py b/pilot/graph_engine/graph_search.py new file mode 100644 index 000000000..9419a4979 --- /dev/null +++ b/pilot/graph_engine/graph_search.py @@ -0,0 +1,197 @@ +import logging +import os +from collections import defaultdict +from concurrent.futures import ThreadPoolExecutor +from typing import List, Optional, Dict, Any, Set, Callable + +from langchain.schema import Document + +from pilot.graph_engine.node import BaseNode, TextNode, NodeWithScore +from pilot.graph_engine.search import BaseSearch, SearchMode + +logger = logging.getLogger(__name__) +DEFAULT_NODE_SCORE = 1000.0 +GLOBAL_EXPLORE_NODE_LIMIT = 3 +REL_TEXT_LIMIT = 30 + + +class RAGGraphSearch(BaseSearch): + """RAG Graph Search. + + args: + graph_engine RAGGraphEngine. + model_name (str): model name + (see :ref:`Prompt-Templates`). + text_qa_template (Optional[BasePromptTemplate]): A Question Answering Prompt + (see :ref:`Prompt-Templates`). + max_keywords_per_query (int): Maximum number of keywords to extract from query. + num_chunks_per_query (int): Maximum number of text chunks to query. + search_mode (Optional[SearchMode]): Specifies whether to use keyowrds, default SearchMode.KEYWORD + embeddings, or both to find relevant triplets. Should be one of "keyword", + "embedding", or "hybrid". + graph_store_query_depth (int): The depth of the graph store query. + extract_subject_entities_fn (Optional[Callback]): extract_subject_entities callback. + """ + + def __init__( + self, + graph_engine, + model_name: str = None, + max_keywords_per_query: int = 10, + num_chunks_per_query: int = 10, + search_mode: Optional[SearchMode] = SearchMode.KEYWORD, + graph_store_query_depth: int = 2, + extract_subject_entities_fn: Optional[Callable] = None, + **kwargs: Any, + ) -> None: + """Initialize params.""" + from pilot.graph_engine.graph_engine import RAGGraphEngine + + self.graph_engine: RAGGraphEngine = graph_engine + self.model_name = model_name or self.graph_engine.model_name + self._index_struct = self.graph_engine.index_struct + self.max_keywords_per_query = max_keywords_per_query + self.num_chunks_per_query = num_chunks_per_query + self._search_mode = search_mode + + self._graph_store = self.graph_engine.graph_store + self.graph_store_query_depth = graph_store_query_depth + self._verbose = kwargs.get("verbose", False) + refresh_schema = kwargs.get("refresh_schema", False) + self.extract_subject_entities_fn = extract_subject_entities_fn + self.executor = ThreadPoolExecutor(max_workers=os.cpu_count() * 5) + try: + self._graph_schema = self._graph_store.get_schema(refresh=refresh_schema) + except NotImplementedError: + self._graph_schema = "" + except Exception as e: + logger.warn(f"can not to find graph schema: {e}") + self._graph_schema = "" + + async def _extract_subject_entities(self, query_str: str) -> Set[str]: + """extract subject entities.""" + if self.extract_subject_entities_fn is not None: + return await self.extract_subject_entities_fn(query_str) + else: + return await self._extract_entities_by_llm(query_str) + + async def _extract_entities_by_llm(self, text: str) -> Set[str]: + """extract subject entities from text by llm""" + from pilot.scene.base import ChatScene + from pilot.common.chat_util import llm_chat_response_nostream + import uuid + + chat_param = { + "chat_session_id": uuid.uuid1(), + "current_user_input": text, + "select_param": "entity", + "model_name": self.model_name, + } + # loop = utils.get_or_create_event_loop() + # entities = loop.run_until_complete( + # llm_chat_response_nostream( + # ChatScene.ExtractEntity.value(), **{"chat_param": chat_param} + # ) + # ) + return await llm_chat_response_nostream( + ChatScene.ExtractEntity.value(), **{"chat_param": chat_param} + ) + + async def _search( + self, + query_str: str, + ) -> List[Document]: + """Get nodes for response.""" + node_visited = set() + keywords = await self._extract_subject_entities(query_str) + print(f"extract entities: {keywords}\n") + rel_texts = [] + cur_rel_map = {} + chunk_indices_count: Dict[str, int] = defaultdict(int) + if self._search_mode != SearchMode.EMBEDDING: + for keyword in keywords: + keyword = keyword.lower() + subjs = set((keyword,)) + # node_ids = self._index_struct.search_node_by_keyword(keyword) + # for node_id in node_ids[:GLOBAL_EXPLORE_NODE_LIMIT]: + # if node_id in node_visited: + # continue + # + # # if self._include_text: + # # chunk_indices_count[node_id] += 1 + # + # node_visited.add(node_id) + + rel_map = self._graph_store.get_rel_map( + list(subjs), self.graph_store_query_depth + ) + logger.debug(f"rel_map: {rel_map}") + + if not rel_map: + continue + rel_texts.extend( + [ + str(rel_obj) + for rel_objs in rel_map.values() + for rel_obj in rel_objs + ] + ) + cur_rel_map.update(rel_map) + + sorted_nodes_with_scores = [] + if not rel_texts: + logger.info("> No relationships found, returning nodes found by keywords.") + if len(sorted_nodes_with_scores) == 0: + logger.info("> No nodes found by keywords, returning empty response.") + return [Document(page_content="No relationships found.")] + + # add relationships as Node + # TODO: make initial text customizable + rel_initial_text = ( + f"The following are knowledge sequence in max depth" + f" {self.graph_store_query_depth} " + f"in the form of directed graph like:\n" + f"`subject -[predicate]->, object, <-[predicate_next_hop]-," + f" object_next_hop ...`" + ) + rel_info = [rel_initial_text] + rel_texts + rel_node_info = { + "kg_rel_texts": rel_texts, + "kg_rel_map": cur_rel_map, + } + if self._graph_schema != "": + rel_node_info["kg_schema"] = {"schema": self._graph_schema} + rel_info_text = "\n".join( + [ + str(item) + for sublist in rel_info + for item in (sublist if isinstance(sublist, list) else [sublist]) + ] + ) + if self._verbose: + print(f"KG context:\n{rel_info_text}\n", color="blue") + rel_text_node = TextNode( + text=rel_info_text, + metadata=rel_node_info, + excluded_embed_metadata_keys=["kg_rel_map", "kg_rel_texts"], + excluded_llm_metadata_keys=["kg_rel_map", "kg_rel_texts"], + ) + # this node is constructed from rel_texts, give high confidence to avoid cutoff + sorted_nodes_with_scores.append( + NodeWithScore(node=rel_text_node, score=DEFAULT_NODE_SCORE) + ) + docs = [ + Document(page_content=node.text, metadata=node.metadata) + for node in sorted_nodes_with_scores + ] + return docs + + def _get_metadata_for_response( + self, nodes: List[BaseNode] + ) -> Optional[Dict[str, Any]]: + """Get metadata for response.""" + for node in nodes: + if node.metadata is None or "kg_rel_map" not in node.metadata: + continue + return node.metadata + raise ValueError("kg_rel_map must be found in at least one Node.") diff --git a/pilot/graph_engine/index_struct.py b/pilot/graph_engine/index_struct.py new file mode 100644 index 000000000..edc47a7ac --- /dev/null +++ b/pilot/graph_engine/index_struct.py @@ -0,0 +1,259 @@ +"""Data structures. + +Nodes are decoupled from the indices. + +""" + +import uuid +from abc import abstractmethod +from dataclasses import dataclass, field +from typing import Dict, List, Optional, Sequence, Set + +from dataclasses_json import DataClassJsonMixin + + +from pilot.graph_engine.index_type import IndexStructType +from pilot.graph_engine.node import TextNode, BaseNode + +# TODO: legacy backport of old Node class +Node = TextNode + + +@dataclass +class IndexStruct(DataClassJsonMixin): + """A base data struct for a LlamaIndex.""" + + index_id: str = field(default_factory=lambda: str(uuid.uuid4())) + summary: Optional[str] = None + + def get_summary(self) -> str: + """Get text summary.""" + if self.summary is None: + raise ValueError("summary field of the index_struct not set.") + return self.summary + + @classmethod + @abstractmethod + def get_type(cls): + """Get index struct type.""" + + +@dataclass +class IndexGraph(IndexStruct): + """A graph representing the tree-structured index.""" + + # mapping from index in tree to Node doc id. + all_nodes: Dict[int, str] = field(default_factory=dict) + root_nodes: Dict[int, str] = field(default_factory=dict) + node_id_to_children_ids: Dict[str, List[str]] = field(default_factory=dict) + + @property + def node_id_to_index(self) -> Dict[str, int]: + """Map from node id to index.""" + return {node_id: index for index, node_id in self.all_nodes.items()} + + @property + def size(self) -> int: + """Get the size of the graph.""" + return len(self.all_nodes) + + def get_index(self, node: BaseNode) -> int: + """Get index of node.""" + return self.node_id_to_index[node.node_id] + + def insert( + self, + node: BaseNode, + index: Optional[int] = None, + children_nodes: Optional[Sequence[BaseNode]] = None, + ) -> None: + """Insert node.""" + index = index or self.size + node_id = node.node_id + + self.all_nodes[index] = node_id + + if children_nodes is None: + children_nodes = [] + children_ids = [n.node_id for n in children_nodes] + self.node_id_to_children_ids[node_id] = children_ids + + def get_children(self, parent_node: Optional[BaseNode]) -> Dict[int, str]: + """Get children nodes.""" + if parent_node is None: + return self.root_nodes + else: + parent_id = parent_node.node_id + children_ids = self.node_id_to_children_ids[parent_id] + return { + self.node_id_to_index[child_id]: child_id for child_id in children_ids + } + + def insert_under_parent( + self, + node: BaseNode, + parent_node: Optional[BaseNode], + new_index: Optional[int] = None, + ) -> None: + """Insert under parent node.""" + new_index = new_index or self.size + if parent_node is None: + self.root_nodes[new_index] = node.node_id + self.node_id_to_children_ids[node.node_id] = [] + else: + if parent_node.node_id not in self.node_id_to_children_ids: + self.node_id_to_children_ids[parent_node.node_id] = [] + self.node_id_to_children_ids[parent_node.node_id].append(node.node_id) + + self.all_nodes[new_index] = node.node_id + + @classmethod + def get_type(cls) -> IndexStructType: + """Get type.""" + return IndexStructType.TREE + + +@dataclass +class KeywordTable(IndexStruct): + """A table of keywords mapping keywords to text chunks.""" + + table: Dict[str, Set[str]] = field(default_factory=dict) + + def add_node(self, keywords: List[str], node: BaseNode) -> None: + """Add text to table.""" + for keyword in keywords: + if keyword not in self.table: + self.table[keyword] = set() + self.table[keyword].add(node.node_id) + + @property + def node_ids(self) -> Set[str]: + """Get all node ids.""" + return set.union(*self.table.values()) + + @property + def keywords(self) -> Set[str]: + """Get all keywords in the table.""" + return set(self.table.keys()) + + @property + def size(self) -> int: + """Get the size of the table.""" + return len(self.table) + + @classmethod + def get_type(cls) -> IndexStructType: + """Get type.""" + return IndexStructType.KEYWORD_TABLE + + +@dataclass +class IndexList(IndexStruct): + """A list of documents.""" + + nodes: List[str] = field(default_factory=list) + + def add_node(self, node: BaseNode) -> None: + """Add text to table, return current position in list.""" + # don't worry about child indices for now, nodes are all in order + self.nodes.append(node.node_id) + + @classmethod + def get_type(cls) -> IndexStructType: + """Get type.""" + return IndexStructType.LIST + + +@dataclass +class IndexDict(IndexStruct): + """A simple dictionary of documents.""" + + # TODO: slightly deprecated, should likely be a list or set now + # mapping from vector store id to node doc_id + nodes_dict: Dict[str, str] = field(default_factory=dict) + + # TODO: deprecated, not used + # mapping from node doc_id to vector store id + doc_id_dict: Dict[str, List[str]] = field(default_factory=dict) + + # TODO: deprecated, not used + # this should be empty for all other indices + embeddings_dict: Dict[str, List[float]] = field(default_factory=dict) + + def add_node( + self, + node: BaseNode, + text_id: Optional[str] = None, + ) -> str: + """Add text to table, return current position in list.""" + # # don't worry about child indices for now, nodes are all in order + # self.nodes_dict[int_id] = node + vector_id = text_id if text_id is not None else node.node_id + self.nodes_dict[vector_id] = node.node_id + + return vector_id + + def delete(self, doc_id: str) -> None: + """Delete a Node.""" + del self.nodes_dict[doc_id] + + @classmethod + def get_type(cls) -> IndexStructType: + """Get type.""" + return IndexStructType.VECTOR_STORE + + +@dataclass +class KG(IndexStruct): + """A table of keywords mapping keywords to text chunks.""" + + # Unidirectional + + # table of keywords to node ids + table: Dict[str, Set[str]] = field(default_factory=dict) + + # TODO: legacy attribute, remove in future releases + rel_map: Dict[str, List[List[str]]] = field(default_factory=dict) + + # TBD, should support vector store, now we just persist the embedding memory + # maybe chainable abstractions for *_stores could be designed + embedding_dict: Dict[str, List[float]] = field(default_factory=dict) + + @property + def node_ids(self) -> Set[str]: + """Get all node ids.""" + return set.union(*self.table.values()) + + def add_to_embedding_dict(self, triplet_str: str, embedding: List[float]) -> None: + """Add embedding to dict.""" + self.embedding_dict[triplet_str] = embedding + + def add_node(self, keywords: List[str], node: BaseNode) -> None: + """Add text to table.""" + node_id = node.node_id + for keyword in keywords: + keyword = keyword.lower() + if keyword not in self.table: + self.table[keyword] = set() + self.table[keyword].add(node_id) + + def search_node_by_keyword(self, keyword: str) -> List[str]: + """Search for nodes by keyword.""" + if keyword not in self.table: + return [] + return list(self.table[keyword]) + + @classmethod + def get_type(cls) -> IndexStructType: + """Get type.""" + return IndexStructType.KG + + +@dataclass +class EmptyIndexStruct(IndexStruct): + """Empty index.""" + + @classmethod + def get_type(cls) -> IndexStructType: + """Get type.""" + return IndexStructType.EMPTY diff --git a/pilot/graph_engine/index_type.py b/pilot/graph_engine/index_type.py new file mode 100644 index 000000000..939066be9 --- /dev/null +++ b/pilot/graph_engine/index_type.py @@ -0,0 +1,48 @@ +"""IndexStructType class.""" + +from enum import Enum + + +class IndexStructType(str, Enum): + """Index struct type. Identifier for a "type" of index. + + Attributes: + TREE ("tree"): Tree index. See :ref:`Ref-Indices-Tree` for tree indices. + LIST ("list"): Summary index. See :ref:`Ref-Indices-List` for summary indices. + KEYWORD_TABLE ("keyword_table"): Keyword table index. See + :ref:`Ref-Indices-Table` + for keyword table indices. + DICT ("dict"): Faiss Vector Store Index. See + :ref:`Ref-Indices-VectorStore` + for more information on the faiss vector store index. + SIMPLE_DICT ("simple_dict"): Simple Vector Store Index. See + :ref:`Ref-Indices-VectorStore` + for more information on the simple vector store index. + KG ("kg"): Knowledge Graph index. + See :ref:`Ref-Indices-Knowledge-Graph` for KG indices. + DOCUMENT_SUMMARY ("document_summary"): Document Summary Index. + See :ref:`Ref-Indices-Document-Summary` for Summary Indices. + + """ + + # TODO: refactor so these are properties on the base class + + NODE = "node" + TREE = "tree" + LIST = "list" + KEYWORD_TABLE = "keyword_table" + + DICT = "dict" + # simple + SIMPLE_DICT = "simple_dict" + # for KG index + KG = "kg" + SIMPLE_KG = "simple_kg" + NEBULAGRAPH = "nebulagraph" + FALKORDB = "falkordb" + + # EMPTY + EMPTY = "empty" + COMPOSITE = "composite" + + DOCUMENT_SUMMARY = "document_summary" diff --git a/pilot/graph_engine/kv_index.py b/pilot/graph_engine/kv_index.py new file mode 100644 index 000000000..7b44b7d04 --- /dev/null +++ b/pilot/graph_engine/kv_index.py @@ -0,0 +1,74 @@ +from typing import List, Optional +from llama_index.data_structs.data_structs import IndexStruct +from llama_index.storage.index_store.utils import ( + index_struct_to_json, + json_to_index_struct, +) +from llama_index.storage.kvstore.types import BaseKVStore + +DEFAULT_NAMESPACE = "index_store" + + +class KVIndexStore: + """Key-Value Index store. + + Args: + kvstore (BaseKVStore): key-value store + namespace (str): namespace for the index store + + """ + + def __init__(self, kvstore: BaseKVStore, namespace: Optional[str] = None) -> None: + """Init a KVIndexStore.""" + self._kvstore = kvstore + self._namespace = namespace or DEFAULT_NAMESPACE + self._collection = f"{self._namespace}/data" + + def add_index_struct(self, index_struct: IndexStruct) -> None: + """Add an index struct. + + Args: + index_struct (IndexStruct): index struct + + """ + key = index_struct.index_id + data = index_struct_to_json(index_struct) + self._kvstore.put(key, data, collection=self._collection) + + def delete_index_struct(self, key: str) -> None: + """Delete an index struct. + + Args: + key (str): index struct key + + """ + self._kvstore.delete(key, collection=self._collection) + + def get_index_struct( + self, struct_id: Optional[str] = None + ) -> Optional[IndexStruct]: + """Get an index struct. + + Args: + struct_id (Optional[str]): index struct id + + """ + if struct_id is None: + structs = self.index_structs() + assert len(structs) == 1 + return structs[0] + else: + json = self._kvstore.get(struct_id, collection=self._collection) + if json is None: + return None + return json_to_index_struct(json) + + def index_structs(self) -> List[IndexStruct]: + """Get all index structs. + + Returns: + List[IndexStruct]: index structs + + """ + jsons = self._kvstore.get_all(collection=self._collection) + return [json_to_index_struct(json) for json in jsons.values()] diff --git a/pilot/graph_engine/node.py b/pilot/graph_engine/node.py new file mode 100644 index 000000000..b23681010 --- /dev/null +++ b/pilot/graph_engine/node.py @@ -0,0 +1,570 @@ +"""Base schema for data structures.""" +import json +import textwrap +import uuid +from abc import abstractmethod +from enum import Enum, auto +from hashlib import sha256 +from typing import Any, Dict, List, Optional, Union + +from langchain.schema import Document +from pydantic import BaseModel, Field, root_validator +from typing_extensions import Self + + +DEFAULT_TEXT_NODE_TMPL = "{metadata_str}\n\n{content}" +DEFAULT_METADATA_TMPL = "{key}: {value}" +# NOTE: for pretty printing +TRUNCATE_LENGTH = 350 +WRAP_WIDTH = 70 + + +class BaseComponent(BaseModel): + """Base component object to caputure class names.""" + + """reference llama-index""" + + @classmethod + @abstractmethod + def class_name(cls) -> str: + """Get class name.""" + + def to_dict(self, **kwargs: Any) -> Dict[str, Any]: + data = self.dict(**kwargs) + data["class_name"] = self.class_name() + return data + + def to_json(self, **kwargs: Any) -> str: + data = self.to_dict(**kwargs) + return json.dumps(data) + + # TODO: return type here not supported by current mypy version + @classmethod + def from_dict(cls, data: Dict[str, Any], **kwargs: Any) -> Self: # type: ignore + if isinstance(kwargs, dict): + data.update(kwargs) + + data.pop("class_name", None) + return cls(**data) + + @classmethod + def from_json(cls, data_str: str, **kwargs: Any) -> Self: # type: ignore + data = json.loads(data_str) + return cls.from_dict(data, **kwargs) + + +class NodeRelationship(str, Enum): + """Node relationships used in `BaseNode` class. + + Attributes: + SOURCE: The node is the source document. + PREVIOUS: The node is the previous node in the document. + NEXT: The node is the next node in the document. + PARENT: The node is the parent node in the document. + CHILD: The node is a child node in the document. + + """ + + SOURCE = auto() + PREVIOUS = auto() + NEXT = auto() + PARENT = auto() + CHILD = auto() + + +class ObjectType(str, Enum): + TEXT = auto() + IMAGE = auto() + INDEX = auto() + DOCUMENT = auto() + + +class MetadataMode(str, Enum): + ALL = auto() + EMBED = auto() + LLM = auto() + NONE = auto() + + +class RelatedNodeInfo(BaseComponent): + node_id: str + node_type: Optional[ObjectType] = None + metadata: Dict[str, Any] = Field(default_factory=dict) + hash: Optional[str] = None + + @classmethod + def class_name(cls) -> str: + """Get class name.""" + return "RelatedNodeInfo" + + +RelatedNodeType = Union[RelatedNodeInfo, List[RelatedNodeInfo]] + + +# Node classes for indexes +class BaseNode(BaseComponent): + """Base node Object. + + Generic abstract interface for retrievable nodes + + """ + + class Config: + allow_population_by_field_name = True + + id_: str = Field( + default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the node." + ) + embedding: Optional[List[float]] = Field( + default=None, description="Embedding of the node." + ) + + """" + metadata fields + - injected as part of the text shown to LLMs as context + - injected as part of the text for generating embeddings + - used by vector DBs for metadata filtering + + """ + metadata: Dict[str, Any] = Field( + default_factory=dict, + description="A flat dictionary of metadata fields", + alias="extra_info", + ) + excluded_embed_metadata_keys: List[str] = Field( + default_factory=list, + description="Metadata keys that are exluded from text for the embed model.", + ) + excluded_llm_metadata_keys: List[str] = Field( + default_factory=list, + description="Metadata keys that are exluded from text for the LLM.", + ) + relationships: Dict[NodeRelationship, RelatedNodeType] = Field( + default_factory=dict, + description="A mapping of relationships to other node information.", + ) + hash: str = Field(default="", description="Hash of the node content.") + + @classmethod + @abstractmethod + def get_type(cls) -> str: + """Get Object type.""" + + @abstractmethod + def get_content(self, metadata_mode: MetadataMode = MetadataMode.ALL) -> str: + """Get object content.""" + + @abstractmethod + def get_metadata_str(self, mode: MetadataMode = MetadataMode.ALL) -> str: + """Metadata string.""" + + @abstractmethod + def set_content(self, value: Any) -> None: + """Set the content of the node.""" + + @property + def node_id(self) -> str: + return self.id_ + + @node_id.setter + def node_id(self, value: str) -> None: + self.id_ = value + + @property + def source_node(self) -> Optional[RelatedNodeInfo]: + """Source object node. + + Extracted from the relationships field. + + """ + if NodeRelationship.SOURCE not in self.relationships: + return None + + relation = self.relationships[NodeRelationship.SOURCE] + if isinstance(relation, list): + raise ValueError("Source object must be a single RelatedNodeInfo object") + return relation + + @property + def prev_node(self) -> Optional[RelatedNodeInfo]: + """Prev node.""" + if NodeRelationship.PREVIOUS not in self.relationships: + return None + + relation = self.relationships[NodeRelationship.PREVIOUS] + if not isinstance(relation, RelatedNodeInfo): + raise ValueError("Previous object must be a single RelatedNodeInfo object") + return relation + + @property + def next_node(self) -> Optional[RelatedNodeInfo]: + """Next node.""" + if NodeRelationship.NEXT not in self.relationships: + return None + + relation = self.relationships[NodeRelationship.NEXT] + if not isinstance(relation, RelatedNodeInfo): + raise ValueError("Next object must be a single RelatedNodeInfo object") + return relation + + @property + def parent_node(self) -> Optional[RelatedNodeInfo]: + """Parent node.""" + if NodeRelationship.PARENT not in self.relationships: + return None + + relation = self.relationships[NodeRelationship.PARENT] + if not isinstance(relation, RelatedNodeInfo): + raise ValueError("Parent object must be a single RelatedNodeInfo object") + return relation + + @property + def child_nodes(self) -> Optional[List[RelatedNodeInfo]]: + """Child nodes.""" + if NodeRelationship.CHILD not in self.relationships: + return None + + relation = self.relationships[NodeRelationship.CHILD] + if not isinstance(relation, list): + raise ValueError("Child objects must be a list of RelatedNodeInfo objects.") + return relation + + @property + def ref_doc_id(self) -> Optional[str]: + """Deprecated: Get ref doc id.""" + source_node = self.source_node + if source_node is None: + return None + return source_node.node_id + + @property + def extra_info(self) -> Dict[str, Any]: + """TODO: DEPRECATED: Extra info.""" + return self.metadata + + def __str__(self) -> str: + source_text_truncated = truncate_text( + self.get_content().strip(), TRUNCATE_LENGTH + ) + source_text_wrapped = textwrap.fill( + f"Text: {source_text_truncated}\n", width=WRAP_WIDTH + ) + return f"Node ID: {self.node_id}\n{source_text_wrapped}" + + def truncate_text(text: str, max_length: int) -> str: + """Truncate text to a maximum length.""" + if len(text) <= max_length: + return text + return text[: max_length - 3] + "..." + + def get_embedding(self) -> List[float]: + """Get embedding. + + Errors if embedding is None. + + """ + if self.embedding is None: + raise ValueError("embedding not set.") + return self.embedding + + def as_related_node_info(self) -> RelatedNodeInfo: + """Get node as RelatedNodeInfo.""" + return RelatedNodeInfo( + node_id=self.node_id, metadata=self.metadata, hash=self.hash + ) + + +class TextNode(BaseNode): + text: str = Field(default="", description="Text content of the node.") + start_char_idx: Optional[int] = Field( + default=None, description="Start char index of the node." + ) + end_char_idx: Optional[int] = Field( + default=None, description="End char index of the node." + ) + text_template: str = Field( + default=DEFAULT_TEXT_NODE_TMPL, + description=( + "Template for how text is formatted, with {content} and " + "{metadata_str} placeholders." + ), + ) + metadata_template: str = Field( + default=DEFAULT_METADATA_TMPL, + description=( + "Template for how metadata is formatted, with {key} and " + "{value} placeholders." + ), + ) + metadata_seperator: str = Field( + default="\n", + description="Seperator between metadata fields when converting to string.", + ) + + @classmethod + def class_name(cls) -> str: + """Get class name.""" + return "TextNode" + + @root_validator + def _check_hash(cls, values: dict) -> dict: + """Generate a hash to represent the node.""" + text = values.get("text", "") + metadata = values.get("metadata", {}) + doc_identity = str(text) + str(metadata) + values["hash"] = str( + sha256(doc_identity.encode("utf-8", "surrogatepass")).hexdigest() + ) + return values + + @classmethod + def get_type(cls) -> str: + """Get Object type.""" + return ObjectType.TEXT + + def get_content(self, metadata_mode: MetadataMode = MetadataMode.NONE) -> str: + """Get object content.""" + metadata_str = self.get_metadata_str(mode=metadata_mode).strip() + if not metadata_str: + return self.text + + return self.text_template.format( + content=self.text, metadata_str=metadata_str + ).strip() + + def get_metadata_str(self, mode: MetadataMode = MetadataMode.ALL) -> str: + """metadata info string.""" + if mode == MetadataMode.NONE: + return "" + + usable_metadata_keys = set(self.metadata.keys()) + if mode == MetadataMode.LLM: + for key in self.excluded_llm_metadata_keys: + if key in usable_metadata_keys: + usable_metadata_keys.remove(key) + elif mode == MetadataMode.EMBED: + for key in self.excluded_embed_metadata_keys: + if key in usable_metadata_keys: + usable_metadata_keys.remove(key) + + return self.metadata_seperator.join( + [ + self.metadata_template.format(key=key, value=str(value)) + for key, value in self.metadata.items() + if key in usable_metadata_keys + ] + ) + + def set_content(self, value: str) -> None: + """Set the content of the node.""" + self.text = value + + def get_node_info(self) -> Dict[str, Any]: + """Get node info.""" + return {"start": self.start_char_idx, "end": self.end_char_idx} + + def get_text(self) -> str: + return self.get_content(metadata_mode=MetadataMode.NONE) + + @property + def node_info(self) -> Dict[str, Any]: + """Deprecated: Get node info.""" + return self.get_node_info() + + +# TODO: legacy backport of old Node class +Node = TextNode + + +class ImageNode(TextNode): + """Node with image.""" + + # TODO: store reference instead of actual image + # base64 encoded image str + image: Optional[str] = None + + @classmethod + def get_type(cls) -> str: + return ObjectType.IMAGE + + @classmethod + def class_name(cls) -> str: + """Get class name.""" + return "ImageNode" + + +class IndexNode(TextNode): + """Node with reference to any object. + + This can include other indices, query engines, retrievers. + + This can also include other nodes (though this is overlapping with `relationships` + on the Node class). + + """ + + index_id: str + + @classmethod + def from_text_node( + cls, + node: TextNode, + index_id: str, + ) -> "IndexNode": + """Create index node from text node.""" + # copy all attributes from text node, add index id + return cls( + **node.dict(), + index_id=index_id, + ) + + @classmethod + def get_type(cls) -> str: + return ObjectType.INDEX + + @classmethod + def class_name(cls) -> str: + """Get class name.""" + return "IndexNode" + + +class NodeWithScore(BaseComponent): + node: BaseNode + score: Optional[float] = None + + def __str__(self) -> str: + return f"{self.node}\nScore: {self.score: 0.3f}\n" + + def get_score(self, raise_error: bool = False) -> float: + """Get score.""" + if self.score is None: + if raise_error: + raise ValueError("Score not set.") + else: + return 0.0 + else: + return self.score + + @classmethod + def class_name(cls) -> str: + """Get class name.""" + return "NodeWithScore" + + ##### pass through methods to BaseNode ##### + @property + def node_id(self) -> str: + return self.node.node_id + + @property + def id_(self) -> str: + return self.node.id_ + + @property + def text(self) -> str: + if isinstance(self.node, TextNode): + return self.node.text + else: + raise ValueError("Node must be a TextNode to get text.") + + @property + def metadata(self) -> Dict[str, Any]: + return self.node.metadata + + @property + def embedding(self) -> Optional[List[float]]: + return self.node.embedding + + def get_text(self) -> str: + if isinstance(self.node, TextNode): + return self.node.get_text() + else: + raise ValueError("Node must be a TextNode to get text.") + + def get_content(self, metadata_mode: MetadataMode = MetadataMode.NONE) -> str: + return self.node.get_content(metadata_mode=metadata_mode) + + def get_embedding(self) -> List[float]: + return self.node.get_embedding() + + +# Document Classes for Readers + + +class Document(TextNode): + """Generic interface for a data document. + + This document connects to data sources. + + """ + + # TODO: A lot of backwards compatibility logic here, clean up + id_: str = Field( + default_factory=lambda: str(uuid.uuid4()), + description="Unique ID of the node.", + alias="doc_id", + ) + + _compat_fields = {"doc_id": "id_", "extra_info": "metadata"} + + @classmethod + def get_type(cls) -> str: + """Get Document type.""" + return ObjectType.DOCUMENT + + @property + def doc_id(self) -> str: + """Get document ID.""" + return self.id_ + + def __str__(self) -> str: + source_text_truncated = truncate_text( + self.get_content().strip(), TRUNCATE_LENGTH + ) + source_text_wrapped = textwrap.fill( + f"Text: {source_text_truncated}\n", width=WRAP_WIDTH + ) + return f"Doc ID: {self.doc_id}\n{source_text_wrapped}" + + def get_doc_id(self) -> str: + """TODO: Deprecated: Get document ID.""" + return self.id_ + + def __setattr__(self, name: str, value: object) -> None: + if name in self._compat_fields: + name = self._compat_fields[name] + super().__setattr__(name, value) + + def to_langchain_format(self) -> Document: + """Convert struct to LangChain document format.""" + metadata = self.metadata or {} + return Document(page_content=self.text, metadata=metadata) + + @classmethod + def from_langchain_format(cls, doc: Document) -> "Document": + """Convert struct from LangChain document format.""" + return cls(text=doc.page_content, metadata=doc.metadata) + + @classmethod + def example(cls) -> "Document": + document = Document( + text="", + metadata={"filename": "README.md", "category": "codebase"}, + ) + return document + + @classmethod + def class_name(cls) -> str: + """Get class name.""" + return "Document" + + +class ImageDocument(Document): + """Data document containing an image.""" + + # base64 encoded image str + image: Optional[str] = None + + @classmethod + def class_name(cls) -> str: + """Get class name.""" + return "ImageDocument" diff --git a/pilot/graph_engine/search.py b/pilot/graph_engine/search.py new file mode 100644 index 000000000..297620b00 --- /dev/null +++ b/pilot/graph_engine/search.py @@ -0,0 +1,44 @@ +from abc import ABC, abstractmethod +from enum import Enum + + +class SearchMode(str, Enum): + """Query mode enum for Knowledge Graphs. + + Can be passed as the enum struct, or as the underlying string. + + Attributes: + KEYWORD ("keyword"): Default query mode, using keywords to find triplets. + EMBEDDING ("embedding"): Embedding mode, using embeddings to find + similar triplets. + HYBRID ("hybrid"): Hyrbid mode, combining both keywords and embeddings + to find relevant triplets. + """ + + KEYWORD = "keyword" + EMBEDDING = "embedding" + HYBRID = "hybrid" + + +class BaseSearch(ABC): + """Base Search.""" + + async def search(self, query: str): + """Retrieve nodes given query. + + Args: + query (QueryType): Either a query string or + a QueryBundle object. + + """ + # if isinstance(query, str): + return await self._search(query) + + @abstractmethod + async def _search(self, query: str): + """search nodes given query. + + Implemented by the user. + + """ + pass diff --git a/pilot/logs.py b/pilot/logs.py index 673799307..32528bdaa 100644 --- a/pilot/logs.py +++ b/pilot/logs.py @@ -249,7 +249,8 @@ def remove_color_codes(s: str) -> str: return ansi_escape.sub("", s) -logger: Logger = Logger() +# Remove current logger +# logger: Logger = Logger() def print_assistant_thoughts( diff --git a/pilot/memory/chat_history/chat_hisotry_factory.py b/pilot/memory/chat_history/chat_hisotry_factory.py index 64d30e971..c1a8f9cab 100644 --- a/pilot/memory/chat_history/chat_hisotry_factory.py +++ b/pilot/memory/chat_history/chat_hisotry_factory.py @@ -1,5 +1,6 @@ from .base import MemoryStoreType from pilot.configs.config import Config +from pilot.memory.chat_history.base import BaseChatHistoryMemory CFG = Config() @@ -18,7 +19,15 @@ class ChatHistory: self.mem_store_class_map[DbHistoryMemory.store_type] = DbHistoryMemory self.mem_store_class_map[MemHistoryMemory.store_type] = MemHistoryMemory - def get_store_instance(self, chat_session_id): + def get_store_instance(self, chat_session_id: str) -> BaseChatHistoryMemory: + """New store instance for store chat histories + + Args: + chat_session_id (str): conversation session id + + Returns: + BaseChatHistoryMemory: Store instance + """ return self.mem_store_class_map.get(CFG.CHAT_HISTORY_STORE_TYPE)( chat_session_id ) diff --git a/pilot/memory/chat_history/chat_history_db.py b/pilot/memory/chat_history/chat_history_db.py index 2f9b2999c..8ef898c27 100644 --- a/pilot/memory/chat_history/chat_history_db.py +++ b/pilot/memory/chat_history/chat_history_db.py @@ -1,9 +1,15 @@ -from pilot.base_modules.meta_data.base_dao import BaseDao -from pilot.base_modules.meta_data.meta_data import Base, engine, session from typing import List from sqlalchemy import Column, Integer, String, Index, DateTime, func, Boolean, Text from sqlalchemy import UniqueConstraint +from pilot.base_modules.meta_data.base_dao import BaseDao +from pilot.base_modules.meta_data.meta_data import ( + Base, + engine, + session, + META_DATA_DATABASE, +) + class ChatHistoryEntity(Base): __tablename__ = "chat_history" @@ -23,7 +29,9 @@ class ChatHistoryEntity(Base): chat_mode = Column(String(255), nullable=False, comment="Conversation scene mode") summary = Column(String(255), nullable=False, comment="Conversation record summary") user_name = Column(String(255), nullable=True, comment="interlocutor") - messages = Column(Text, nullable=True, comment="Conversation details") + messages = Column( + Text(length=2**31 - 1), nullable=True, comment="Conversation details" + ) UniqueConstraint("conv_uid", name="uk_conversation") Index("idx_q_user", "user_name") @@ -34,7 +42,10 @@ class ChatHistoryEntity(Base): class ChatHistoryDao(BaseDao[ChatHistoryEntity]): def __init__(self): super().__init__( - database="dbgpt", orm_base=Base, db_engine=engine, session=session + database=META_DATA_DATABASE, + orm_base=Base, + db_engine=engine, + session=session, ) def list_last_20(self, user_name: str = None): diff --git a/pilot/memory/chat_history/store_type/meta_db_history.py b/pilot/memory/chat_history/store_type/meta_db_history.py index cdf631229..8570f37a5 100644 --- a/pilot/memory/chat_history/store_type/meta_db_history.py +++ b/pilot/memory/chat_history/store_type/meta_db_history.py @@ -14,7 +14,7 @@ from ..chat_history_db import ChatHistoryEntity, ChatHistoryDao from pilot.memory.chat_history.base import MemoryStoreType CFG = Config() -logger = logging.getLogger("db_chat_history") +logger = logging.getLogger(__name__) class DbHistoryMemory(BaseChatHistoryMemory): @@ -47,9 +47,7 @@ class DbHistoryMemory(BaseChatHistoryMemory): logger.error("init create conversation log error！" + str(e)) def append(self, once_message: OnceConversation, user_id: str = None) -> None: - logger.info(f"db history append, chat_mode: {once_message.chat_mode}, messages: {once_message.messages}, start_date: {once_message.start_date}, " \ - f"chat_order: {once_message.chat_order}, model_name: {once_message.model_name}, param_type: {once_message.param_type}, " \ - f"param_value: {once_message.param_value}, cost: {once_message.cost}, tokens: {once_message.tokens}") + logger.debug(f"db history append: {once_message}") chat_history: ChatHistoryEntity = self.chat_history_dao.get_by_uid( self.chat_seesion_id ) diff --git a/pilot/mock_datas/db-gpt-test.db b/pilot/mock_datas/db-gpt-test.db deleted file mode 100644 index 4dd7921b4..000000000 Binary files a/pilot/mock_datas/db-gpt-test.db and /dev/null differ diff --git a/pilot/model/adapter.py b/pilot/model/adapter.py index 69b159a13..5ce5b2173 100644 --- a/pilot/model/adapter.py +++ b/pilot/model/adapter.py @@ -320,6 +320,19 @@ class Llama2Adapter(BaseLLMAdaper): return model, tokenizer +class CodeLlamaAdapter(BaseLLMAdaper): + """The model adapter for codellama""" + + def match(self, model_path: str): + return "codellama" in model_path.lower() + + def loader(self, model_path: str, from_pretrained_kwargs: dict): + model, tokenizer = super().loader(model_path, from_pretrained_kwargs) + model.config.eos_token_id = tokenizer.eos_token_id + model.config.pad_token_id = tokenizer.pad_token_id + return model, tokenizer + + class BaichuanAdapter(BaseLLMAdaper): """The model adapter for Baichuan models (e.g., baichuan-inc/Baichuan-13B-Chat)""" @@ -420,6 +433,7 @@ register_llm_model_adapters(FalconAdapater) register_llm_model_adapters(GorillaAdapter) register_llm_model_adapters(GPT4AllAdapter) register_llm_model_adapters(Llama2Adapter) +register_llm_model_adapters(CodeLlamaAdapter) register_llm_model_adapters(BaichuanAdapter) register_llm_model_adapters(WizardLMAdapter) register_llm_model_adapters(LlamaCppAdapater) diff --git a/pilot/model/base.py b/pilot/model/base.py index e89b243c9..d8f020f72 100644 --- a/pilot/model/base.py +++ b/pilot/model/base.py @@ -2,10 +2,13 @@ # -*- coding: utf-8 -*- from enum import Enum -from typing import TypedDict, Optional, Dict, List +from typing import TypedDict, Optional, Dict, List, Any + from dataclasses import dataclass, asdict +import time from datetime import datetime from pilot.utils.parameter_utils import ParameterDescription +from pilot.utils.model_utils import GPUInfo class Message(TypedDict): @@ -47,11 +50,101 @@ class WorkerApplyType(str, Enum): UPDATE_PARAMS = "update_params" +@dataclass +class ModelInferenceMetrics: + """A class to represent metrics for assessing the inference performance of a LLM.""" + + collect_index: Optional[int] = 0 + + start_time_ms: Optional[int] = None + """The timestamp (in milliseconds) when the model inference starts.""" + + end_time_ms: Optional[int] = None + """The timestamp (in milliseconds) when the model inference ends.""" + + current_time_ms: Optional[int] = None + """The current timestamp (in milliseconds) when the model inference return partially output(stream).""" + + first_token_time_ms: Optional[int] = None + """The timestamp (in milliseconds) when the first token is generated.""" + + first_completion_time_ms: Optional[int] = None + """The timestamp (in milliseconds) when the first completion is generated.""" + + first_completion_tokens: Optional[int] = None + """The number of tokens when the first completion is generated.""" + + prompt_tokens: Optional[int] = None + """The number of tokens in the input prompt.""" + + completion_tokens: Optional[int] = None + """The number of tokens in the generated completion.""" + + total_tokens: Optional[int] = None + """The total number of tokens (prompt plus completion).""" + + speed_per_second: Optional[float] = None + """The average number of tokens generated per second.""" + + current_gpu_infos: Optional[List[GPUInfo]] = None + """Current gpu information, all devices""" + + avg_gpu_infos: Optional[List[GPUInfo]] = None + """Average memory usage across all collection points""" + + @staticmethod + def create_metrics( + last_metrics: Optional["ModelInferenceMetrics"] = None, + ) -> "ModelInferenceMetrics": + start_time_ms = last_metrics.start_time_ms if last_metrics else None + first_token_time_ms = last_metrics.first_token_time_ms if last_metrics else None + first_completion_time_ms = ( + last_metrics.first_completion_time_ms if last_metrics else None + ) + first_completion_tokens = ( + last_metrics.first_completion_tokens if last_metrics else None + ) + prompt_tokens = last_metrics.prompt_tokens if last_metrics else None + completion_tokens = last_metrics.completion_tokens if last_metrics else None + total_tokens = last_metrics.total_tokens if last_metrics else None + speed_per_second = last_metrics.speed_per_second if last_metrics else None + current_gpu_infos = last_metrics.current_gpu_infos if last_metrics else None + avg_gpu_infos = last_metrics.avg_gpu_infos if last_metrics else None + + if not start_time_ms: + start_time_ms = time.time_ns() // 1_000_000 + current_time_ms = time.time_ns() // 1_000_000 + end_time_ms = current_time_ms + + return ModelInferenceMetrics( + start_time_ms=start_time_ms, + end_time_ms=end_time_ms, + current_time_ms=current_time_ms, + first_token_time_ms=first_token_time_ms, + first_completion_time_ms=first_completion_time_ms, + first_completion_tokens=first_completion_tokens, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=total_tokens, + speed_per_second=speed_per_second, + current_gpu_infos=current_gpu_infos, + avg_gpu_infos=avg_gpu_infos, + ) + + def to_dict(self) -> Dict: + return asdict(self) + + @dataclass class ModelOutput: text: str error_code: int model_context: Dict = None + finish_reason: str = None + usage: Dict[str, Any] = None + metrics: Optional[ModelInferenceMetrics] = None + + """Some metrics for model inference""" def to_dict(self) -> Dict: return asdict(self) diff --git a/pilot/model/cli.py b/pilot/model/cli.py index 1030adfc2..79b47db82 100644 --- a/pilot/model/cli.py +++ b/pilot/model/cli.py @@ -8,6 +8,7 @@ from pilot.configs.model_config import LOGDIR from pilot.model.base import WorkerApplyType from pilot.model.parameter import ( ModelControllerParameters, + ModelAPIServerParameters, ModelWorkerParameters, ModelParameters, BaseParameters, @@ -441,15 +442,27 @@ def stop_model_worker(port: int): @click.command(name="apiserver") +@EnvArgumentParser.create_click_option(ModelAPIServerParameters) def start_apiserver(**kwargs): - """Start apiserver(TODO)""" - raise NotImplementedError + """Start apiserver""" + + if kwargs["daemon"]: + log_file = os.path.join(LOGDIR, "model_apiserver_uvicorn.log") + _run_current_with_daemon("ModelAPIServer", log_file) + else: + from pilot.model.cluster import run_apiserver + + run_apiserver() @click.command(name="apiserver") -def stop_apiserver(**kwargs): - """Start apiserver(TODO)""" - raise NotImplementedError +@add_stop_server_options +def stop_apiserver(port: int): + """Stop apiserver""" + name = "ModelAPIServer" + if port: + name = f"{name}-{port}" + _stop_service("apiserver", name, port=port) def _stop_all_model_server(**kwargs): diff --git a/pilot/model/cluster/__init__.py b/pilot/model/cluster/__init__.py index 9937ffa0b..a777a8d4b 100644 --- a/pilot/model/cluster/__init__.py +++ b/pilot/model/cluster/__init__.py @@ -21,6 +21,7 @@ from pilot.model.cluster.controller.controller import ( run_model_controller, BaseModelController, ) +from pilot.model.cluster.apiserver.api import run_apiserver from pilot.model.cluster.worker.remote_manager import RemoteWorkerManager @@ -40,4 +41,5 @@ __all__ = [ "ModelRegistryClient", "RemoteWorkerManager", "run_model_controller", + "run_apiserver", ] diff --git a/pilot/model/cluster/apiserver/__init__.py b/pilot/model/cluster/apiserver/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/model/cluster/apiserver/api.py b/pilot/model/cluster/apiserver/api.py new file mode 100644 index 000000000..148a51eed --- /dev/null +++ b/pilot/model/cluster/apiserver/api.py @@ -0,0 +1,443 @@ +"""A server that provides OpenAI-compatible RESTful APIs. It supports: +- Chat Completions. (Reference: https://platform.openai.com/docs/api-reference/chat) + +Adapted from https://github.com/lm-sys/FastChat/blob/main/fastchat/serve/openai_api_server.py +""" +from typing import Optional, List, Dict, Any, Generator + +import logging +import asyncio +import shortuuid +import json +from fastapi import APIRouter, FastAPI +from fastapi import Depends, HTTPException +from fastapi.exceptions import RequestValidationError +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import StreamingResponse +from fastapi.security.http import HTTPAuthorizationCredentials, HTTPBearer + +from pydantic import BaseSettings + +from fastchat.protocol.openai_api_protocol import ( + ChatCompletionResponse, + ChatCompletionResponseStreamChoice, + ChatCompletionStreamResponse, + ChatMessage, + ChatCompletionResponseChoice, + DeltaMessage, + EmbeddingsRequest, + EmbeddingsResponse, + ErrorResponse, + ModelCard, + ModelList, + ModelPermission, + UsageInfo, +) +from fastchat.protocol.api_protocol import ( + APIChatCompletionRequest, + APITokenCheckRequest, + APITokenCheckResponse, + APITokenCheckResponseItem, +) +from fastchat.serve.openai_api_server import create_error_response, check_requests +from fastchat.constants import ErrorCode + +from pilot.component import BaseComponent, ComponentType, SystemApp +from pilot.utils.parameter_utils import EnvArgumentParser +from pilot.scene.base_message import ModelMessage, ModelMessageRoleType +from pilot.model.base import ModelInstance, ModelOutput +from pilot.model.parameter import ModelAPIServerParameters, WorkerType +from pilot.model.cluster import ModelRegistry, ModelRegistryClient +from pilot.model.cluster.manager_base import WorkerManager, WorkerManagerFactory +from pilot.utils.utils import setup_logging + +logger = logging.getLogger(__name__) + + +class APIServerException(Exception): + def __init__(self, code: int, message: str): + self.code = code + self.message = message + + +class APISettings(BaseSettings): + api_keys: Optional[List[str]] = None + + +api_settings = APISettings() +get_bearer_token = HTTPBearer(auto_error=False) + + +async def check_api_key( + auth: Optional[HTTPAuthorizationCredentials] = Depends(get_bearer_token), +) -> str: + if api_settings.api_keys: + if auth is None or (token := auth.credentials) not in api_settings.api_keys: + raise HTTPException( + status_code=401, + detail={ + "error": { + "message": "", + "type": "invalid_request_error", + "param": None, + "code": "invalid_api_key", + } + }, + ) + return token + else: + # api_keys not set; allow all + return None + + +class APIServer(BaseComponent): + name = ComponentType.MODEL_API_SERVER + + def init_app(self, system_app: SystemApp): + self.system_app = system_app + + def get_worker_manager(self) -> WorkerManager: + """Get the worker manager component instance + + Raises: + APIServerException: If can't get worker manager component instance + """ + worker_manager = self.system_app.get_component( + ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory + ).create() + if not worker_manager: + raise APIServerException( + ErrorCode.INTERNAL_ERROR, + f"Could not get component {ComponentType.WORKER_MANAGER_FACTORY} from system_app", + ) + return worker_manager + + def get_model_registry(self) -> ModelRegistry: + """Get the model registry component instance + + Raises: + APIServerException: If can't get model registry component instance + """ + + controller = self.system_app.get_component( + ComponentType.MODEL_REGISTRY, ModelRegistry + ) + if not controller: + raise APIServerException( + ErrorCode.INTERNAL_ERROR, + f"Could not get component {ComponentType.MODEL_REGISTRY} from system_app", + ) + return controller + + async def get_model_instances_or_raise( + self, model_name: str + ) -> List[ModelInstance]: + """Get healthy model instances with request model name + + Args: + model_name (str): Model name + + Raises: + APIServerException: If can't get healthy model instances with request model name + """ + registry = self.get_model_registry() + registry_model_name = f"{model_name}@llm" + model_instances = await registry.get_all_instances( + registry_model_name, healthy_only=True + ) + if not model_instances: + all_instances = await registry.get_all_model_instances(healthy_only=True) + models = [ + ins.model_name.split("@llm")[0] + for ins in all_instances + if ins.model_name.endswith("@llm") + ] + if models: + models = "&&".join(models) + message = f"Only {models} allowed now, your model {model_name}" + else: + message = f"No models allowed now, your model {model_name}" + raise APIServerException(ErrorCode.INVALID_MODEL, message) + return model_instances + + async def get_available_models(self) -> ModelList: + """Return available models + + Just include LLM and embedding models. + + Returns: + List[ModelList]: The list of models. + """ + registry = self.get_model_registry() + model_instances = await registry.get_all_model_instances(healthy_only=True) + model_name_set = set() + for inst in model_instances: + name, worker_type = WorkerType.parse_worker_key(inst.model_name) + if worker_type == WorkerType.LLM or worker_type == WorkerType.TEXT2VEC: + model_name_set.add(name) + models = list(model_name_set) + models.sort() + # TODO: return real model permission details + model_cards = [] + for m in models: + model_cards.append( + ModelCard( + id=m, root=m, owned_by="DB-GPT", permission=[ModelPermission()] + ) + ) + return ModelList(data=model_cards) + + async def chat_completion_stream_generator( + self, model_name: str, params: Dict[str, Any], n: int + ) -> Generator[str, Any, None]: + """Chat stream completion generator + + Args: + model_name (str): Model name + params (Dict[str, Any]): The parameters pass to model worker + n (int): How many completions to generate for each prompt. + """ + worker_manager = self.get_worker_manager() + id = f"chatcmpl-{shortuuid.random()}" + finish_stream_events = [] + for i in range(n): + # First chunk with role + choice_data = ChatCompletionResponseStreamChoice( + index=i, + delta=DeltaMessage(role="assistant"), + finish_reason=None, + ) + chunk = ChatCompletionStreamResponse( + id=id, choices=[choice_data], model=model_name + ) + yield f"data: {chunk.json(exclude_unset=True, ensure_ascii=False)}\n\n" + + previous_text = "" + async for model_output in worker_manager.generate_stream(params): + model_output: ModelOutput = model_output + if model_output.error_code != 0: + yield f"data: {json.dumps(model_output.to_dict(), ensure_ascii=False)}\n\n" + yield "data: [DONE]\n\n" + return + decoded_unicode = model_output.text.replace("\ufffd", "") + delta_text = decoded_unicode[len(previous_text) :] + previous_text = ( + decoded_unicode + if len(decoded_unicode) > len(previous_text) + else previous_text + ) + + if len(delta_text) == 0: + delta_text = None + choice_data = ChatCompletionResponseStreamChoice( + index=i, + delta=DeltaMessage(content=delta_text), + finish_reason=model_output.finish_reason, + ) + chunk = ChatCompletionStreamResponse( + id=id, choices=[choice_data], model=model_name + ) + if delta_text is None: + if model_output.finish_reason is not None: + finish_stream_events.append(chunk) + continue + yield f"data: {chunk.json(exclude_unset=True, ensure_ascii=False)}\n\n" + # There is not "content" field in the last delta message, so exclude_none to exclude field "content". + for finish_chunk in finish_stream_events: + yield f"data: {finish_chunk.json(exclude_none=True, ensure_ascii=False)}\n\n" + yield "data: [DONE]\n\n" + + async def chat_completion_generate( + self, model_name: str, params: Dict[str, Any], n: int + ) -> ChatCompletionResponse: + """Generate completion + Args: + model_name (str): Model name + params (Dict[str, Any]): The parameters pass to model worker + n (int): How many completions to generate for each prompt. + """ + worker_manager: WorkerManager = self.get_worker_manager() + choices = [] + chat_completions = [] + for i in range(n): + model_output = asyncio.create_task(worker_manager.generate(params)) + chat_completions.append(model_output) + try: + all_tasks = await asyncio.gather(*chat_completions) + except Exception as e: + return create_error_response(ErrorCode.INTERNAL_ERROR, str(e)) + usage = UsageInfo() + for i, model_output in enumerate(all_tasks): + model_output: ModelOutput = model_output + if model_output.error_code != 0: + return create_error_response(model_output.error_code, model_output.text) + choices.append( + ChatCompletionResponseChoice( + index=i, + message=ChatMessage(role="assistant", content=model_output.text), + finish_reason=model_output.finish_reason or "stop", + ) + ) + if model_output.usage: + task_usage = UsageInfo.parse_obj(model_output.usage) + for usage_key, usage_value in task_usage.dict().items(): + setattr(usage, usage_key, getattr(usage, usage_key) + usage_value) + + return ChatCompletionResponse(model=model_name, choices=choices, usage=usage) + + +def get_api_server() -> APIServer: + api_server = global_system_app.get_component( + ComponentType.MODEL_API_SERVER, APIServer, default_component=None + ) + if not api_server: + global_system_app.register(APIServer) + return global_system_app.get_component(ComponentType.MODEL_API_SERVER, APIServer) + + +router = APIRouter() + + +@router.get("/v1/models", dependencies=[Depends(check_api_key)]) +async def get_available_models(api_server: APIServer = Depends(get_api_server)): + return await api_server.get_available_models() + + +@router.post("/v1/chat/completions", dependencies=[Depends(check_api_key)]) +async def create_chat_completion( + request: APIChatCompletionRequest, api_server: APIServer = Depends(get_api_server) +): + await api_server.get_model_instances_or_raise(request.model) + error_check_ret = check_requests(request) + if error_check_ret is not None: + return error_check_ret + params = { + "model": request.model, + "messages": ModelMessage.to_dict_list( + ModelMessage.from_openai_messages(request.messages) + ), + "echo": False, + } + if request.temperature: + params["temperature"] = request.temperature + if request.top_p: + params["top_p"] = request.top_p + if request.max_tokens: + params["max_new_tokens"] = request.max_tokens + if request.stop: + params["stop"] = request.stop + if request.user: + params["user"] = request.user + + # TODO check token length + if request.stream: + generator = api_server.chat_completion_stream_generator( + request.model, params, request.n + ) + return StreamingResponse(generator, media_type="text/event-stream") + return await api_server.chat_completion_generate(request.model, params, request.n) + + +def _initialize_all(controller_addr: str, system_app: SystemApp): + from pilot.model.cluster import RemoteWorkerManager, ModelRegistryClient + from pilot.model.cluster.worker.manager import _DefaultWorkerManagerFactory + + if not system_app.get_component( + ComponentType.MODEL_REGISTRY, ModelRegistry, default_component=None + ): + # Register model registry if not exist + registry = ModelRegistryClient(controller_addr) + registry.name = ComponentType.MODEL_REGISTRY.value + system_app.register_instance(registry) + + registry = system_app.get_component( + ComponentType.MODEL_REGISTRY, ModelRegistry, default_component=None + ) + worker_manager = RemoteWorkerManager(registry) + + # Register worker manager component if not exist + system_app.get_component( + ComponentType.WORKER_MANAGER_FACTORY, + WorkerManagerFactory, + or_register_component=_DefaultWorkerManagerFactory, + worker_manager=worker_manager, + ) + # Register api server component if not exist + system_app.get_component( + ComponentType.MODEL_API_SERVER, APIServer, or_register_component=APIServer + ) + + +def initialize_apiserver( + controller_addr: str, + app=None, + system_app: SystemApp = None, + host: str = None, + port: int = None, + api_keys: List[str] = None, +): + global global_system_app + global api_settings + embedded_mod = True + if not app: + embedded_mod = False + app = FastAPI() + app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["GET", "POST", "PUT", "PATCH", "DELETE", "OPTIONS"], + allow_headers=["*"], + ) + + if not system_app: + system_app = SystemApp(app) + global_system_app = system_app + + if api_keys: + api_settings.api_keys = api_keys + + app.include_router(router, prefix="/api", tags=["APIServer"]) + + @app.exception_handler(APIServerException) + async def validation_apiserver_exception_handler(request, exc: APIServerException): + return create_error_response(exc.code, exc.message) + + @app.exception_handler(RequestValidationError) + async def validation_exception_handler(request, exc): + return create_error_response(ErrorCode.VALIDATION_TYPE_ERROR, str(exc)) + + _initialize_all(controller_addr, system_app) + + if not embedded_mod: + import uvicorn + + uvicorn.run(app, host=host, port=port, log_level="info") + + +def run_apiserver(): + parser = EnvArgumentParser() + env_prefix = "apiserver_" + apiserver_params: ModelAPIServerParameters = parser.parse_args_into_dataclass( + ModelAPIServerParameters, + env_prefixes=[env_prefix], + ) + setup_logging( + "pilot", + logging_level=apiserver_params.log_level, + logger_filename=apiserver_params.log_file, + ) + api_keys = None + if apiserver_params.api_keys: + api_keys = apiserver_params.api_keys.strip().split(",") + + initialize_apiserver( + apiserver_params.controller_addr, + host=apiserver_params.host, + port=apiserver_params.port, + api_keys=api_keys, + ) + + +if __name__ == "__main__": + run_apiserver() diff --git a/pilot/model/cluster/apiserver/tests/__init__.py b/pilot/model/cluster/apiserver/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/model/cluster/apiserver/tests/test_api.py b/pilot/model/cluster/apiserver/tests/test_api.py new file mode 100644 index 000000000..281a8aff6 --- /dev/null +++ b/pilot/model/cluster/apiserver/tests/test_api.py @@ -0,0 +1,248 @@ +import pytest +import pytest_asyncio +from aioresponses import aioresponses +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from httpx import AsyncClient, HTTPError + +from pilot.component import SystemApp +from pilot.utils.openai_utils import chat_completion_stream, chat_completion + +from pilot.model.cluster.apiserver.api import ( + api_settings, + initialize_apiserver, + ModelList, + UsageInfo, + ChatCompletionResponse, + ChatCompletionResponseStreamChoice, + ChatCompletionStreamResponse, + ChatMessage, + ChatCompletionResponseChoice, + DeltaMessage, +) +from pilot.model.cluster.tests.conftest import _new_cluster + +from pilot.model.cluster.worker.manager import _DefaultWorkerManagerFactory + +app = FastAPI() +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["GET", "POST", "PUT", "PATCH", "DELETE", "OPTIONS"], + allow_headers=["*"], +) + + +@pytest_asyncio.fixture +async def system_app(): + return SystemApp(app) + + +@pytest_asyncio.fixture +async def client(request, system_app: SystemApp): + param = getattr(request, "param", {}) + api_keys = param.get("api_keys", []) + client_api_key = param.get("client_api_key") + if "num_workers" not in param: + param["num_workers"] = 2 + if "api_keys" in param: + del param["api_keys"] + headers = {} + if client_api_key: + headers["Authorization"] = "Bearer " + client_api_key + print(f"param: {param}") + if api_settings: + # Clear global api keys + api_settings.api_keys = [] + async with AsyncClient(app=app, base_url="http://test", headers=headers) as client: + async with _new_cluster(**param) as cluster: + worker_manager, model_registry = cluster + system_app.register(_DefaultWorkerManagerFactory, worker_manager) + system_app.register_instance(model_registry) + # print(f"Instances {model_registry.registry}") + initialize_apiserver(None, app, system_app, api_keys=api_keys) + yield client + + +@pytest.mark.asyncio +async def test_get_all_models(client: AsyncClient): + res = await client.get("/api/v1/models") + res.status_code == 200 + model_lists = ModelList.parse_obj(res.json()) + print(f"model list json: {res.json()}") + assert model_lists.object == "list" + assert len(model_lists.data) == 2 + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "client, expected_messages", + [ + ({"stream_messags": ["Hello", " world."]}, "Hello world."), + ({"stream_messags": ["你好，我是", "张三。"]}, "你好，我是张三。"), + ], + indirect=["client"], +) +async def test_chat_completions(client: AsyncClient, expected_messages): + chat_data = { + "model": "test-model-name-0", + "messages": [{"role": "user", "content": "Hello"}], + "stream": True, + } + full_text = "" + async for text in chat_completion_stream( + "/api/v1/chat/completions", chat_data, client + ): + full_text += text + assert full_text == expected_messages + + assert ( + await chat_completion("/api/v1/chat/completions", chat_data, client) + == expected_messages + ) + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "client, expected_messages, client_api_key", + [ + ( + {"stream_messags": ["Hello", " world."], "api_keys": ["abc"]}, + "Hello world.", + "abc", + ), + ({"stream_messags": ["你好，我是", "张三。"], "api_keys": ["abc"]}, "你好，我是张三。", "abc"), + ], + indirect=["client"], +) +async def test_chat_completions_with_openai_lib_async_no_stream( + client: AsyncClient, expected_messages: str, client_api_key: str +): + import openai + + openai.api_key = client_api_key + openai.api_base = "http://test/api/v1" + + model_name = "test-model-name-0" + + with aioresponses() as mocked: + mock_message = {"text": expected_messages} + one_res = ChatCompletionResponseChoice( + index=0, + message=ChatMessage(role="assistant", content=expected_messages), + finish_reason="stop", + ) + data = ChatCompletionResponse( + model=model_name, choices=[one_res], usage=UsageInfo() + ) + mock_message = f"{data.json(exclude_unset=True, ensure_ascii=False)}\n\n" + # Mock http request + mocked.post( + "http://test/api/v1/chat/completions", status=200, body=mock_message + ) + completion = await openai.ChatCompletion.acreate( + model=model_name, + messages=[{"role": "user", "content": "Hello! What is your name?"}], + ) + assert completion.choices[0].message.content == expected_messages + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "client, expected_messages, client_api_key", + [ + ( + {"stream_messags": ["Hello", " world."], "api_keys": ["abc"]}, + "Hello world.", + "abc", + ), + ({"stream_messags": ["你好，我是", "张三。"], "api_keys": ["abc"]}, "你好，我是张三。", "abc"), + ], + indirect=["client"], +) +async def test_chat_completions_with_openai_lib_async_stream( + client: AsyncClient, expected_messages: str, client_api_key: str +): + import openai + + openai.api_key = client_api_key + openai.api_base = "http://test/api/v1" + + model_name = "test-model-name-0" + + with aioresponses() as mocked: + mock_message = {"text": expected_messages} + choice_data = ChatCompletionResponseStreamChoice( + index=0, + delta=DeltaMessage(content=expected_messages), + finish_reason="stop", + ) + chunk = ChatCompletionStreamResponse( + id=0, choices=[choice_data], model=model_name + ) + mock_message = f"data: {chunk.json(exclude_unset=True, ensure_ascii=False)}\n\n" + mocked.post( + "http://test/api/v1/chat/completions", + status=200, + body=mock_message, + content_type="text/event-stream", + ) + + stream_stream_resp = "" + async for stream_resp in await openai.ChatCompletion.acreate( + model=model_name, + messages=[{"role": "user", "content": "Hello! What is your name?"}], + stream=True, + ): + stream_stream_resp = stream_resp.choices[0]["delta"].get("content", "") + assert stream_stream_resp == expected_messages + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "client, expected_messages, api_key_is_error", + [ + ( + { + "stream_messags": ["Hello", " world."], + "api_keys": ["abc", "xx"], + "client_api_key": "abc", + }, + "Hello world.", + False, + ), + ({"stream_messags": ["你好，我是", "张三。"]}, "你好，我是张三。", False), + ( + {"stream_messags": ["你好，我是", "张三。"], "api_keys": ["abc", "xx"]}, + "你好，我是张三。", + True, + ), + ( + { + "stream_messags": ["你好，我是", "张三。"], + "api_keys": ["abc", "xx"], + "client_api_key": "error_api_key", + }, + "你好，我是张三。", + True, + ), + ], + indirect=["client"], +) +async def test_chat_completions_with_api_keys( + client: AsyncClient, expected_messages: str, api_key_is_error: bool +): + chat_data = { + "model": "test-model-name-0", + "messages": [{"role": "user", "content": "Hello"}], + "stream": True, + } + if api_key_is_error: + with pytest.raises(HTTPError): + await chat_completion("/api/v1/chat/completions", chat_data, client) + else: + assert ( + await chat_completion("/api/v1/chat/completions", chat_data, client) + == expected_messages + ) diff --git a/pilot/model/cluster/base.py b/pilot/model/cluster/base.py index 9d22161b1..45e46ab3e 100644 --- a/pilot/model/cluster/base.py +++ b/pilot/model/cluster/base.py @@ -17,8 +17,12 @@ class PromptRequest(BaseModel): temperature: float = None max_new_tokens: int = None stop: str = None + stop_token_ids: List[int] = [] + context_len: int = None echo: bool = True span_id: str = None + metrics: bool = False + """Whether to return metrics of inference""" class EmbeddingsRequest(BaseModel): diff --git a/pilot/model/cluster/controller/controller.py b/pilot/model/cluster/controller/controller.py index 35a91ee3c..0006d91a0 100644 --- a/pilot/model/cluster/controller/controller.py +++ b/pilot/model/cluster/controller/controller.py @@ -13,7 +13,7 @@ from pilot.utils.api_utils import ( _api_remote as api_remote, _sync_api_remote as sync_api_remote, ) -from pilot.utils.utils import setup_logging +from pilot.utils.utils import setup_logging, setup_http_service_logging logger = logging.getLogger(__name__) @@ -66,7 +66,9 @@ class LocalModelController(BaseModelController): f"Get all instances with {model_name}, healthy_only: {healthy_only}" ) if not model_name: - return await self.registry.get_all_model_instances() + return await self.registry.get_all_model_instances( + healthy_only=healthy_only + ) else: return await self.registry.get_all_instances(model_name, healthy_only) @@ -98,8 +100,10 @@ class _RemoteModelController(BaseModelController): class ModelRegistryClient(_RemoteModelController, ModelRegistry): - async def get_all_model_instances(self) -> List[ModelInstance]: - return await self.get_all_instances() + async def get_all_model_instances( + self, healthy_only: bool = False + ) -> List[ModelInstance]: + return await self.get_all_instances(healthy_only=healthy_only) @sync_api_remote(path="/api/controller/models") def sync_get_all_instances( @@ -149,6 +153,7 @@ def initialize_controller( else: import uvicorn + setup_http_service_logging() app = FastAPI() app.include_router(router, prefix="/api", tags=["Model"]) uvicorn.run(app, host=host, port=port, log_level="info") @@ -179,7 +184,8 @@ def run_model_controller(): parser = EnvArgumentParser() env_prefix = "controller_" controller_params: ModelControllerParameters = parser.parse_args_into_dataclass( - ModelControllerParameters, env_prefix=env_prefix + ModelControllerParameters, + env_prefixes=[env_prefix], ) setup_logging( diff --git a/pilot/model/cluster/registry.py b/pilot/model/cluster/registry.py index 398882eb9..eb5f1e415 100644 --- a/pilot/model/cluster/registry.py +++ b/pilot/model/cluster/registry.py @@ -1,22 +1,37 @@ import random import threading import time +import logging from abc import ABC, abstractmethod from collections import defaultdict from datetime import datetime, timedelta -from typing import Dict, List, Tuple +from typing import Dict, List, Optional, Tuple import itertools +from pilot.component import BaseComponent, ComponentType, SystemApp from pilot.model.base import ModelInstance -class ModelRegistry(ABC): +logger = logging.getLogger(__name__) + + +class ModelRegistry(BaseComponent, ABC): """ Abstract base class for a model registry. It provides an interface for registering, deregistering, fetching instances, and sending heartbeats for instances. """ + name = ComponentType.MODEL_REGISTRY + + def __init__(self, system_app: SystemApp | None = None): + self.system_app = system_app + super().__init__(system_app) + + def init_app(self, system_app: SystemApp): + """Initialize the component with the main application.""" + self.system_app = system_app + @abstractmethod async def register_instance(self, instance: ModelInstance) -> bool: """ @@ -65,9 +80,11 @@ class ModelRegistry(ABC): """Fetch all instances of a given model. Optionally, fetch only the healthy instances.""" @abstractmethod - async def get_all_model_instances(self) -> List[ModelInstance]: + async def get_all_model_instances( + self, healthy_only: bool = False + ) -> List[ModelInstance]: """ - Fetch all instances of all models + Fetch all instances of all models, Optionally, fetch only the healthy instances. Returns: - List[ModelInstance]: A list of instances for the all models. @@ -105,8 +122,12 @@ class ModelRegistry(ABC): class EmbeddedModelRegistry(ModelRegistry): def __init__( - self, heartbeat_interval_secs: int = 60, heartbeat_timeout_secs: int = 120 + self, + system_app: SystemApp | None = None, + heartbeat_interval_secs: int = 60, + heartbeat_timeout_secs: int = 120, ): + super().__init__(system_app) self.registry: Dict[str, List[ModelInstance]] = defaultdict(list) self.heartbeat_interval_secs = heartbeat_interval_secs self.heartbeat_timeout_secs = heartbeat_timeout_secs @@ -180,9 +201,14 @@ class EmbeddedModelRegistry(ModelRegistry): instances = [ins for ins in instances if ins.healthy == True] return instances - async def get_all_model_instances(self) -> List[ModelInstance]: - print(self.registry) - return list(itertools.chain(*self.registry.values())) + async def get_all_model_instances( + self, healthy_only: bool = False + ) -> List[ModelInstance]: + logger.debug("Current registry metadata:\n{self.registry}") + instances = list(itertools.chain(*self.registry.values())) + if healthy_only: + instances = [ins for ins in instances if ins.healthy == True] + return instances async def send_heartbeat(self, instance: ModelInstance) -> bool: _, exist_ins = self._get_instances( diff --git a/pilot/model/cluster/tests/__init__.py b/pilot/model/cluster/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/model/cluster/worker/tests/base_tests.py b/pilot/model/cluster/tests/conftest.py similarity index 71% rename from pilot/model/cluster/worker/tests/base_tests.py rename to pilot/model/cluster/tests/conftest.py index 21821d9f9..f614387ac 100644 --- a/pilot/model/cluster/worker/tests/base_tests.py +++ b/pilot/model/cluster/tests/conftest.py @@ -6,6 +6,7 @@ from pilot.model.parameter import ModelParameters, ModelWorkerParameters, Worker from pilot.model.base import ModelOutput from pilot.model.cluster.worker_base import ModelWorker from pilot.model.cluster.worker.manager import ( + WorkerManager, LocalWorkerManager, RegisterFunc, DeregisterFunc, @@ -13,6 +14,23 @@ from pilot.model.cluster.worker.manager import ( ApplyFunction, ) +from pilot.model.base import ModelInstance +from pilot.model.cluster.registry import ModelRegistry, EmbeddedModelRegistry + + +@pytest.fixture +def model_registry(request): + return EmbeddedModelRegistry() + + +@pytest.fixture +def model_instance(): + return ModelInstance( + model_name="test_model", + host="192.168.1.1", + port=5000, + ) + class MockModelWorker(ModelWorker): def __init__( @@ -51,8 +69,10 @@ class MockModelWorker(ModelWorker): raise Exception("Stop worker error for mock") def generate_stream(self, params: Dict) -> Iterator[ModelOutput]: + full_text = "" for msg in self.stream_messags: - yield ModelOutput(text=msg, error_code=0) + full_text += msg + yield ModelOutput(text=full_text, error_code=0) def generate(self, params: Dict) -> ModelOutput: output = None @@ -67,6 +87,8 @@ class MockModelWorker(ModelWorker): _TEST_MODEL_NAME = "vicuna-13b-v1.5" _TEST_MODEL_PATH = "/app/models/vicuna-13b-v1.5" +ClusterType = Tuple[WorkerManager, ModelRegistry] + def _new_worker_params( model_name: str = _TEST_MODEL_NAME, @@ -85,7 +107,9 @@ def _create_workers( worker_type: str = WorkerType.LLM.value, stream_messags: List[str] = None, embeddings: List[List[float]] = None, -) -> List[Tuple[ModelWorker, ModelWorkerParameters]]: + host: str = "127.0.0.1", + start_port=8001, +) -> List[Tuple[ModelWorker, ModelWorkerParameters, ModelInstance]]: workers = [] for i in range(num_workers): model_name = f"test-model-name-{i}" @@ -98,10 +122,16 @@ def _create_workers( stream_messags=stream_messags, embeddings=embeddings, ) + model_instance = ModelInstance( + model_name=WorkerType.to_worker_key(model_name, worker_type), + host=host, + port=start_port + i, + healthy=True, + ) worker_params = _new_worker_params( model_name, model_path, worker_type=worker_type ) - workers.append((worker, worker_params)) + workers.append((worker, worker_params, model_instance)) return workers @@ -127,12 +157,12 @@ async def _start_worker_manager(**kwargs): model_registry=model_registry, ) - for worker, worker_params in _create_workers( + for worker, worker_params, model_instance in _create_workers( num_workers, error_worker, stop_error, stream_messags, embeddings ): worker_manager.add_worker(worker, worker_params) if workers: - for worker, worker_params in workers: + for worker, worker_params, model_instance in workers: worker_manager.add_worker(worker, worker_params) if start: @@ -143,6 +173,15 @@ async def _start_worker_manager(**kwargs): await worker_manager.stop() +async def _create_model_registry( + workers: List[Tuple[ModelWorker, ModelWorkerParameters, ModelInstance]] +) -> ModelRegistry: + registry = EmbeddedModelRegistry() + for _, _, inst in workers: + assert await registry.register_instance(inst) == True + return registry + + @pytest_asyncio.fixture async def manager_2_workers(request): param = getattr(request, "param", {}) @@ -166,3 +205,27 @@ async def manager_2_embedding_workers(request): ) async with _start_worker_manager(workers=workers, **param) as worker_manager: yield (worker_manager, workers) + + +@asynccontextmanager +async def _new_cluster(**kwargs) -> ClusterType: + num_workers = kwargs.get("num_workers", 0) + workers = _create_workers( + num_workers, stream_messags=kwargs.get("stream_messags", []) + ) + if "num_workers" in kwargs: + del kwargs["num_workers"] + registry = await _create_model_registry( + workers, + ) + async with _start_worker_manager(workers=workers, **kwargs) as worker_manager: + yield (worker_manager, registry) + + +@pytest_asyncio.fixture +async def cluster_2_workers(request): + param = getattr(request, "param", {}) + workers = _create_workers(2) + registry = await _create_model_registry(workers) + async with _start_worker_manager(workers=workers, **param) as worker_manager: + yield (worker_manager, registry) diff --git a/pilot/model/cluster/worker/default_worker.py b/pilot/model/cluster/worker/default_worker.py index 04b47cbdb..89d064bcf 100644 --- a/pilot/model/cluster/worker/default_worker.py +++ b/pilot/model/cluster/worker/default_worker.py @@ -1,14 +1,17 @@ import os import logging -from typing import Dict, Iterator, List + +from typing import Dict, Iterator, List, Optional +import time +import traceback from pilot.configs.model_config import get_device from pilot.model.model_adapter import get_llm_model_adapter, LLMModelAdaper -from pilot.model.base import ModelOutput +from pilot.model.base import ModelOutput, ModelInferenceMetrics from pilot.model.loader import ModelLoader, _get_model_real_path from pilot.model.parameter import ModelParameters from pilot.model.cluster.worker_base import ModelWorker -from pilot.utils.model_utils import _clear_model_cache +from pilot.utils.model_utils import _clear_model_cache, _get_current_cuda_memory from pilot.utils.parameter_utils import EnvArgumentParser, _get_dict_from_obj from pilot.utils.tracer import root_tracer, SpanType, SpanTypeRunName from pilot.utils.system_utils import get_system_info @@ -60,7 +63,7 @@ class DefaultModelWorker(ModelWorker): self.ml: ModelLoader = ModelLoader( model_path=self.model_path, model_name=self.model_name ) - # TODO read context len from model config + # Default model context len self.context_len = 2048 def model_param_class(self) -> ModelParameters: @@ -76,7 +79,7 @@ class DefaultModelWorker(ModelWorker): model_type = self.llm_adapter.model_type() model_params: ModelParameters = model_args.parse_args_into_dataclass( param_cls, - env_prefix=env_prefix, + env_prefixes=[env_prefix, "LLM_"], command_args=command_args, model_name=self.model_name, model_path=self.model_path, @@ -111,6 +114,12 @@ class DefaultModelWorker(ModelWorker): self.model, self.tokenizer = self.ml.loader_with_params( model_params, self.llm_adapter ) + model_max_length = _parse_model_max_length(self.model, self.tokenizer) + if model_max_length: + logger.info( + f"Parse model max length {model_max_length} from model {self.model_name}." + ) + self.context_len = model_max_length def stop(self) -> None: if not self.model: @@ -138,14 +147,29 @@ class DefaultModelWorker(ModelWorker): ) previous_response = "" + last_metrics = ModelInferenceMetrics.create_metrics() + is_first_generate = True + context_len = params.get("context_len") or self.context_len for output in generate_stream_func( - self.model, self.tokenizer, params, get_device(), self.context_len + self.model, self.tokenizer, params, get_device(), context_len ): - model_output, incremental_output, output_str = self._handle_output( - output, previous_response, model_context + ( + model_output, + incremental_output, + output_str, + current_metrics, + ) = self._handle_output( + output, + previous_response, + model_context, + last_metrics, + is_first_generate, ) + if is_first_generate: + is_first_generate = False previous_response = output_str + last_metrics = current_metrics yield model_output print( f"\n\nfull stream output:\n{previous_response}\n\nmodel generate_stream params:\n{params}" @@ -183,14 +207,30 @@ class DefaultModelWorker(ModelWorker): ) previous_response = "" + context_len = params.get("context_len") or self.context_len + last_metrics = ModelInferenceMetrics.create_metrics() + is_first_generate = True async for output in generate_stream_func( - self.model, self.tokenizer, params, get_device(), self.context_len + self.model, self.tokenizer, params, get_device(), context_len ): - model_output, incremental_output, output_str = self._handle_output( - output, previous_response, model_context + ( + model_output, + incremental_output, + output_str, + current_metrics, + ) = self._handle_output( + output, + previous_response, + model_context, + last_metrics, + is_first_generate, ) + if is_first_generate: + is_first_generate = False + previous_response = output_str + last_metrics = current_metrics yield model_output print( f"\n\nfull stream output:\n{previous_response}\n\nmodel generate_stream params:\n{params}" @@ -213,6 +253,7 @@ class DefaultModelWorker(ModelWorker): params, self.model_name, self.model_path, + self.tokenizer, prompt_template=self.ml.prompt_template, ) stream_type = "" @@ -229,7 +270,9 @@ class DefaultModelWorker(ModelWorker): self.model, self.model_path ) str_prompt = params.get("prompt") - print(f"model prompt: \n\n{str_prompt}\n\n{stream_type}stream output:\n") + print( + f"llm_adapter: {str(self.llm_adapter)}\n\nmodel prompt: \n\n{str_prompt}\n\n{stream_type}stream output:\n" + ) generate_stream_func_str_name = "{}.{}".format( generate_stream_func.__module__, generate_stream_func.__name__ @@ -255,28 +298,141 @@ class DefaultModelWorker(ModelWorker): return params, model_context, generate_stream_func, model_span - def _handle_output(self, output, previous_response, model_context): + def _handle_output( + self, + output, + previous_response, + model_context, + last_metrics: ModelInferenceMetrics, + is_first_generate: bool, + ): + finish_reason = None + usage = None if isinstance(output, dict): finish_reason = output.get("finish_reason") + usage = output.get("usage") output = output["text"] if finish_reason is not None: logger.info(f"finish_reason: {finish_reason}") incremental_output = output[len(previous_response) :] print(incremental_output, end="", flush=True) + + metrics = _new_metrics_from_model_output(last_metrics, is_first_generate, usage) model_output = ModelOutput( - text=output, error_code=0, model_context=model_context + text=output, + error_code=0, + model_context=model_context, + finish_reason=finish_reason, + usage=usage, + metrics=metrics, ) - return model_output, incremental_output, output + return model_output, incremental_output, output, metrics def _handle_exception(self, e): # Check if the exception is a torch.cuda.CudaError and if torch was imported. if _torch_imported and isinstance(e, torch.cuda.CudaError): model_output = ModelOutput( - text="**GPU OutOfMemory, Please Refresh.**", error_code=0 + text="**GPU OutOfMemory, Please Refresh.**", error_code=1 ) else: + msg = traceback.format_exc() + logger.error(f"Model inference error, detail: {msg}") model_output = ModelOutput( text=f"**LLMServer Generate Error, Please CheckErrorInfo.**: {e}", - error_code=0, + error_code=1, ) return model_output + + +def _parse_model_max_length(model, tokenizer) -> Optional[int]: + if not (tokenizer or model): + return None + try: + if tokenizer and hasattr(tokenizer, "model_max_length"): + return tokenizer.model_max_length + if model and hasattr(model, "config"): + model_config = model.config + if hasattr(model_config, "max_sequence_length"): + return model_config.max_sequence_length + if hasattr(model_config, "max_position_embeddings"): + return model_config.max_position_embeddings + except Exception: + return None + + +def _new_metrics_from_model_output( + last_metric: ModelInferenceMetrics, + is_first_generate: bool, + usage: Optional[Dict] = None, +) -> ModelInferenceMetrics: + metrics = ModelInferenceMetrics.create_metrics(last_metric) + metrics.collect_index = last_metric.collect_index + 1 + if is_first_generate: + logger.info(f"is_first_generate, usage: {usage}") + metrics.first_completion_time_ms = time.time_ns() // 1_000_000 + + if not usage or not isinstance(usage, dict): + return metrics + prompt_tokens = usage.get("prompt_tokens") + completion_tokens = usage.get("completion_tokens") + total_tokens = usage.get("total_tokens") + + if prompt_tokens is None: + prompt_tokens = metrics.prompt_tokens + if completion_tokens is None: + completion_tokens = metrics.completion_tokens + if total_tokens is None: + total_tokens = metrics.total_tokens + + if is_first_generate and (completion_tokens is not None): + # completion_tokens == 0 is prefill + metrics.first_completion_tokens = completion_tokens + if completion_tokens == 1: + metrics.first_token_time_ms = metrics.first_completion_time_ms + if ( + not is_first_generate + and metrics.first_token_time_ms is None + and completion_tokens == 1 + ): + # Case: first generate has 0 token, and second generate has 1 token + metrics.first_token_time_ms = time.time_ns() // 1_000_000 + + if prompt_tokens: + metrics.prompt_tokens = prompt_tokens + if completion_tokens: + metrics.completion_tokens = completion_tokens + if total_tokens: + metrics.total_tokens = total_tokens + elif prompt_tokens and completion_tokens: + total_tokens = prompt_tokens + completion_tokens + metrics.total_tokens = total_tokens + + if total_tokens: + # time cost(seconds) + duration = (metrics.current_time_ms - metrics.start_time_ms) / 1000.0 + metrics.speed_per_second = total_tokens / duration + + current_gpu_infos = _get_current_cuda_memory() + metrics.current_gpu_infos = current_gpu_infos + if not metrics.avg_gpu_infos: + metrics.avg_gpu_infos = current_gpu_infos + elif current_gpu_infos: + for i, last_avg in enumerate(metrics.avg_gpu_infos): + allocated_memory_gb = ( + last_avg.allocated_memory_gb * (metrics.collect_index - 1) + + current_gpu_infos[i].allocated_memory_gb + ) + metrics.avg_gpu_infos[i].allocated_memory_gb = ( + allocated_memory_gb / metrics.collect_index + ) + metrics.avg_gpu_infos[i].total_memory_gb = current_gpu_infos[ + i + ].total_memory_gb + metrics.avg_gpu_infos[i].cached_memory_gb = current_gpu_infos[ + i + ].cached_memory_gb + metrics.avg_gpu_infos[i].available_memory_gb = current_gpu_infos[ + i + ].available_memory_gb + + return metrics diff --git a/pilot/model/cluster/worker/embedding_worker.py b/pilot/model/cluster/worker/embedding_worker.py index 62b799864..22c644034 100644 --- a/pilot/model/cluster/worker/embedding_worker.py +++ b/pilot/model/cluster/worker/embedding_worker.py @@ -106,7 +106,7 @@ def _parse_embedding_params( env_prefix = EnvArgumentParser.get_env_prefix(model_name) model_params: BaseEmbeddingModelParameters = model_args.parse_args_into_dataclass( param_cls, - env_prefix=env_prefix, + env_prefixes=[env_prefix], command_args=command_args, model_name=model_name, model_path=model_path, diff --git a/pilot/model/cluster/worker/manager.py b/pilot/model/cluster/worker/manager.py index cc5ef97d6..9c1c4f7b3 100644 --- a/pilot/model/cluster/worker/manager.py +++ b/pilot/model/cluster/worker/manager.py @@ -38,7 +38,7 @@ from pilot.utils.parameter_utils import ( _dict_to_command_args, _get_dict_from_obj, ) -from pilot.utils.utils import setup_logging +from pilot.utils.utils import setup_logging, setup_http_service_logging from pilot.utils.tracer import initialize_tracer, root_tracer, SpanType, SpanTypeRunName from pilot.utils.system_utils import get_system_info @@ -99,9 +99,7 @@ class LocalWorkerManager(WorkerManager): ) def _worker_key(self, worker_type: str, model_name: str) -> str: - if isinstance(worker_type, WorkerType): - worker_type = worker_type.value - return f"{model_name}@{worker_type}" + return WorkerType.to_worker_key(model_name, worker_type) async def run_blocking_func(self, func, *args): if asyncio.iscoroutinefunction(func): @@ -121,7 +119,10 @@ class LocalWorkerManager(WorkerManager): _async_heartbeat_sender(self.run_data, 20, self.send_heartbeat_func) ) for listener in self.start_listeners: - listener(self) + if asyncio.iscoroutinefunction(listener): + await listener(self) + else: + listener(self) async def stop(self, ignore_exception: bool = False): if not self.run_data.stop_event.is_set(): @@ -327,7 +328,7 @@ class LocalWorkerManager(WorkerManager): except Exception as e: yield ModelOutput( text=f"**LLMServer Generate Error, Please CheckErrorInfo.**: {e}", - error_code=0, + error_code=1, ) return async with worker_run_data.semaphore: @@ -357,7 +358,7 @@ class LocalWorkerManager(WorkerManager): except Exception as e: return ModelOutput( text=f"**LLMServer Generate Error, Please CheckErrorInfo.**: {e}", - error_code=0, + error_code=1, ) async with worker_run_data.semaphore: if worker_run_data.worker.support_async(): @@ -735,6 +736,8 @@ def _setup_fastapi( ): if not app: app = FastAPI() + setup_http_service_logging() + if worker_params.standalone: from pilot.model.cluster.controller.controller import initialize_controller from pilot.model.cluster.controller.controller import ( @@ -781,7 +784,7 @@ def _parse_worker_params( env_prefix = EnvArgumentParser.get_env_prefix(model_name) worker_params: ModelWorkerParameters = worker_args.parse_args_into_dataclass( ModelWorkerParameters, - env_prefix=env_prefix, + env_prefixes=[env_prefix], model_name=model_name, model_path=model_path, **kwargs, @@ -790,7 +793,7 @@ def _parse_worker_params( # Read parameters agein with prefix of model name. new_worker_params = worker_args.parse_args_into_dataclass( ModelWorkerParameters, - env_prefix=env_prefix, + env_prefixes=[env_prefix], model_name=worker_params.model_name, model_path=worker_params.model_path, **kwargs, @@ -996,11 +999,17 @@ def run_worker_manager( port: int = None, embedding_model_name: str = None, embedding_model_path: str = None, + start_listener: Callable[["WorkerManager"], None] = None, + **kwargs, ): global worker_manager worker_params: ModelWorkerParameters = _parse_worker_params( - model_name=model_name, model_path=model_path, standalone=standalone, port=port + model_name=model_name, + model_path=model_path, + standalone=standalone, + port=port, + **kwargs, ) setup_logging( @@ -1021,6 +1030,7 @@ def run_worker_manager( system_app, os.path.join(LOGDIR, worker_params.tracer_file), root_operation_name="DB-GPT-WorkerManager-Entry", + tracer_storage_cls=worker_params.tracer_storage_cls, ) _start_local_worker(worker_manager, worker_params) @@ -1028,6 +1038,8 @@ def run_worker_manager( worker_manager, embedding_model_name, embedding_model_path ) + worker_manager.after_start(start_listener) + if include_router: app.include_router(router, prefix="/api") diff --git a/pilot/model/cluster/worker/remote_manager.py b/pilot/model/cluster/worker/remote_manager.py index 61b608cc7..4047f428e 100644 --- a/pilot/model/cluster/worker/remote_manager.py +++ b/pilot/model/cluster/worker/remote_manager.py @@ -15,7 +15,10 @@ class RemoteWorkerManager(LocalWorkerManager): async def start(self): for listener in self.start_listeners: - listener(self) + if asyncio.iscoroutinefunction(listener): + await listener(self) + else: + listener(self) async def stop(self, ignore_exception: bool = False): pass diff --git a/pilot/model/cluster/worker/remote_worker.py b/pilot/model/cluster/worker/remote_worker.py index f974ba714..80898abdf 100644 --- a/pilot/model/cluster/worker/remote_worker.py +++ b/pilot/model/cluster/worker/remote_worker.py @@ -13,7 +13,7 @@ class RemoteModelWorker(ModelWorker): def __init__(self) -> None: self.headers = {} # TODO Configured by ModelParameters - self.timeout = 180 + self.timeout = 3600 self.host = None self.port = None diff --git a/pilot/model/cluster/worker/tests/test_manager.py b/pilot/model/cluster/worker/tests/test_manager.py index 919e64f99..681fb49a3 100644 --- a/pilot/model/cluster/worker/tests/test_manager.py +++ b/pilot/model/cluster/worker/tests/test_manager.py @@ -3,7 +3,7 @@ import pytest from typing import List, Iterator, Dict, Tuple from dataclasses import asdict from pilot.model.parameter import ModelParameters, ModelWorkerParameters, WorkerType -from pilot.model.base import ModelOutput, WorkerApplyType +from pilot.model.base import ModelOutput, WorkerApplyType, ModelInstance from pilot.model.cluster.base import WorkerApplyRequest, WorkerStartupRequest from pilot.model.cluster.worker_base import ModelWorker from pilot.model.cluster.manager_base import WorkerRunData @@ -14,7 +14,7 @@ from pilot.model.cluster.worker.manager import ( SendHeartbeatFunc, ApplyFunction, ) -from pilot.model.cluster.worker.tests.base_tests import ( +from pilot.model.cluster.tests.conftest import ( MockModelWorker, manager_2_workers, manager_with_2_workers, @@ -216,7 +216,7 @@ async def test__remove_worker(): workers = _create_workers(3) async with _start_worker_manager(workers=workers, stop=False) as manager: assert len(manager.workers) == 3 - for _, worker_params in workers: + for _, worker_params, _ in workers: manager._remove_worker(worker_params) not_exist_parmas = _new_worker_params( model_name="this is a not exist worker params" @@ -229,7 +229,7 @@ async def test__remove_worker(): async def test_model_startup(mock_build_worker): async with _start_worker_manager() as manager: workers = _create_workers(1) - worker, worker_params = workers[0] + worker, worker_params, model_instance = workers[0] mock_build_worker.return_value = worker req = WorkerStartupRequest( @@ -245,7 +245,7 @@ async def test_model_startup(mock_build_worker): async with _start_worker_manager() as manager: workers = _create_workers(1, error_worker=True) - worker, worker_params = workers[0] + worker, worker_params, model_instance = workers[0] mock_build_worker.return_value = worker req = WorkerStartupRequest( host="127.0.0.1", @@ -263,7 +263,7 @@ async def test_model_startup(mock_build_worker): async def test_model_shutdown(mock_build_worker): async with _start_worker_manager(start=False, stop=False) as manager: workers = _create_workers(1) - worker, worker_params = workers[0] + worker, worker_params, model_instance = workers[0] mock_build_worker.return_value = worker req = WorkerStartupRequest( @@ -298,7 +298,7 @@ async def test_get_model_instances(is_async): workers = _create_workers(3) async with _start_worker_manager(workers=workers, stop=False) as manager: assert len(manager.workers) == 3 - for _, worker_params in workers: + for _, worker_params, _ in workers: model_name = worker_params.model_name worker_type = worker_params.worker_type if is_async: @@ -326,7 +326,7 @@ async def test__simple_select( ] ): manager, workers = manager_with_2_workers - for _, worker_params in workers: + for _, worker_params, _ in workers: model_name = worker_params.model_name worker_type = worker_params.worker_type instances = await manager.get_model_instances(worker_type, model_name) @@ -351,7 +351,7 @@ async def test_select_one_instance( ], ): manager, workers = manager_with_2_workers - for _, worker_params in workers: + for _, worker_params, _ in workers: model_name = worker_params.model_name worker_type = worker_params.worker_type if is_async: @@ -376,7 +376,7 @@ async def test__get_model( ], ): manager, workers = manager_with_2_workers - for _, worker_params in workers: + for _, worker_params, _ in workers: model_name = worker_params.model_name worker_type = worker_params.worker_type params = {"model": model_name} @@ -403,13 +403,13 @@ async def test_generate_stream( expected_messages: str, ): manager, workers = manager_with_2_workers - for _, worker_params in workers: + for _, worker_params, _ in workers: model_name = worker_params.model_name worker_type = worker_params.worker_type params = {"model": model_name} text = "" async for out in manager.generate_stream(params): - text += out.text + text = out.text assert text == expected_messages @@ -417,8 +417,8 @@ async def test_generate_stream( @pytest.mark.parametrize( "manager_with_2_workers, expected_messages", [ - ({"stream_messags": ["Hello", " world."]}, " world."), - ({"stream_messags": ["你好，我是", "张三。"]}, "张三。"), + ({"stream_messags": ["Hello", " world."]}, "Hello world."), + ({"stream_messags": ["你好，我是", "张三。"]}, "你好，我是张三。"), ], indirect=["manager_with_2_workers"], ) @@ -429,7 +429,7 @@ async def test_generate( expected_messages: str, ): manager, workers = manager_with_2_workers - for _, worker_params in workers: + for _, worker_params, _ in workers: model_name = worker_params.model_name worker_type = worker_params.worker_type params = {"model": model_name} @@ -454,7 +454,7 @@ async def test_embeddings( is_async: bool, ): manager, workers = manager_2_embedding_workers - for _, worker_params in workers: + for _, worker_params, _ in workers: model_name = worker_params.model_name worker_type = worker_params.worker_type params = {"model": model_name, "input": ["hello", "world"]} @@ -472,7 +472,7 @@ async def test_parameter_descriptions( ] ): manager, workers = manager_with_2_workers - for _, worker_params in workers: + for _, worker_params, _ in workers: model_name = worker_params.model_name worker_type = worker_params.worker_type params = await manager.parameter_descriptions(worker_type, model_name) diff --git a/pilot/model/conversation.py b/pilot/model/conversation.py index b3674e946..5d4309d9f 100644 --- a/pilot/model/conversation.py +++ b/pilot/model/conversation.py @@ -339,6 +339,27 @@ register_conv_template( ) ) + +# codellama template +# reference: https://github.com/facebookresearch/llama/blob/cfc3fc8c1968d390eb830e65c63865e980873a06/llama/generation.py#L212 +# reference2 : https://github.com/eosphoros-ai/DB-GPT-Hub/blob/main/README.zh.md +register_conv_template( + Conversation( + name="codellama", + system="[INST] <>\nI want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request." + "If you don't know the answer to the request, please don't share false information.\n<>\n\n", + roles=("[INST]", "[/INST]"), + messages=(), + offset=0, + sep_style=SeparatorStyle.LLAMA2, + sep=" ", + sep2=" ", + stop_token_ids=[2], + system_formatter=lambda msg: f"[INST] <>\n{msg}\n<>\n\n", + ) +) + + # Alpaca default template register_conv_template( Conversation( diff --git a/pilot/model/llm_out/hf_chat_llm.py b/pilot/model/llm_out/hf_chat_llm.py new file mode 100644 index 000000000..570b20b09 --- /dev/null +++ b/pilot/model/llm_out/hf_chat_llm.py @@ -0,0 +1,54 @@ +import logging +import torch +from threading import Thread +from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer +from pilot.scene.base_message import ModelMessage, ModelMessageRoleType + +logger = logging.getLogger(__name__) + + +@torch.inference_mode() +def huggingface_chat_generate_stream( + model: AutoModelForCausalLM, + tokenizer: AutoTokenizer, + params, + device, + context_len=4096, +): + prompt = params["prompt"] + temperature = float(params.get("temperature", 0.7)) + top_p = float(params.get("top_p", 1.0)) + echo = params.get("echo", False) + max_new_tokens = int(params.get("max_new_tokens", 2048)) + + input_ids = tokenizer(prompt).input_ids + # input_ids = input_ids.to(device) + if model.config.is_encoder_decoder: + max_src_len = context_len + else: # truncate + max_src_len = context_len - max_new_tokens - 1 + input_ids = input_ids[-max_src_len:] + input_echo_len = len(input_ids) + input_ids = torch.as_tensor([input_ids], device=device) + + # messages = params["messages"] + # messages = ModelMessage.to_openai_messages(messages) + # input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') + # input_ids = input_ids.to(device) + + streamer = TextIteratorStreamer( + tokenizer, skip_prompt=not echo, skip_special_tokens=True + ) + generate_kwargs = { + "input_ids": input_ids, + "max_length": context_len, + "temperature": temperature, + "streamer": streamer, + } + + thread = Thread(target=model.generate, kwargs=generate_kwargs) + thread.start() + out = "" + for new_text in streamer: + out += new_text + yield out diff --git a/pilot/model/llm_out/vllm_llm.py b/pilot/model/llm_out/vllm_llm.py index 07d43dc74..de108c87c 100644 --- a/pilot/model/llm_out/vllm_llm.py +++ b/pilot/model/llm_out/vllm_llm.py @@ -1,9 +1,13 @@ from typing import Dict +import os from vllm import AsyncLLMEngine from vllm.utils import random_uuid from vllm.sampling_params import SamplingParams +_IS_BENCHMARK = os.getenv("DB_GPT_MODEL_BENCHMARK", "False").lower() == "true" + + async def generate_stream( model: AsyncLLMEngine, tokenizer, params: Dict, device: str, context_len: int ): @@ -37,15 +41,29 @@ async def generate_stream( top_p = max(top_p, 1e-5) if temperature <= 1e-5: top_p = 1.0 + gen_params = { + "stop": list(stop), + "ignore_eos": False, + } + prompt_token_ids = None + if _IS_BENCHMARK: + gen_params["stop"] = [] + gen_params["ignore_eos"] = True + prompt_len = context_len - max_new_tokens - 2 + prompt_token_ids = tokenizer([prompt]).input_ids[0] + prompt_token_ids = prompt_token_ids[-prompt_len:] sampling_params = SamplingParams( n=1, temperature=temperature, top_p=top_p, use_beam_search=False, - stop=list(stop), max_tokens=max_new_tokens, + **gen_params + ) + + results_generator = model.generate( + prompt, sampling_params, request_id, prompt_token_ids=prompt_token_ids ) - results_generator = model.generate(prompt, sampling_params, request_id) async for request_output in results_generator: prompt = request_output.prompt if echo: @@ -53,4 +71,25 @@ async def generate_stream( else: text_outputs = [output.text for output in request_output.outputs] text_outputs = " ".join(text_outputs) - yield {"text": text_outputs, "error_code": 0, "usage": {}} + + # Note: usage is not supported yet + prompt_tokens = len(request_output.prompt_token_ids) + completion_tokens = sum( + len(output.token_ids) for output in request_output.outputs + ) + usage = { + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + "total_tokens": prompt_tokens + completion_tokens, + } + finish_reason = ( + request_output.outputs[0].finish_reason + if len(request_output.outputs) == 1 + else [output.finish_reason for output in request_output.outputs] + ) + yield { + "text": text_outputs, + "error_code": 0, + "usage": usage, + "finish_reason": finish_reason, + } diff --git a/pilot/model/loader.py b/pilot/model/loader.py index 2f5f10c2d..b7cf57815 100644 --- a/pilot/model/loader.py +++ b/pilot/model/loader.py @@ -95,7 +95,7 @@ class ModelLoader: env_prefix = env_prefix.replace("-", "_") model_params = args_parser.parse_args_into_dataclass( param_cls, - env_prefix=env_prefix, + env_prefixes=[env_prefix], device=self.device, model_path=self.model_path, model_name=self.model_name, diff --git a/pilot/model/model_adapter.py b/pilot/model/model_adapter.py index 57e3cf251..adf6321f0 100644 --- a/pilot/model/model_adapter.py +++ b/pilot/model/model_adapter.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Callable, List, Dict, Type, Tuple, TYPE_CHECKING +from typing import Callable, List, Dict, Type, Tuple, TYPE_CHECKING, Any, Optional import dataclasses import logging import threading @@ -39,14 +39,27 @@ if TYPE_CHECKING: logger = logging.getLogger(__name__) thread_local = threading.local() +_IS_BENCHMARK = os.getenv("DB_GPT_MODEL_BENCHMARK", "False").lower() == "true" _OLD_MODELS = [ "llama-cpp", "proxyllm", "gptj-6b", + "codellama-13b-sql-sft", + "codellama-7b", + "codellama-7b-sql-sft", + "codellama-13b", ] +_NEW_HF_CHAT_MODELS = [ + "yi-34b", + "yi-6b", +] + +# The implementation of some models in fastchat will affect the DB-GPT loading model and will be temporarily added to the blacklist. +_BLACK_LIST_MODLE_PROMPT = ["OpenHermes-2.5-Mistral-7B"] + class LLMModelAdaper: """New Adapter for DB-GPT LLM models""" @@ -95,26 +108,25 @@ class LLMModelAdaper: """Get the default conv template""" raise NotImplementedError - def model_adaptation( + def get_str_prompt( self, params: Dict, + messages: List[ModelMessage], + tokenizer: Any, + prompt_template: str = None, + ) -> Optional[str]: + return None + + def get_prompt_with_template( + self, + params: Dict, + messages: List[ModelMessage], model_name: str, model_path: str, + model_context: Dict, prompt_template: str = None, - ) -> Tuple[Dict, Dict]: - """Params adaptation""" + ): conv = self.get_default_conv_template(model_name, model_path) - messages = params.get("messages") - # Some model scontext to dbgpt server - model_context = {"prompt_echo_len_char": -1, "has_format_prompt": False} - - if messages: - # Dict message to ModelMessage - messages = [ - m if isinstance(m, ModelMessage) else ModelMessage(**m) - for m in messages - ] - params["messages"] = messages if prompt_template: logger.info(f"Use prompt template {prompt_template} from config") @@ -124,10 +136,13 @@ class LLMModelAdaper: logger.info( f"No conv from model_path {model_path} or no messages in params, {self}" ) - return params, model_context + return None, None, None conv = conv.copy() system_messages = [] + user_messages = [] + ai_messages = [] + for message in messages: role, content = None, None if isinstance(message, ModelMessage): @@ -143,17 +158,71 @@ class LLMModelAdaper: # Support for multiple system messages system_messages.append(content) elif role == ModelMessageRoleType.HUMAN: - conv.append_message(conv.roles[0], content) + # conv.append_message(conv.roles[0], content) + user_messages.append(content) elif role == ModelMessageRoleType.AI: - conv.append_message(conv.roles[1], content) + # conv.append_message(conv.roles[1], content) + ai_messages.append(content) else: raise ValueError(f"Unknown role: {role}") + + can_use_systems: [] = [] if system_messages: - conv.set_system_message("".join(system_messages)) + if len(system_messages) > 1: + ## Compatible with dbgpt complex scenarios, the last system will protect more complete information entered by the current user + user_messages[-1] = system_messages[-1] + can_use_systems = system_messages[:-1] + else: + can_use_systems = system_messages + + for i in range(len(user_messages)): + conv.append_message(conv.roles[0], user_messages[i]) + if i < len(ai_messages): + conv.append_message(conv.roles[1], ai_messages[i]) + + if isinstance(conv, Conversation): + conv.set_system_message("".join(can_use_systems)) + else: + conv.update_system_message("".join(can_use_systems)) # Add a blank message for the assistant. conv.append_message(conv.roles[1], None) new_prompt = conv.get_prompt() + return new_prompt, conv.stop_str, conv.stop_token_ids + + def model_adaptation( + self, + params: Dict, + model_name: str, + model_path: str, + tokenizer: Any, + prompt_template: str = None, + ) -> Tuple[Dict, Dict]: + """Params adaptation""" + messages = params.get("messages") + # Some model scontext to dbgpt server + model_context = {"prompt_echo_len_char": -1, "has_format_prompt": False} + if messages: + # Dict message to ModelMessage + messages = [ + m if isinstance(m, ModelMessage) else ModelMessage(**m) + for m in messages + ] + params["messages"] = messages + + new_prompt = self.get_str_prompt(params, messages, tokenizer, prompt_template) + conv_stop_str, conv_stop_token_ids = None, None + if not new_prompt: + ( + new_prompt, + conv_stop_str, + conv_stop_token_ids, + ) = self.get_prompt_with_template( + params, messages, model_name, model_path, model_context, prompt_template + ) + if not new_prompt: + return params, model_context + # Overwrite the original prompt # TODO remote bos token and eos token from tokenizer_config.json of model prompt_echo_len_char = len(new_prompt.replace("", "").replace("", "")) @@ -162,8 +231,12 @@ class LLMModelAdaper: model_context["has_format_prompt"] = True params["prompt"] = new_prompt - # Overwrite model params: - params["stop"] = conv.stop_str + custom_stop = params.get("stop") + custom_stop_token_ids = params.get("stop_token_ids") + + # Prefer the value passed in from the input parameter + params["stop"] = custom_stop or conv_stop_str + params["stop_token_ids"] = custom_stop_token_ids or conv_stop_token_ids return params, model_context @@ -216,9 +289,16 @@ class FastChatLLMModelAdaperWrapper(LLMModelAdaper): return self._adapter.load_model(model_path, from_pretrained_kwargs) def get_generate_stream_function(self, model: "TorchNNModule", model_path: str): - from fastchat.model.model_adapter import get_generate_stream_function + if _IS_BENCHMARK: + from pilot.utils.benchmarks.llm.fastchat_benchmarks_inference import ( + generate_stream, + ) - return get_generate_stream_function(model, model_path) + return generate_stream + else: + from fastchat.model.model_adapter import get_generate_stream_function + + return get_generate_stream_function(model, model_path) def get_default_conv_template( self, model_name: str, model_path: str @@ -233,6 +313,69 @@ class FastChatLLMModelAdaperWrapper(LLMModelAdaper): ) +class NewHFChatModelAdapter(LLMModelAdaper): + def load(self, model_path: str, from_pretrained_kwargs: dict): + try: + import transformers + from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel + except ImportError as exc: + raise ValueError( + "Could not import depend python package " + "Please install it with `pip install transformers`." + ) from exc + if not transformers.__version__ >= "4.34.0": + raise ValueError( + "Current model (Load by HFNewChatAdapter) require transformers.__version__>=4.34.0" + ) + revision = from_pretrained_kwargs.get("revision", "main") + try: + tokenizer = AutoTokenizer.from_pretrained( + model_path, + use_fast=self.use_fast_tokenizer, + revision=revision, + trust_remote_code=True, + ) + except TypeError: + tokenizer = AutoTokenizer.from_pretrained( + model_path, use_fast=False, revision=revision, trust_remote_code=True + ) + try: + model = AutoModelForCausalLM.from_pretrained( + model_path, low_cpu_mem_usage=True, **from_pretrained_kwargs + ) + except NameError: + model = AutoModel.from_pretrained( + model_path, low_cpu_mem_usage=True, **from_pretrained_kwargs + ) + # tokenizer.use_default_system_prompt = False + return model, tokenizer + + def get_generate_stream_function(self, model, model_path: str): + """Get the generate stream function of the model""" + from pilot.model.llm_out.hf_chat_llm import huggingface_chat_generate_stream + + return huggingface_chat_generate_stream + + def get_str_prompt( + self, + params: Dict, + messages: List[ModelMessage], + tokenizer: Any, + prompt_template: str = None, + ) -> Optional[str]: + from transformers import AutoTokenizer + + if not tokenizer: + raise ValueError("tokenizer is is None") + tokenizer: AutoTokenizer = tokenizer + + messages = ModelMessage.to_openai_messages(messages) + str_prompt = tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + return str_prompt + + def get_conv_template(name: str) -> "Conversation": """Get a conversation template.""" from fastchat.conversation import get_conv_template @@ -261,6 +404,11 @@ def get_llm_model_adapter( logger.info("Current model type is vllm, return VLLMModelAdaperWrapper") return VLLMModelAdaperWrapper() + use_new_hf_chat_models = any(m in model_name.lower() for m in _NEW_HF_CHAT_MODELS) + if use_new_hf_chat_models: + logger.info(f"Current model {model_name} use NewHFChatModelAdapter") + return NewHFChatModelAdapter() + must_use_old = any(m in model_name for m in _OLD_MODELS) if use_fastchat and not must_use_old: logger.info("Use fastcat adapter") @@ -297,6 +445,7 @@ def _get_fastchat_model_adapter( if use_fastchat_monkey_patch: model_adapter.get_model_adapter = _fastchat_get_adapter_monkey_patch thread_local.model_name = model_name + _remove_black_list_model_of_fastchat() if caller: return caller(model_path) finally: @@ -340,6 +489,24 @@ def _fastchat_get_adapter_monkey_patch(model_path: str, model_name: str = None): ) +@cache +def _remove_black_list_model_of_fastchat(): + from fastchat.model.model_adapter import model_adapters + + black_list_models = [] + for adapter in model_adapters: + try: + if ( + adapter.get_default_conv_template("/data/not_exist_model_path").name + in _BLACK_LIST_MODLE_PROMPT + ): + black_list_models.append(adapter) + except Exception: + pass + for adapter in black_list_models: + model_adapters.remove(adapter) + + def _dynamic_model_parser() -> Callable[[None], List[Type]]: from pilot.utils.parameter_utils import _SimpleArgParser from pilot.model.parameter import ( @@ -444,17 +611,50 @@ class VLLMModelAdaperWrapper(LLMModelAdaper): # Covering the configuration of fastcaht, we will regularly feedback the code here to fastchat. # We also recommend that you modify it directly in the fastchat repository. + +# source: https://huggingface.co/BAAI/AquilaChat2-34B/blob/4608b75855334b93329a771aee03869dbf7d88cc/predict.py#L212 register_conv_template( Conversation( - name="internlm-chat", - system_message="A chat between a curious <|User|> and an <|Bot|>. The <|Bot|> gives helpful, detailed, and polite answers to the <|User|>'s questions.\n\n", - roles=("<|User|>", "<|Bot|>"), - sep_style=SeparatorStyle.CHATINTERN, - sep="", - sep2="", - stop_token_ids=[1, 103028], - # TODO feedback stop_str to fastchat - stop_str="", + name="aquila-legacy", + system_message="A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n", + roles=("### Human: ", "### Assistant: ", "System"), + messages=(), + offset=0, + sep_style=SeparatorStyle.NO_COLON_TWO, + sep="\n", + sep2="", + stop_str=["", "[UNK]"], + ), + override=True, +) +# source: https://huggingface.co/BAAI/AquilaChat2-34B/blob/4608b75855334b93329a771aee03869dbf7d88cc/predict.py#L227 +register_conv_template( + Conversation( + name="aquila", + system_message="A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions.", + roles=("Human", "Assistant", "System"), + messages=(), + offset=0, + sep_style=SeparatorStyle.ADD_COLON_TWO, + sep="###", + sep2="", + stop_str=["", "[UNK]"], + ), + override=True, +) +# source: https://huggingface.co/BAAI/AquilaChat2-34B/blob/4608b75855334b93329a771aee03869dbf7d88cc/predict.py#L242 +register_conv_template( + Conversation( + name="aquila-v1", + roles=("<|startofpiece|>", "<|endofpiece|>", ""), + messages=(), + offset=0, + sep_style=SeparatorStyle.NO_COLON_TWO, + sep="", + sep2="", + stop_str=["", "<|endoftext|>"], ), override=True, ) diff --git a/pilot/model/operator/__init__.py b/pilot/model/operator/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/model/operator/model_operator.py b/pilot/model/operator/model_operator.py new file mode 100644 index 000000000..d8ee62172 --- /dev/null +++ b/pilot/model/operator/model_operator.py @@ -0,0 +1,311 @@ +from typing import AsyncIterator, Dict, List, Union +import logging +from pilot.awel import ( + BranchFunc, + StreamifyAbsOperator, + BranchOperator, + MapOperator, + TransformStreamAbsOperator, +) +from pilot.component import ComponentType +from pilot.awel.operator.base import BaseOperator +from pilot.model.base import ModelOutput +from pilot.model.cluster import WorkerManager, WorkerManagerFactory +from pilot.cache import LLMCacheClient, CacheManager, LLMCacheKey, LLMCacheValue + +logger = logging.getLogger(__name__) + +_LLM_MODEL_INPUT_VALUE_KEY = "llm_model_input_value" +_LLM_MODEL_OUTPUT_CACHE_KEY = "llm_model_output_cache" + + +class ModelStreamOperator(StreamifyAbsOperator[Dict, ModelOutput]): + """Operator for streaming processing of model outputs. + + Args: + worker_manager (WorkerManager): The manager that handles worker processes for model inference. + **kwargs: Additional keyword arguments. + + Methods: + streamify: Asynchronously processes a stream of inputs, yielding model outputs. + """ + + def __init__(self, worker_manager: WorkerManager = None, **kwargs) -> None: + super().__init__(**kwargs) + self.worker_manager = worker_manager + + async def streamify(self, input_value: Dict) -> AsyncIterator[ModelOutput]: + """Process inputs as a stream and yield model outputs. + + Args: + input_value (Dict): The input value for the model. + + Returns: + AsyncIterator[ModelOutput]: An asynchronous iterator of model outputs. + """ + if not self.worker_manager: + self.worker_manager = self.system_app.get_component( + ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory + ).create() + async for out in self.worker_manager.generate_stream(input_value): + yield out + + +class ModelOperator(MapOperator[Dict, ModelOutput]): + """Operator for map-based processing of model outputs. + + Args: + worker_manager (WorkerManager): Manager for handling worker processes. + **kwargs: Additional keyword arguments. + + Methods: + map: Asynchronously processes a single input and returns the model output. + """ + + def __init__(self, worker_manager: WorkerManager = None, **kwargs) -> None: + super().__init__(**kwargs) + self.worker_manager = worker_manager + + async def map(self, input_value: Dict) -> ModelOutput: + """Process a single input and return the model output. + + Args: + input_value (Dict): The input value for the model. + + Returns: + ModelOutput: The output from the model. + """ + if not self.worker_manager: + self.worker_manager = self.system_app.get_component( + ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory + ).create() + return await self.worker_manager.generate(input_value) + + +class CachedModelStreamOperator(StreamifyAbsOperator[Dict, ModelOutput]): + """Operator for streaming processing of model outputs with caching. + + Args: + cache_manager (CacheManager): The cache manager to handle caching operations. + **kwargs: Additional keyword arguments. + + Methods: + streamify: Processes a stream of inputs with cache support, yielding model outputs. + """ + + def __init__(self, cache_manager: CacheManager, **kwargs) -> None: + super().__init__(**kwargs) + self._cache_manager = cache_manager + self._client = LLMCacheClient(cache_manager) + + async def streamify(self, input_value: Dict) -> AsyncIterator[ModelOutput]: + """Process inputs as a stream with cache support and yield model outputs. + + Args: + input_value (Dict): The input value for the model. + + Returns: + AsyncIterator[ModelOutput]: An asynchronous iterator of model outputs. + """ + cache_dict = _parse_cache_key_dict(input_value) + llm_cache_key: LLMCacheKey = self._client.new_key(**cache_dict) + llm_cache_value: LLMCacheValue = await self._client.get(llm_cache_key) + logger.info(f"llm_cache_value: {llm_cache_value}") + for out in llm_cache_value.get_value().output: + yield out + + +class CachedModelOperator(MapOperator[Dict, ModelOutput]): + """Operator for map-based processing of model outputs with caching. + + Args: + cache_manager (CacheManager): Manager for caching operations. + **kwargs: Additional keyword arguments. + + Methods: + map: Processes a single input with cache support and returns the model output. + """ + + def __init__(self, cache_manager: CacheManager, **kwargs) -> None: + super().__init__(**kwargs) + self._cache_manager = cache_manager + self._client = LLMCacheClient(cache_manager) + + async def map(self, input_value: Dict) -> ModelOutput: + """Process a single input with cache support and return the model output. + + Args: + input_value (Dict): The input value for the model. + + Returns: + ModelOutput: The output from the model. + """ + cache_dict = _parse_cache_key_dict(input_value) + llm_cache_key: LLMCacheKey = self._client.new_key(**cache_dict) + llm_cache_value: LLMCacheValue = await self._client.get(llm_cache_key) + logger.info(f"llm_cache_value: {llm_cache_value}") + return llm_cache_value.get_value().output + + +class ModelCacheBranchOperator(BranchOperator[Dict, Dict]): + """ + A branch operator that decides whether to use cached data or to process data using the model. + + Args: + cache_manager (CacheManager): The cache manager for managing cache operations. + model_task_name (str): The name of the task to process data using the model. + cache_task_name (str): The name of the task to process data using the cache. + **kwargs: Additional keyword arguments. + """ + + def __init__( + self, + cache_manager: CacheManager, + model_task_name: str, + cache_task_name: str, + **kwargs, + ): + super().__init__(branches=None, **kwargs) + self._cache_manager = cache_manager + self._client = LLMCacheClient(cache_manager) + self._model_task_name = model_task_name + self._cache_task_name = cache_task_name + + async def branchs(self) -> Dict[BranchFunc[Dict], Union[BaseOperator, str]]: + """Defines branch logic based on cache availability. + + Returns: + Dict[BranchFunc[Dict], Union[BaseOperator, str]]: A dictionary mapping branch functions to task names. + """ + + async def check_cache_true(input_value: Dict) -> bool: + # Check if the cache contains the result for the given input + if not input_value["model_cache_enable"]: + return False + cache_dict = _parse_cache_key_dict(input_value) + cache_key: LLMCacheKey = self._client.new_key(**cache_dict) + cache_value = await self._client.get(cache_key) + logger.debug( + f"cache_key: {cache_key}, hash key: {hash(cache_key)}, cache_value: {cache_value}" + ) + await self.current_dag_context.save_to_share_data( + _LLM_MODEL_INPUT_VALUE_KEY, cache_key + ) + return True if cache_value else False + + async def check_cache_false(input_value: Dict): + # Inverse of check_cache_true + return not await check_cache_true(input_value) + + return { + check_cache_true: self._cache_task_name, + check_cache_false: self._model_task_name, + } + + +class ModelStreamSaveCacheOperator( + TransformStreamAbsOperator[ModelOutput, ModelOutput] +): + """An operator to save the stream of model outputs to cache. + + Args: + cache_manager (CacheManager): The cache manager for handling cache operations. + **kwargs: Additional keyword arguments. + """ + + def __init__(self, cache_manager: CacheManager, **kwargs): + self._cache_manager = cache_manager + self._client = LLMCacheClient(cache_manager) + super().__init__(**kwargs) + + async def transform_stream( + self, input_value: AsyncIterator[ModelOutput] + ) -> AsyncIterator[ModelOutput]: + """Transforms the input stream by saving the outputs to cache. + + Args: + input_value (AsyncIterator[ModelOutput]): An asynchronous iterator of model outputs. + + Returns: + AsyncIterator[ModelOutput]: The same input iterator, but the outputs are saved to cache. + """ + llm_cache_key: LLMCacheKey = None + outputs = [] + async for out in input_value: + if not llm_cache_key: + llm_cache_key = await self.current_dag_context.get_share_data( + _LLM_MODEL_INPUT_VALUE_KEY + ) + outputs.append(out) + yield out + if llm_cache_key and _is_success_model_output(outputs): + llm_cache_value: LLMCacheValue = self._client.new_value(output=outputs) + await self._client.set(llm_cache_key, llm_cache_value) + + +class ModelSaveCacheOperator(MapOperator[ModelOutput, ModelOutput]): + """An operator to save a single model output to cache. + + Args: + cache_manager (CacheManager): The cache manager for handling cache operations. + **kwargs: Additional keyword arguments. + """ + + def __init__(self, cache_manager: CacheManager, **kwargs): + self._cache_manager = cache_manager + self._client = LLMCacheClient(cache_manager) + super().__init__(**kwargs) + + async def map(self, input_value: ModelOutput) -> ModelOutput: + """Saves a single model output to cache and returns it. + + Args: + input_value (ModelOutput): The output from the model to be cached. + + Returns: + ModelOutput: The same input model output. + """ + llm_cache_key: LLMCacheKey = await self.current_dag_context.get_share_data( + _LLM_MODEL_INPUT_VALUE_KEY + ) + llm_cache_value: LLMCacheValue = self._client.new_value(output=input_value) + if llm_cache_key and _is_success_model_output(input_value): + await self._client.set(llm_cache_key, llm_cache_value) + return input_value + + +def _parse_cache_key_dict(input_value: Dict) -> Dict: + """Parses and extracts relevant fields from input to form a cache key dictionary. + + Args: + input_value (Dict): The input dictionary containing model and prompt parameters. + + Returns: + Dict: A dictionary used for generating cache keys. + """ + prompt: str = input_value.get("prompt") + if prompt: + prompt = prompt.strip() + return { + "prompt": prompt, + "model_name": input_value.get("model"), + "temperature": input_value.get("temperature"), + "max_new_tokens": input_value.get("max_new_tokens"), + "top_p": input_value.get("top_p", "1.0"), + # TODO pass model_type + "model_type": input_value.get("model_type", "huggingface"), + } + + +def _is_success_model_output(out: Union[Dict, ModelOutput, List[ModelOutput]]) -> bool: + if not out: + return False + if isinstance(out, list): + # check last model output + out = out[-1] + error_code = 0 + if isinstance(out, ModelOutput): + error_code = out.error_code + else: + error_code = int(out.get("error_code", 0)) + return error_code == 0 diff --git a/pilot/model/parameter.py b/pilot/model/parameter.py index ba0000435..79558e02c 100644 --- a/pilot/model/parameter.py +++ b/pilot/model/parameter.py @@ -1,9 +1,10 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- + import os from dataclasses import dataclass, field from enum import Enum -from typing import Dict, Optional +from typing import Dict, Optional, Union, Tuple from pilot.model.conversation import conv_templates from pilot.utils.parameter_utils import BaseParameters @@ -19,6 +20,35 @@ class WorkerType(str, Enum): def values(): return [item.value for item in WorkerType] + @staticmethod + def to_worker_key(worker_name, worker_type: Union[str, "WorkerType"]) -> str: + """Generate worker key from worker name and worker type + + Args: + worker_name (str): Worker name(eg., chatglm2-6b) + worker_type (Union[str, "WorkerType"]): Worker type(eg., 'llm', or [`WorkerType.LLM`]) + + Returns: + str: Generated worker key + """ + if "@" in worker_name: + raise ValueError(f"Invaild symbol '@' in your worker name {worker_name}") + if isinstance(worker_type, WorkerType): + worker_type = worker_type.value + return f"{worker_name}@{worker_type}" + + @staticmethod + def parse_worker_key(worker_key: str) -> Tuple[str, str]: + """Parse worker name and worker type from worker key + + Args: + worker_key (str): Worker key generated by [`WorkerType.to_worker_key`] + + Returns: + Tuple[str, str]: Worker name and worker type + """ + return tuple(worker_key.split("@")) + @dataclass class ModelControllerParameters(BaseParameters): @@ -58,6 +88,68 @@ class ModelControllerParameters(BaseParameters): "help": "The filename to store tracer span records", }, ) + tracer_storage_cls: Optional[str] = field( + default=None, + metadata={ + "help": "The storage class to storage tracer span records", + }, + ) + + +@dataclass +class ModelAPIServerParameters(BaseParameters): + host: Optional[str] = field( + default="0.0.0.0", metadata={"help": "Model API server deploy host"} + ) + port: Optional[int] = field( + default=8100, metadata={"help": "Model API server deploy port"} + ) + daemon: Optional[bool] = field( + default=False, metadata={"help": "Run Model API server in background"} + ) + controller_addr: Optional[str] = field( + default="http://127.0.0.1:8000", + metadata={"help": "The Model controller address to connect"}, + ) + + api_keys: Optional[str] = field( + default=None, + metadata={"help": "Optional list of comma separated API keys"}, + ) + + log_level: Optional[str] = field( + default=None, + metadata={ + "help": "Logging level", + "valid_values": [ + "FATAL", + "ERROR", + "WARNING", + "WARNING", + "INFO", + "DEBUG", + "NOTSET", + ], + }, + ) + log_file: Optional[str] = field( + default="dbgpt_model_apiserver.log", + metadata={ + "help": "The filename to store log", + }, + ) + tracer_file: Optional[str] = field( + default="dbgpt_model_apiserver_tracer.jsonl", + metadata={ + "help": "The filename to store tracer span records", + }, + ) + tracer_storage_cls: Optional[str] = field( + default=None, + metadata={ + "help": "The storage class to storage tracer span records", + }, + ) @dataclass @@ -146,6 +238,12 @@ class ModelWorkerParameters(BaseModelParameters): "help": "The filename to store tracer span records", }, ) + tracer_storage_cls: Optional[str] = field( + default=None, + metadata={ + "help": "The storage class to storage tracer span records", + }, + ) @dataclass @@ -318,6 +416,13 @@ class ProxyModelParameters(BaseModelParameters): }, ) + proxy_api_app_id: Optional[str] = field( + default=None, + metadata={ + "help": "The app id for current proxy LLM(Just for spark proxy LLM now)." + }, + ) + proxy_api_type: Optional[str] = field( default=None, metadata={ diff --git a/pilot/model/proxy/llms/chatgpt.py b/pilot/model/proxy/llms/chatgpt.py index 994986b5c..4c2a371ce 100644 --- a/pilot/model/proxy/llms/chatgpt.py +++ b/pilot/model/proxy/llms/chatgpt.py @@ -4,18 +4,25 @@ import os from typing import List import logging - -import openai - +import importlib.metadata as metadata from pilot.model.proxy.llms.proxy_model import ProxyModel from pilot.model.parameter import ProxyModelParameters from pilot.scene.base_message import ModelMessage, ModelMessageRoleType from pilot.server.monitor.api_key_db import ApiKeyDao +import httpx logger = logging.getLogger(__name__) def _initialize_openai(params: ProxyModelParameters): + try: + import openai + except ImportError as exc: + raise ValueError( + "Could not import python package: openai " + "Please install openai by command `pip install openai` " + ) from exc + api_type = params.proxy_api_type or os.getenv("OPENAI_API_TYPE", "open_ai") api_base = params.proxy_api_base or os.getenv( @@ -55,14 +62,45 @@ def _initialize_openai(params: ProxyModelParameters): return openai_params +def _initialize_openai_v1(params: ProxyModelParameters): + try: + from openai import OpenAI + except ImportError as exc: + raise ValueError( + "Could not import python package: openai " + "Please install openai by command `pip install openai" + ) + + api_type = params.proxy_api_type or os.getenv("OPENAI_API_TYPE", "open_ai") + + base_url = params.proxy_api_base or os.getenv( + "OPENAI_API_TYPE", + os.getenv("AZURE_OPENAI_ENDPOINT") if api_type == "azure" else None, + ) + api_key = params.proxy_api_key or os.getenv( + "OPENAI_API_KEY", + os.getenv("AZURE_OPENAI_KEY") if api_type == "azure" else None, + ) + api_version = params.proxy_api_version or os.getenv("OPENAI_API_VERSION") + + if not base_url and params.proxy_server_url: + # Adapt previous proxy_server_url configuration + base_url = params.proxy_server_url.split("/chat/completions")[0] + + proxies = params.http_proxy + openai_params = { + "api_key": api_key, + "base_url": base_url, + } + return openai_params, api_type, api_version, proxies + + def _build_request(model: ProxyModel, params): history = [] model_params = model.get_params() logger.info(f"Model: {model}, model_params: {model_params}") - openai_params = _initialize_openai(model_params) - messages: List[ModelMessage] = params["messages"] # Add history conversation for message in messages: @@ -93,45 +131,113 @@ def _build_request(model: ProxyModel, params): } proxyllm_backend = model_params.proxyllm_backend - if openai_params["api_type"] == "azure": - # engine = "deployment_name". - proxyllm_backend = proxyllm_backend or "gpt-35-turbo" - payloads["engine"] = proxyllm_backend - else: + if metadata.version("openai") >= "1.0.0": + openai_params, api_type, api_version, proxies = _initialize_openai_v1( + model_params + ) proxyllm_backend = proxyllm_backend or "gpt-3.5-turbo" payloads["model"] = proxyllm_backend + else: + openai_params = _initialize_openai(model_params) + if openai_params["api_type"] == "azure": + # engine = "deployment_name". + proxyllm_backend = proxyllm_backend or "gpt-35-turbo" + payloads["engine"] = proxyllm_backend + else: + proxyllm_backend = proxyllm_backend or "gpt-3.5-turbo" + payloads["model"] = proxyllm_backend - logger.info( - f"Send request to real model {proxyllm_backend}, openai_params: {openai_params}" - ) + logger.info(f"Send request to real model {proxyllm_backend}") return history, payloads def chatgpt_generate_stream( model: ProxyModel, tokenizer, params, device, context_len=2048 ): - history, payloads = _build_request(model, params) + if metadata.version("openai") >= "1.0.0": + model_params = model.get_params() + openai_params, api_type, api_version, proxies = _initialize_openai_v1( + model_params + ) + history, payloads = _build_request(model, params) + if api_type == "azure": + from openai import AzureOpenAI - res = openai.ChatCompletion.create(messages=history, **payloads) + client = AzureOpenAI( + api_key=openai_params["api_key"], + api_version=api_version, + azure_endpoint=openai_params["base_url"], + http_client=httpx.Client(proxies=proxies), + ) + else: + from openai import OpenAI - text = "" - for r in res: - if r["choices"][0]["delta"].get("content") is not None: - content = r["choices"][0]["delta"]["content"] - text += content - yield text + client = OpenAI(**openai_params, http_client=httpx.Client(proxies=proxies)) + res = client.chat.completions.create(messages=history, **payloads) + text = "" + for r in res: + if r.choices[0].delta.content is not None: + content = r.choices[0].delta.content + text += content + yield text + + else: + import openai + + history, payloads = _build_request(model, params) + + res = openai.ChatCompletion.create(messages=history, **payloads) + + text = "" + for r in res: + if r["choices"][0]["delta"].get("content") is not None: + content = r["choices"][0]["delta"]["content"] + text += content + yield text async def async_chatgpt_generate_stream( model: ProxyModel, tokenizer, params, device, context_len=2048 ): - history, payloads = _build_request(model, params) + if metadata.version("openai") >= "1.0.0": + model_params = model.get_params() + openai_params, api_type, api_version, proxies = _initialize_openai_v1( + model_params + ) + history, payloads = _build_request(model, params) + if api_type == "azure": + from openai import AsyncAzureOpenAI - res = await openai.ChatCompletion.acreate(messages=history, **payloads) + client = AsyncAzureOpenAI( + api_key=openai_params["api_key"], + api_version=api_version, + azure_endpoint=openai_params["base_url"], + http_client=httpx.AsyncClient(proxies=proxies), + ) + else: + from openai import AsyncOpenAI - text = "" - async for r in res: - if r["choices"][0]["delta"].get("content") is not None: - content = r["choices"][0]["delta"]["content"] - text += content - yield text + client = AsyncOpenAI( + **openai_params, http_client=httpx.AsyncClient(proxies=proxies) + ) + + res = await client.chat.completions.create(messages=history, **payloads) + text = "" + for r in res: + if r.choices[0].delta.content is not None: + content = r.choices[0].delta.content + text += content + yield text + else: + import openai + + history, payloads = _build_request(model, params) + + res = await openai.ChatCompletion.acreate(messages=history, **payloads) + + text = "" + async for r in res: + if r["choices"][0]["delta"].get("content") is not None: + content = r["choices"][0]["delta"]["content"] + text += content + yield text diff --git a/pilot/model/proxy/llms/tongyi.py b/pilot/model/proxy/llms/tongyi.py index fb826e49c..5bc0a97f3 100644 --- a/pilot/model/proxy/llms/tongyi.py +++ b/pilot/model/proxy/llms/tongyi.py @@ -7,6 +7,35 @@ from pilot.scene.base_message import ModelMessage, ModelMessageRoleType logger = logging.getLogger(__name__) +def __convert_2_tongyi_messages(messages: List[ModelMessage]): + chat_round = 0 + tongyi_messages = [] + + last_usr_message = "" + system_messages = [] + + for message in messages: + if message.role == ModelMessageRoleType.HUMAN: + last_usr_message = message.content + elif message.role == ModelMessageRoleType.SYSTEM: + system_messages.append(message.content) + elif message.role == ModelMessageRoleType.AI: + last_ai_message = message.content + tongyi_messages.append({"role": "user", "content": last_usr_message}) + tongyi_messages.append({"role": "assistant", "content": last_ai_message}) + if len(system_messages) > 0: + if len(system_messages) < 2: + tongyi_messages.insert(0, {"role": "system", "content": system_messages[0]}) + else: + tongyi_messages.append({"role": "user", "content": system_messages[1]}) + else: + last_message = messages[-1] + if last_message.role == ModelMessageRoleType.HUMAN: + tongyi_messages.append({"role": "user", "content": last_message.content}) + + return tongyi_messages + + def tongyi_generate_stream( model: ProxyModel, tokenizer, params, device, context_len=2048 ): @@ -23,40 +52,9 @@ def tongyi_generate_stream( if not proxyllm_backend: proxyllm_backend = Generation.Models.qwen_turbo # By Default qwen_turbo - history = [] - messages: List[ModelMessage] = params["messages"] - # Add history conversation - - if len(messages) > 1 and messages[0].role == ModelMessageRoleType.SYSTEM: - role_define = messages.pop(0) - history.append({"role": "system", "content": role_define.content}) - else: - message = messages.pop(0) - if message.role == ModelMessageRoleType.HUMAN: - history.append({"role": "user", "content": message.content}) - for message in messages: - if message.role == ModelMessageRoleType.SYSTEM: - history.append({"role": "user", "content": message.content}) - # elif message.role == ModelMessageRoleType.HUMAN: - # history.append({"role": "user", "content": message.content}) - elif message.role == ModelMessageRoleType.AI: - history.append({"role": "assistant", "content": message.content}) - else: - pass - - # temp_his = history[::-1] - temp_his = history - last_user_input = None - for m in temp_his: - if m["role"] == "user": - last_user_input = m - break - - if last_user_input: - history.remove(last_user_input) - history.append(last_user_input) + history = __convert_2_tongyi_messages(messages) gen = Generation() res = gen.call( proxyllm_backend, diff --git a/pilot/model/proxy/llms/wenxin.py b/pilot/model/proxy/llms/wenxin.py index acc82907c..cfd47fd18 100644 --- a/pilot/model/proxy/llms/wenxin.py +++ b/pilot/model/proxy/llms/wenxin.py @@ -26,6 +26,41 @@ def _build_access_token(api_key: str, secret_key: str) -> str: return res.json().get("access_token") +def __convert_2_wenxin_messages(messages: List[ModelMessage]): + chat_round = 0 + wenxin_messages = [] + + last_usr_message = "" + system_messages = [] + + for message in messages: + if message.role == ModelMessageRoleType.HUMAN: + last_usr_message = message.content + elif message.role == ModelMessageRoleType.SYSTEM: + system_messages.append(message.content) + elif message.role == ModelMessageRoleType.AI: + last_ai_message = message.content + wenxin_messages.append({"role": "user", "content": last_usr_message}) + wenxin_messages.append({"role": "assistant", "content": last_ai_message}) + + # build last user messge + + if len(system_messages) > 0: + if len(system_messages) > 1: + end_message = system_messages[-1] + else: + last_message = messages[-1] + if last_message.role == ModelMessageRoleType.HUMAN: + end_message = system_messages[-1] + "\n" + last_message.content + else: + end_message = system_messages[-1] + else: + last_message = messages[-1] + end_message = last_message.content + wenxin_messages.append({"role": "user", "content": end_message}) + return wenxin_messages, system_messages + + def wenxin_generate_stream( model: ProxyModel, tokenizer, params, device, context_len=2048 ): @@ -40,8 +75,9 @@ def wenxin_generate_stream( if not model_version: yield f"Unsupport model version {model_name}" - proxy_api_key = model_params.proxy_api_key - proxy_api_secret = model_params.proxy_api_secret + keys: [] = model_params.proxy_api_key.split(";") + proxy_api_key = keys[0] + proxy_api_secret = keys[1] access_token = _build_access_token(proxy_api_key, proxy_api_secret) headers = {"Content-Type": "application/json", "Accept": "application/json"} @@ -51,40 +87,42 @@ def wenxin_generate_stream( if not access_token: yield "Failed to get access token. please set the correct api_key and secret key." - history = [] - messages: List[ModelMessage] = params["messages"] # Add history conversation + # system = "" + # if len(messages) > 1 and messages[0].role == ModelMessageRoleType.SYSTEM: + # role_define = messages.pop(0) + # system = role_define.content + # else: + # message = messages.pop(0) + # if message.role == ModelMessageRoleType.HUMAN: + # history.append({"role": "user", "content": message.content}) + # for message in messages: + # if message.role == ModelMessageRoleType.SYSTEM: + # history.append({"role": "user", "content": message.content}) + # # elif message.role == ModelMessageRoleType.HUMAN: + # # history.append({"role": "user", "content": message.content}) + # elif message.role == ModelMessageRoleType.AI: + # history.append({"role": "assistant", "content": message.content}) + # else: + # pass + # + # # temp_his = history[::-1] + # temp_his = history + # last_user_input = None + # for m in temp_his: + # if m["role"] == "user": + # last_user_input = m + # break + # + # if last_user_input: + # history.remove(last_user_input) + # history.append(last_user_input) + # + history, systems = __convert_2_wenxin_messages(messages) system = "" - if len(messages) > 1 and messages[0].role == ModelMessageRoleType.SYSTEM: - role_define = messages.pop(0) - system = role_define.content - else: - message = messages.pop(0) - if message.role == ModelMessageRoleType.HUMAN: - history.append({"role": "user", "content": message.content}) - for message in messages: - if message.role == ModelMessageRoleType.SYSTEM: - history.append({"role": "user", "content": message.content}) - # elif message.role == ModelMessageRoleType.HUMAN: - # history.append({"role": "user", "content": message.content}) - elif message.role == ModelMessageRoleType.AI: - history.append({"role": "assistant", "content": message.content}) - else: - pass - - # temp_his = history[::-1] - temp_his = history - last_user_input = None - for m in temp_his: - if m["role"] == "user": - last_user_input = m - break - - if last_user_input: - history.remove(last_user_input) - history.append(last_user_input) - + if systems and len(systems) > 0: + system = systems[0] payload = { "messages": history, "system": system, diff --git a/pilot/model/proxy/llms/zhipu.py b/pilot/model/proxy/llms/zhipu.py index 89e7dd9a0..c5fabe8ed 100644 --- a/pilot/model/proxy/llms/zhipu.py +++ b/pilot/model/proxy/llms/zhipu.py @@ -8,6 +8,41 @@ from pilot.scene.base_message import ModelMessage, ModelMessageRoleType CHATGLM_DEFAULT_MODEL = "chatglm_pro" +def __convert_2_wenxin_messages(messages: List[ModelMessage]): + chat_round = 0 + wenxin_messages = [] + + last_usr_message = "" + system_messages = [] + + for message in messages: + if message.role == ModelMessageRoleType.HUMAN: + last_usr_message = message.content + elif message.role == ModelMessageRoleType.SYSTEM: + system_messages.append(message.content) + elif message.role == ModelMessageRoleType.AI: + last_ai_message = message.content + wenxin_messages.append({"role": "user", "content": last_usr_message}) + wenxin_messages.append({"role": "assistant", "content": last_ai_message}) + + # build last user messge + + if len(system_messages) > 0: + if len(system_messages) > 1: + end_message = system_messages[-1] + else: + last_message = messages[-1] + if last_message.role == ModelMessageRoleType.HUMAN: + end_message = system_messages[-1] + "\n" + last_message.content + else: + end_message = system_messages[-1] + else: + last_message = messages[-1] + end_message = last_message.content + wenxin_messages.append({"role": "user", "content": end_message}) + return wenxin_messages, system_messages + + def zhipu_generate_stream( model: ProxyModel, tokenizer, params, device, context_len=2048 ): @@ -22,40 +57,40 @@ def zhipu_generate_stream( import zhipuai zhipuai.api_key = proxy_api_key - history = [] messages: List[ModelMessage] = params["messages"] # Add history conversation - system = "" - if len(messages) > 1 and messages[0].role == ModelMessageRoleType.SYSTEM: - role_define = messages.pop(0) - system = role_define.content - else: - message = messages.pop(0) - if message.role == ModelMessageRoleType.HUMAN: - history.append({"role": "user", "content": message.content}) - for message in messages: - if message.role == ModelMessageRoleType.SYSTEM: - history.append({"role": "user", "content": message.content}) - # elif message.role == ModelMessageRoleType.HUMAN: - # history.append({"role": "user", "content": message.content}) - elif message.role == ModelMessageRoleType.AI: - history.append({"role": "assistant", "content": message.content}) - else: - pass - - # temp_his = history[::-1] - temp_his = history - last_user_input = None - for m in temp_his: - if m["role"] == "user": - last_user_input = m - break - - if last_user_input: - history.remove(last_user_input) - history.append(last_user_input) + # system = "" + # if len(messages) > 1 and messages[0].role == ModelMessageRoleType.SYSTEM: + # role_define = messages.pop(0) + # system = role_define.content + # else: + # message = messages.pop(0) + # if message.role == ModelMessageRoleType.HUMAN: + # history.append({"role": "user", "content": message.content}) + # for message in messages: + # if message.role == ModelMessageRoleType.SYSTEM: + # history.append({"role": "user", "content": message.content}) + # # elif message.role == ModelMessageRoleType.HUMAN: + # # history.append({"role": "user", "content": message.content}) + # elif message.role == ModelMessageRoleType.AI: + # history.append({"role": "assistant", "content": message.content}) + # else: + # pass + # + # # temp_his = history[::-1] + # temp_his = history + # last_user_input = None + # for m in temp_his: + # if m["role"] == "user": + # last_user_input = m + # break + # + # if last_user_input: + # history.remove(last_user_input) + # history.append(last_user_input) + history, systems = __convert_2_wenxin_messages(messages) res = zhipuai.model_api.sse_invoke( model=proxyllm_backend, prompt=history, diff --git a/pilot/openapi/api_v1/api_v1.py b/pilot/openapi/api_v1/api_v1.py index 640033ea1..fde191953 100644 --- a/pilot/openapi/api_v1/api_v1.py +++ b/pilot/openapi/api_v1/api_v1.py @@ -21,6 +21,7 @@ from fastapi.responses import StreamingResponse from fastapi.exceptions import RequestValidationError from typing import List import tempfile +from concurrent.futures import Executor from pilot.component import ComponentType from pilot.openapi.api_view_model import ( @@ -52,6 +53,8 @@ from pilot.memory.chat_history.chat_hisotry_factory import ChatHistory from pilot.model.cluster import BaseModelController, WorkerManager, WorkerManagerFactory from pilot.model.base import FlatSupportedModel from pilot.user import UserDao, UserRequest, get_user_from_headers +from pilot.utils.tracer import root_tracer, SpanType +from pilot.utils.executor_utils import ExecutorFactory, blocking_func_to_async router = APIRouter() CFG = Config() @@ -81,10 +84,13 @@ def __new_conversation(chat_mode, user_id) -> ConversationVo: def get_db_list(user_id: str = None): dbs = CFG.LOCAL_DB_MANAGE.get_db_list(user_id=user_id) - params: dict = {} + db_params = [] for item in dbs: - params.update({item["db_name"]: item["db_name"]}) - return params + params: dict = {} + params.update({"param": item["db_name"]}) + params.update({"type": item["db_type"]}) + db_params.append(params) + return db_params def plugins_select_info(): @@ -116,12 +122,15 @@ def knowledge_list_info(): def knowledge_list(): """return knowledge space list""" - params: dict = {} request = KnowledgeSpaceRequest() spaces = knowledge_service.get_knowledge_space(request) + space_list = [] for space in spaces: - params.update({space.name: space.name}) - return params + params: dict = {} + params.update({"param": space.name}) + params.update({"type": "space"}) + space_list.append(params) + return space_list def get_model_controller() -> BaseModelController: @@ -138,6 +147,13 @@ def get_worker_manager() -> WorkerManager: return worker_manager +def get_executor() -> Executor: + """Get the global default executor""" + return CFG.SYSTEM_APP.get_component( + ComponentType.EXECUTOR_DEFAULT, ExecutorFactory + ).create() + + @router.get("/v1/chat/db/list", response_model=Result[DBConfig]) async def db_connect_list(user_token: UserRequest = Depends(get_user_from_headers)): results = CFG.LOCAL_DB_MANAGE.get_db_list(user_token.user_id) @@ -170,14 +186,16 @@ async def async_db_summary_embedding(db_name, db_type): @router.post("/v1/chat/db/test/connect", response_model=Result[bool]) async def test_connect(db_config: DBConfig = Body()): try: + # TODO Change the synchronous call to the asynchronous call CFG.LOCAL_DB_MANAGE.test_connect(db_config) return Result.succ(True) except Exception as e: - return Result.faild(code="E1001", msg=str(e)) + return Result.failed(code="E1001", msg=str(e)) @router.post("/v1/chat/db/summary", response_model=Result[bool]) async def db_summary(db_name: str, db_type: str): + # TODO Change the synchronous call to the asynchronous call async_db_summary_embedding(db_name, db_type) return Result.succ(True) @@ -227,8 +245,8 @@ async def dialogue_scenes(): scene_vos: List[ChatSceneVo] = [] new_modes: List[ChatScene] = [ ChatScene.ChatWithDbExecute, - ChatScene.ChatExcel, ChatScene.ChatWithDbQA, + ChatScene.ChatExcel, ChatScene.ChatKnowledge, ChatScene.ChatDashboard, ChatScene.ChatAgent, @@ -267,6 +285,10 @@ async def params_list(chat_mode: str = ChatScene.ChatNormal.value(), user_token: result = plugins_select_info() elif ChatScene.ChatKnowledge.value() == chat_mode: result = knowledge_list() + elif ChatScene.ChatKnowledge.ExtractRefineSummary.value() == chat_mode: + result = knowledge_list() + else: + return Result.succ(None) if result and result.get("dbgpt"): del result["dbgpt"] if result and result.get("auth"): @@ -302,14 +324,14 @@ async def params_load( select_param=doc_file.filename, model_name=model_name, ) - chat: BaseChat = get_chat_instance(dialogue, user_token.user_id) + chat: BaseChat = await get_chat_instance(dialogue, user_token.user_id) resp = await chat.prepare() ### refresh messages return Result.succ(get_hist_messages(conv_uid)) except Exception as e: logger.error("excel load error!", e) - return Result.faild(code="E000X", msg=f"File Load Error {e}") + return Result.failed(code="E000X", msg=f"File Load Error {e}") @router.post("/v1/user/add") @@ -330,6 +352,7 @@ async def add_user(user_req: UserRequest): async def dialogue_delete(con_uid: str): history_fac = ChatHistory() history_mem = history_fac.get_store_instance(con_uid) + # TODO Change the synchronous call to the asynchronous call history_mem.delete() return Result.succ(None) @@ -342,7 +365,6 @@ def get_hist_messages(conv_uid: str): history_messages: List[OnceConversation] = history_mem.get_messages() if history_messages: for once in history_messages: - print(f"once:{once}") model_name = once.get("model_name", CFG.LLM_MODEL) once_message_vos = [ message2Vo(element, once["chat_order"], model_name) @@ -355,10 +377,12 @@ def get_hist_messages(conv_uid: str): @router.get("/v1/chat/dialogue/messages/history", response_model=Result[MessageVo]) async def dialogue_history_messages(con_uid: str): print(f"dialogue_history_messages:{con_uid}") + # TODO Change the synchronous call to the asynchronous call return Result.succ(get_hist_messages(con_uid)) -def get_chat_instance(dialogue: ConversationVo = Body(), user_id: str = None) -> BaseChat: +# def get_chat_instance(dialogue: ConversationVo = Body(), user_id: str = None) -> BaseChat: +async def get_chat_instance(dialogue: ConversationVo = Body(), user_id: str = None) -> BaseChat: logger.info(f"get_chat_instance:{dialogue}") if not dialogue.chat_mode: dialogue.chat_mode = ChatScene.ChatNormal.value() @@ -368,7 +392,7 @@ def get_chat_instance(dialogue: ConversationVo = Body(), user_id: str = None) -> if not ChatScene.is_valid_mode(dialogue.chat_mode): raise StopAsyncIteration( - Result.faild("Unsupported Chat Mode," + dialogue.chat_mode + "!") + Result.failed("Unsupported Chat Mode," + dialogue.chat_mode + "!") ) chat_param = { @@ -378,8 +402,14 @@ def get_chat_instance(dialogue: ConversationVo = Body(), user_id: str = None) -> "model_name": dialogue.model_name, "user_id": user_id, } - chat: BaseChat = CHAT_FACTORY.get_implementation( - dialogue.chat_mode, **{"chat_param": chat_param} + # chat: BaseChat = CHAT_FACTORY.get_implementation( + # dialogue.chat_mode, **{"chat_param": chat_param} + # ) + chat: BaseChat = await blocking_func_to_async( + get_executor(), + CHAT_FACTORY.get_implementation, + dialogue.chat_mode, + **{"chat_param": chat_param}, ) return chat @@ -389,7 +419,7 @@ async def chat_prepare(dialogue: ConversationVo = Body(), user_token: UserReques # dialogue.model_name = CFG.LLM_MODEL logger.info(f"chat_prepare:{dialogue}") ## check conv_uid - chat: BaseChat = get_chat_instance(dialogue, user_token.user_id) + chat: BaseChat = await get_chat_instance(dialogue, user_token.user_id) if len(chat.history_message) > 0: return Result.succ(None) resp = await chat.prepare() @@ -402,6 +432,15 @@ async def stream_error(msg): @router.post("/v1/chat/completions") async def chat_completions(dialogue: ConversationVo = Body(), user_token: UserRequest = Depends(get_user_from_headers)): + print( + f"chat_completions:{dialogue.chat_mode},{dialogue.select_param},{dialogue.model_name}" + ) + with root_tracer.start_span( + "get_chat_instance", span_type=SpanType.CHAT, metadata=dialogue.dict() + ): + chat: BaseChat = await get_chat_instance(dialogue) + # background_tasks = BackgroundTasks() + # background_tasks.add_task(release_model_semaphore) headers = { "Content-Type": "text/event-stream", "Cache-Control": "no-cache", @@ -454,7 +493,7 @@ async def model_types(controller: BaseModelController = Depends(get_model_contro return Result.succ(list(types)) except Exception as e: - return Result.faild(code="E000X", msg=f"controller model types error {e}") + return Result.failed(code="E000X", msg=f"controller model types error {e}") @router.get("/v1/model/supports") @@ -464,7 +503,7 @@ async def model_supports(worker_manager: WorkerManager = Depends(get_worker_mana models = await worker_manager.supported_models() return Result.succ(FlatSupportedModel.from_supports(models)) except Exception as e: - return Result.faild(code="E000X", msg=f"Fetch supportd models error {e}") + return Result.failed(code="E000X", msg=f"Fetch supportd models error {e}") @router.get("/v1/github/callback") @@ -498,8 +537,9 @@ async def github_access_token(code: str = None): async def no_stream_generator(chat): - msg = await chat.nostream_call() - yield f"data: {msg}\n\n" + with root_tracer.start_span("no_stream_generator"): + msg = await chat.nostream_call() + yield f"data: {msg}\n\n" async def stream_generator(chat, incremental: bool, model_name: str): @@ -516,6 +556,7 @@ async def stream_generator(chat, incremental: bool, model_name: str): Yields: _type_: streaming responses """ + span = root_tracer.start_span("stream_generator") msg = "[LLM_ERROR]: llm server has no output, maybe your prompt template is wrong." stream_id = f"chatcmpl-{str(uuid.uuid1())}" @@ -541,6 +582,7 @@ async def stream_generator(chat, incremental: bool, model_name: str): await asyncio.sleep(0.02) if incremental: yield "data: [DONE]\n\n" + span.end() def message2Vo(message: dict, order, model_name) -> MessageVo: diff --git a/pilot/openapi/api_v1/editor/api_editor_v1.py b/pilot/openapi/api_v1/editor/api_editor_v1.py index e41998942..9653a1bad 100644 --- a/pilot/openapi/api_v1/editor/api_editor_v1.py +++ b/pilot/openapi/api_v1/editor/api_editor_v1.py @@ -107,7 +107,7 @@ async def get_editor_sql(con_uid: str, round: int): .replace("\n", " ") ) return Result.succ(json.loads(context)) - return Result.faild(msg="not have sql!") + return Result.failed(msg="not have sql!") @router.post("/v1/editor/sql/run", response_model=Result[SqlRunData]) @@ -116,7 +116,7 @@ async def editor_sql_run(run_param: dict = Body()): db_name = run_param["db_name"] sql = run_param["sql"] if not db_name and not sql: - return Result.faild("SQL run param error！") + return Result.failed("SQL run param error！") conn = CFG.LOCAL_DB_MANAGE.get_connect(db_name) try: @@ -134,6 +134,7 @@ async def editor_sql_run(run_param: dict = Body()): ) return Result.succ(sql_run_data) except Exception as e: + logging.error("editor_sql_run exception!" + str(e)) return Result.succ( SqlRunData(result_info=str(e), run_cost=0, colunms=[], values=[]) ) @@ -165,11 +166,12 @@ async def sql_editor_submit(sql_edit_context: ChatSqlEditContext = Body()): if element["type"] == "view": data_loader = DbDataLoader() element["data"]["content"] = data_loader.get_table_view_by_conn( - conn.run(sql_edit_context.new_sql), sql_edit_context.new_speak + conn.run_to_df(sql_edit_context.new_sql), + sql_edit_context.new_speak, ) history_mem.update(history_messages) return Result.succ(None) - return Result.faild(msg="Edit Faild!") + return Result.failed(msg="Edit Failed!") @router.get("/v1/editor/chart/list", response_model=Result[ChartList]) @@ -191,7 +193,7 @@ async def get_editor_chart_list(con_uid: str): charts=json.loads(element["data"]["content"]), ) return Result.succ(chart_list) - return Result.faild(msg="Not have charts!") + return Result.failed(msg="Not have charts!") @router.post("/v1/editor/chart/info", response_model=Result[ChartDetail]) @@ -210,7 +212,7 @@ async def get_editor_chart_info(param: dict = Body()): logger.error( "this dashboard dialogue version too old, can't support editor!" ) - return Result.faild( + return Result.failed( msg="this dashboard dialogue version too old, can't support editor!" ) for element in last_round["messages"]: @@ -234,7 +236,7 @@ async def get_editor_chart_info(param: dict = Body()): ) return Result.succ(detail) - return Result.faild(msg="Can't Find Chart Detail Info!") + return Result.failed(msg="Can't Find Chart Detail Info!") @router.post("/v1/editor/chart/run", response_model=Result[ChartRunData]) @@ -244,7 +246,7 @@ async def editor_chart_run(run_param: dict = Body()): sql = run_param["sql"] chart_type = run_param["chart_type"] if not db_name and not sql: - return Result.faild("SQL run param error！") + return Result.failed("SQL run param error！") try: dashboard_data_loader: DashboardDataLoader = DashboardDataLoader() db_conn = CFG.LOCAL_DB_MANAGE.get_connect(db_name) @@ -334,7 +336,7 @@ async def chart_editor_submit(chart_edit_context: ChatChartEditContext = Body()) ) except Exception as e: logger.error(f"edit chart exception!{str(e)}", e) - return Result.faild(msg=f"Edit chart exception!{str(e)}") + return Result.failed(msg=f"Edit chart exception!{str(e)}") history_mem.update(history_messages) return Result.succ(None) - return Result.faild(msg="Edit Faild!") + return Result.failed(msg="Edit Failed!") diff --git a/pilot/openapi/api_v1/feedback/feed_back_db.py b/pilot/openapi/api_v1/feedback/feed_back_db.py index 3d697263b..02afb3215 100644 --- a/pilot/openapi/api_v1/feedback/feed_back_db.py +++ b/pilot/openapi/api_v1/feedback/feed_back_db.py @@ -3,7 +3,12 @@ from datetime import datetime from sqlalchemy import Column, Integer, Text, String, DateTime from pilot.base_modules.meta_data.base_dao import BaseDao -from pilot.base_modules.meta_data.meta_data import Base, engine, session +from pilot.base_modules.meta_data.meta_data import ( + Base, + engine, + session, + META_DATA_DATABASE, +) from pilot.openapi.api_v1.feedback.feed_back_model import FeedBackBody @@ -36,7 +41,10 @@ class ChatFeedBackEntity(Base): class ChatFeedBackDao(BaseDao): def __init__(self): super().__init__( - database="dbgpt", orm_base=Base, db_engine=engine, session=session + database=META_DATA_DATABASE, + orm_base=Base, + db_engine=engine, + session=session, ) def create_or_update_chat_feed_back(self, feed_back: FeedBackBody): diff --git a/pilot/openapi/api_view_model.py b/pilot/openapi/api_view_model.py index 60065f2f2..af1aa4b9c 100644 --- a/pilot/openapi/api_view_model.py +++ b/pilot/openapi/api_view_model.py @@ -17,11 +17,11 @@ class Result(Generic[T], BaseModel): return Result(success=True, err_code=None, err_msg=None, data=data) @classmethod - def faild(cls, msg): + def failed(cls, msg): return Result(success=False, err_code="E000X", err_msg=msg, data=None) @classmethod - def faild(cls, code, msg): + def failed(cls, code, msg): return Result(success=False, err_code=code, err_msg=msg, data=None) diff --git a/pilot/openapi/base.py b/pilot/openapi/base.py index 506254ec7..d8c814787 100644 --- a/pilot/openapi/base.py +++ b/pilot/openapi/base.py @@ -7,4 +7,4 @@ async def validation_exception_handler(request: Request, exc: RequestValidationE message = "" for error in exc.errors(): message += ".".join(error.get("loc")) + ":" + error.get("msg") + ";" - return Result.faild(code="E0001", msg=message) + return Result.failed(code="E0001", msg=message) diff --git a/pilot/out_parser/base.py b/pilot/out_parser/base.py index 78286f3d3..a7268ab68 100644 --- a/pilot/out_parser/base.py +++ b/pilot/out_parser/base.py @@ -26,6 +26,10 @@ class BaseOutputParser(ABC): def __init__(self, sep: str, is_stream_out: bool = True): self.sep = sep self.is_stream_out = is_stream_out + self.data_schema = None + + def update(self, data_schema): + self.data_schema = data_schema def __post_process_code(self, code): sep = "\n```" @@ -115,7 +119,9 @@ class BaseOutputParser(ABC): print("un_stream ai response:", ai_response) return ai_response else: - raise ValueError("Model server error!code=" + resp_obj_ex["error_code"]) + raise ValueError( + f"""Model server error!code={resp_obj_ex["error_code"]}, errmsg is {resp_obj_ex["text"]}""" + ) def __illegal_json_ends(self, s): temp_json = s @@ -202,16 +208,23 @@ class BaseOutputParser(ABC): if not cleaned_output.startswith("{") or not cleaned_output.endswith("}"): logger.info("illegal json processing:\n" + cleaned_output) cleaned_output = self.__extract_json(cleaned_output) + + if not cleaned_output or len(cleaned_output) <= 0: + return model_out_text + cleaned_output = ( cleaned_output.strip() .replace("\\n", " ") .replace("\n", " ") .replace("\\", " ") + .replace("\_", "_") ) cleaned_output = self.__illegal_json_ends(cleaned_output) return cleaned_output - def parse_view_response(self, ai_text, data) -> str: + def parse_view_response( + self, ai_text, data, parse_prompt_response: Any = None + ) -> str: """ parse the ai response info to user view Args: @@ -242,7 +255,9 @@ class BaseOutputParser(ABC): def _parse_model_response(response: ResponseTye): - if isinstance(response, ModelOutput): + if response is None: + resp_obj_ex = "" + elif isinstance(response, ModelOutput): resp_obj_ex = asdict(response) elif isinstance(response, str): resp_obj_ex = json.loads(response) diff --git a/pilot/scene/base.py b/pilot/scene/base.py index b56176991..e3478f7c3 100644 --- a/pilot/scene/base.py +++ b/pilot/scene/base.py @@ -82,6 +82,30 @@ class ChatScene(Enum): "Dialogue through natural language and private documents and knowledge bases.", ["Knowledge Space Select"], ) + ExtractTriplet = Scene( + "extract_triplet", + "Extract Triplet", + "Extract Triplet", + ["Extract Select"], + True, + ) + ExtractSummary = Scene( + "extract_summary", + "Extract Summary", + "Extract Summary", + ["Extract Select"], + True, + ) + ExtractRefineSummary = Scene( + "extract_refine_summary", + "Extract Summary", + "Extract Summary", + ["Extract Select"], + True, + ) + ExtractEntity = Scene( + "extract_entity", "Extract Entity", "Extract Entity", ["Extract Select"], True + ) @staticmethod def of_mode(mode): diff --git a/pilot/scene/base_chat.py b/pilot/scene/base_chat.py index 571706b88..deb397dcf 100644 --- a/pilot/scene/base_chat.py +++ b/pilot/scene/base_chat.py @@ -12,8 +12,12 @@ from pilot.prompts.prompt_new import PromptTemplate from pilot.scene.base_message import ModelMessage, ModelMessageRoleType from pilot.scene.message import OnceConversation from pilot.utils import get_or_create_event_loop +from pilot.utils.executor_utils import ExecutorFactory, blocking_func_to_async +from pilot.utils.tracer import root_tracer, trace from pydantic import Extra from pilot.memory.chat_history.chat_hisotry_factory import ChatHistory +from pilot.awel import BaseOperator, SimpleCallDataInputSource, InputOperator, DAG +from pilot.model.operator.model_operator import ModelOperator, ModelStreamOperator logger = logging.getLogger(__name__) headers = {"User-Agent": "dbgpt Client"} @@ -37,6 +41,7 @@ class BaseChat(ABC): arbitrary_types_allowed = True + @trace("BaseChat.__init__") def __init__(self, chat_param: Dict): """Chat Module Initialization Args: @@ -53,6 +58,7 @@ class BaseChat(ABC): chat_param["model_name"] if chat_param["model_name"] else CFG.LLM_MODEL ) self.llm_echo = False + self.model_cache_enable = chat_param.get("model_cache_enable", False) ### load prompt template # self.prompt_template: PromptTemplate = CFG.prompt_templates[ @@ -62,7 +68,7 @@ class BaseChat(ABC): CFG.prompt_template_registry.get_prompt_template( self.chat_mode.value(), language=CFG.LANGUAGE, - model_name=CFG.LLM_MODEL, + model_name=self.llm_model, proxyllm_backend=CFG.PROXYLLM_BACKEND, ) ) @@ -82,6 +88,15 @@ class BaseChat(ABC): self.current_tokens_used: int = 0 if chat_param["user_id"]: self.user_id = chat_param["user_id"] + # The executor to submit blocking function + self._executor = CFG.SYSTEM_APP.get_component( + ComponentType.EXECUTOR_DEFAULT, ExecutorFactory + ).create() + + self._model_operator: BaseOperator = _build_model_operator() + self._model_stream_operator: BaseOperator = _build_model_operator( + is_stream=True, dag_name="llm_stream_model_dag" + ) class Config: """Configuration for this pydantic object.""" @@ -94,12 +109,21 @@ class BaseChat(ABC): raise NotImplementedError("Not supported for this chat type.") @abstractmethod - def generate_input_values(self): - pass + async def generate_input_values(self) -> Dict: + """Generate input to LLM + + Please note that you must not perform any blocking operations in this function + + Returns: + a dictionary to be formatted by prompt template + """ def do_action(self, prompt_response): return prompt_response + def message_adjust(self): + pass + def get_llm_speak(self, prompt_define_response): if hasattr(prompt_define_response, "thoughts"): if isinstance(prompt_define_response.thoughts, dict): @@ -118,18 +142,24 @@ class BaseChat(ABC): speak_to_user = prompt_define_response return speak_to_user - def __call_base(self): - input_values = self.generate_input_values() + async def __call_base(self): + input_values = await self.generate_input_values() ### Chat sequence advance self.current_message.chat_order = len(self.history_message) + 1 self.current_message.add_user_message(self.current_user_input) self.current_message.start_date = datetime.datetime.now().strftime( "%Y-%m-%d %H:%M:%S" ) - self.current_message.tokens = 0 if self.prompt_template.template: - current_prompt = self.prompt_template.format(**input_values) + metadata = { + "template_scene": self.prompt_template.template_scene, + "input_values": input_values, + } + with root_tracer.start_span( + "BaseChat.__call_base.prompt_template.format", metadata=metadata + ): + current_prompt = self.prompt_template.format(**input_values) self.current_message.add_system_message(current_prompt) llm_messages = self.generate_llm_messages() @@ -137,14 +167,13 @@ class BaseChat(ABC): # Not new server mode, we convert the message format(List[ModelMessage]) to list of dict # fix the error of "Object of type ModelMessage is not JSON serializable" when passing the payload to request.post llm_messages = list(map(lambda m: m.dict(), llm_messages)) - payload = { "model": self.llm_model, "prompt": self.generate_llm_text(), "messages": llm_messages, "temperature": float(self.prompt_template.temperature), "max_new_tokens": int(self.prompt_template.max_new_tokens), - "stop": self.prompt_template.sep, + # "stop": self.prompt_template.sep, "echo": self.llm_echo, } return payload @@ -152,6 +181,9 @@ class BaseChat(ABC): def stream_plugin_call(self, text): return text + def stream_call_reinforce_fn(self, text): + return text + async def check_iterator_end(iterator): try: await asyncio.anext(iterator) @@ -159,20 +191,30 @@ class BaseChat(ABC): except StopAsyncIteration: return True # 迭代器已经执行结束 + def _get_span_metadata(self, payload: Dict) -> Dict: + metadata = {k: v for k, v in payload.items()} + del metadata["prompt"] + metadata["messages"] = list( + map(lambda m: m if isinstance(m, dict) else m.dict(), metadata["messages"]) + ) + return metadata + async def stream_call(self): # TODO Retry when server connection error - payload = self.__call_base() + payload = await self.__call_base() self.skip_echo_len = len(payload.get("prompt").replace("", " ")) + 11 logger.info(f"Requert: \n{payload}") ai_response_text = "" + span = root_tracer.start_span( + "BaseChat.stream_call", metadata=self._get_span_metadata(payload) + ) + payload["span_id"] = span.span_id + payload["model_cache_enable"] = self.model_cache_enable try: - from pilot.model.cluster import WorkerManagerFactory - - worker_manager = CFG.SYSTEM_APP.get_component( - ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory - ).create() - async for output in worker_manager.generate_stream(payload): + async for output in await self._model_stream_operator.call_stream( + call_data={"data": payload} + ): ### Plug-in research in result generation msg = self.prompt_template.output_parser.parse_model_stream_resp_ex( output, self.skip_echo_len @@ -181,28 +223,33 @@ class BaseChat(ABC): view_msg = view_msg.replace("\n", "\\n") yield view_msg self.current_message.add_ai_message(msg) + view_msg = self.stream_call_reinforce_fn(view_msg) self.current_message.add_view_message(view_msg) + span.end() except Exception as e: print(traceback.format_exc()) - logger.error("model response parase faild！" + str(e)) + logger.error("model response parase failed！" + str(e)) self.current_message.add_view_message( f"""ERROR!{str(e)}\n {ai_response_text} """ ) ### store current conversation + span.end(metadata={"error": str(e)}) self.memory.append(self.current_message, self.user_id) async def nostream_call(self): - payload = self.__call_base() + payload = await self.__call_base() logger.info(f"Request: \n{payload}") ai_response_text = "" + span = root_tracer.start_span( + "BaseChat.nostream_call", metadata=self._get_span_metadata(payload) + ) + payload["span_id"] = span.span_id + payload["model_cache_enable"] = self.model_cache_enable try: - from pilot.model.cluster import WorkerManagerFactory - - worker_manager = CFG.SYSTEM_APP.get_component( - ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory - ).create() - - model_output = await worker_manager.generate(payload) + with root_tracer.start_span("BaseChat.invoke_worker_manager.generate"): + model_output = await self._model_operator.call( + call_data={"data": payload} + ) ### output parse ai_response_text = ( @@ -217,27 +264,78 @@ class BaseChat(ABC): ai_response_text ) ) - ### run - result = self.do_action(prompt_define_response) + metadata = { + "model_output": model_output.to_dict(), + "ai_response_text": ai_response_text, + "prompt_define_response": self._parse_prompt_define_response( + prompt_define_response + ), + } + with root_tracer.start_span("BaseChat.do_action", metadata=metadata): + ### run + result = await blocking_func_to_async( + self._executor, self.do_action, prompt_define_response + ) ### llm speaker speak_to_user = self.get_llm_speak(prompt_define_response) - view_message = self.prompt_template.output_parser.parse_view_response( - speak_to_user, result + # view_message = self.prompt_template.output_parser.parse_view_response( + # speak_to_user, result + # ) + view_message = await blocking_func_to_async( + self._executor, + self.prompt_template.output_parser.parse_view_response, + speak_to_user, + result, + prompt_define_response, ) + view_message = view_message.replace("\n", "\\n") self.current_message.add_view_message(view_message) + self.message_adjust() + + span.end() except Exception as e: print(traceback.format_exc()) logger.error("model response parase faild！" + str(e)) self.current_message.add_view_message( f"""ERROR!{str(e)}\n {ai_response_text} """ ) + span.end(metadata={"error": str(e)}) ### store dialogue self.memory.append(self.current_message, self.user_id) return self.current_ai_response() + async def get_llm_response(self): + payload = await self.__call_base() + logger.info(f"Request: \n{payload}") + ai_response_text = "" + payload["model_cache_enable"] = self.model_cache_enable + try: + model_output = await self._model_operator.call(call_data={"data": payload}) + ### output parse + ai_response_text = ( + self.prompt_template.output_parser.parse_model_nostream_resp( + model_output, self.prompt_template.sep + ) + ) + ### model result deal + self.current_message.add_ai_message(ai_response_text) + prompt_define_response = None + prompt_define_response = ( + self.prompt_template.output_parser.parse_prompt_response( + ai_response_text + ) + ) + except Exception as e: + print(traceback.format_exc()) + logger.error("model response parse failed！" + str(e)) + self.current_message.add_view_message( + f"""model response parse failed！{str(e)}\n {ai_response_text} """ + ) + return prompt_define_response + def _blocking_stream_call(self): logger.warn( "_blocking_stream_call is only temporarily used in webserver and will be deleted soon, please use stream_call to replace it for higher performance" @@ -277,16 +375,18 @@ class BaseChat(ABC): if self.prompt_template.template_define: text += self.prompt_template.template_define + self.prompt_template.sep ### Load prompt - text += self.__load_system_message() + text += _load_system_message(self.current_message, self.prompt_template) ### Load examples - text += self.__load_example_messages() + text += _load_example_messages(self.prompt_template) ### Load History - text += self.__load_histroy_messages() + text += _load_history_messages( + self.prompt_template, self.history_message, self.chat_retention_rounds + ) ### Load User Input - text += self.__load_user_message() + text += _load_user_message(self.current_message, self.prompt_template) return text def generate_llm_messages(self) -> List[ModelMessage]: @@ -304,137 +404,26 @@ class BaseChat(ABC): ) ) ### Load prompt - messages += self.__load_system_message(str_message=False) + messages += _load_system_message( + self.current_message, self.prompt_template, str_message=False + ) ### Load examples - messages += self.__load_example_messages(str_message=False) + messages += _load_example_messages(self.prompt_template, str_message=False) ### Load History - messages += self.__load_histroy_messages(str_message=False) + messages += _load_history_messages( + self.prompt_template, + self.history_message, + self.chat_retention_rounds, + str_message=False, + ) ### Load User Input - messages += self.__load_user_message(str_message=False) + messages += _load_user_message( + self.current_message, self.prompt_template, str_message=False + ) return messages - def __load_system_message(self, str_message: bool = True): - system_convs = self.current_message.get_system_conv() - system_text = "" - system_messages = [] - for system_conv in system_convs: - system_text += ( - system_conv.type + ":" + system_conv.content + self.prompt_template.sep - ) - system_messages.append( - ModelMessage(role=system_conv.type, content=system_conv.content) - ) - return system_text if str_message else system_messages - - def __load_user_message(self, str_message: bool = True): - user_conv = self.current_message.get_user_conv() - user_messages = [] - if user_conv: - user_text = ( - user_conv.type + ":" + user_conv.content + self.prompt_template.sep - ) - user_messages.append( - ModelMessage(role=user_conv.type, content=user_conv.content) - ) - return user_text if str_message else user_messages - else: - raise ValueError("Hi! What do you want to talk about？") - - def __load_example_messages(self, str_message: bool = True): - example_text = "" - example_messages = [] - if self.prompt_template.example_selector: - for round_conv in self.prompt_template.example_selector.examples(): - for round_message in round_conv["messages"]: - if not round_message["type"] in [ - ModelMessageRoleType.VIEW, - ModelMessageRoleType.SYSTEM, - ]: - message_type = round_message["type"] - message_content = round_message["data"]["content"] - example_text += ( - message_type - + ":" - + message_content - + self.prompt_template.sep - ) - example_messages.append( - ModelMessage(role=message_type, content=message_content) - ) - return example_text if str_message else example_messages - - def __load_histroy_messages(self, str_message: bool = True): - history_text = "" - history_messages = [] - if self.prompt_template.need_historical_messages: - if self.history_message: - logger.info( - f"There are already {len(self.history_message)} rounds of conversations! Will use {self.chat_retention_rounds} rounds of content as history!" - ) - if len(self.history_message) > self.chat_retention_rounds: - for first_message in self.history_message[0]["messages"]: - if not first_message["type"] in [ - ModelMessageRoleType.VIEW, - ModelMessageRoleType.SYSTEM, - ]: - message_type = first_message["type"] - message_content = first_message["data"]["content"] - history_text += ( - message_type - + ":" - + message_content - + self.prompt_template.sep - ) - history_messages.append( - ModelMessage(role=message_type, content=message_content) - ) - if self.chat_retention_rounds > 1: - index = self.chat_retention_rounds - 1 - for round_conv in self.history_message[-index:]: - for round_message in round_conv["messages"]: - if not round_message["type"] in [ - ModelMessageRoleType.VIEW, - ModelMessageRoleType.SYSTEM, - ]: - message_type = round_message["type"] - message_content = round_message["data"]["content"] - history_text += ( - message_type - + ":" - + message_content - + self.prompt_template.sep - ) - history_messages.append( - ModelMessage( - role=message_type, content=message_content - ) - ) - - else: - ### user all history - for conversation in self.history_message: - for message in conversation["messages"]: - ### histroy message not have promot and view info - if not message["type"] in [ - ModelMessageRoleType.VIEW, - ModelMessageRoleType.SYSTEM, - ]: - message_type = message["type"] - message_content = message["data"]["content"] - history_text += ( - message_type - + ":" - + message_content - + self.prompt_template.sep - ) - history_messages.append( - ModelMessage(role=message_type, content=message_content) - ) - - return history_text if str_message else history_messages - def current_ai_response(self) -> str: for message in self.current_message.messages: if message.type == "view": @@ -451,3 +440,230 @@ class BaseChat(ABC): """ pass + + def _parse_prompt_define_response(self, prompt_define_response: Any) -> Any: + if not prompt_define_response: + return "" + if isinstance(prompt_define_response, str) or isinstance( + prompt_define_response, dict + ): + return prompt_define_response + if isinstance(prompt_define_response, tuple): + if hasattr(prompt_define_response, "_asdict"): + # namedtuple + return prompt_define_response._asdict() + else: + return dict( + zip(range(len(prompt_define_response)), prompt_define_response) + ) + else: + return prompt_define_response + + +def _build_model_operator( + is_stream: bool = False, dag_name: str = "llm_model_dag" +) -> BaseOperator: + """Builds and returns a model processing workflow (DAG) operator. + + This function constructs a Directed Acyclic Graph (DAG) for processing data using a model. + It includes caching and branching logic to either fetch results from a cache or process + data using the model. It supports both streaming and non-streaming modes. + + .. code-block:: python + input_node >> cache_check_branch_node + cache_check_branch_node >> model_node >> save_cached_node >> join_node + cache_check_branch_node >> cached_node >> join_node + + equivalent to:: + + -> model_node -> save_cached_node -> + / \ + input_node -> cache_check_branch_node ---> join_node + \ / + -> cached_node ------------------- -> + + Args: + is_stream (bool): Flag to determine if the operator should process data in streaming mode. + dag_name (str): Name of the DAG. + + Returns: + BaseOperator: The final operator in the constructed DAG, typically a join node. + """ + from pilot.model.cluster import WorkerManagerFactory + from pilot.awel import JoinOperator + from pilot.model.operator.model_operator import ( + ModelCacheBranchOperator, + CachedModelStreamOperator, + CachedModelOperator, + ModelSaveCacheOperator, + ModelStreamSaveCacheOperator, + ) + from pilot.cache import CacheManager + + # Fetch worker and cache managers from the system configuration + worker_manager = CFG.SYSTEM_APP.get_component( + ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory + ).create() + cache_manager: CacheManager = CFG.SYSTEM_APP.get_component( + ComponentType.MODEL_CACHE_MANAGER, CacheManager + ) + # Define task names for the model and cache nodes + model_task_name = "llm_model_node" + cache_task_name = "llm_model_cache_node" + + with DAG(dag_name): + # Create an input node + input_node = InputOperator(SimpleCallDataInputSource()) + # Determine if the workflow should operate in streaming mode + if is_stream: + model_node = ModelStreamOperator(worker_manager, task_name=model_task_name) + cached_node = CachedModelStreamOperator( + cache_manager, task_name=cache_task_name + ) + save_cached_node = ModelStreamSaveCacheOperator(cache_manager) + else: + model_node = ModelOperator(worker_manager, task_name=model_task_name) + cached_node = CachedModelOperator(cache_manager, task_name=cache_task_name) + save_cached_node = ModelSaveCacheOperator(cache_manager) + + # Create a branch node to decide between fetching from cache or processing with the model + cache_check_branch_node = ModelCacheBranchOperator( + cache_manager, + model_task_name="llm_model_node", + cache_task_name="llm_model_cache_node", + ) + # Create a join node to merge outputs from the model and cache nodes, just keep the first not empty output + join_node = JoinOperator( + combine_function=lambda model_out, cache_out: cache_out or model_out + ) + + # Define the workflow structure using the >> operator + input_node >> cache_check_branch_node + cache_check_branch_node >> model_node >> save_cached_node >> join_node + cache_check_branch_node >> cached_node >> join_node + + return join_node + + +def _load_system_message( + current_message: OnceConversation, + prompt_template: PromptTemplate, + str_message: bool = True, +): + system_convs = current_message.get_system_conv() + system_text = "" + system_messages = [] + for system_conv in system_convs: + system_text += ( + system_conv.type + ":" + system_conv.content + prompt_template.sep + ) + system_messages.append( + ModelMessage(role=system_conv.type, content=system_conv.content) + ) + return system_text if str_message else system_messages + + +def _load_user_message( + current_message: OnceConversation, + prompt_template: PromptTemplate, + str_message: bool = True, +): + user_conv = current_message.get_user_conv() + user_messages = [] + if user_conv: + user_text = user_conv.type + ":" + user_conv.content + prompt_template.sep + user_messages.append( + ModelMessage(role=user_conv.type, content=user_conv.content) + ) + return user_text if str_message else user_messages + else: + raise ValueError("Hi! What do you want to talk about？") + + +def _load_example_messages(prompt_template: PromptTemplate, str_message: bool = True): + example_text = "" + example_messages = [] + if prompt_template.example_selector: + for round_conv in prompt_template.example_selector.examples(): + for round_message in round_conv["messages"]: + if not round_message["type"] in [ + ModelMessageRoleType.VIEW, + ModelMessageRoleType.SYSTEM, + ]: + message_type = round_message["type"] + message_content = round_message["data"]["content"] + example_text += ( + message_type + ":" + message_content + prompt_template.sep + ) + example_messages.append( + ModelMessage(role=message_type, content=message_content) + ) + return example_text if str_message else example_messages + + +def _load_history_messages( + prompt_template: PromptTemplate, + history_message: List[OnceConversation], + chat_retention_rounds: int, + str_message: bool = True, +): + history_text = "" + history_messages = [] + if prompt_template.need_historical_messages: + if history_message: + logger.info( + f"There are already {len(history_message)} rounds of conversations! Will use {chat_retention_rounds} rounds of content as history!" + ) + if len(history_message) > chat_retention_rounds: + for first_message in history_message[0]["messages"]: + if not first_message["type"] in [ + ModelMessageRoleType.VIEW, + ModelMessageRoleType.SYSTEM, + ]: + message_type = first_message["type"] + message_content = first_message["data"]["content"] + history_text += ( + message_type + ":" + message_content + prompt_template.sep + ) + history_messages.append( + ModelMessage(role=message_type, content=message_content) + ) + if chat_retention_rounds > 1: + index = chat_retention_rounds - 1 + for round_conv in history_message[-index:]: + for round_message in round_conv["messages"]: + if not round_message["type"] in [ + ModelMessageRoleType.VIEW, + ModelMessageRoleType.SYSTEM, + ]: + message_type = round_message["type"] + message_content = round_message["data"]["content"] + history_text += ( + message_type + + ":" + + message_content + + prompt_template.sep + ) + history_messages.append( + ModelMessage(role=message_type, content=message_content) + ) + + else: + ### user all history + for conversation in history_message: + for message in conversation["messages"]: + ### histroy message not have promot and view info + if not message["type"] in [ + ModelMessageRoleType.VIEW, + ModelMessageRoleType.SYSTEM, + ]: + message_type = message["type"] + message_content = message["data"]["content"] + history_text += ( + message_type + ":" + message_content + prompt_template.sep + ) + history_messages.append( + ModelMessage(role=message_type, content=message_content) + ) + + return history_text if str_message else history_messages diff --git a/pilot/scene/base_message.py b/pilot/scene/base_message.py index eeb42a285..c4c10459c 100644 --- a/pilot/scene/base_message.py +++ b/pilot/scene/base_message.py @@ -1,7 +1,7 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import Any, Dict, List, Tuple, Optional +from typing import Any, Dict, List, Tuple, Optional, Union from pydantic import BaseModel, Field, root_validator @@ -70,14 +70,6 @@ class SystemMessage(BaseMessage): return "system" -class ModelMessage(BaseModel): - """Type of message that interaction between dbgpt-server and llm-server""" - - """Similar to openai's message format""" - role: str - content: str - - class ModelMessageRoleType: """ "Type of ModelMessage role""" @@ -87,6 +79,77 @@ class ModelMessageRoleType: VIEW = "view" +class ModelMessage(BaseModel): + """Type of message that interaction between dbgpt-server and llm-server""" + + """Similar to openai's message format""" + role: str + content: str + + @staticmethod + def from_openai_messages( + messages: Union[str, List[Dict[str, str]]] + ) -> List["ModelMessage"]: + """Openai message format to current ModelMessage format""" + if isinstance(messages, str): + return [ModelMessage(role=ModelMessageRoleType.HUMAN, content=messages)] + result = [] + for message in messages: + msg_role = message["role"] + content = message["content"] + if msg_role == "system": + result.append( + ModelMessage(role=ModelMessageRoleType.SYSTEM, content=content) + ) + elif msg_role == "user": + result.append( + ModelMessage(role=ModelMessageRoleType.HUMAN, content=content) + ) + elif msg_role == "assistant": + result.append( + ModelMessage(role=ModelMessageRoleType.AI, content=content) + ) + else: + raise ValueError(f"Unknown role: {msg_role}") + return result + + @staticmethod + def to_openai_messages(messages: List["ModelMessage"]) -> List[Dict[str, str]]: + """Convert to OpenAI message format and + hugggingface [Templates of Chat Models](https://huggingface.co/docs/transformers/v4.34.1/en/chat_templating) + """ + history = [] + # Add history conversation + for message in messages: + if message.role == ModelMessageRoleType.HUMAN: + history.append({"role": "user", "content": message.content}) + elif message.role == ModelMessageRoleType.SYSTEM: + history.append({"role": "system", "content": message.content}) + elif message.role == ModelMessageRoleType.AI: + history.append({"role": "assistant", "content": message.content}) + else: + pass + # Move the last user's information to the end + temp_his = history[::-1] + last_user_input = None + for m in temp_his: + if m["role"] == "user": + last_user_input = m + break + if last_user_input: + history.remove(last_user_input) + history.append(last_user_input) + return history + + @staticmethod + def to_dict_list(messages: List["ModelMessage"]) -> List[Dict[str, str]]: + return list(map(lambda m: m.dict(), messages)) + + @staticmethod + def build_human_message(content: str) -> "ModelMessage": + return ModelMessage(role=ModelMessageRoleType.HUMAN, content=content) + + class Generation(BaseModel): """Output of a single generation.""" diff --git a/pilot/scene/chat_agent/chat.py b/pilot/scene/chat_agent/chat.py index 0fc5a0375..09968d8f3 100644 --- a/pilot/scene/chat_agent/chat.py +++ b/pilot/scene/chat_agent/chat.py @@ -11,6 +11,7 @@ from pilot.common.string_utils import extract_content from .prompt import prompt from pilot.component import ComponentType from pilot.base_modules.agent.controller import ModuleAgent +from pilot.utils.tracer import root_tracer, trace CFG = Config() @@ -51,7 +52,8 @@ class ChatAgent(BaseChat): self.api_call = ApiCall(plugin_generator=self.plugins_prompt_generator) - def generate_input_values(self): + @trace() + async def generate_input_values(self) -> Dict[str, str]: input_values = { "user_goal": self.current_user_input, "expand_constraints": self.__list_to_prompt_str( @@ -62,8 +64,16 @@ class ChatAgent(BaseChat): return input_values def stream_plugin_call(self, text): - text = text.replace("\n", " ") - return self.api_call.run(text) + text = ( + text.replace("\\n", " ") + .replace("\n", " ") + .replace("\_", "_") + .replace("\\", " ") + ) + with root_tracer.start_span( + "ChatAgent.stream_plugin_call.api_call", metadata={"text": text} + ): + return self.api_call.run(text) def __list_to_prompt_str(self, list: List) -> str: return "\n".join(f"{i + 1 + 1}. {item}" for i, item in enumerate(list)) diff --git a/pilot/scene/chat_agent/out_parser.py b/pilot/scene/chat_agent/out_parser.py index ecea328bb..962a40fbc 100644 --- a/pilot/scene/chat_agent/out_parser.py +++ b/pilot/scene/chat_agent/out_parser.py @@ -1,11 +1,6 @@ import json from typing import Dict, NamedTuple -from pilot.utils import build_logger from pilot.out_parser.base import BaseOutputParser, T -from pilot.configs.model_config import LOGDIR - - -logger = build_logger("webserver", LOGDIR + "DbChatOutputParser.log") class PluginAction(NamedTuple): @@ -15,7 +10,7 @@ class PluginAction(NamedTuple): class PluginChatOutputParser(BaseOutputParser): - def parse_view_response(self, speak, data) -> str: + def parse_view_response(self, speak, data, prompt_response) -> str: ### tool out data to table view print(f"parse_view_response:{speak},{str(data)}") view_text = f"##### {speak}" + "\n" + str(data) diff --git a/pilot/scene/chat_agent/prompt.py b/pilot/scene/chat_agent/prompt.py index 7d01bfd2f..94151544a 100644 --- a/pilot/scene/chat_agent/prompt.py +++ b/pilot/scene/chat_agent/prompt.py @@ -42,7 +42,8 @@ _DEFAULT_TEMPLATE_ZH = """ 3.根据上面约束的方式生成每个工具的调用，对于工具使用的提示文本，需要在工具使用前生成 4.如果用户目标无法理解和意图不明确，优先使用搜索引擎工具 5.参数内容可能需要根据用户的目标推理得到，不仅仅是从文本提取 - 6.约束条件和工具信息作为推理过程的辅助信息，不要表达在给用户的输出内容中 + 6.约束条件和工具信息作为推理过程的辅助信息，对应内容不要表达在给用户的输出内容中 + 7.不要把部分内容放在markdown标签里 {expand_constraints} 工具列表: diff --git a/pilot/scene/chat_dashboard/chat.py b/pilot/scene/chat_dashboard/chat.py index 7e4433670..6771fb3fc 100644 --- a/pilot/scene/chat_dashboard/chat.py +++ b/pilot/scene/chat_dashboard/chat.py @@ -12,6 +12,8 @@ from pilot.scene.chat_dashboard.data_preparation.report_schma import ( ) from pilot.scene.chat_dashboard.prompt import prompt from pilot.scene.chat_dashboard.data_loader import DashboardDataLoader +from pilot.utils.executor_utils import blocking_func_to_async +from pilot.utils.tracer import root_tracer, trace CFG = Config() @@ -52,7 +54,8 @@ class ChatDashboard(BaseChat): data = f.read() return json.loads(data) - def generate_input_values(self): + @trace() + async def generate_input_values(self) -> Dict: try: from pilot.summary.db_summary_client import DBSummaryClient except ImportError: @@ -60,9 +63,16 @@ class ChatDashboard(BaseChat): client = DBSummaryClient(system_app=CFG.SYSTEM_APP) try: - table_infos = client.get_similar_tables( - dbname=self.db_name, query=self.current_user_input, topk=self.top_k + table_infos = await blocking_func_to_async( + self._executor, + client.get_similar_tables, + self.db_name, + self.current_user_input, + self.top_k, ) + # table_infos = client.get_similar_tables( + # dbname=self.db_name, query=self.current_user_input, topk=self.top_k + # ) print("dashboard vector find tables:{}", table_infos) except Exception as e: print("db summary find error!" + str(e)) diff --git a/pilot/scene/chat_dashboard/data_loader.py b/pilot/scene/chat_dashboard/data_loader.py index faabe542a..970fc92dd 100644 --- a/pilot/scene/chat_dashboard/data_loader.py +++ b/pilot/scene/chat_dashboard/data_loader.py @@ -52,8 +52,8 @@ class DashboardDataLoader: values.append(value_item) return field_names, values except Exception as e: - logger.debug("Prepare Chart Data Faild!" + str(e)) - raise ValueError("Prepare Chart Data Faild!") + logger.debug("Prepare Chart Data Failed!" + str(e)) + raise ValueError("Prepare Chart Data Failed!") def get_chart_values_by_db(self, db_name: str, chart_sql: str): logger.info(f"get_chart_values_by_db:{db_name},{chart_sql}") diff --git a/pilot/scene/chat_dashboard/out_parser.py b/pilot/scene/chat_dashboard/out_parser.py index d593333e5..a3fee7886 100644 --- a/pilot/scene/chat_dashboard/out_parser.py +++ b/pilot/scene/chat_dashboard/out_parser.py @@ -38,7 +38,7 @@ class ChatDashboardOutputParser(BaseOutputParser): ) return chart_items - def parse_view_response(self, speak, data) -> str: + def parse_view_response(self, speak, data, prompt_response) -> str: return json.dumps(data.prepare_dict()) @property diff --git a/pilot/scene/chat_dashboard/prompt.py b/pilot/scene/chat_dashboard/prompt.py index 9fed97f8f..7f1dd4090 100644 --- a/pilot/scene/chat_dashboard/prompt.py +++ b/pilot/scene/chat_dashboard/prompt.py @@ -42,7 +42,7 @@ RESPONSE_FORMAT = [ PROMPT_SEP = SeparatorStyle.SINGLE.value -PROMPT_NEED_NEED_STREAM_OUT = False +PROMPT_NEED_STREAM_OUT = False prompt = PromptTemplate( template_scene=ChatScene.ChatDashboard.value(), @@ -50,9 +50,9 @@ prompt = PromptTemplate( response_format=json.dumps(RESPONSE_FORMAT, indent=4), template_define=PROMPT_SCENE_DEFINE, template=_DEFAULT_TEMPLATE, - stream_out=PROMPT_NEED_NEED_STREAM_OUT, + stream_out=PROMPT_NEED_STREAM_OUT, output_parser=ChatDashboardOutputParser( - sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT ), ) CFG.prompt_template_registry.register(prompt, is_default=True) diff --git a/pilot/scene/chat_data/chat_excel/excel_analyze/chat.py b/pilot/scene/chat_data/chat_excel/excel_analyze/chat.py index 12465998f..036ca5517 100644 --- a/pilot/scene/chat_data/chat_excel/excel_analyze/chat.py +++ b/pilot/scene/chat_data/chat_excel/excel_analyze/chat.py @@ -14,6 +14,7 @@ from pilot.scene.chat_data.chat_excel.excel_learning.chat import ExcelLearning from pilot.common.path_utils import has_path from pilot.configs.model_config import LLM_MODEL_CONFIG, KNOWLEDGE_UPLOAD_ROOT_PATH from pilot.base_modules.agent.common.schema import Status +from pilot.utils.tracer import root_tracer, trace CFG = Config() @@ -22,7 +23,7 @@ class ChatExcel(BaseChat): """a Excel analyzer to analyze Excel Data""" chat_scene: str = ChatScene.ChatExcel.value() - chat_retention_rounds = 1 + chat_retention_rounds = 2 def __init__(self, chat_param: Dict): """Chat Excel Module Initialization @@ -50,19 +51,50 @@ class ChatExcel(BaseChat): super().__init__(chat_param=chat_param) def _generate_numbered_list(self) -> str: - command_strings = [] - if CFG.command_disply: - for name, item in CFG.command_disply.commands.items(): - if item.enabled: - command_strings.append(f"{name}:{item.description}") - # command_strings += [ - # str(item) - # for item in CFG.command_disply.commands.values() - # if item.enabled - # ] - return "\n".join(f"{i+1}. {item}" for i, item in enumerate(command_strings)) + antv_charts = [ + {"response_line_chart": "used to display comparative trend analysis data"}, + { + "response_pie_chart": "suitable for scenarios such as proportion and distribution statistics" + }, + { + "response_table": "suitable for display with many display columns or non-numeric columns" + }, + # {"response_data_text":" the default display method, suitable for single-line or simple content display"}, + { + "response_scatter_plot": "Suitable for exploring relationships between variables, detecting outliers, etc." + }, + { + "response_bubble_chart": "Suitable for relationships between multiple variables, highlighting outliers or special situations, etc." + }, + { + "response_donut_chart": "Suitable for hierarchical structure representation, category proportion display and highlighting key categories, etc." + }, + { + "response_area_chart": "Suitable for visualization of time series data, comparison of multiple groups of data, analysis of data change trends, etc." + }, + { + "response_heatmap": "Suitable for visual analysis of time series data, large-scale data sets, distribution of classified data, etc." + }, + ] - def generate_input_values(self): + # command_strings = [] + # if CFG.command_disply: + # for name, item in CFG.command_disply.commands.items(): + # if item.enabled: + # command_strings.append(f"{name}:{item.description}") + # command_strings += [ + # str(item) + # for item in CFG.command_disply.commands.values() + # if item.enabled + # ] + return "\n".join( + f"{key}:{value}" + for dict_item in antv_charts + for key, value in dict_item.items() + ) + + @trace() + async def generate_input_values(self) -> Dict: input_values = { "user_input": self.current_user_input, "table_name": self.excel_reader.table_name, @@ -76,7 +108,7 @@ class ChatExcel(BaseChat): return None chat_param = { "chat_session_id": self.chat_session_id, - "user_input": "[" + self.excel_reader.excel_file_name + "]" + " Analysis！", + "user_input": "[" + self.excel_reader.excel_file_name + "]" + " Analyze！", "parent_mode": self.chat_mode, "select_param": self.excel_reader.excel_file_name, "excel_reader": self.excel_reader, @@ -88,5 +120,15 @@ class ChatExcel(BaseChat): return result def stream_plugin_call(self, text): - text = text.replace("\n", " ") - return self.api_call.run_display_sql(text, self.excel_reader.get_df_by_sql_ex) + text = ( + text.replace("\\n", " ") + .replace("\n", " ") + .replace("\_", "_") + .replace("\\", " ") + ) + with root_tracer.start_span( + "ChatExcel.stream_plugin_call.run_display_sql", metadata={"text": text} + ): + return self.api_call.display_sql_llmvis( + text, self.excel_reader.get_df_by_sql_ex + ) diff --git a/pilot/scene/chat_data/chat_excel/excel_analyze/out_parser.py b/pilot/scene/chat_data/chat_excel/excel_analyze/out_parser.py index 691c652fa..c3368f6f5 100644 --- a/pilot/scene/chat_data/chat_excel/excel_analyze/out_parser.py +++ b/pilot/scene/chat_data/chat_excel/excel_analyze/out_parser.py @@ -36,6 +36,6 @@ class ChatExcelOutputParser(BaseOutputParser): except Exception as e: raise ValueError(f"LLM Response Can't Parser! \n") - def parse_view_response(self, speak, data) -> str: + def parse_view_response(self, speak, data, prompt_response) -> str: ### tool out data to table view return data diff --git a/pilot/scene/chat_data/chat_excel/excel_analyze/prompt.py b/pilot/scene/chat_data/chat_excel/excel_analyze/prompt.py index 23c86bd4d..924a78b68 100644 --- a/pilot/scene/chat_data/chat_excel/excel_analyze/prompt.py +++ b/pilot/scene/chat_data/chat_excel/excel_analyze/prompt.py @@ -12,30 +12,34 @@ CFG = Config() _PROMPT_SCENE_DEFINE_EN = "You are a data analysis expert. " _DEFAULT_TEMPLATE_EN = """ -Please use the data structure and column information in the above historical dialogue and combine it with data analysis to answer the user's questions while satisfying the constraints. +Please use the data structure column analysis information generated in the above historical dialogue to answer the user's questions through duckdb sql data analysis under the following constraints.. Constraint: - 1.Please output your thinking process and analysis ideas first, and then output the specific data analysis results. The data analysis results are output in the following format:display typeCorrect duckdb data analysis sql - 2.For the available display methods of data analysis results, please choose the most appropriate one from the following display methods. If you are not sure, use 'response_data_text' as the display. The available display types are as follows:{disply_type} - 3.The table name that needs to be used in SQL is: {table_name}, please make sure not to use column names that are not in the data structure. + 1.Please fully understand the user's problem and use duckdb sql for analysis. The analysis content is returned in the output format required below. Please output the sql in the corresponding sql parameter. + 2.Please choose the best one from the display methods given below for data rendering, and put the type name into the name parameter value that returns the required format. If you cannot find the most suitable one, use 'Table' as the display method. , the available data display methods are as follows: {disply_type} + 3.The table name that needs to be used in SQL is: {table_name}. Please check the sql you generated and do not use column names that are not in the data structure. 4.Give priority to answering using data analysis. If the user's question does not involve data analysis, you can answer according to your understanding. - + 5.The sql part of the output content is converted to: [data display mode][correct duckdb data analysis sql] For this format, please refer to the return format requirements. + +Please think step by step and give your answer, and make sure your answer is formatted as follows: + thoughts summary to say to user.[Data display method][Correct duckdb data analysis sql] + User Questions: {user_input} """ _PROMPT_SCENE_DEFINE_ZH = """你是一个数据分析专家！""" _DEFAULT_TEMPLATE_ZH = """ -请使用上述历史对话中的数据结构信息，在满足下面约束条件下结合数据分析回答用户的问题。 +请使用历史对话中的数据结构信息，在满足下面约束条件下通过duckdb sql数据分析回答用户的问题。 约束条件: - 1.请先输出你的分析思路内容，再输出具体的数据分析结果。如果有数据数据分析时，请确保在输出的结果中包含如下格式内容:[数据展示方式][正确的duckdb数据分析sql] - 2.请确保数据分析结果格式的内容在整个回答中只出现一次,确保上述结构稳定，把[]部分内容替换为对应的值 - 3.数据分析结果可用的展示方式请在下面的展示方式中选择最合适的一种,放入数据分析结果的name字段内如果无法确定，则使用'Text'作为显示，可用数据展示方式如下: {disply_type} - 4.SQL中需要使用的表名是: {table_name},请不要使用没在数据结构中的列名。 - 5.优先使用数据分析的方式回答，如果用户问题不涉及数据分析内容，你可以按你的理解进行回答 - 6.请确保你的输出内容有良好排版，输出内容均为普通markdown文本,不要用```或者```python这种标签来包围的输出内容 -请确保你的输出格式如下: - 分析思路简介.[数据展示方式][正确的duckdb数据分析sql] + 1.请充分理解用户的问题，使用duckdb sql的方式进行分析， 分析内容按下面要求的输出格式返回，sql请输出在对应的sql参数中 + 2.请从如下给出的展示方式种选择最优的一种用以进行数据渲染，将类型名称放入返回要求格式的name参数值种，如果找不到最合适的则使用'Table'作为展示方式，可用数据展示方式如下: {disply_type} + 3.SQL中需要使用的表名是: {table_name},请检查你生成的sql，不要使用没在数据结构中的列名 + 4.优先使用数据分析的方式回答，如果用户问题不涉及数据分析内容，你可以按你的理解进行回答 + 5.输出内容中sql部分转换为：[数据显示方式][正确的duckdb数据分析sql] 这样的格式，参考返回格式要求 + +请一步一步思考，给出回答，并确保你的回答内容格式如下: + 对用户说的想法摘要.[数据展示方式][正确的duckdb数据分析sql] 用户问题：{user_input} """ @@ -51,21 +55,21 @@ _PROMPT_SCENE_DEFINE = ( PROMPT_SEP = SeparatorStyle.SINGLE.value -PROMPT_NEED_NEED_STREAM_OUT = True +PROMPT_NEED_STREAM_OUT = True # Temperature is a configuration hyperparameter that controls the randomness of language model output. # A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output. # For example, if you adjust the temperature to 0.5, the model will usually generate text that is more predictable and less creative than if you set the temperature to 1.0. -PROMPT_TEMPERATURE = 0.8 +PROMPT_TEMPERATURE = 0.3 prompt = PromptTemplate( template_scene=ChatScene.ChatExcel.value(), input_variables=["user_input", "table_name", "disply_type"], template_define=_PROMPT_SCENE_DEFINE, template=_DEFAULT_TEMPLATE, - stream_out=PROMPT_NEED_NEED_STREAM_OUT, + stream_out=PROMPT_NEED_STREAM_OUT, output_parser=ChatExcelOutputParser( - sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT ), need_historical_messages=True, # example_selector=sql_data_example, diff --git a/pilot/scene/chat_data/chat_excel/excel_learning/chat.py b/pilot/scene/chat_data/chat_excel/excel_learning/chat.py index 646704d32..dc94f4574 100644 --- a/pilot/scene/chat_data/chat_excel/excel_learning/chat.py +++ b/pilot/scene/chat_data/chat_excel/excel_learning/chat.py @@ -1,11 +1,7 @@ import json -import os -from typing import Any +from typing import Any, Dict -from pilot.scene.base_message import ( - HumanMessage, - ViewMessage, -) +from pilot.scene.base_message import HumanMessage, ViewMessage, AIMessage from pilot.scene.base_chat import BaseChat from pilot.scene.base import ChatScene from pilot.common.sql_database import Database @@ -13,6 +9,8 @@ from pilot.configs.config import Config from pilot.scene.chat_data.chat_excel.excel_learning.prompt import prompt from pilot.scene.chat_data.chat_excel.excel_reader import ExcelReader from pilot.json_utils.utilities import DateTimeEncoder +from pilot.utils.executor_utils import blocking_func_to_async +from pilot.utils.tracer import root_tracer, trace CFG = Config() @@ -46,13 +44,28 @@ class ExcelLearning(BaseChat): if parent_mode: self.current_message.chat_mode = parent_mode.value() - def generate_input_values(self): - colunms, datas = self.excel_reader.get_sample_data() + @trace() + async def generate_input_values(self) -> Dict: + # colunms, datas = self.excel_reader.get_sample_data() + colunms, datas = await blocking_func_to_async( + self._executor, self.excel_reader.get_sample_data + ) + self.prompt_template.output_parser.update(colunms) datas.insert(0, colunms) input_values = { - "data_example": json.dumps( - self.excel_reader.get_sample_data(), cls=DateTimeEncoder - ), + "data_example": json.dumps(datas, cls=DateTimeEncoder), + "file_name": self.excel_reader.excel_file_name, } return input_values + + def message_adjust(self): + ### adjust learning result in messages + view_message = "" + for message in self.current_message.messages: + if message.type == ViewMessage.type: + view_message = message.content + + for message in self.current_message.messages: + if message.type == AIMessage.type: + message.content = view_message diff --git a/pilot/scene/chat_data/chat_excel/excel_learning/out_parser.py b/pilot/scene/chat_data/chat_excel/excel_learning/out_parser.py index e3bcfb8ea..b52558e47 100644 --- a/pilot/scene/chat_data/chat_excel/excel_learning/out_parser.py +++ b/pilot/scene/chat_data/chat_excel/excel_learning/out_parser.py @@ -19,11 +19,12 @@ logger = logging.getLogger(__name__) class LearningExcelOutputParser(BaseOutputParser): def __init__(self, sep: str, is_stream_out: bool): super().__init__(sep=sep, is_stream_out=is_stream_out) + self.is_downgraded = False def parse_prompt_response(self, model_out_text): - clean_str = super().parse_prompt_response(model_out_text) - print("clean prompt response:", clean_str) try: + clean_str = super().parse_prompt_response(model_out_text) + logger.info(f"parse_prompt_response:{model_out_text},{model_out_text}") response = json.loads(clean_str) for key in sorted(response): if key.strip() == "DataAnalysis": @@ -34,27 +35,40 @@ class LearningExcelOutputParser(BaseOutputParser): plans = response[key] return ExcelResponse(desciption=desciption, clounms=clounms, plans=plans) except Exception as e: - return model_out_text + logger.error(f"parse_prompt_response Faild!{str(e)}") + clounms = [] + for name in self.data_schema: + clounms.append({name: "-"}) + return ExcelResponse(desciption=model_out_text, clounms=clounms, plans=None) - def parse_view_response(self, speak, data) -> str: + def __build_colunms_html(self, clounms_data): + html_colunms = f"### **Data Structure**\n" + column_index = 0 + for item in clounms_data: + column_index += 1 + keys = item.keys() + for key in keys: + html_colunms = ( + html_colunms + f"- **{column_index}.[{key}]** _{item[key]}_\n" + ) + return html_colunms + + def __build_plans_html(self, plans_data): + html_plans = f"### **Analysis plans**\n" + index = 0 + if plans_data: + for item in plans_data: + index += 1 + html_plans = html_plans + f"{item} \n" + return html_plans + + def parse_view_response(self, speak, data, prompt_response) -> str: if data and not isinstance(data, str): ### tool out data to table view html_title = f"### **Data Summary**\n{data.desciption} " - html_colunms = f"### **Data Structure**\n" - column_index = 0 - for item in data.clounms: - column_index += 1 - keys = item.keys() - for key in keys: - html_colunms = ( - html_colunms + f"- **{column_index}.[{key}]** _{item[key]}_\n" - ) + html_colunms = self.__build_colunms_html(data.clounms) + html_plans = self.__build_plans_html(data.plans) - html_plans = f"### **Recommended analysis plan**\n" - index = 0 - for item in data.plans: - index += 1 - html_plans = html_plans + f"{item} \n" html = f"""{html_title}\n{html_colunms}\n{html_plans}""" return html else: diff --git a/pilot/scene/chat_data/chat_excel/excel_learning/prompt.py b/pilot/scene/chat_data/chat_excel/excel_learning/prompt.py index ee82b51a0..9339f7cc4 100644 --- a/pilot/scene/chat_data/chat_excel/excel_learning/prompt.py +++ b/pilot/scene/chat_data/chat_excel/excel_learning/prompt.py @@ -12,32 +12,49 @@ CFG = Config() _PROMPT_SCENE_DEFINE_EN = "You are a data analysis expert. " _DEFAULT_TEMPLATE_EN = """ -This is an example data，please learn to understand the structure and content of this data: +The following is part of the data of the user file {file_name}. Please learn to understand the structure and content of the data and output the parsing results as required: {data_example} -Explain the meaning and function of each column, and give a simple and clear explanation of the technical terms. -Provide some analysis options,please think step by step. +Explain the meaning and function of each column, and give a simple and clear explanation of the technical terms， If it is a Date column, please summarize the Date format like: yyyy-MM-dd HH:MM:ss. +Use the column name as the attribute name and the analysis explanation as the attribute value to form a json array and output it in the ColumnAnalysis attribute that returns the json content. +Please do not modify or translate the column names, make sure they are consistent with the given data column names. +Provide some useful analysis ideas to users from different dimensions for data. -Please return your answer in JSON format, the return format is as follows: +Please think step by step and give your answer. Make sure to answer only in JSON format，the format is as follows: {response} """ _PROMPT_SCENE_DEFINE_ZH = "你是一个数据分析专家. " _DEFAULT_TEMPLATE_ZH = """ -下面是一份示例数据，请学习理解该数据的结构和内容: +下面是用户文件{file_name}的一部分数据，请学习理解该数据的结构和内容，按要求输出解析结果: {data_example} -分析各列数据的含义和作用，并对专业术语进行简单明了的解释。 -提供一些分析方案思路，请一步一步思考。 +分析各列数据的含义和作用，并对专业术语进行简单明了的解释, 如果是时间类型请给出时间格式类似:yyyy-MM-dd HH:MM:ss. +将列名作为属性名，分析解释作为属性值,组成json数组，并输出在返回json内容的ColumnAnalysis属性中. +请不要修改或者翻译列名，确保和给出数据列名一致. +针对数据从不同维度提供一些有用的分析思路给用户。 -请以JSON格式返回您的答案，返回格式如下： +请一步一步思考,确保只以JSON格式回答，具体格式如下： {response} """ -RESPONSE_FORMAT_SIMPLE = { +_RESPONSE_FORMAT_SIMPLE_ZH = { "DataAnalysis": "数据内容分析总结", - "ColumnAnalysis": [{"column name1": "字段1介绍，专业术语解释(请尽量简单明了)"}], - "AnalysisProgram": ["1.分析方案1，图表展示方式1", "2.分析方案2，图表展示方式2"], + "ColumnAnalysis": [{"column name": "字段1介绍，专业术语解释(请尽量简单明了)"}], + "AnalysisProgram": ["1.分析方案1", "2.分析方案2"], } +_RESPONSE_FORMAT_SIMPLE_EN = { + "DataAnalysis": "Data content analysis summary", + "ColumnAnalysis": [ + { + "column name": "Introduction to Column 1 and explanation of professional terms (please try to be as simple and clear as possible)" + } + ], + "AnalysisProgram": ["1. Analysis plan ", "2. Analysis plan "], +} + +RESPONSE_FORMAT_SIMPLE = ( + _RESPONSE_FORMAT_SIMPLE_EN if CFG.LANGUAGE == "en" else _RESPONSE_FORMAT_SIMPLE_ZH +) _DEFAULT_TEMPLATE = ( @@ -51,12 +68,12 @@ PROMPT_SCENE_DEFINE = ( PROMPT_SEP = SeparatorStyle.SINGLE.value -PROMPT_NEED_NEED_STREAM_OUT = False +PROMPT_NEED_STREAM_OUT = False # Temperature is a configuration hyperparameter that controls the randomness of language model output. # A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output. # For example, if you adjust the temperature to 0.5, the model will usually generate text that is more predictable and less creative than if you set the temperature to 1.0. -PROMPT_TEMPERATURE = 0.5 +PROMPT_TEMPERATURE = 0.8 prompt = PromptTemplate( template_scene=ChatScene.ExcelLearning.value(), @@ -64,9 +81,9 @@ prompt = PromptTemplate( response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, ensure_ascii=False, indent=4), template_define=PROMPT_SCENE_DEFINE, template=_DEFAULT_TEMPLATE, - stream_out=PROMPT_NEED_NEED_STREAM_OUT, + stream_out=PROMPT_NEED_STREAM_OUT, output_parser=LearningExcelOutputParser( - sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT ), # example_selector=sql_data_example, temperature=PROMPT_TEMPERATURE, diff --git a/pilot/scene/chat_data/chat_excel/excel_learning/test.py b/pilot/scene/chat_data/chat_excel/excel_learning/test.py deleted file mode 100644 index fdfaf146a..000000000 --- a/pilot/scene/chat_data/chat_excel/excel_learning/test.py +++ /dev/null @@ -1,271 +0,0 @@ -import os -import duckdb -import pandas as pd -import numpy as np -import matplotlib -import seaborn as sns -import uuid - -from pandas import DataFrame - -import matplotlib.pyplot as plt -import matplotlib.ticker as mtick -from matplotlib import font_manager -from matplotlib.font_manager import FontManager - -matplotlib.use("Agg") -import time -from fsspec import filesystem -import spatial - -from pilot.scene.chat_data.chat_excel.excel_reader import ExcelReader - - -def data_pre_classification(df: DataFrame): - ## Data pre-classification - columns = df.columns.tolist() - - number_columns = [] - non_numeric_colums = [] - - # 收集数据分类小于10个的列 - non_numeric_colums_value_map = {} - numeric_colums_value_map = {} - df_filtered = df.dropna() - for column_name in columns: - print(np.issubdtype(df_filtered[column_name].dtype, np.number)) - # if pd.to_numeric(df[column_name], errors='coerce').notna().all(): - # if np.issubdtype(df_filtered[column_name].dtype, np.number): - if pd.api.types.is_numeric_dtype(df[column_name].dtypes): - number_columns.append(column_name) - unique_values = df[column_name].unique() - numeric_colums_value_map.update({column_name: len(unique_values)}) - else: - non_numeric_colums.append(column_name) - unique_values = df[column_name].unique() - non_numeric_colums_value_map.update({column_name: len(unique_values)}) - - sorted_numeric_colums_value_map = dict( - sorted(numeric_colums_value_map.items(), key=lambda x: x[1]) - ) - numeric_colums_sort_list = list(sorted_numeric_colums_value_map.keys()) - - sorted_colums_value_map = dict( - sorted(non_numeric_colums_value_map.items(), key=lambda x: x[1]) - ) - non_numeric_colums_sort_list = list(sorted_colums_value_map.keys()) - - # Analyze x-coordinate - if len(non_numeric_colums_sort_list) > 0: - x_cloumn = non_numeric_colums_sort_list[-1] - non_numeric_colums_sort_list.remove(x_cloumn) - else: - x_cloumn = number_columns[0] - numeric_colums_sort_list.remove(x_cloumn) - - # Analyze y-coordinate - if len(numeric_colums_sort_list) > 0: - y_column = numeric_colums_sort_list[0] - numeric_colums_sort_list.remove(y_column) - else: - raise ValueError("Not enough numeric columns for chart！") - - return x_cloumn, y_column, non_numeric_colums_sort_list, numeric_colums_sort_list - - # - # if len(non_numeric_colums) <=0: - # sorted_colums_value_map = dict(sorted(numeric_colums_value_map.items(), key=lambda x: x[1])) - # numeric_colums_sort_list = list(sorted_colums_value_map.keys()) - # x_column = number_columns[0] - # hue_column = numeric_colums_sort_list[0] - # y_column = numeric_colums_sort_list[1] - # cols = numeric_colums_sort_list[2:] - # elif len(number_columns) <=0: - # raise ValueError("Have No numeric Column！") - # else: - # # 数字和非数字都存在多列，放弃部分数字列 - # x_column = non_numeric_colums[0] - # y_column = number_columns[0] - # if len(non_numeric_colums) > 1: - # sorted_colums_value_map = dict(sorted(non_numeric_colums_value_map.items(), key=lambda x: x[1])) - # non_numeric_colums_sort_list = list(sorted_colums_value_map.keys()) - # non_numeric_colums_sort_list.remove(non_numeric_colums[0]) - # hue_column = non_numeric_colums_sort_list[0] - # if len(number_columns) > 1: - # # try multiple charts - # cols = number_columns.remove( number_columns[0]) - # - # else: - # sorted_colums_value_map = dict(sorted(numeric_colums_value_map.items(), key=lambda x: x[1])) - # numeric_colums_sort_list = list(sorted_colums_value_map.keys()) - # numeric_colums_sort_list.remove(number_columns[0]) - # if sorted_colums_value_map[numeric_colums_sort_list[0]].value < 5: - # hue_column = numeric_colums_sort_list[0] - # if len(number_columns) > 2: - # # try multiple charts - # cols = numeric_colums_sort_list.remove(numeric_colums_sort_list[0]) - # - # print(x_column, y_column, hue_column, cols) - # return x_column, y_column, hue_column - - -if __name__ == "__main__": - # connect = duckdb.connect("/Users/tuyang.yhj/Downloads/example.xlsx") - # - - # fonts = fm.findSystemFonts() - # for font in fonts: - # if 'Hei' in font: - # print(font) - - # fm = FontManager() - # mat_fonts = set(f.name for f in fm.ttflist) - # for i in mat_fonts: - # print(i) - # print(len(mat_fonts)) - # 获取系统中的默认中文字体名称 - # default_font = fm.fontManager.defaultFontProperties.get_family() - - # 创建一个示例 DataFrame - df = pd.DataFrame( - { - "A": [1, 2, 3, None, 5], - "B": [10, 20, 30, 40, 50], - "C": [1.1, 2.2, None, 4.4, 5.5], - "D": ["a", "b", "c", "d", "e"], - } - ) - - # 判断列是否为数字列 - column_name = "A" # 要判断的列名 - is_numeric = pd.to_numeric(df[column_name], errors="coerce").notna().all() - - if is_numeric: - print( - f"Column '{column_name}' is a numeric column (ignoring null and NaN values in some elements)." - ) - else: - print( - f"Column '{column_name}' is not a numeric column (ignoring null and NaN values in some elements)." - ) - - # - # excel_reader = ExcelReader("/Users/tuyang.yhj/Downloads/example.xlsx") - excel_reader = ExcelReader("/Users/tuyang.yhj/Downloads/yhj-zx.csv") - # - # # colunms, datas = excel_reader.run( "SELECT CONCAT(Year, '-', Quarter) AS QuarterYear, SUM(Sales) AS TotalSales FROM example GROUP BY QuarterYear ORDER BY QuarterYear") - # # colunms, datas = excel_reader.run( """ SELECT Year, SUM(Sales) AS Total_Sales FROM example GROUP BY Year ORDER BY Year; """) - # df = excel_reader.get_df_by_sql_ex(""" SELECT Segment, Country, SUM(Sales) AS Total_Sales, SUM(Profit) AS Total_Profit FROM example GROUP BY Segment, Country """) - df = excel_reader.get_df_by_sql_ex( - """ SELECT 大项, AVG(实际) AS 平均实际支出, AVG(已支出) AS 平均已支出 FROM yhj-zx GROUP BY 大项""" - ) - - for column_name in df.columns.tolist(): - print(column_name + ":" + str(df[column_name].dtypes)) - print( - column_name - + ":" - + str(pd.api.types.is_numeric_dtype(df[column_name].dtypes)) - ) - - columns = df.columns.tolist() - font_names = [ - "Heiti TC", - "Songti SC", - "STHeiti Light", - "Microsoft YaHei", - "SimSun", - "SimHei", - "KaiTi", - ] - fm = FontManager() - mat_fonts = set(f.name for f in fm.ttflist) - can_use_fonts = [] - for font_name in font_names: - if font_name in mat_fonts: - can_use_fonts.append(font_name) - if len(can_use_fonts) > 0: - plt.rcParams["font.sans-serif"] = can_use_fonts - - rc = {"font.sans-serif": can_use_fonts} - plt.rcParams["axes.unicode_minus"] = False # 解决无法显示符号的问题 - sns.set(font="Heiti TC", font_scale=0.8) # 解决Seaborn中文显示问题 - sns.set_palette("Set3") # 设置颜色主题 - sns.set_style("dark") - sns.color_palette("hls", 10) - sns.hls_palette(8, l=0.5, s=0.7) - sns.set(context="notebook", style="ticks", rc=rc) - - fig, ax = plt.subplots(figsize=(8, 5), dpi=100) - # plt.ticklabel_format(style='plain') - # ax = df.plot(kind='bar', ax=ax) - # sns.barplot(df, x=x, y="Total_Sales", hue='Country', ax=ax) - # sns.barplot(df, x=x, y="Total_Profit", hue='Country', ax=ax) - - # sns.catplot(data=df, x=x, y=y, hue='Country', kind='bar') - # x, y, non_num_columns, num_colmns = data_pre_classification(df) - # print(x, y, str(non_num_columns), str(num_colmns)) - ## 复杂折线图实现 - # if len(num_colmns) > 0: - # num_colmns.append(y) - # df_melted = pd.melt( - # df, id_vars=x, value_vars=num_colmns, var_name="line", value_name="Value" - # ) - # sns.lineplot(data=df_melted, x=x, y="Value", hue="line", ax=ax, palette="Set2") - # else: - # sns.lineplot(data=df, x=x, y=y, ax=ax, palette="Set2") - - hue = None - ## 复杂柱状图实现 - x, y, non_num_columns, num_colmns = data_pre_classification(df) - - if len(non_num_columns) >= 1: - hue = non_num_columns[0] - - if len(num_colmns) >= 1: - if hue: - if len(num_colmns) >= 2: - can_use_columns = num_colmns[:2] - else: - can_use_columns = num_colmns - sns.barplot(data=df, x=x, y=y, hue=hue, palette="Set2", ax=ax) - for sub_y_column in can_use_columns: - sns.barplot( - data=df, x=x, y=sub_y_column, hue=hue, palette="Set2", ax=ax - ) - else: - if len(num_colmns) > 5: - can_use_columns = num_colmns[:5] - else: - can_use_columns = num_colmns - can_use_columns.append(y) - - df_melted = pd.melt( - df, - id_vars=x, - value_vars=can_use_columns, - var_name="line", - value_name="Value", - ) - sns.barplot( - data=df_melted, x=x, y="Value", hue="line", palette="Set2", ax=ax - ) - else: - sns.barplot(data=df, x=x, y=y, hue=hue, palette="Set2", ax=ax) - - # # 转换 DataFrame 格式 - # df_melted = pd.melt(df, id_vars=x, value_vars=['Total_Sales', 'Total_Profit'], var_name='line', value_name='y') - # - # # 绘制多列柱状图 - # - # sns.barplot(data=df, x=x, y="Total_Sales", hue = "Country", palette="Set2", ax=ax) - # sns.barplot(data=df, x=x, y="Total_Profit", hue = "Country", palette="Set1", ax=ax) - - # 设置 y 轴刻度格式为普通数字格式 - ax.yaxis.set_major_formatter(mtick.FuncFormatter(lambda x, _: "{:,.0f}".format(x))) - - chart_name = "bar_" + str(uuid.uuid1()) + ".png" - chart_path = chart_name - plt.savefig(chart_path, bbox_inches="tight", dpi=100) - - # diff --git a/pilot/scene/chat_data/chat_excel/excel_reader.py b/pilot/scene/chat_data/chat_excel/excel_reader.py index 4037840af..00cb27a2b 100644 --- a/pilot/scene/chat_data/chat_excel/excel_reader.py +++ b/pilot/scene/chat_data/chat_excel/excel_reader.py @@ -4,9 +4,8 @@ import duckdb import os import re import sqlparse - -import chardet import pandas as pd +import chardet import numpy as np from pyparsing import ( CaselessKeyword, @@ -27,6 +26,8 @@ from pyparsing import ( from pilot.common.pd_utils import csv_colunm_foramt from pilot.common.string_utils import is_chinese_include_number +logger = logging.getLogger(__name__) + def excel_colunm_format(old_name: str) -> str: new_column = old_name.strip() @@ -240,33 +241,45 @@ if __name__ == "__main__": # print(add_quotes_to_chinese_columns(sql_2)) # sql = """ SELECT 省份, 2021年, 2022年 as GDP FROM excel_data """ - sql = """ SELECT 省份, 2022年, 2021年 FROM excel_data """ - sql_2 = """ SELECT "省份", "2022年" as "2022年实际GDP增速", "2021年" as "2021年实际GDP增速" FROM excel_data """ - sql_3 = """ SELECT "省份", ("2022年" / ("2022年" + "2021年")) * 100 as "2022年实际GDP增速占比", ("2021年" / ("2022年" + "2021年")) * 100 as "2021年实际GDP增速占比" FROM excel_data """ + # sql = """ SELECT 省份, 2022年, 2021年 FROM excel_data """ + # sql_2 = """ SELECT "省份", "2022年" as "2022年实际GDP增速", "2021年" as "2021年实际GDP增速" FROM excel_data """ + # sql_3 = """ SELECT "省份", ("2022年" / ("2022年" + "2021年")) * 100 as "2022年实际GDP增速占比", ("2021年" / ("2022年" + "2021年")) * 100 as "2021年实际GDP增速占比" FROM excel_data """ + # + # sql = add_quotes_to_chinese_columns(sql_3) + # print(f"excute sql:{sql}") - sql = add_quotes_to_chinese_columns(sql_3) - print(f"excute sql:{sql}") + my_list = [ + {"name": "John", "age": 30}, + {"name": "Alice", "age": 25}, + {"name": "Bob", "age": 35}, + ] + + for dict_item in my_list: + for key, value in dict_item.items(): + print(key, value) class ExcelReader: def __init__(self, file_path): file_name = os.path.basename(file_path) - file_name_without_extension = os.path.splitext(file_name)[0] + self.file_name_without_extension = os.path.splitext(file_name)[0] encoding, confidence = detect_encoding(file_path) - logging.error(f"Detected Encoding: {encoding} (Confidence: {confidence})") + logger.error(f"Detected Encoding: {encoding} (Confidence: {confidence})") self.excel_file_name = file_name self.extension = os.path.splitext(file_name)[1] # read excel file if file_path.endswith(".xlsx") or file_path.endswith(".xls"): - df_tmp = pd.read_excel(file_path) + df_tmp = pd.read_excel(file_path, index_col=False) self.df = pd.read_excel( file_path, + index_col=False, converters={i: csv_colunm_foramt for i in range(df_tmp.shape[1])}, ) elif file_path.endswith(".csv"): - df_tmp = pd.read_csv(file_path, encoding=encoding) + df_tmp = pd.read_csv(file_path, index_col=False, encoding=encoding) self.df = pd.read_csv( file_path, + index_col=False, encoding=encoding, converters={i: csv_colunm_foramt for i in range(df_tmp.shape[1])}, ) @@ -278,10 +291,11 @@ class ExcelReader: for column_name in df_tmp.columns: self.columns_map.update({column_name: excel_colunm_format(column_name)}) try: - self.df[column_name] = pd.to_numeric(self.df[column_name]) + if not pd.api.types.is_datetime64_ns_dtype(self.df[column_name]): + self.df[column_name] = pd.to_numeric(self.df[column_name]) self.df[column_name] = self.df[column_name].fillna(0) except Exception as e: - print("transfor column error！" + column_name) + print("can't transfor numeric column" + column_name) self.df = self.df.rename(columns=lambda x: x.strip().replace(" ", "_")) @@ -292,6 +306,12 @@ class ExcelReader: # write data in duckdb self.db.register(self.table_name, self.df) + # 获取结果并打印表结构信息 + result = self.db.execute(f"DESCRIBE {self.table_name}") + columns = result.fetchall() + for column in columns: + print(column) + def run(self, sql): try: if f'"{self.table_name}"' in sql: @@ -304,7 +324,7 @@ class ExcelReader: colunms.append(descrip[0]) return colunms, results.fetchall() except Exception as e: - logging.error("excel sql run error!", e) + logger.error(f"excel sql run error!, {str(e)}") raise ValueError(f"Data Query Exception!\\nSQL[{sql}].\\nError:{str(e)}") def get_df_by_sql_ex(self, sql): diff --git a/pilot/scene/chat_db/auto_execute/chat.py b/pilot/scene/chat_db/auto_execute/chat.py index f92df7a3a..00ddce1a2 100644 --- a/pilot/scene/chat_db/auto_execute/chat.py +++ b/pilot/scene/chat_db/auto_execute/chat.py @@ -5,6 +5,9 @@ from pilot.scene.base import ChatScene from pilot.common.sql_database import Database from pilot.configs.config import Config from pilot.scene.chat_db.auto_execute.prompt import prompt +from pilot.utils.executor_utils import blocking_func_to_async +from pilot.utils.tracer import root_tracer, trace +from pilot.base_modules.agent.commands.command_mange import ApiCall CFG = Config() @@ -34,11 +37,15 @@ class ChatWithDbAutoExecute(BaseChat): raise ValueError( f"{ChatScene.ChatWithDbExecute.value} mode should chose db!" ) + with root_tracer.start_span( + "ChatWithDbAutoExecute.get_connect", metadata={"db_name": self.db_name} + ): + self.database = CFG.LOCAL_DB_MANAGE.get_connect(self.db_name) - self.database = CFG.LOCAL_DB_MANAGE.get_connect(self.db_name) - self.top_k: int = 200 + self.top_k: int = 50 - def generate_input_values(self): + @trace() + async def generate_input_values(self) -> Dict: """ generate input values """ @@ -48,27 +55,35 @@ class ChatWithDbAutoExecute(BaseChat): raise ValueError("Could not import DBSummaryClient. ") client = DBSummaryClient(system_app=CFG.SYSTEM_APP) try: - table_infos = client.get_db_summary( - dbname=self.db_name, - query=self.current_user_input, - topk=CFG.KNOWLEDGE_SEARCH_TOP_SIZE, - ) + with root_tracer.start_span("ChatWithDbAutoExecute.get_db_summary"): + table_infos = await blocking_func_to_async( + self._executor, + client.get_db_summary, + self.db_name, + self.current_user_input, + CFG.KNOWLEDGE_SEARCH_TOP_SIZE, + ) except Exception as e: print("db summary find error!" + str(e)) - table_infos = self.database.table_simple_info() if not table_infos: - table_infos = self.database.table_simple_info() - - # table_infos = self.database.table_simple_info() + table_infos = await blocking_func_to_async( + self._executor, self.database.table_simple_info + ) input_values = { - "input": self.current_user_input, + "db_name": self.db_name, + "user_input": self.current_user_input, "top_k": str(self.top_k), "dialect": self.database.dialect, "table_info": table_infos, } return input_values + def stream_plugin_call(self, text): + text = text.replace("\n", " ") + print(f"stream_plugin_call:{text}") + return self.api_call.display_sql_llmvis(text, self.database.run_to_df) + def do_action(self, prompt_response): print(f"do_action:{prompt_response}") - return self.database.run(prompt_response.sql) + return self.database.run_to_df diff --git a/pilot/scene/chat_db/auto_execute/out_parser.py b/pilot/scene/chat_db/auto_execute/out_parser.py index 577cac1ef..bd1dd9de8 100644 --- a/pilot/scene/chat_db/auto_execute/out_parser.py +++ b/pilot/scene/chat_db/auto_execute/out_parser.py @@ -1,6 +1,9 @@ import json from typing import Dict, NamedTuple import logging +import sqlparse +import xml.etree.ElementTree as ET +from pilot.common.json_utils import serialize from pilot.out_parser.base import BaseOutputParser, T from pilot.configs.config import Config from pilot.scene.chat_db.data_loader import DbDataLoader @@ -12,6 +15,9 @@ class SqlAction(NamedTuple): sql: str thoughts: Dict + def to_dict(self) -> Dict[str, Dict]: + return {"sql": self.sql, "thoughts": self.thoughts} + logger = logging.getLogger(__name__) @@ -20,32 +26,63 @@ class DbChatOutputParser(BaseOutputParser): def __init__(self, sep: str, is_stream_out: bool): super().__init__(sep=sep, is_stream_out=is_stream_out) + def is_sql_statement(self, statement): + parsed = sqlparse.parse(statement) + if not parsed: + return False + for stmt in parsed: + if stmt.get_type() != "UNKNOWN": + return True + return False + def parse_prompt_response(self, model_out_text): clean_str = super().parse_prompt_response(model_out_text) - print("clean prompt response:", clean_str) - response = json.loads(clean_str) - for key in sorted(response): - if key.strip() == "sql": - sql = response[key] - if key.strip() == "thoughts": - thoughts = response[key] - return SqlAction(sql, thoughts) - - def parse_view_response(self, speak, data) -> str: - import pandas as pd - - ### tool out data to table view - data_loader = DbDataLoader() - if len(data) < 1: - data.insert(0, []) - df = pd.DataFrame(data[1:], columns=data[0]) - if not CFG.NEW_SERVER_MODE and not CFG.SERVER_LIGHT_MODE: - table_style = """""" - html_table = df.to_html(index=False, escape=False) - html = f"{table_style}{html_table}" - view_text = f"##### {str(speak)}" + "\n" + html.replace("\n", " ") - return view_text + logger.info(f"clean prompt response: {clean_str}") + # Compatible with community pure sql output model + if self.is_sql_statement(clean_str): + return SqlAction(clean_str, "") else: - return data_loader.get_table_view_by_conn(data, speak) + try: + response = json.loads(clean_str) + for key in sorted(response): + if key.strip() == "sql": + sql = response[key] + if key.strip() == "thoughts": + thoughts = response[key] + return SqlAction(sql, thoughts) + except Exception as e: + logger.error("json load faild") + return SqlAction("", clean_str) + + def parse_view_response(self, speak, data, prompt_response) -> str: + param = {} + api_call_element = ET.Element("chart-view") + err_msg = None + try: + if not prompt_response.sql or len(prompt_response.sql) <= 0: + return f"""{speak}""" + + df = data(prompt_response.sql) + param["type"] = "response_table" + param["sql"] = prompt_response.sql + param["data"] = json.loads( + df.to_json(orient="records", date_format="iso", date_unit="s") + ) + view_json_str = json.dumps(param, default=serialize, ensure_ascii=False) + except Exception as e: + logger.error("parse_view_response error!" + str(e)) + err_param = {} + err_param["sql"] = f"{prompt_response.sql}" + err_param["type"] = "response_table" + # err_param["err_msg"] = str(e) + err_param["data"] = [] + err_msg = str(e) + view_json_str = json.dumps(err_param, default=serialize, ensure_ascii=False) + + # api_call_element.text = view_json_str + api_call_element.set("content", view_json_str) + result = ET.tostring(api_call_element, encoding="utf-8") + if err_msg: + return f"""{speak} \\n ERROR!{err_msg} \n {result.decode("utf-8")}""" + else: + return speak + "\n" + result.decode("utf-8") diff --git a/pilot/scene/chat_db/auto_execute/prompt.py b/pilot/scene/chat_db/auto_execute/prompt.py index abc889cec..e7f0e0ee0 100644 --- a/pilot/scene/chat_db/auto_execute/prompt.py +++ b/pilot/scene/chat_db/auto_execute/prompt.py @@ -8,24 +8,61 @@ from pilot.scene.chat_db.auto_execute.example import sql_data_example CFG = Config() -PROMPT_SCENE_DEFINE = "You are a SQL expert. " -_DEFAULT_TEMPLATE = """ -Given an input question, create a syntactically correct {dialect} sql. +_PROMPT_SCENE_DEFINE_EN = "You are a database expert. " +_PROMPT_SCENE_DEFINE_ZH = "你是一个数据库专家. " -Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. -Use as few tables as possible when querying. -Only use the following tables schema to generate sql: -{table_info} -Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table. +_DEFAULT_TEMPLATE_EN = """ +Please answer the user's question based on the database selected by the user and some of the available table structure definitions of the database. +Database name: + {db_name} +Table structure definition: + {table_info} -Question: {input} +Constraint: + 1.Please understand the user's intention based on the user's question, and use the given table structure definition to create a grammatically correct {dialect} sql. If sql is not required, answer the user's question directly.. + 2.Always limit the query to a maximum of {top_k} results unless the user specifies in the question the specific number of rows of data he wishes to obtain. + 3.You can only use the tables provided in the table structure information to generate sql. If you cannot generate sql based on the provided table structure, please say: "The table structure information provided is not enough to generate sql queries." It is prohibited to fabricate information at will. + 4.Please be careful not to mistake the relationship between tables and columns when generating SQL. + 5.Please check the correctness of the SQL and ensure that the query performance is optimized under correct conditions. + +User Question: + {user_input} +Please think step by step and respond according to the following JSON format: + {response} +Ensure the response is correct json and can be parsed by Python json.loads. -Respond in JSON format as following format: -{response} -Ensure the response is correct json and can be parsed by Python json.loads """ +_DEFAULT_TEMPLATE_ZH = """ +请根据用户选择的数据库和该库的部分可用表结构定义来回答用户问题. +数据库名: + {db_name} +表结构定义: + {table_info} + +约束: + 1. 请根据用户问题理解用户意图，使用给出表结构定义创建一个语法正确的 {dialect} sql，如果不需要sql，则直接回答用户问题。 + 2. 除非用户在问题中指定了他希望获得的具体数据行数，否则始终将查询限制为最多 {top_k} 个结果。 + 3. 只能使用表结构信息中提供的表来生成 sql，如果无法根据提供的表结构中生成 sql ，请说：“提供的表结构信息不足以生成 sql 查询。” 禁止随意捏造信息。 + 4. 请注意生成SQL时不要弄错表和列的关系 + 5. 请检查SQL的正确性，并保证正确的情况下优化查询性能 +用户问题: + {user_input} +请一步步思考并按照以下JSON格式回复： + {response} +确保返回正确的json并且可以被Python json.loads方法解析. + +""" + +_DEFAULT_TEMPLATE = ( + _DEFAULT_TEMPLATE_EN if CFG.LANGUAGE == "en" else _DEFAULT_TEMPLATE_ZH +) + +PROMPT_SCENE_DEFINE = ( + _PROMPT_SCENE_DEFINE_EN if CFG.LANGUAGE == "en" else _PROMPT_SCENE_DEFINE_ZH +) + RESPONSE_FORMAT_SIMPLE = { "thoughts": "thoughts summary to say to user", "sql": "SQL Query to run", @@ -33,7 +70,7 @@ RESPONSE_FORMAT_SIMPLE = { PROMPT_SEP = SeparatorStyle.SINGLE.value -PROMPT_NEED_NEED_STREAM_OUT = False +PROMPT_NEED_STREAM_OUT = False # Temperature is a configuration hyperparameter that controls the randomness of language model output. # A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output. @@ -42,16 +79,17 @@ PROMPT_TEMPERATURE = 0.5 prompt = PromptTemplate( template_scene=ChatScene.ChatWithDbExecute.value(), - input_variables=["input", "table_info", "dialect", "top_k", "response"], + input_variables=["table_info", "dialect", "top_k", "response"], response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, ensure_ascii=False, indent=4), template_define=PROMPT_SCENE_DEFINE, template=_DEFAULT_TEMPLATE, - stream_out=PROMPT_NEED_NEED_STREAM_OUT, + stream_out=PROMPT_NEED_STREAM_OUT, output_parser=DbChatOutputParser( - sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT ), # example_selector=sql_data_example, temperature=PROMPT_TEMPERATURE, + need_historical_messages=True, ) CFG.prompt_template_registry.register(prompt, is_default=True) from . import prompt_baichuan diff --git a/pilot/scene/chat_db/auto_execute/prompt_baichuan.py b/pilot/scene/chat_db/auto_execute/prompt_baichuan.py index 95aa962fa..4888cbd7f 100644 --- a/pilot/scene/chat_db/auto_execute/prompt_baichuan.py +++ b/pilot/scene/chat_db/auto_execute/prompt_baichuan.py @@ -36,7 +36,7 @@ RESPONSE_FORMAT_SIMPLE = { PROMPT_SEP = SeparatorStyle.SINGLE.value -PROMPT_NEED_NEED_STREAM_OUT = False +PROMPT_NEED_STREAM_OUT = False # Temperature is a configuration hyperparameter that controls the randomness of language model output. # A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output. @@ -50,9 +50,9 @@ prompt = PromptTemplate( template_is_strict=False, template_define=PROMPT_SCENE_DEFINE, template=_DEFAULT_TEMPLATE, - stream_out=PROMPT_NEED_NEED_STREAM_OUT, + stream_out=PROMPT_NEED_STREAM_OUT, output_parser=DbChatOutputParser( - sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT ), # example_selector=sql_data_example, temperature=PROMPT_TEMPERATURE, diff --git a/pilot/scene/chat_db/data_loader.py b/pilot/scene/chat_db/data_loader.py index a9e4450f5..63aa3c12f 100644 --- a/pilot/scene/chat_db/data_loader.py +++ b/pilot/scene/chat_db/data_loader.py @@ -1,13 +1,48 @@ -class DbDataLoader: - def get_table_view_by_conn(self, data, speak): - import pandas as pd +import xml.etree.ElementTree as ET +import json +import logging - ### tool out data to table view - if len(data) < 1: - data.insert(0, ["result"]) - df = pd.DataFrame(data[1:], columns=data[0]) - html_table = df.to_html(index=False, escape=False, sparsify=False) - table_str = "".join(html_table.split()) - html = f"""
{table_str}
""" - view_text = f"##### {str(speak)}" + "\n" + html.replace("\n", " ") - return view_text +from pilot.common.json_utils import serialize + + +class DbDataLoader: + def get_table_view_by_conn(self, data, speak, sql: str = None): + # import pandas as pd + # + # ### tool out data to table view + # if len(data) < 1: + # data.insert(0, ["result"]) + # df = pd.DataFrame(data[1:], columns=data[0]) + # html_table = df.to_html(index=False, escape=False, sparsify=False) + # table_str = "".join(html_table.split()) + # html = f"""
{table_str}
""" + # view_text = f"##### {str(speak)}" + "\n" + html.replace("\n", " ") + # return view_text + + param = {} + api_call_element = ET.Element("chart-view") + err_msg = None + try: + param["type"] = "response_table" + param["sql"] = sql + param["data"] = json.loads( + data.to_json(orient="records", date_format="iso", date_unit="s") + ) + view_json_str = json.dumps(param, default=serialize, ensure_ascii=False) + except Exception as e: + logging.error("parse_view_response error!" + str(e)) + err_param = {} + err_param["sql"] = f"{sql}" + err_param["type"] = "response_table" + err_param["err_msg"] = str(e) + err_param["data"] = [] + err_msg = str(e) + view_json_str = json.dumps(err_param, default=serialize, ensure_ascii=False) + + # api_call_element.text = view_json_str + api_call_element.set("content", view_json_str) + result = ET.tostring(api_call_element, encoding="utf-8") + if err_msg: + return f"""{speak} \\n ERROR!{err_msg} \n {result.decode("utf-8")}""" + else: + return speak + "\n" + result.decode("utf-8") diff --git a/pilot/scene/chat_db/professional_qa/chat.py b/pilot/scene/chat_db/professional_qa/chat.py index abdfd9f00..fde28d91b 100644 --- a/pilot/scene/chat_db/professional_qa/chat.py +++ b/pilot/scene/chat_db/professional_qa/chat.py @@ -5,6 +5,8 @@ from pilot.scene.base import ChatScene from pilot.common.sql_database import Database from pilot.configs.config import Config from pilot.scene.chat_db.professional_qa.prompt import prompt +from pilot.utils.executor_utils import blocking_func_to_async +from pilot.utils.tracer import root_tracer, trace CFG = Config() @@ -38,7 +40,8 @@ class ChatWithDbQA(BaseChat): else len(self.tables) ) - def generate_input_values(self): + @trace() + async def generate_input_values(self) -> Dict: table_info = "" dialect = "mysql" try: @@ -48,12 +51,22 @@ class ChatWithDbQA(BaseChat): if self.db_name: client = DBSummaryClient(system_app=CFG.SYSTEM_APP) try: - table_infos = client.get_db_summary( - dbname=self.db_name, query=self.current_user_input, topk=self.top_k + # table_infos = client.get_db_summary( + # dbname=self.db_name, query=self.current_user_input, topk=self.top_k + # ) + table_infos = await blocking_func_to_async( + self._executor, + client.get_db_summary, + self.db_name, + self.current_user_input, + self.top_k, ) except Exception as e: print("db summary find error!" + str(e)) - table_infos = self.database.table_simple_info() + # table_infos = self.database.table_simple_info() + table_infos = await blocking_func_to_async( + self._executor, self.database.table_simple_info + ) # table_infos = self.database.table_simple_info() dialect = self.database.dialect diff --git a/pilot/scene/chat_db/professional_qa/prompt.py b/pilot/scene/chat_db/professional_qa/prompt.py index ca4110398..c84f2eb7a 100644 --- a/pilot/scene/chat_db/professional_qa/prompt.py +++ b/pilot/scene/chat_db/professional_qa/prompt.py @@ -54,7 +54,7 @@ _DEFAULT_TEMPLATE = ( PROMPT_SEP = SeparatorStyle.SINGLE.value -PROMPT_NEED_NEED_STREAM_OUT = True +PROMPT_NEED_STREAM_OUT = True prompt = PromptTemplate( template_scene=ChatScene.ChatWithDbQA.value(), @@ -62,9 +62,9 @@ prompt = PromptTemplate( response_format=None, template_define=PROMPT_SCENE_DEFINE, template=_DEFAULT_TEMPLATE, - stream_out=PROMPT_NEED_NEED_STREAM_OUT, + stream_out=PROMPT_NEED_STREAM_OUT, output_parser=NormalChatOutputParser( - sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT ), ) diff --git a/pilot/scene/chat_execution/chat.py b/pilot/scene/chat_execution/chat.py index c6d7bbe2f..2615918ff 100644 --- a/pilot/scene/chat_execution/chat.py +++ b/pilot/scene/chat_execution/chat.py @@ -6,6 +6,7 @@ from pilot.configs.config import Config from pilot.base_modules.agent.commands.command import execute_command from pilot.base_modules.agent import PluginPromptGenerator from .prompt import prompt +from pilot.utils.tracer import root_tracer, trace CFG = Config() @@ -50,7 +51,8 @@ class ChatWithPlugin(BaseChat): self.plugins_prompt_generator ) - def generate_input_values(self): + @trace() + async def generate_input_values(self) -> Dict: input_values = { "input": self.current_user_input, "constraints": self.__list_to_prompt_str( diff --git a/pilot/scene/chat_execution/out_parser.py b/pilot/scene/chat_execution/out_parser.py index 3826b35df..60260a9d3 100644 --- a/pilot/scene/chat_execution/out_parser.py +++ b/pilot/scene/chat_execution/out_parser.py @@ -35,7 +35,7 @@ class PluginChatOutputParser(BaseOutputParser): speak = response[key] return PluginAction(command, speak, thoughts) - def parse_view_response(self, speak, data) -> str: + def parse_view_response(self, speak, data, prompt_response) -> str: ### tool out data to table view print(f"parse_view_response:{speak},{str(data)}") view_text = f"##### {speak}" + "\n" + str(data) diff --git a/pilot/scene/chat_factory.py b/pilot/scene/chat_factory.py index d33f89e76..10a588c04 100644 --- a/pilot/scene/chat_factory.py +++ b/pilot/scene/chat_factory.py @@ -1,5 +1,6 @@ from pilot.scene.base_chat import BaseChat from pilot.singleton import Singleton +from pilot.utils.tracer import root_tracer class ChatFactory(metaclass=Singleton): @@ -13,6 +14,10 @@ class ChatFactory(metaclass=Singleton): from pilot.scene.chat_dashboard.chat import ChatDashboard from pilot.scene.chat_knowledge.v1.chat import ChatKnowledge from pilot.scene.chat_knowledge.inner_db_summary.chat import InnerChatDBSummary + from pilot.scene.chat_knowledge.extract_triplet.chat import ExtractTriplet + from pilot.scene.chat_knowledge.extract_entity.chat import ExtractEntity + from pilot.scene.chat_knowledge.summary.chat import ExtractSummary + from pilot.scene.chat_knowledge.refine_summary.chat import ExtractRefineSummary from pilot.scene.chat_data.chat_excel.excel_analyze.chat import ChatExcel from pilot.scene.chat_agent.chat import ChatAgent @@ -20,7 +25,11 @@ class ChatFactory(metaclass=Singleton): implementation = None for cls in chat_classes: if cls.chat_scene == chat_mode: - implementation = cls(**kwargs) + metadata = {"cls": str(cls)} + with root_tracer.start_span( + "get_implementation_of_chat", metadata=metadata + ): + implementation = cls(**kwargs) if implementation == None: raise Exception(f"Invalid implementation name:{chat_mode}") return implementation diff --git a/pilot/scene/chat_knowledge/extract_entity/__init__.py b/pilot/scene/chat_knowledge/extract_entity/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/scene/chat_knowledge/extract_entity/chat.py b/pilot/scene/chat_knowledge/extract_entity/chat.py new file mode 100644 index 000000000..373bb4e5d --- /dev/null +++ b/pilot/scene/chat_knowledge/extract_entity/chat.py @@ -0,0 +1,35 @@ +from typing import Dict + +from pilot.scene.base_chat import BaseChat +from pilot.scene.base import ChatScene +from pilot.configs.config import Config + +from pilot.scene.chat_knowledge.extract_entity.prompt import prompt + +CFG = Config() + + +class ExtractEntity(BaseChat): + chat_scene: str = ChatScene.ExtractEntity.value() + + """extracting entities by llm""" + + def __init__(self, chat_param: Dict): + """ """ + chat_param["chat_mode"] = ChatScene.ExtractEntity + super().__init__( + chat_param=chat_param, + ) + + self.user_input = chat_param["current_user_input"] + self.extract_mode = chat_param["select_param"] + + async def generate_input_values(self): + input_values = { + "text": self.user_input, + } + return input_values + + @property + def chat_type(self) -> str: + return ChatScene.ExtractEntity.value diff --git a/pilot/scene/chat_knowledge/extract_entity/out_parser.py b/pilot/scene/chat_knowledge/extract_entity/out_parser.py new file mode 100644 index 000000000..4093e460f --- /dev/null +++ b/pilot/scene/chat_knowledge/extract_entity/out_parser.py @@ -0,0 +1,39 @@ +import json +import logging +from typing import Set + +from pilot.out_parser.base import BaseOutputParser, T +from pilot.configs.config import Config + +CFG = Config() + + +logger = logging.getLogger(__name__) + + +class ExtractEntityParser(BaseOutputParser): + def __init__(self, sep: str, is_stream_out: bool): + super().__init__(sep=sep, is_stream_out=is_stream_out) + + def parse_prompt_response(self, response, max_length: int = 128) -> Set[str]: + lowercase = True + # clean_str = super().parse_prompt_response(response) + print("clean prompt response:", response) + + results = [] + response = response.strip() # Strip newlines from responses. + + if response.startswith("KEYWORDS:"): + response = response[len("KEYWORDS:") :] + + keywords = response.split(",") + for k in keywords: + rk = k + if lowercase: + rk = rk.lower() + results.append(rk.strip()) + + return set(results) + + def parse_view_response(self, speak, data) -> str: + return data diff --git a/pilot/scene/chat_knowledge/extract_entity/prompt.py b/pilot/scene/chat_knowledge/extract_entity/prompt.py new file mode 100644 index 000000000..77349bd28 --- /dev/null +++ b/pilot/scene/chat_knowledge/extract_entity/prompt.py @@ -0,0 +1,52 @@ +import json + +from pilot.prompts.prompt_new import PromptTemplate +from pilot.configs.config import Config +from pilot.scene.base import ChatScene +from pilot.common.schema import SeparatorStyle +from pilot.scene.chat_knowledge.extract_entity.out_parser import ExtractEntityParser + +from pilot.scene.chat_knowledge.extract_triplet.out_parser import ( + ExtractTripleParser, +) + + +CFG = Config() + +PROMPT_SCENE_DEFINE = """""" + +_DEFAULT_TEMPLATE = """ +"A question is provided below. Given the question, extract up to 10 " + "keywords from the text. Focus on extracting the keywords that we can use " + "to best lookup answers to the question. Avoid stopwords.\n" + "Example:" + "Text: Alice is Bob's mother." + "KEYWORDS:Alice,mother,Bob\n" + "---------------------\n" + "{text}\n" + "---------------------\n" + "Provide keywords in the following comma-separated format: 'KEYWORDS: '\n" +""" +PROMPT_RESPONSE = """""" + + +RESPONSE_FORMAT = """""" + + +PROMPT_SEP = SeparatorStyle.SINGLE.value + +PROMPT_NEED_NEED_STREAM_OUT = False + +prompt = PromptTemplate( + template_scene=ChatScene.ExtractEntity.value(), + input_variables=["text"], + response_format="", + template_define=PROMPT_SCENE_DEFINE, + template=_DEFAULT_TEMPLATE + PROMPT_RESPONSE, + stream_out=PROMPT_NEED_NEED_STREAM_OUT, + output_parser=ExtractEntityParser( + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + ), +) + +CFG.prompt_template_registry.register(prompt, is_default=True) diff --git a/pilot/scene/chat_knowledge/extract_triplet/__init__.py b/pilot/scene/chat_knowledge/extract_triplet/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/scene/chat_knowledge/extract_triplet/chat.py b/pilot/scene/chat_knowledge/extract_triplet/chat.py new file mode 100644 index 000000000..28152b92e --- /dev/null +++ b/pilot/scene/chat_knowledge/extract_triplet/chat.py @@ -0,0 +1,35 @@ +from typing import Dict + +from pilot.scene.base_chat import BaseChat +from pilot.scene.base import ChatScene +from pilot.configs.config import Config + +from pilot.scene.chat_knowledge.extract_triplet.prompt import prompt + +CFG = Config() + + +class ExtractTriplet(BaseChat): + chat_scene: str = ChatScene.ExtractTriplet.value() + + """extracting triplets by llm""" + + def __init__(self, chat_param: Dict): + """ """ + chat_param["chat_mode"] = ChatScene.ExtractTriplet + super().__init__( + chat_param=chat_param, + ) + + self.user_input = chat_param["current_user_input"] + self.extract_mode = chat_param["select_param"] + + async def generate_input_values(self): + input_values = { + "text": self.user_input, + } + return input_values + + @property + def chat_type(self) -> str: + return ChatScene.ExtractTriplet.value diff --git a/pilot/scene/chat_knowledge/extract_triplet/out_parser.py b/pilot/scene/chat_knowledge/extract_triplet/out_parser.py new file mode 100644 index 000000000..75606bd0f --- /dev/null +++ b/pilot/scene/chat_knowledge/extract_triplet/out_parser.py @@ -0,0 +1,57 @@ +import json +import logging +import re +from typing import List, Tuple + +from pilot.out_parser.base import BaseOutputParser, T +from pilot.configs.config import Config + +CFG = Config() + + +logger = logging.getLogger(__name__) + + +class ExtractTripleParser(BaseOutputParser): + def __init__(self, sep: str, is_stream_out: bool): + super().__init__(sep=sep, is_stream_out=is_stream_out) + + def parse_prompt_response( + self, response, max_length: int = 128 + ) -> List[Tuple[str, str, str]]: + # clean_str = super().parse_prompt_response(response) + print("clean prompt response:", response) + + if response.startswith("Triplets:"): + response = response[len("Triplets:") :] + pattern = r"\([^()]+\)" + response = re.findall(pattern, response) + # response = response.strip().split("\n") + print("parse prompt response:", response) + results = [] + for text in response: + if not text or text[0] != "(" or text[-1] != ")": + # skip empty lines and non-triplets + continue + tokens = text[1:-1].split(",") + if len(tokens) != 3: + continue + + if any(len(s.encode("utf-8")) > max_length for s in tokens): + # We count byte-length instead of len() for UTF-8 chars, + # will skip if any of the tokens are too long. + # This is normally due to a poorly formatted triplet + # extraction, in more serious KG building cases + # we'll need NLP models to better extract triplets. + continue + + subject, predicate, obj = map(str.strip, tokens) + if not subject or not predicate or not obj: + # skip partial triplets + continue + results.append((subject.lower(), predicate.lower(), obj.lower())) + return results + + def parse_view_response(self, speak, data) -> str: + ### tool out data to table view + return data diff --git a/pilot/scene/chat_knowledge/extract_triplet/prompt.py b/pilot/scene/chat_knowledge/extract_triplet/prompt.py new file mode 100644 index 000000000..dd391bce8 --- /dev/null +++ b/pilot/scene/chat_knowledge/extract_triplet/prompt.py @@ -0,0 +1,57 @@ +import json + +from pilot.prompts.prompt_new import PromptTemplate +from pilot.configs.config import Config +from pilot.scene.base import ChatScene +from pilot.common.schema import SeparatorStyle + +from pilot.scene.chat_knowledge.extract_triplet.out_parser import ( + ExtractTripleParser, +) + + +CFG = Config() + +PROMPT_SCENE_DEFINE = """""" + +_DEFAULT_TEMPLATE = """ +"Some text is provided below. Given the text, extract up to 10" + "knowledge triplets in the form of (subject, predicate, object). Avoid stopwords.\n" + "---------------------\n" + "Example:" + "Text: Alice is Bob's mother." + "Triplets:\n(Alice, is mother of, Bob)\n" + "Text: Philz is a coffee shop founded in Berkeley in 1982.\n" + "Triplets:\n" + "(Philz, is, coffee shop)\n" + "(Philz, founded in, Berkeley)\n" + "(Philz, founded in, 1982)\n" + "---------------------\n" + "Text: {text}\n" + "Triplets:\n" + ensure Respond in the following List(Tuple) format: + '(Stephen Curry, plays for, Golden State Warriors)\n(Stephen Curry, known for, shooting skills)\n(Stephen Curry, attended, Davidson College)\n(Stephen Curry, led, team to success)' +""" +PROMPT_RESPONSE = """""" + + +RESPONSE_FORMAT = """""" + + +PROMPT_SEP = SeparatorStyle.SINGLE.value + +PROMPT_NEED_NEED_STREAM_OUT = False + +prompt = PromptTemplate( + template_scene=ChatScene.ExtractTriplet.value(), + input_variables=["text"], + response_format="", + template_define=PROMPT_SCENE_DEFINE, + template=_DEFAULT_TEMPLATE + PROMPT_RESPONSE, + stream_out=PROMPT_NEED_NEED_STREAM_OUT, + output_parser=ExtractTripleParser( + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + ), +) + +CFG.prompt_template_registry.register(prompt, is_default=True) diff --git a/pilot/scene/chat_knowledge/inner_db_summary/chat.py b/pilot/scene/chat_knowledge/inner_db_summary/chat.py index 34c8260e3..f7c81bd77 100644 --- a/pilot/scene/chat_knowledge/inner_db_summary/chat.py +++ b/pilot/scene/chat_knowledge/inner_db_summary/chat.py @@ -1,8 +1,10 @@ +from typing import Dict from pilot.scene.base_chat import BaseChat from pilot.scene.base import ChatScene from pilot.configs.config import Config from pilot.scene.chat_knowledge.inner_db_summary.prompt import prompt +from pilot.utils.tracer import root_tracer, trace CFG = Config() @@ -30,7 +32,8 @@ class InnerChatDBSummary(BaseChat): self.db_input = db_select self.db_summary = db_summary - def generate_input_values(self): + @trace() + async def generate_input_values(self) -> Dict: input_values = { "db_input": self.db_input, "db_profile_summary": self.db_summary, diff --git a/pilot/scene/chat_knowledge/inner_db_summary/prompt.py b/pilot/scene/chat_knowledge/inner_db_summary/prompt.py index 924fab2c6..3f81906c0 100644 --- a/pilot/scene/chat_knowledge/inner_db_summary/prompt.py +++ b/pilot/scene/chat_knowledge/inner_db_summary/prompt.py @@ -33,7 +33,7 @@ RESPONSE_FORMAT = {"table": ["orders", "products"]} PROMPT_SEP = SeparatorStyle.SINGLE.value -PROMPT_NEED_NEED_STREAM_OUT = False +PROMPT_NEED_STREAM_OUT = False prompt = PromptTemplate( template_scene=ChatScene.InnerChatDBSummary.value(), @@ -41,9 +41,9 @@ prompt = PromptTemplate( response_format=json.dumps(RESPONSE_FORMAT, indent=4), template_define=PROMPT_SCENE_DEFINE, template=_DEFAULT_TEMPLATE + PROMPT_RESPONSE, - stream_out=PROMPT_NEED_NEED_STREAM_OUT, + stream_out=PROMPT_NEED_STREAM_OUT, output_parser=NormalChatOutputParser( - sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT ), ) diff --git a/pilot/scene/chat_knowledge/refine_summary/__init__.py b/pilot/scene/chat_knowledge/refine_summary/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/scene/chat_knowledge/refine_summary/chat.py b/pilot/scene/chat_knowledge/refine_summary/chat.py new file mode 100644 index 000000000..2f3181d5e --- /dev/null +++ b/pilot/scene/chat_knowledge/refine_summary/chat.py @@ -0,0 +1,39 @@ +from typing import Dict + +from pilot.scene.base_chat import BaseChat +from pilot.scene.base import ChatScene +from pilot.configs.config import Config + +from pilot.scene.chat_knowledge.refine_summary.prompt import prompt + +CFG = Config() + + +class ExtractRefineSummary(BaseChat): + chat_scene: str = ChatScene.ExtractRefineSummary.value() + + """extract final summary by llm""" + + def __init__(self, chat_param: Dict): + """ """ + chat_param["chat_mode"] = ChatScene.ExtractRefineSummary + super().__init__( + chat_param=chat_param, + ) + + self.existing_answer = chat_param["select_param"] + + async def generate_input_values(self): + input_values = { + # "context": self.user_input, + "existing_answer": self.existing_answer, + } + return input_values + + def stream_plugin_call(self, text): + """return summary label""" + return f"{text}" + + @property + def chat_type(self) -> str: + return ChatScene.ExtractRefineSummary.value diff --git a/pilot/scene/chat_knowledge/refine_summary/out_parser.py b/pilot/scene/chat_knowledge/refine_summary/out_parser.py new file mode 100644 index 000000000..104419e88 --- /dev/null +++ b/pilot/scene/chat_knowledge/refine_summary/out_parser.py @@ -0,0 +1,57 @@ +import json +import logging +import re +from typing import List, Tuple + +from pilot.out_parser.base import BaseOutputParser, T +from pilot.configs.config import Config + +CFG = Config() + + +logger = logging.getLogger(__name__) + + +class ExtractRefineSummaryParser(BaseOutputParser): + def __init__(self, sep: str, is_stream_out: bool): + super().__init__(sep=sep, is_stream_out=is_stream_out) + + def parse_prompt_response( + self, response, max_length: int = 128 + ) -> List[Tuple[str, str, str]]: + # clean_str = super().parse_prompt_response(response) + print("clean prompt response:", response) + + # if response.startswith("Triplets:"): + # response = response[len("Triplets:") :] + # pattern = r"\([^()]+\)" + # response = re.findall(pattern, response) + # # response = response.strip().split("\n") + # print("parse prompt response:", response) + # results = [] + # for text in response: + # if not text or text[0] != "(" or text[-1] != ")": + # # skip empty lines and non-triplets + # continue + # tokens = text[1:-1].split(",") + # if len(tokens) != 3: + # continue + # + # if any(len(s.encode("utf-8")) > max_length for s in tokens): + # # We count byte-length instead of len() for UTF-8 chars, + # # will skip if any of the tokens are too long. + # # This is normally due to a poorly formatted triplet + # # extraction, in more serious KG building cases + # # we'll need NLP models to better extract triplets. + # continue + # + # subject, predicate, obj = map(str.strip, tokens) + # if not subject or not predicate or not obj: + # # skip partial triplets + # continue + # results.append((subject.lower(), predicate.lower(), obj.lower())) + return response + + def parse_view_response(self, speak, data) -> str: + ### tool out data to table view + return data diff --git a/pilot/scene/chat_knowledge/refine_summary/prompt.py b/pilot/scene/chat_knowledge/refine_summary/prompt.py new file mode 100644 index 000000000..8029efcef --- /dev/null +++ b/pilot/scene/chat_knowledge/refine_summary/prompt.py @@ -0,0 +1,49 @@ +from pilot.prompts.prompt_new import PromptTemplate +from pilot.configs.config import Config +from pilot.scene.base import ChatScene +from pilot.common.schema import SeparatorStyle + +from pilot.scene.chat_knowledge.refine_summary.out_parser import ( + ExtractRefineSummaryParser, +) + +CFG = Config() + + +PROMPT_SCENE_DEFINE = """A chat between a curious user and an artificial intelligence assistant, who very familiar with database related knowledge. +The assistant gives helpful, detailed, professional and polite answers to the user's questions.""" + +_DEFAULT_TEMPLATE_ZH = ( + """我们已经提供了一个到某一点的现有总结:{existing_answer}\n 请根据你之前推理的内容进行最终的总结,总结回答的时候最好按照1.2.3.进行.""" +) + +_DEFAULT_TEMPLATE_EN = """ +We have provided an existing summary up to a certain point: {existing_answer}\nWe have the opportunity to refine the existing summary (only if needed) with some more context below. +\nBased on the previous reasoning, please summarize the final conclusion in accordance with points 1.2.and 3. + +""" + +_DEFAULT_TEMPLATE = ( + _DEFAULT_TEMPLATE_EN if CFG.LANGUAGE == "en" else _DEFAULT_TEMPLATE_ZH +) + +PROMPT_RESPONSE = """""" + +PROMPT_SEP = SeparatorStyle.SINGLE.value + +PROMPT_NEED_NEED_STREAM_OUT = True + +prompt = PromptTemplate( + template_scene=ChatScene.ExtractRefineSummary.value(), + input_variables=["existing_answer"], + response_format=None, + template_define=PROMPT_SCENE_DEFINE, + template=_DEFAULT_TEMPLATE + PROMPT_RESPONSE, + stream_out=PROMPT_NEED_NEED_STREAM_OUT, + output_parser=ExtractRefineSummaryParser( + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + ), +) + +CFG.prompt_template_registry.register(prompt, is_default=True) +from ..v1 import prompt_chatglm diff --git a/pilot/scene/chat_knowledge/summary/__init__.py b/pilot/scene/chat_knowledge/summary/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/scene/chat_knowledge/summary/chat.py b/pilot/scene/chat_knowledge/summary/chat.py new file mode 100644 index 000000000..be4ee00c3 --- /dev/null +++ b/pilot/scene/chat_knowledge/summary/chat.py @@ -0,0 +1,34 @@ +from typing import Dict + +from pilot.scene.base_chat import BaseChat +from pilot.scene.base import ChatScene +from pilot.configs.config import Config + +from pilot.scene.chat_knowledge.summary.prompt import prompt + +CFG = Config() + + +class ExtractSummary(BaseChat): + chat_scene: str = ChatScene.ExtractSummary.value() + + """get summary by llm""" + + def __init__(self, chat_param: Dict): + """ """ + chat_param["chat_mode"] = ChatScene.ExtractSummary + super().__init__( + chat_param=chat_param, + ) + + self.user_input = chat_param["select_param"] + + async def generate_input_values(self): + input_values = { + "context": self.user_input, + } + return input_values + + @property + def chat_type(self) -> str: + return ChatScene.ExtractSummary.value diff --git a/pilot/scene/chat_knowledge/summary/out_parser.py b/pilot/scene/chat_knowledge/summary/out_parser.py new file mode 100644 index 000000000..cc4de7356 --- /dev/null +++ b/pilot/scene/chat_knowledge/summary/out_parser.py @@ -0,0 +1,32 @@ +import logging +from typing import List, Tuple + +from pilot.out_parser.base import BaseOutputParser, T, ResponseTye +from pilot.configs.config import Config + +CFG = Config() + + +logger = logging.getLogger(__name__) + + +class ExtractSummaryParser(BaseOutputParser): + def __init__(self, sep: str, is_stream_out: bool): + super().__init__(sep=sep, is_stream_out=is_stream_out) + + def parse_prompt_response( + self, response, max_length: int = 128 + ) -> List[Tuple[str, str, str]]: + # clean_str = super().parse_prompt_response(response) + print("clean prompt response:", response) + return response + + def parse_view_response(self, speak, data) -> str: + ### tool out data to table view + return data + + def parse_model_nostream_resp(self, response: ResponseTye, sep: str) -> str: + try: + return super().parse_model_nostream_resp(response, sep) + except Exception as e: + return str(e) diff --git a/pilot/scene/chat_knowledge/summary/prompt.py b/pilot/scene/chat_knowledge/summary/prompt.py new file mode 100644 index 000000000..404b9079f --- /dev/null +++ b/pilot/scene/chat_knowledge/summary/prompt.py @@ -0,0 +1,53 @@ +from pilot.prompts.prompt_new import PromptTemplate +from pilot.configs.config import Config +from pilot.scene.base import ChatScene +from pilot.common.schema import SeparatorStyle + +from pilot.scene.chat_knowledge.summary.out_parser import ExtractSummaryParser + +CFG = Config() + +# PROMPT_SCENE_DEFINE = """You are an expert Q&A system that is trusted around the world.\nAlways answer the query using the provided context information, and not prior knowledge.\nSome rules to follow:\n1. Never directly reference the given context in your answer.\n2. Avoid statements like 'Based on the context, ...' or 'The context information ...' or anything along those lines.""" + +PROMPT_SCENE_DEFINE = """A chat between a curious user and an artificial intelligence assistant, who very familiar with database related knowledge. +The assistant gives helpful, detailed, professional and polite answers to the user's questions.""" + +_DEFAULT_TEMPLATE_ZH = """请根据提供的上下文信息的进行精简地总结: +{context} +答案尽量精确和简单,不要过长，长度控制在100字左右 +""" + +_DEFAULT_TEMPLATE_EN = """ +Write a quick summary of the following context: +{context} +the summary should be as concise as possible and not overly lengthy.Please keep the answer within approximately 200 characters. +""" + +_DEFAULT_TEMPLATE = ( + _DEFAULT_TEMPLATE_EN if CFG.LANGUAGE == "en" else _DEFAULT_TEMPLATE_ZH +) + +PROMPT_RESPONSE = """""" + + +RESPONSE_FORMAT = """""" + + +PROMPT_SEP = SeparatorStyle.SINGLE.value + +PROMPT_NEED_NEED_STREAM_OUT = False + +prompt = PromptTemplate( + template_scene=ChatScene.ExtractSummary.value(), + input_variables=["context"], + response_format=None, + template_define=PROMPT_SCENE_DEFINE, + template=_DEFAULT_TEMPLATE + PROMPT_RESPONSE, + stream_out=PROMPT_NEED_NEED_STREAM_OUT, + output_parser=ExtractSummaryParser( + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + ), +) + +CFG.prompt_template_registry.register(prompt, is_default=True) +from ..v1 import prompt_chatglm diff --git a/pilot/scene/chat_knowledge/v1/chat.py b/pilot/scene/chat_knowledge/v1/chat.py index 321fb27ac..d2936cd28 100644 --- a/pilot/scene/chat_knowledge/v1/chat.py +++ b/pilot/scene/chat_knowledge/v1/chat.py @@ -1,6 +1,8 @@ +import json import os -from typing import Dict +from typing import Dict, List +from pilot.component import ComponentType from pilot.scene.base_chat import BaseChat from pilot.scene.base import ChatScene from pilot.configs.config import Config @@ -12,13 +14,14 @@ from pilot.configs.model_config import ( from pilot.scene.chat_knowledge.v1.prompt import prompt from pilot.server.knowledge.service import KnowledgeService +from pilot.utils.executor_utils import blocking_func_to_async +from pilot.utils.tracer import root_tracer, trace CFG = Config() class ChatKnowledge(BaseChat): chat_scene: str = ChatScene.ChatKnowledge.value() - """KBQA Chat Module""" def __init__(self, chat_param: Dict): @@ -44,10 +47,9 @@ class ChatKnowledge(BaseChat): if self.space_context is None else int(self.space_context["embedding"]["topk"]) ) - # self.recall_score = CFG.KNOWLEDGE_SEARCH_TOP_SIZE if self.space_context is None else self.space_context["embedding"]["recall_score"] self.max_token = ( CFG.KNOWLEDGE_SEARCH_MAX_TOKEN - if self.space_context is None + if self.space_context is None or self.space_context.get("prompt") is None else int(self.space_context["prompt"]["max_token"]) ) vector_store_config = { @@ -65,7 +67,7 @@ class ChatKnowledge(BaseChat): self.prompt_template.template_is_strict = False async def stream_call(self): - input_values = self.generate_input_values() + input_values = await self.generate_input_values() # Source of knowledge file relations = input_values.get("relations") last_output = None @@ -83,20 +85,34 @@ class ChatKnowledge(BaseChat): last_output.text = ( last_output.text + "\n\nrelations:\n\n" + ",".join(relations) ) - yield last_output + reference = f"\n\n{self.parse_source_view(self.sources)}" + last_output = last_output + reference + yield last_output - def generate_input_values(self): - if self.space_context: + def stream_call_reinforce_fn(self, text): + """return reference""" + return text + f"\n\n{self.parse_source_view(self.sources)}" + + @trace() + async def generate_input_values(self) -> Dict: + if self.space_context and self.space_context.get("prompt"): self.prompt_template.template_define = self.space_context["prompt"]["scene"] self.prompt_template.template = self.space_context["prompt"]["template"] - docs = self.knowledge_embedding_client.similar_search( - self.current_user_input, self.top_k + docs = await blocking_func_to_async( + self._executor, + self.knowledge_embedding_client.similar_search, + self.current_user_input, + self.top_k, ) - if not docs: - raise ValueError( - "you have no knowledge space, please add your knowledge space" - ) - context = [d.page_content for d in docs] + self.sources = _merge_by_key( + list(map(lambda doc: doc.metadata, docs)), "source" + ) + + if not docs or len(docs) == 0: + print("no relevant docs to retrieve") + context = "no relevant docs to retrieve" + else: + context = [d.page_content for d in docs] context = context[: self.max_token] relations = list( set([os.path.basename(str(d.metadata.get("source", ""))) for d in docs]) @@ -108,6 +124,31 @@ class ChatKnowledge(BaseChat): } return input_values + def parse_source_view(self, sources: List): + """ + build knowledge reference view message to web + { + "title":"References", + "references":[{ + "name":"aa.pdf", + "pages":["1","2","3"] + }] + } + """ + references = {"title": "References", "references": []} + for item in sources: + reference = {} + source = item["source"] if "source" in item else "" + reference["name"] = source + pages = item["pages"] if "pages" in item else [] + if len(pages) > 0: + reference["pages"] = pages + references["references"].append(reference) + html = ( + f"""{json.dumps(references, ensure_ascii=False)}""" + ) + return html + @property def chat_type(self) -> str: return ChatScene.ChatKnowledge.value() @@ -115,3 +156,27 @@ class ChatKnowledge(BaseChat): def get_space_context(self, space_name): service = KnowledgeService() return service.get_space_context(space_name, self.user_id) + + +def _merge_by_key(data, key): + result = {} + for item in data: + if item.get(key): + item_key = os.path.basename(item.get(key)) + if item_key in result: + if "pages" in result[item_key] and "page" in item: + result[item_key]["pages"].append(str(item["page"])) + elif "page" in item: + result[item_key]["pages"] = [ + result[item_key]["pages"], + str(item["page"]), + ] + else: + if "page" in item: + result[item_key] = { + "source": item_key, + "pages": [str(item["page"])], + } + else: + result[item_key] = {"source": item_key} + return list(result.values()) diff --git a/pilot/scene/chat_knowledge/v1/prompt.py b/pilot/scene/chat_knowledge/v1/prompt.py index 6788d6d35..55d74297f 100644 --- a/pilot/scene/chat_knowledge/v1/prompt.py +++ b/pilot/scene/chat_knowledge/v1/prompt.py @@ -13,17 +13,17 @@ The assistant gives helpful, detailed, professional and polite answers to the us _DEFAULT_TEMPLATE_ZH = """ 基于以下已知的信息, 专业、简要的回答用户的问题, - 如果无法从提供的内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题" 禁止胡乱编造。 + 如果无法从提供的内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题" 禁止胡乱编造, 回答的时候最好按照1.2.3.点进行总结。 已知内容: {context} 问题: - {question} + {question},请使用和用户相同的语言进行回答. """ -_DEFAULT_TEMPLATE_EN = """ Based on the known information below, provide users with professional and concise answers to their questions. If the answer cannot be obtained from the provided content, please say: "The information provided in the knowledge base is not sufficient to answer this question." It is forbidden to make up information randomly. +_DEFAULT_TEMPLATE_EN = """ Based on the known information below, provide users with professional and concise answers to their questions. If the answer cannot be obtained from the provided content, please say: "The information provided in the knowledge base is not sufficient to answer this question." It is forbidden to make up information randomly. When answering, it is best to summarize according to points 1.2.3. known information: {context} question: - {question} + {question},when answering, use the same language as the "user". """ _DEFAULT_TEMPLATE = ( @@ -33,7 +33,7 @@ _DEFAULT_TEMPLATE = ( PROMPT_SEP = SeparatorStyle.SINGLE.value -PROMPT_NEED_NEED_STREAM_OUT = True +PROMPT_NEED_STREAM_OUT = True prompt = PromptTemplate( template_scene=ChatScene.ChatKnowledge.value(), @@ -41,9 +41,9 @@ prompt = PromptTemplate( response_format=None, template_define=PROMPT_SCENE_DEFINE, template=_DEFAULT_TEMPLATE, - stream_out=PROMPT_NEED_NEED_STREAM_OUT, + stream_out=PROMPT_NEED_STREAM_OUT, output_parser=NormalChatOutputParser( - sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT ), ) diff --git a/pilot/scene/chat_knowledge/v1/prompt_chatglm.py b/pilot/scene/chat_knowledge/v1/prompt_chatglm.py index 7f66c1e6f..898699e89 100644 --- a/pilot/scene/chat_knowledge/v1/prompt_chatglm.py +++ b/pilot/scene/chat_knowledge/v1/prompt_chatglm.py @@ -33,7 +33,7 @@ _DEFAULT_TEMPLATE = ( PROMPT_SEP = SeparatorStyle.SINGLE.value -PROMPT_NEED_NEED_STREAM_OUT = True +PROMPT_NEED_STREAM_OUT = True prompt = PromptTemplate( template_scene=ChatScene.ChatKnowledge.value(), @@ -41,9 +41,9 @@ prompt = PromptTemplate( response_format=None, template_define=None, template=_DEFAULT_TEMPLATE, - stream_out=PROMPT_NEED_NEED_STREAM_OUT, + stream_out=PROMPT_NEED_STREAM_OUT, output_parser=NormalChatOutputParser( - sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT ), ) diff --git a/pilot/scene/chat_normal/chat.py b/pilot/scene/chat_normal/chat.py index 47d1e70b5..0191ef943 100644 --- a/pilot/scene/chat_normal/chat.py +++ b/pilot/scene/chat_normal/chat.py @@ -5,6 +5,7 @@ from pilot.scene.base import ChatScene from pilot.configs.config import Config from pilot.scene.chat_normal.prompt import prompt +from pilot.utils.tracer import root_tracer, trace CFG = Config() @@ -21,7 +22,8 @@ class ChatNormal(BaseChat): chat_param=chat_param, ) - def generate_input_values(self): + @trace() + async def generate_input_values(self) -> Dict: input_values = {"input": self.current_user_input} return input_values diff --git a/pilot/scene/chat_normal/prompt.py b/pilot/scene/chat_normal/prompt.py index ad0724874..dae412987 100644 --- a/pilot/scene/chat_normal/prompt.py +++ b/pilot/scene/chat_normal/prompt.py @@ -11,7 +11,7 @@ CFG = Config() PROMPT_SEP = SeparatorStyle.SINGLE.value -PROMPT_NEED_NEED_STREAM_OUT = True +PROMPT_NEED_STREAM_OUT = True prompt = PromptTemplate( template_scene=ChatScene.ChatNormal.value(), @@ -19,9 +19,9 @@ prompt = PromptTemplate( response_format=None, template_define=PROMPT_SCENE_DEFINE, template=None, - stream_out=PROMPT_NEED_NEED_STREAM_OUT, + stream_out=PROMPT_NEED_STREAM_OUT, output_parser=NormalChatOutputParser( - sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT + sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT ), ) diff --git a/pilot/scene/operator/__init__.py b/pilot/scene/operator/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pilot/scene/operator/_experimental.py b/pilot/scene/operator/_experimental.py new file mode 100644 index 000000000..f0ee06179 --- /dev/null +++ b/pilot/scene/operator/_experimental.py @@ -0,0 +1,255 @@ +from typing import Dict, Optional, List, Any +from dataclasses import dataclass +import datetime +import os +from pilot.awel import MapOperator +from pilot.prompts.prompt_new import PromptTemplate +from pilot.configs.config import Config +from pilot.scene.base import ChatScene +from pilot.scene.message import OnceConversation +from pilot.scene.base_message import ModelMessage, ModelMessageRoleType + + +from pilot.memory.chat_history.base import BaseChatHistoryMemory +from pilot.memory.chat_history.chat_hisotry_factory import ChatHistory + +# TODO move global config +CFG = Config() + + +@dataclass +class ChatContext: + current_user_input: str + model_name: Optional[str] + chat_session_id: Optional[str] = None + select_param: Optional[str] = None + chat_scene: Optional[ChatScene] = ChatScene.ChatNormal + prompt_template: Optional[PromptTemplate] = None + chat_retention_rounds: Optional[int] = 0 + history_storage: Optional[BaseChatHistoryMemory] = None + history_manager: Optional["ChatHistoryManager"] = None + # The input values for prompt template + input_values: Optional[Dict] = None + echo: Optional[bool] = False + + def build_model_payload(self) -> Dict: + if not self.input_values: + raise ValueError("The input value can't be empty") + llm_messages = self.history_manager._new_chat(self.input_values) + return { + "model": self.model_name, + "prompt": "", + "messages": llm_messages, + "temperature": float(self.prompt_template.temperature), + "max_new_tokens": int(self.prompt_template.max_new_tokens), + "echo": self.echo, + } + + +class ChatHistoryManager: + def __init__( + self, + chat_ctx: ChatContext, + prompt_template: PromptTemplate, + history_storage: BaseChatHistoryMemory, + chat_retention_rounds: Optional[int] = 0, + ) -> None: + self._chat_ctx = chat_ctx + self.chat_retention_rounds = chat_retention_rounds + self.current_message: OnceConversation = OnceConversation( + chat_ctx.chat_scene.value() + ) + self.prompt_template = prompt_template + self.history_storage: BaseChatHistoryMemory = history_storage + self.history_message: List[OnceConversation] = history_storage.messages() + self.current_message.model_name = chat_ctx.model_name + if chat_ctx.select_param: + if len(chat_ctx.chat_scene.param_types()) > 0: + self.current_message.param_type = chat_ctx.chat_scene.param_types()[0] + self.current_message.param_value = chat_ctx.select_param + + def _new_chat(self, input_values: Dict) -> List[ModelMessage]: + self.current_message.chat_order = len(self.history_message) + 1 + self.current_message.add_user_message(self._chat_ctx.current_user_input) + self.current_message.start_date = datetime.datetime.now().strftime( + "%Y-%m-%d %H:%M:%S" + ) + self.current_message.tokens = 0 + if self.prompt_template.template: + current_prompt = self.prompt_template.format(**input_values) + self.current_message.add_system_message(current_prompt) + return self._generate_llm_messages() + + def _generate_llm_messages(self) -> List[ModelMessage]: + from pilot.scene.base_chat import ( + _load_system_message, + _load_example_messages, + _load_history_messages, + _load_user_message, + ) + + messages = [] + ### Load scene setting or character definition as system message + if self.prompt_template.template_define: + messages.append( + ModelMessage( + role=ModelMessageRoleType.SYSTEM, + content=self.prompt_template.template_define, + ) + ) + ### Load prompt + messages += _load_system_message( + self.current_message, self.prompt_template, str_message=False + ) + ### Load examples + messages += _load_example_messages(self.prompt_template, str_message=False) + + ### Load History + messages += _load_history_messages( + self.prompt_template, + self.history_message, + self.chat_retention_rounds, + str_message=False, + ) + + ### Load User Input + messages += _load_user_message( + self.current_message, self.prompt_template, str_message=False + ) + return messages + + +class PromptManagerOperator(MapOperator[ChatContext, ChatContext]): + def __init__(self, prompt_template: PromptTemplate = None, **kwargs): + super().__init__(**kwargs) + self._prompt_template = prompt_template + + async def map(self, input_value: ChatContext) -> ChatContext: + if not self._prompt_template: + self._prompt_template: PromptTemplate = ( + CFG.prompt_template_registry.get_prompt_template( + input_value.chat_scene.value(), + language=CFG.LANGUAGE, + model_name=input_value.model_name, + proxyllm_backend=CFG.PROXYLLM_BACKEND, + ) + ) + input_value.prompt_template = self._prompt_template + return input_value + + +class ChatHistoryStorageOperator(MapOperator[ChatContext, ChatContext]): + def __init__(self, history: BaseChatHistoryMemory = None, **kwargs): + super().__init__(**kwargs) + self._history = history + + async def map(self, input_value: ChatContext) -> ChatContext: + if self._history: + return self._history + chat_history_fac = ChatHistory() + input_value.history_storage = chat_history_fac.get_store_instance( + input_value.chat_session_id + ) + return input_value + + +class ChatHistoryOperator(MapOperator[ChatContext, ChatContext]): + def __init__(self, history: BaseChatHistoryMemory = None, **kwargs): + super().__init__(**kwargs) + self._history = history + + async def map(self, input_value: ChatContext) -> ChatContext: + history_storage = self._history or input_value.history_storage + if not history_storage: + from pilot.memory.chat_history.store_type.mem_history import ( + MemHistoryMemory, + ) + + history_storage = MemHistoryMemory(input_value.chat_session_id) + input_value.history_storage = history_storage + input_value.history_manager = ChatHistoryManager( + input_value, + input_value.prompt_template, + history_storage, + input_value.chat_retention_rounds, + ) + return input_value + + +class EmbeddingEngingOperator(MapOperator[ChatContext, ChatContext]): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + async def map(self, input_value: ChatContext) -> ChatContext: + from pilot.configs.model_config import EMBEDDING_MODEL_CONFIG + from pilot.embedding_engine.embedding_engine import EmbeddingEngine + from pilot.embedding_engine.embedding_factory import EmbeddingFactory + from pilot.scene.chat_knowledge.v1.chat import _merge_by_key + + # TODO, decompose the current operator into some atomic operators + knowledge_space = input_value.select_param + vector_store_config = { + "vector_store_name": knowledge_space, + "vector_store_type": CFG.VECTOR_STORE_TYPE, + } + embedding_factory = self.system_app.get_component( + "embedding_factory", EmbeddingFactory + ) + knowledge_embedding_client = EmbeddingEngine( + model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL], + vector_store_config=vector_store_config, + embedding_factory=embedding_factory, + ) + space_context = await self._get_space_context(knowledge_space) + top_k = ( + CFG.KNOWLEDGE_SEARCH_TOP_SIZE + if space_context is None + else int(space_context["embedding"]["topk"]) + ) + max_token = ( + CFG.KNOWLEDGE_SEARCH_MAX_TOKEN + if space_context is None or space_context.get("prompt") is None + else int(space_context["prompt"]["max_token"]) + ) + input_value.prompt_template.template_is_strict = False + if space_context and space_context.get("prompt"): + input_value.prompt_template.template_define = space_context["prompt"][ + "scene" + ] + input_value.prompt_template.template = space_context["prompt"]["template"] + + docs = await self.blocking_func_to_async( + knowledge_embedding_client.similar_search, + input_value.current_user_input, + top_k, + ) + sources = _merge_by_key(list(map(lambda doc: doc.metadata, docs)), "source") + if not docs or len(docs) == 0: + print("no relevant docs to retrieve") + context = "no relevant docs to retrieve" + else: + context = [d.page_content for d in docs] + context = context[:max_token] + relations = list( + set([os.path.basename(str(d.metadata.get("source", ""))) for d in docs]) + ) + input_value.input_values = { + "context": context, + "question": input_value.current_user_input, + "relations": relations, + } + return input_value + + async def _get_space_context(self, space_name): + from pilot.server.knowledge.service import KnowledgeService + + service = KnowledgeService() + return await self.blocking_func_to_async(service.get_space_context, space_name) + + +class BaseChatOperator(MapOperator[ChatContext, Dict]): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + async def map(self, input_value: ChatContext) -> Dict: + return input_value.build_model_payload() diff --git a/pilot/server/base.py b/pilot/server/base.py index e66efcf43..488c919c3 100644 --- a/pilot/server/base.py +++ b/pilot/server/base.py @@ -6,7 +6,6 @@ from typing import Optional, Any from dataclasses import dataclass, field from pilot.configs.config import Config -from pilot.configs.model_config import PLUGINS_DIR from pilot.component import SystemApp from pilot.utils.parameter_utils import BaseParameters from pilot.base_modules.meta_data.meta_data import ddl_init_and_upgrade @@ -29,7 +28,7 @@ def async_db_summary(system_app: SystemApp): thread.start() -def server_init(args, system_app: SystemApp): +def server_init(param: "WebWerverParameters", system_app: SystemApp): from pilot.base_modules.agent.commands.command_mange import CommandRegistry # logger.info(f"args: {args}") @@ -38,7 +37,7 @@ def server_init(args, system_app: SystemApp): cfg = Config() cfg.SYSTEM_APP = system_app - ddl_init_and_upgrade() + ddl_init_and_upgrade(param.disable_alembic_upgrade) # load_native_plugins(cfg) signal.signal(signal.SIGINT, signal_handler) @@ -148,3 +147,15 @@ class WebWerverParameters(BaseParameters): "help": "The filename to store tracer span records", }, ) + tracer_storage_cls: Optional[str] = field( + default=None, + metadata={ + "help": "The storage class to storage tracer span records", + }, + ) + disable_alembic_upgrade: Optional[bool] = field( + default=False, + metadata={ + "help": "Whether to disable alembic to initialize and upgrade database metadata", + }, + ) diff --git a/pilot/server/chat_adapter.py b/pilot/server/chat_adapter.py index cb486021b..64b72739b 100644 --- a/pilot/server/chat_adapter.py +++ b/pilot/server/chat_adapter.py @@ -215,6 +215,16 @@ class Llama2ChatAdapter(BaseChatAdpter): return get_conv_template("llama-2") +class CodeLlamaChatAdapter(BaseChatAdpter): + """The model ChatAdapter for codellama .""" + + def match(self, model_path: str): + return "codellama" in model_path.lower() + + def get_conv_template(self, model_path: str) -> Conversation: + return get_conv_template("codellama") + + class BaichuanChatAdapter(BaseChatAdpter): def match(self, model_path: str): return "baichuan" in model_path.lower() @@ -268,6 +278,7 @@ register_llm_model_chat_adapter(FalconChatAdapter) register_llm_model_chat_adapter(GorillaChatAdapter) register_llm_model_chat_adapter(GPT4AllChatAdapter) register_llm_model_chat_adapter(Llama2ChatAdapter) +register_llm_model_chat_adapter(CodeLlamaChatAdapter) register_llm_model_chat_adapter(BaichuanChatAdapter) register_llm_model_chat_adapter(WizardLMChatAdapter) register_llm_model_chat_adapter(LlamaCppChatAdapter) diff --git a/pilot/server/component_configs.py b/pilot/server/component_configs.py index d700de94c..4b7f1bb2b 100644 --- a/pilot/server/component_configs.py +++ b/pilot/server/component_configs.py @@ -5,6 +5,8 @@ from typing import TYPE_CHECKING, Any, Type import os from pilot.component import ComponentType, SystemApp +from pilot.configs.config import Config +from pilot.configs.model_config import MODEL_DISK_CACHE_DIR from pilot.utils.executor_utils import DefaultExecutorFactory from pilot.embedding_engine.embedding_factory import EmbeddingFactory from pilot.server.base import WebWerverParameters @@ -15,6 +17,8 @@ if TYPE_CHECKING: logger = logging.getLogger(__name__) +CFG = Config() + def initialize_components( param: WebWerverParameters, @@ -28,6 +32,11 @@ def initialize_components( system_app.register(DefaultExecutorFactory) system_app.register_instance(controller) + # Register global default RAGGraphFactory + # from pilot.graph_engine.graph_factory import DefaultRAGGraphFactory + + # system_app.register(DefaultRAGGraphFactory) + from pilot.base_modules.agent.controller import module_agent system_app.register_instance(module_agent) @@ -35,6 +44,8 @@ def initialize_components( _initialize_embedding_model( param, system_app, embedding_model_name, embedding_model_path ) + _initialize_model_cache(system_app) + _initialize_awel(system_app) def _initialize_embedding_model( @@ -126,3 +137,23 @@ class LocalEmbeddingFactory(EmbeddingFactory): loader = EmbeddingLoader() # Ignore model_name args return loader.load(self._default_model_name, model_params) + + +def _initialize_model_cache(system_app: SystemApp): + from pilot.cache import initialize_cache + + if not CFG.MODEL_CACHE_ENABLE: + logger.info("Model cache is not enable") + return + + storage_type = CFG.MODEL_CACHE_STORAGE_TYPE or "disk" + max_memory_mb = CFG.MODEL_CACHE_MAX_MEMORY_MB or 256 + persist_dir = CFG.MODEL_CACHE_STORAGE_DISK_DIR or MODEL_DISK_CACHE_DIR + initialize_cache(system_app, storage_type, max_memory_mb, persist_dir) + + +def _initialize_awel(system_app: SystemApp): + from pilot.awel import initialize_awel + from pilot.configs.model_config import _DAG_DEFINITION_DIR + + initialize_awel(system_app, _DAG_DEFINITION_DIR) diff --git a/pilot/server/dbgpt_server.py b/pilot/server/dbgpt_server.py index c6e084a93..c26803cfd 100644 --- a/pilot/server/dbgpt_server.py +++ b/pilot/server/dbgpt_server.py @@ -2,6 +2,7 @@ import os import argparse import sys from typing import List +import logging ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) sys.path.append(ROOT_PATH) @@ -39,6 +40,7 @@ from pilot.utils.utils import ( setup_logging, _get_logging_level, logging_str_to_uvicorn_level, + setup_http_service_logging, ) from pilot.utils.tracer import root_tracer, initialize_tracer, SpanType, SpanTypeRunName from pilot.utils.parameter_utils import _get_dict_from_obj @@ -127,6 +129,7 @@ def initialize_app(param: WebWerverParameters = None, args: List[str] = None): setup_logging( "pilot", logging_level=param.log_level, logger_filename=param.log_file ) + # Before start system_app.before_start() @@ -141,7 +144,7 @@ def initialize_app(param: WebWerverParameters = None, args: List[str] = None): model_name = param.model_name or CFG.LLM_MODEL - model_path = LLM_MODEL_CONFIG.get(model_name) + model_path = CFG.LLM_MODEL_PATH or LLM_MODEL_CONFIG.get(model_name) if not param.light: print("Model Unified Deployment Mode!") if not param.remote_embedding: @@ -180,6 +183,7 @@ def initialize_app(param: WebWerverParameters = None, args: List[str] = None): def run_uvicorn(param: WebWerverParameters): import uvicorn + setup_http_service_logging() uvicorn.run( app, host=param.host, @@ -191,7 +195,11 @@ def run_uvicorn(param: WebWerverParameters): def run_webserver(param: WebWerverParameters = None): if not param: param = _get_webserver_params() - initialize_tracer(system_app, os.path.join(LOGDIR, param.tracer_file)) + initialize_tracer( + system_app, + os.path.join(LOGDIR, param.tracer_file), + tracer_storage_cls=param.tracer_storage_cls, + ) with root_tracer.start_span( "run_webserver", diff --git a/pilot/server/knowledge/api.py b/pilot/server/knowledge/api.py index c0966cb62..f2fc0abc2 100644 --- a/pilot/server/knowledge/api.py +++ b/pilot/server/knowledge/api.py @@ -10,6 +10,7 @@ from pilot.configs.model_config import ( EMBEDDING_MODEL_CONFIG, KNOWLEDGE_UPLOAD_ROOT_PATH, ) +from pilot.openapi.api_v1.api_v1 import no_stream_generator, stream_generator from pilot.openapi.api_view_model import Result from pilot.embedding_engine.embedding_engine import EmbeddingEngine @@ -24,10 +25,13 @@ from pilot.server.knowledge.request.request import ( ChunkQueryRequest, DocumentQueryRequest, SpaceArgumentRequest, + EntityExtractRequest, + DocumentSummaryRequest, ) from pilot.server.knowledge.request.request import KnowledgeSpaceRequest from pilot.user import UserRequest, get_user_from_headers +from pilot.utils.tracer import root_tracer, SpanType logger = logging.getLogger(__name__) @@ -46,7 +50,7 @@ def space_add(request: KnowledgeSpaceRequest, user_token: UserRequest = Depends( knowledge_space_service.create_knowledge_space(request) return Result.succ([]) except Exception as e: - return Result.faild(code="E000X", msg=f"space add error {e}") + return Result.failed(code="E000X", msg=f"space add error {e}") @router.post("/knowledge/space/list") @@ -56,7 +60,7 @@ def space_list(request: KnowledgeSpaceRequest, user_token: UserRequest = Depends request.user_id = user_token.user_id return Result.succ(knowledge_space_service.get_knowledge_space(request)) except Exception as e: - return Result.faild(code="E000X", msg=f"space list error {e}") + return Result.failed(code="E000X", msg=f"space list error {e}") @router.post("/knowledge/space/delete") @@ -70,7 +74,7 @@ def space_delete(request: KnowledgeSpaceRequest, user_token: UserRequest = Depen try: return Result.succ(knowledge_space_service.delete_space(spaces[0].id)) except Exception as e: - return Result.faild(code="E000X", msg=f"space list error {e}") + return Result.failed(code="E000X", msg=f"space list error {e}") @router.post("/knowledge/{space_name}/arguments") @@ -79,7 +83,7 @@ def arguments(space_name: str, user_token: UserRequest = Depends(get_user_from_h try: return Result.succ(knowledge_space_service.arguments(space_name, user_token.user_id)) except Exception as e: - return Result.faild(code="E000X", msg=f"space list error {e}") + return Result.failed(code="E000X", msg=f"space list error {e}") @router.post("/knowledge/{space_name}/argument/save") @@ -90,7 +94,7 @@ def arguments_save(space_name: str, argument_request: SpaceArgumentRequest, user knowledge_space_service.argument_save(space_name, argument_request, user_token.user_id) ) except Exception as e: - return Result.faild(code="E000X", msg=f"space list error {e}") + return Result.failed(code="E000X", msg=f"space list error {e}") @router.post("/knowledge/{space_name}/document/add") @@ -107,7 +111,7 @@ def document_add(space_name: str, request: KnowledgeDocumentRequest, user_token: ) ) except Exception as e: - return Result.faild(code="E000X", msg=f"document add error {e}") + return Result.failed(code="E000X", msg=f"document add error {e}") @router.post("/knowledge/{space_name}/document/list") @@ -123,7 +127,7 @@ def document_list(space_name: str, query_request: DocumentQueryRequest, user_tok knowledge_space_service.get_knowledge_documents(spaces[0].id, query_request) ) except Exception as e: - return Result.faild(code="E000X", msg=f"document list error {e}") + return Result.failed(code="E000X", msg=f"document list error {e}") @router.post("/knowledge/{space_name}/document/delete") @@ -139,7 +143,7 @@ def document_delete(space_name: str, query_request: DocumentQueryRequest, user_t knowledge_space_service.delete_document(spaces[0].id, query_request.doc_name) ) except Exception as e: - return Result.faild(code="E000X", msg=f"document list error {e}") + return Result.failed(code="E000X", msg=f"document list error {e}") @router.post("/knowledge/{space_name}/document/upload") @@ -177,15 +181,28 @@ async def document_upload( request.content = os.path.join( KNOWLEDGE_UPLOAD_ROOT_PATH, space_name_dir, doc_file.filename ) + space_res = knowledge_space_service.get_knowledge_space( + KnowledgeSpaceRequest(name=space_name) + ) + if len(space_res) == 0: + # create default space + if "default" != space_name: + raise Exception(f"you have not create your knowledge space.") + knowledge_space_service.create_knowledge_space( + KnowledgeSpaceRequest( + name=space_name, + desc="first db-gpt rag application", + owner="dbgpt", + ) + ) return Result.succ( knowledge_space_service.create_knowledge_document( space_id=spaces[0].id, request=request ) ) - # return Result.succ([]) - return Result.faild(code="E000X", msg=f"doc_file is None") + return Result.failed(code="E000X", msg=f"doc_file is None") except Exception as e: - return Result.faild(code="E000X", msg=f"document add error {e}") + return Result.failed(code="E000X", msg=f"document add error {e}") @router.post("/knowledge/{space_name}/document/sync") @@ -202,7 +219,7 @@ def document_sync(space_name: str, request: DocumentSyncRequest, user_token: Use ) return Result.succ([]) except Exception as e: - return Result.faild(code="E000X", msg=f"document sync error {e}") + return Result.failed(code="E000X", msg=f"document sync error {e}") @router.post("/knowledge/{space_name}/chunk/list") @@ -211,7 +228,7 @@ def document_list(space_name: str, query_request: ChunkQueryRequest, user_token: try: return Result.succ(knowledge_space_service.get_document_chunks(query_request)) except Exception as e: - return Result.faild(code="E000X", msg=f"document chunk list error {e}") + return Result.failed(code="E000X", msg=f"document chunk list error {e}") @router.post("/knowledge/{vector_name}/query") @@ -231,3 +248,58 @@ def similar_query(space_name: str, query_request: KnowledgeQueryRequest, user_to for d in docs ] return {"response": res} + + +@router.post("/knowledge/document/summary") +async def document_summary(request: DocumentSummaryRequest): + print(f"/document/summary params: {request}") + try: + with root_tracer.start_span( + "get_chat_instance", span_type=SpanType.CHAT, metadata=request + ): + chat = await knowledge_space_service.document_summary(request=request) + headers = { + "Content-Type": "text/event-stream", + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "Transfer-Encoding": "chunked", + } + from starlette.responses import StreamingResponse + + if not chat.prompt_template.stream_out: + return StreamingResponse( + no_stream_generator(chat), + headers=headers, + media_type="text/event-stream", + ) + else: + return StreamingResponse( + stream_generator(chat, False, request.model_name), + headers=headers, + media_type="text/plain", + ) + except Exception as e: + return Result.failed(code="E000X", msg=f"document summary error {e}") + + +@router.post("/knowledge/entity/extract") +async def entity_extract(request: EntityExtractRequest): + logger.info(f"Received params: {request}") + try: + from pilot.scene.base import ChatScene + from pilot.common.chat_util import llm_chat_response_nostream + import uuid + + chat_param = { + "chat_session_id": uuid.uuid1(), + "current_user_input": request.text, + "select_param": "entity", + "model_name": request.model_name, + } + + res = await llm_chat_response_nostream( + ChatScene.ExtractEntity.value(), **{"chat_param": chat_param} + ) + return Result.succ(res) + except Exception as e: + return Result.failed(code="E000X", msg=f"entity extract error {e}") diff --git a/pilot/server/knowledge/chunk_db.py b/pilot/server/knowledge/chunk_db.py index f1e792377..1df3cf895 100644 --- a/pilot/server/knowledge/chunk_db.py +++ b/pilot/server/knowledge/chunk_db.py @@ -4,7 +4,12 @@ from typing import List from sqlalchemy import Column, String, DateTime, Integer, Text, func from pilot.base_modules.meta_data.base_dao import BaseDao -from pilot.base_modules.meta_data.meta_data import Base, engine, session +from pilot.base_modules.meta_data.meta_data import ( + Base, + engine, + session, + META_DATA_DATABASE, +) from pilot.configs.config import Config CFG = Config() @@ -32,7 +37,10 @@ class DocumentChunkEntity(Base): class DocumentChunkDao(BaseDao): def __init__(self): super().__init__( - database="dbgpt", orm_base=Base, db_engine=engine, session=session + database=META_DATA_DATABASE, + orm_base=Base, + db_engine=engine, + session=session, ) def create_documents_chunks(self, documents: List): @@ -75,7 +83,7 @@ class DocumentChunkDao(BaseDao): DocumentChunkEntity.meta_info == query.meta_info ) - document_chunks = document_chunks.order_by(DocumentChunkEntity.id.desc()) + document_chunks = document_chunks.order_by(DocumentChunkEntity.id.asc()) document_chunks = document_chunks.offset((page - 1) * page_size).limit( page_size ) diff --git a/pilot/server/knowledge/document_db.py b/pilot/server/knowledge/document_db.py index 232f75bb4..39208ced4 100644 --- a/pilot/server/knowledge/document_db.py +++ b/pilot/server/knowledge/document_db.py @@ -3,7 +3,12 @@ from datetime import datetime from sqlalchemy import Column, String, DateTime, Integer, Text, func from pilot.base_modules.meta_data.base_dao import BaseDao -from pilot.base_modules.meta_data.meta_data import Base, engine, session +from pilot.base_modules.meta_data.meta_data import ( + Base, + engine, + session, + META_DATA_DATABASE, +) from pilot.configs.config import Config CFG = Config() @@ -26,17 +31,21 @@ class KnowledgeDocumentEntity(Base): content = Column(Text) result = Column(Text) vector_ids = Column(Text) + summary = Column(Text) gmt_created = Column(DateTime) gmt_modified = Column(DateTime) def __repr__(self): - return f"KnowledgeDocumentEntity(id={self.id}, space_id={self.space_id}, doc_name='{self.doc_name}', doc_type='{self.doc_type}', chunk_size='{self.chunk_size}', status='{self.status}', last_sync='{self.last_sync}', content='{self.content}', result='{self.result}', gmt_created='{self.gmt_created}', gmt_modified='{self.gmt_modified}')" + return f"KnowledgeDocumentEntity(id={self.id}, space_id={self.space_id}, doc_name='{self.doc_name}', doc_type='{self.doc_type}', chunk_size='{self.chunk_size}', status='{self.status}', last_sync='{self.last_sync}', content='{self.content}', result='{self.result}', summary='{self.summary}', gmt_created='{self.gmt_created}', gmt_modified='{self.gmt_modified}')" class KnowledgeDocumentDao(BaseDao): def __init__(self): super().__init__( - database="dbgpt", orm_base=Base, db_engine=engine, session=session + database=META_DATA_DATABASE, + orm_base=Base, + db_engine=engine, + session=session, ) def create_knowledge_document(self, document: KnowledgeDocumentEntity): diff --git a/pilot/server/knowledge/request/request.py b/pilot/server/knowledge/request/request.py index a0cb4ae17..fccb85c58 100644 --- a/pilot/server/knowledge/request/request.py +++ b/pilot/server/knowledge/request/request.py @@ -63,6 +63,8 @@ class DocumentSyncRequest(BaseModel): """doc_ids: doc ids""" doc_ids: List + model_name: Optional[str] = None + """Preseparator, this separator is used for pre-splitting before the document is actually split by the text splitter. Preseparator are not included in the vectorized text. """ @@ -108,3 +110,19 @@ class SpaceArgumentRequest(BaseModel): """argument: argument""" argument: str + + +class DocumentSummaryRequest(BaseModel): + """Sync request""" + + """doc_ids: doc ids""" + doc_id: int + model_name: str + conv_uid: str + + +class EntityExtractRequest(BaseModel): + """argument: argument""" + + text: str + model_name: str diff --git a/pilot/server/knowledge/request/response.py b/pilot/server/knowledge/request/response.py index fb7aa55e9..5c1c7efd1 100644 --- a/pilot/server/knowledge/request/response.py +++ b/pilot/server/knowledge/request/response.py @@ -7,6 +7,8 @@ class ChunkQueryResponse(BaseModel): """data: data""" data: List = None + """summary: document summary""" + summary: str = None """total: total size""" total: int = None """page: current page""" diff --git a/pilot/server/knowledge/service.py b/pilot/server/knowledge/service.py index 59d8855a6..97bd4d7d4 100644 --- a/pilot/server/knowledge/service.py +++ b/pilot/server/knowledge/service.py @@ -10,7 +10,7 @@ from pilot.configs.model_config import ( KNOWLEDGE_UPLOAD_ROOT_PATH, ) from pilot.component import ComponentType -from pilot.utils.executor_utils import ExecutorFactory +from pilot.utils.executor_utils import ExecutorFactory, blocking_func_to_async from pilot.server.knowledge.chunk_db import ( DocumentChunkEntity, @@ -31,6 +31,7 @@ from pilot.server.knowledge.request.request import ( ChunkQueryRequest, SpaceArgumentRequest, DocumentSyncRequest, + DocumentSummaryRequest, ) from enum import Enum @@ -200,6 +201,7 @@ class KnowledgeService: # import langchain is very very slow!!! doc_ids = sync_request.doc_ids + self.model_name = sync_request.model_name or CFG.LLM_MODEL for doc_id in doc_ids: query = KnowledgeDocumentEntity( id=doc_id, @@ -290,7 +292,6 @@ class KnowledgeService: ComponentType.EXECUTOR_DEFAULT, ExecutorFactory ).create() executor.submit(self.async_doc_embedding, client, chunk_docs, doc) - logger.info(f"begin save document chunks, doc:{doc.doc_name}") # save chunk details chunk_entities = [ @@ -307,7 +308,33 @@ class KnowledgeService: ] document_chunk_dao.create_documents_chunks(chunk_entities) - return True + return doc.id + + async def document_summary(self, request: DocumentSummaryRequest): + """get document summary + Args: + - request: DocumentSummaryRequest + """ + doc_query = KnowledgeDocumentEntity(id=request.doc_id) + documents = knowledge_document_dao.get_documents(doc_query) + if len(documents) != 1: + raise Exception(f"can not found document for {request.doc_id}") + document = documents[0] + query = DocumentChunkEntity( + document_id=request.doc_id, + ) + chunks = document_chunk_dao.get_document_chunks(query, page=1, page_size=100) + if len(chunks) == 0: + raise Exception(f"can not found chunks for {request.doc_id}") + from langchain.schema import Document + + chunk_docs = [Document(page_content=chunk.content) for chunk in chunks] + return await self.async_document_summary( + model_name=request.model_name, + chunk_docs=chunk_docs, + doc=document, + conn_uid=request.conv_uid, + ) def update_knowledge_space( self, space_id: int, space_request: KnowledgeSpaceRequest @@ -390,23 +417,82 @@ class KnowledgeService: doc_name=request.doc_name, doc_type=request.doc_type, ) + document_query = KnowledgeDocumentEntity(id=request.document_id) + documents = knowledge_document_dao.get_documents(document_query) + res = ChunkQueryResponse() res.data = document_chunk_dao.get_document_chunks( query, page=request.page, page_size=request.page_size ) + res.summary = documents[0].summary res.total = document_chunk_dao.get_document_chunks_count(query) res.page = request.page return res + def async_knowledge_graph(self, chunk_docs, doc): + """async document extract triplets and save into graph db + Args: + - chunk_docs: List[Document] + - doc: KnowledgeDocumentEntity + """ + logger.info( + f"async_knowledge_graph, doc:{doc.doc_name}, chunk_size:{len(chunk_docs)}, begin embedding to graph store" + ) + try: + from pilot.graph_engine.graph_factory import RAGGraphFactory + + rag_engine = CFG.SYSTEM_APP.get_component( + ComponentType.RAG_GRAPH_DEFAULT.value, RAGGraphFactory + ).create() + rag_engine.knowledge_graph(chunk_docs) + doc.status = SyncStatus.FINISHED.name + doc.result = "document build graph success" + except Exception as e: + doc.status = SyncStatus.FAILED.name + doc.result = "document build graph failed" + str(e) + logger.error(f"document build graph failed:{doc.doc_name}, {str(e)}") + return knowledge_document_dao.update_knowledge_document(doc) + + async def async_document_summary(self, model_name, chunk_docs, doc, conn_uid): + """async document extract summary + Args: + - model_name: str + - chunk_docs: List[Document] + - doc: KnowledgeDocumentEntity + """ + texts = [doc.page_content for doc in chunk_docs] + from pilot.common.prompt_util import PromptHelper + + prompt_helper = PromptHelper() + from pilot.scene.chat_knowledge.summary.prompt import prompt + + texts = prompt_helper.repack(prompt_template=prompt.template, text_chunks=texts) + logger.info( + f"async_document_summary, doc:{doc.doc_name}, chunk_size:{len(texts)}, begin generate summary" + ) + space_context = self.get_space_context(doc.space) + if space_context and space_context.get("summary"): + summary = await self._mapreduce_extract_summary( + docs=texts, + model_name=model_name, + max_iteration=int(space_context["summary"]["max_iteration"]), + concurrency_limit=int(space_context["summary"]["concurrency_limit"]), + ) + else: + summary = await self._mapreduce_extract_summary( + docs=texts, model_name=model_name + ) + return await self._llm_extract_summary(summary, conn_uid, model_name) + def async_doc_embedding(self, client, chunk_docs, doc): """async document embedding into vector db Args: - client: EmbeddingEngine Client - chunk_docs: List[Document] - - doc: doc + - doc: KnowledgeDocumentEntity """ logger.info( - f"async_doc_embedding, doc:{doc.doc_name}, chunk_size:{len(chunk_docs)}, begin embedding to vector store-{CFG.VECTOR_STORE_TYPE}" + f"async doc sync, doc:{doc.doc_name}, chunk_size:{len(chunk_docs)}, begin embedding to vector store-{CFG.VECTOR_STORE_TYPE}" ) try: vector_ids = client.knowledge_embedding_batch(chunk_docs) @@ -441,6 +527,10 @@ class KnowledgeService: "scene": PROMPT_SCENE_DEFINE, "template": _DEFAULT_TEMPLATE, }, + "summary": { + "max_iteration": 5, + "concurrency_limit": 3, + }, } context_template_string = json.dumps(context_template, indent=4) return context_template_string @@ -462,3 +552,98 @@ class KnowledgeService: if space.context is not None: return json.loads(spaces[0].context) return None + + async def _llm_extract_summary( + self, doc: str, conn_uid: str, model_name: str = None + ): + """Extract triplets from text by llm + Args: + doc: Document + conn_uid: str,chat conversation id + model_name: str, model name + Returns: + chat: BaseChat, refine summary chat. + """ + from pilot.scene.base import ChatScene + + chat_param = { + "chat_session_id": conn_uid, + "current_user_input": "", + "select_param": doc, + "model_name": model_name, + "model_cache_enable": False, + } + executor = CFG.SYSTEM_APP.get_component( + ComponentType.EXECUTOR_DEFAULT, ExecutorFactory + ).create() + from pilot.openapi.api_v1.api_v1 import CHAT_FACTORY + + chat = await blocking_func_to_async( + executor, + CHAT_FACTORY.get_implementation, + ChatScene.ExtractRefineSummary.value(), + **{"chat_param": chat_param}, + ) + return chat + + async def _mapreduce_extract_summary( + self, + docs, + model_name: str = None, + max_iteration: int = 5, + concurrency_limit: int = 3, + ): + """Extract summary by mapreduce mode + map -> multi async call llm to generate summary + reduce -> merge the summaries by map process + Args: + docs:List[str] + model_name:model name str + max_iteration:max iteration will call llm to summary + concurrency_limit:the max concurrency threads to call llm + Returns: + Document: refine summary context document. + """ + from pilot.scene.base import ChatScene + from pilot.common.chat_util import llm_chat_response_nostream + import uuid + + tasks = [] + if len(docs) == 1: + return docs[0] + else: + max_iteration = max_iteration if len(docs) > max_iteration else len(docs) + for doc in docs[0:max_iteration]: + chat_param = { + "chat_session_id": uuid.uuid1(), + "current_user_input": "", + "select_param": doc, + "model_name": model_name, + "model_cache_enable": True, + } + tasks.append( + llm_chat_response_nostream( + ChatScene.ExtractSummary.value(), **{"chat_param": chat_param} + ) + ) + from pilot.common.chat_util import run_async_tasks + + summary_iters = await run_async_tasks( + tasks=tasks, concurrency_limit=concurrency_limit + ) + summary_iters = list( + filter( + lambda content: "LLMServer Generate Error" not in content, + summary_iters, + ) + ) + from pilot.common.prompt_util import PromptHelper + from pilot.scene.chat_knowledge.summary.prompt import prompt + + prompt_helper = PromptHelper() + summary_iters = prompt_helper.repack( + prompt_template=prompt.template, text_chunks=summary_iters + ) + return await self._mapreduce_extract_summary( + summary_iters, model_name, max_iteration, concurrency_limit + ) diff --git a/pilot/server/knowledge/space_db.py b/pilot/server/knowledge/space_db.py index 3f8ad957d..a14df5405 100644 --- a/pilot/server/knowledge/space_db.py +++ b/pilot/server/knowledge/space_db.py @@ -3,7 +3,12 @@ from datetime import datetime from sqlalchemy import Column, Integer, Text, String, DateTime from pilot.base_modules.meta_data.base_dao import BaseDao -from pilot.base_modules.meta_data.meta_data import Base, engine, session +from pilot.base_modules.meta_data.meta_data import ( + Base, + engine, + session, + META_DATA_DATABASE, +) from pilot.configs.config import Config from pilot.server.knowledge.request.request import KnowledgeSpaceRequest @@ -33,7 +38,10 @@ class KnowledgeSpaceEntity(Base): class KnowledgeSpaceDao(BaseDao): def __init__(self): super().__init__( - database="dbgpt", orm_base=Base, db_engine=engine, session=session + database=META_DATA_DATABASE, + orm_base=Base, + db_engine=engine, + session=session, ) def create_knowledge_space(self, space: KnowledgeSpaceRequest): diff --git a/pilot/server/llm_manage/api.py b/pilot/server/llm_manage/api.py index f5602d3c4..617018642 100644 --- a/pilot/server/llm_manage/api.py +++ b/pilot/server/llm_manage/api.py @@ -33,9 +33,9 @@ async def model_params(): params.append(model_dict) return Result.succ(params) if not worker_instance: - return Result.faild(code="E000X", msg=f"can not find worker manager") + return Result.failed(code="E000X", msg=f"can not find worker manager") except Exception as e: - return Result.faild(code="E000X", msg=f"model stop failed {e}") + return Result.failed(code="E000X", msg=f"model stop failed {e}") @router.get("/v1/worker/model/list") @@ -78,7 +78,7 @@ async def model_list(): return Result.succ(responses) except Exception as e: - return Result.faild(code="E000X", msg=f"model list error {e}") + return Result.failed(code="E000X", msg=f"model list error {e}") @router.post("/v1/worker/model/stop") @@ -91,11 +91,11 @@ async def model_stop(request: WorkerStartupRequest): ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory ).create() if not worker_manager: - return Result.faild(code="E000X", msg=f"can not find worker manager") + return Result.failed(code="E000X", msg=f"can not find worker manager") request.params = {} return Result.succ(await worker_manager.model_shutdown(request)) except Exception as e: - return Result.faild(code="E000X", msg=f"model stop failed {e}") + return Result.failed(code="E000X", msg=f"model stop failed {e}") @router.post("/v1/worker/model/start") @@ -106,7 +106,7 @@ async def model_start(request: WorkerStartupRequest): ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory ).create() if not worker_manager: - return Result.faild(code="E000X", msg=f"can not find worker manager") + return Result.failed(code="E000X", msg=f"can not find worker manager") return Result.succ(await worker_manager.model_startup(request)) except Exception as e: - return Result.faild(code="E000X", msg=f"model start failed {e}") + return Result.failed(code="E000X", msg=f"model start failed {e}") diff --git a/pilot/server/prompt/api.py b/pilot/server/prompt/api.py index 18189f6e1..901050bd8 100644 --- a/pilot/server/prompt/api.py +++ b/pilot/server/prompt/api.py @@ -12,13 +12,13 @@ prompt_manage_service = PromptManageService() @router.post("/prompt/add") def prompt_add(request: PromptManageRequest, user_token: UserRequest = Depends(get_user_from_headers)): - print(f"/space/add params: {request}") + print(f"/prompt/add params: {request}") request.user_id = user_token.user_id try: prompt_manage_service.create_prompt(request) return Result.succ([]) except Exception as e: - return Result.faild(code="E010X", msg=f"prompt add error {e}") + return Result.failed(code="E010X", msg=f"prompt add error {e}") @router.post("/prompt/list") @@ -28,7 +28,7 @@ def prompt_list(request: PromptManageRequest, user_token: UserRequest = Depends( request.user_id = user_token.user_id return Result.succ(prompt_manage_service.get_prompts(request)) except Exception as e: - return Result.faild(code="E010X", msg=f"prompt list error {e}") + return Result.failed(code="E010X", msg=f"prompt list error {e}") @router.post("/prompt/update") @@ -38,7 +38,7 @@ def prompt_update(request: PromptManageRequest, user_token: UserRequest = Depend try: return Result.succ(prompt_manage_service.update_prompt(request)) except Exception as e: - return Result.faild(code="E010X", msg=f"prompt update error {e}") + return Result.failed(code="E010X", msg=f"prompt update error {e}") @router.post("/prompt/delete") @@ -47,4 +47,4 @@ def prompt_delete(request: PromptManageRequest, user_token: UserRequest = Depend try: return Result.succ(prompt_manage_service.delete_prompt(request.prompt_name, user_token.user_id)) except Exception as e: - return Result.faild(code="E010X", msg=f"prompt delete error {e}") + return Result.failed(code="E010X", msg=f"prompt delete error {e}") diff --git a/pilot/server/prompt/prompt_manage_db.py b/pilot/server/prompt/prompt_manage_db.py index 28828e43a..5a1a531fe 100644 --- a/pilot/server/prompt/prompt_manage_db.py +++ b/pilot/server/prompt/prompt_manage_db.py @@ -3,7 +3,12 @@ from datetime import datetime from sqlalchemy import Column, Integer, Text, String, DateTime from pilot.base_modules.meta_data.base_dao import BaseDao -from pilot.base_modules.meta_data.meta_data import Base, engine, session +from pilot.base_modules.meta_data.meta_data import ( + Base, + engine, + session, + META_DATA_DATABASE, +) from pilot.configs.config import Config from pilot.server.prompt.request.request import PromptManageRequest @@ -35,7 +40,10 @@ class PromptManageEntity(Base): class PromptManageDao(BaseDao): def __init__(self): super().__init__( - database="dbgpt", orm_base=Base, db_engine=engine, session=session + database=META_DATA_DATABASE, + orm_base=Base, + db_engine=engine, + session=session, ) def create_prompt(self, prompt: PromptManageRequest): diff --git a/pilot/server/static/404.html b/pilot/server/static/404.html index d4360fb5c..d252dd264 100644 --- a/pilot/server/static/404.html +++ b/pilot/server/static/404.html @@ -1 +1 @@ -404: This page could not be found

404

This page could not be found.

\ No newline at end of file +404: This page could not be found

404

This page could not be found.

diff --git a/pilot/server/static/404/index.html b/pilot/server/static/404/index.html index d4360fb5c..d252dd264 100644 --- a/pilot/server/static/404/index.html +++ b/pilot/server/static/404/index.html @@ -1 +1 @@ -404: This page could not be found

404

This page could not be found.

\ No newline at end of file +404: This page could not be found

404

This page could not be found.

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this.projectionMode=t,this},t.prototype.setTrackingMode=function(t){if(this.type!==A.TRACKING)throw Error("Impossible to set a tracking mode if the camera is not of tracking type");return this.trackingMode=t,this},t.prototype.setWorldRotation=function(t){return this.rotateWorld=t,this._getAngles(),this},t.prototype.getViewTransform=function(){return q.invert(q.create(),this.matrix)},t.prototype.getWorldTransform=function(){return this.matrix},t.prototype.jitterProjectionMatrix=function(t,e){var n=q.fromTranslation(q.create(),[t,e,0]);this.jitteredProjectionMatrix=q.multiply(q.create(),n,this.projectionMatrix)},t.prototype.clearJitterProjectionMatrix=function(){this.jitteredProjectionMatrix=void 0},t.prototype.setMatrix=function(t){return this.matrix=t,this._update(),this},t.prototype.setFov=function(t){return this.setPerspective(this.near,this.far,t,this.aspect),this},t.prototype.setAspect=function(t){return this.setPerspective(this.near,this.far,this.fov,t),this},t.prototype.setNear=function(t){return this.projectionMode===L.PERSPECTIVE?this.setPerspective(t,this.far,this.fov,this.aspect):this.setOrthographic(this.left,this.rright,this.top,this.bottom,t,this.far),this},t.prototype.setFar=function(t){return this.projectionMode===L.PERSPECTIVE?this.setPerspective(this.near,t,this.fov,this.aspect):this.setOrthographic(this.left,this.rright,this.top,this.bottom,this.near,t),this},t.prototype.setViewOffset=function(t,e,n,r,i,o){return this.aspect=t/e,void 0===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=r,this.view.width=i,this.view.height=o,this.projectionMode===L.PERSPECTIVE?this.setPerspective(this.near,this.far,this.fov,this.aspect):this.setOrthographic(this.left,this.rright,this.top,this.bottom,this.near,this.far),this},t.prototype.clearViewOffset=function(){return void 0!==this.view&&(this.view.enabled=!1),this.projectionMode===L.PERSPECTIVE?this.setPerspective(this.near,this.far,this.fov,this.aspect):this.setOrthographic(this.left,this.rright,this.top,this.bottom,this.near,this.far),this},t.prototype.setZoom=function(t){return this.zoom=t,this.projectionMode===L.ORTHOGRAPHIC?this.setOrthographic(this.left,this.rright,this.top,this.bottom,this.near,this.far):this.projectionMode===L.PERSPECTIVE&&this.setPerspective(this.near,this.far,this.fov,this.aspect),this},t.prototype.setZoomByViewportPoint=function(t,e){var n=this.canvas.viewport2Canvas({x:e[0],y:e[1]}),r=n.x,i=n.y,o=this.roll;this.rotate(0,0,-o),this.setPosition(r,i),this.setFocalPoint(r,i),this.setZoom(t),this.rotate(0,0,o);var a=this.canvas.viewport2Canvas({x:e[0],y:e[1]}),s=a.x,l=a.y,c=K.al(s-r,l-i,0),u=K.AK(c,this.right)/K.kE(this.right),h=K.AK(c,this.up)/K.kE(this.up);return this.pan(-u,-h),this},t.prototype.setPerspective=function(t,e,n,r){this.projectionMode=L.PERSPECTIVE,this.fov=n,this.near=t,this.far=e,this.aspect=r;var i,o,a,s,l,c,u,h,p,f=this.near*Math.tan(tD(.5*this.fov))/this.zoom,d=2*f,v=this.aspect*d,y=-.5*v;if(null===(p=this.view)||void 0===p?void 0:p.enabled){var g=this.view.fullWidth,m=this.view.fullHeight;y+=this.view.offsetX*v/g,f-=this.view.offsetY*d/m,v*=this.view.width/g,d*=this.view.height/m}return i=this.projectionMatrix,o=y,a=y+v,s=f,l=f-d,c=this.far,this.clipSpaceNearZ===k.ZERO?(u=-c/(c-t),h=-c*t/(c-t)):(u=-(c+t)/(c-t),h=-2*c*t/(c-t)),i[0]=2*t/(a-o),i[1]=0,i[2]=0,i[3]=0,i[4]=0,i[5]=2*t/(s-l),i[6]=0,i[7]=0,i[8]=(a+o)/(a-o),i[9]=(s+l)/(s-l),i[10]=u,i[11]=-1,i[12]=0,i[13]=0,i[14]=h,i[15]=0,q.scale(this.projectionMatrix,this.projectionMatrix,K.al(1,-1,1)),q.invert(this.projectionMatrixInverse,this.projectionMatrix),this.triggerUpdate(),this},t.prototype.setOrthographic=function(t,e,n,r,i,o){this.projectionMode=L.ORTHOGRAPHIC,this.rright=e,this.left=t,this.top=n,this.bottom=r,this.near=i,this.far=o;var a,s=(this.rright-this.left)/(2*this.zoom),l=(this.top-this.bottom)/(2*this.zoom),c=(this.rright+this.left)/2,u=(this.top+this.bottom)/2,h=c-s,p=c+s,f=u+l,d=u-l;if(null===(a=this.view)||void 0===a?void 0:a.enabled){var v=(this.rright-this.left)/this.view.fullWidth/this.zoom,y=(this.top-this.bottom)/this.view.fullHeight/this.zoom;h+=v*this.view.offsetX,p=h+v*this.view.width,f-=y*this.view.offsetY,d=f-y*this.view.height}return this.clipSpaceNearZ===k.NEGATIVE_ONE?q.ortho(this.projectionMatrix,h,p,d,f,i,o):q.orthoZO(this.projectionMatrix,h,p,d,f,i,o),q.scale(this.projectionMatrix,this.projectionMatrix,K.al(1,-1,1)),q.invert(this.projectionMatrixInverse,this.projectionMatrix),this._getOrthoMatrix(),this.triggerUpdate(),this},t.prototype.setPosition=function(t,e,n){void 0===e&&(e=this.position[1]),void 0===n&&(n=this.position[2]);var r=tI(t,e,n);return this._setPosition(r),this.setFocalPoint(this.focalPoint),this.triggerUpdate(),this},t.prototype.setFocalPoint=function(t,e,n){void 0===e&&(e=this.focalPoint[1]),void 0===n&&(n=this.focalPoint[2]);var r=K.al(0,1,0);if(this.focalPoint=tI(t,e,n),this.trackingMode===O.CINEMATIC){var i=K.$X(K.Ue(),this.focalPoint,this.position);t=i[0],e=i[1],n=i[2];var o=tG(Math.asin(e/K.kE(i))),a=90+tG(Math.atan2(n,t)),s=q.create();q.rotateY(s,s,tD(a)),q.rotateX(s,s,tD(o)),r=K.fF(K.Ue(),[0,1,0],s)}return q.invert(this.matrix,q.lookAt(q.create(),this.position,this.focalPoint,r)),this._getAxes(),this._getDistance(),this._getAngles(),this.triggerUpdate(),this},t.prototype.getDistance=function(){return this.distance},t.prototype.getDistanceVector=function(){return this.distanceVector},t.prototype.setDistance=function(t){if(this.distance===t||t<0)return this;this.distance=t,this.distance<2e-4&&(this.distance=2e-4),this.dollyingStep=this.distance/100;var e=K.Ue();t=this.distance;var n=this.forward,r=this.focalPoint;return e[0]=t*n[0]+r[0],e[1]=t*n[1]+r[1],e[2]=t*n[2]+r[2],this._setPosition(e),this.triggerUpdate(),this},t.prototype.setMaxDistance=function(t){return this.maxDistance=t,this},t.prototype.setMinDistance=function(t){return this.minDistance=t,this},t.prototype.setAzimuth=function(t){return this.azimuth=tL(t),this.computeMatrix(),this._getAxes(),this.type===A.ORBITING||this.type===A.EXPLORING?this._getPosition():this.type===A.TRACKING&&this._getFocalPoint(),this.triggerUpdate(),this},t.prototype.getAzimuth=function(){return this.azimuth},t.prototype.setElevation=function(t){return this.elevation=tL(t),this.computeMatrix(),this._getAxes(),this.type===A.ORBITING||this.type===A.EXPLORING?this._getPosition():this.type===A.TRACKING&&this._getFocalPoint(),this.triggerUpdate(),this},t.prototype.getElevation=function(){return this.elevation},t.prototype.setRoll=function(t){return this.roll=tL(t),this.computeMatrix(),this._getAxes(),this.type===A.ORBITING||this.type===A.EXPLORING?this._getPosition():this.type===A.TRACKING&&this._getFocalPoint(),this.triggerUpdate(),this},t.prototype.getRoll=function(){return this.roll},t.prototype._update=function(){this._getAxes(),this._getPosition(),this._getDistance(),this._getAngles(),this._getOrthoMatrix(),this.triggerUpdate()},t.prototype.computeMatrix=function(){var t=Q.yY(Q.Ue(),[0,0,1],tD(this.roll));q.identity(this.matrix);var e=Q.yY(Q.Ue(),[1,0,0],tD((this.rotateWorld&&this.type!==A.TRACKING||this.type===A.TRACKING?1:-1)*this.elevation)),n=Q.yY(Q.Ue(),[0,1,0],tD((this.rotateWorld&&this.type!==A.TRACKING||this.type===A.TRACKING?1:-1)*this.azimuth)),r=Q.Jp(Q.Ue(),n,e);r=Q.Jp(Q.Ue(),r,t);var i=q.fromQuat(q.create(),r);this.type===A.ORBITING||this.type===A.EXPLORING?(q.translate(this.matrix,this.matrix,this.focalPoint),q.multiply(this.matrix,this.matrix,i),q.translate(this.matrix,this.matrix,[0,0,this.distance])):this.type===A.TRACKING&&(q.translate(this.matrix,this.matrix,this.position),q.multiply(this.matrix,this.matrix,i))},t.prototype._setPosition=function(t,e,n){this.position=tI(t,e,n);var r=this.matrix;r[12]=this.position[0],r[13]=this.position[1],r[14]=this.position[2],r[15]=1,this._getOrthoMatrix()},t.prototype._getAxes=function(){K.JG(this.right,tI($.fF($.Ue(),[1,0,0,0],this.matrix))),K.JG(this.up,tI($.fF($.Ue(),[0,1,0,0],this.matrix))),K.JG(this.forward,tI($.fF($.Ue(),[0,0,1,0],this.matrix))),K.Fv(this.right,this.right),K.Fv(this.up,this.up),K.Fv(this.forward,this.forward)},t.prototype._getAngles=function(){var t=this.distanceVector[0],e=this.distanceVector[1],n=this.distanceVector[2],r=K.kE(this.distanceVector);if(0===r){this.elevation=0,this.azimuth=0;return}this.type===A.TRACKING?(this.elevation=tG(Math.asin(e/r)),this.azimuth=tG(Math.atan2(-t,-n))):this.rotateWorld?(this.elevation=tG(Math.asin(e/r)),this.azimuth=tG(Math.atan2(-t,-n))):(this.elevation=-tG(Math.asin(e/r)),this.azimuth=-tG(Math.atan2(-t,-n)))},t.prototype._getPosition=function(){K.JG(this.position,tI($.fF($.Ue(),[0,0,0,1],this.matrix))),this._getDistance()},t.prototype._getFocalPoint=function(){K.kK(this.distanceVector,[0,0,-this.distance],J.xO(J.Ue(),this.matrix)),K.IH(this.focalPoint,this.position,this.distanceVector),this._getDistance()},t.prototype._getDistance=function(){this.distanceVector=K.$X(K.Ue(),this.focalPoint,this.position),this.distance=K.kE(this.distanceVector),this.dollyingStep=this.distance/100},t.prototype._getOrthoMatrix=function(){if(this.projectionMode===L.ORTHOGRAPHIC){var t=this.position,e=Q.yY(Q.Ue(),[0,0,1],-this.roll*Math.PI/180);q.fromRotationTranslationScaleOrigin(this.orthoMatrix,e,K.al((this.rright-this.left)/2-t[0],(this.top-this.bottom)/2-t[1],0),K.al(this.zoom,this.zoom,1),t)}},t.prototype.triggerUpdate=function(){if(this.enableUpdate){var t=this.getViewTransform(),e=q.multiply(q.create(),this.getPerspective(),t);this.getFrustum().extractFromVPMatrix(e),this.eventEmitter.emit(t$.UPDATED)}},t.prototype.rotate=function(t,e,n){throw Error(tq)},t.prototype.pan=function(t,e){throw Error(tq)},t.prototype.dolly=function(t){throw Error(tq)},t.prototype.createLandmark=function(t,e){throw Error(tq)},t.prototype.gotoLandmark=function(t,e){throw Error(tq)},t.prototype.cancelLandmarkAnimation=function(){throw Error(tq)},t}();function t0(t,e){if("function"!=typeof t||null!=e&&"function"!=typeof e)throw TypeError("Expected a function");var n=function(){for(var r=[],i=0;i=I.kEms&&t=B.kUnitType&&this.getType()<=B.kClampType},t}(),t8=function(t){function 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p=n.clonePointerEvent(e,"pointerenter");for(p.eventPhase=p.AT_TARGET;p.target&&p.target!==o&&p.target!==(rT.isNode(n.rootTarget)&&n.rootTarget.parentNode);)p.currentTarget=p.target,n.notifyTarget(p),r&&n.notifyTarget(p,"mouseenter"),rT.isNode(p.target)&&(p.target=p.target.parentNode);n.freeEvent(p)}n.freeEvent(u)}n.dispatchEvent(e,"pointermove"),"touch"===e.pointerType&&n.dispatchEvent(e,"touchmove"),r&&(n.dispatchEvent(e,"mousemove"),n.cursor=n.getCursor(e.target)),i.overTargets=e.composedPath(),n.freeEvent(e)},this.onPointerOut=function(t){var e=n.trackingData(t.pointerId);if(e.overTargets){var r="mouse"===t.pointerType||"pen"===t.pointerType,i=n.findMountedTarget(e.overTargets),o=n.createPointerEvent(t,"pointerout",i||void 0);n.dispatchEvent(o),r&&n.dispatchEvent(o,"mouseout");var a=n.createPointerEvent(t,"pointerleave",i||void 0);for(a.eventPhase=a.AT_TARGET;a.target&&a.target!==(rT.isNode(n.rootTarget)&&n.rootTarget.parentNode);)a.currentTarget=a.target,n.notifyTarget(a),r&&n.notifyTarget(a,"mouseleave"),rT.isNode(a.target)&&(a.target=a.target.parentNode);e.overTargets=null,n.freeEvent(o),n.freeEvent(a)}n.cursor=null},this.onPointerOver=function(t){var e=n.trackingData(t.pointerId),r=n.createPointerEvent(t),i="mouse"===r.pointerType||"pen"===r.pointerType;n.dispatchEvent(r,"pointerover"),i&&n.dispatchEvent(r,"mouseover"),"mouse"===r.pointerType&&(n.cursor=n.getCursor(r.target));var o=n.clonePointerEvent(r,"pointerenter");for(o.eventPhase=o.AT_TARGET;o.target&&o.target!==(rT.isNode(n.rootTarget)&&n.rootTarget.parentNode);)o.currentTarget=o.target,n.notifyTarget(o),i&&n.notifyTarget(o,"mouseenter"),rT.isNode(o.target)&&(o.target=o.target.parentNode);e.overTargets=r.composedPath(),n.freeEvent(r),n.freeEvent(o)},this.onPointerUpOutside=function(t){var e=n.trackingData(t.pointerId),r=n.findMountedTarget(e.pressTargetsByButton[t.button]),i=n.createPointerEvent(t);if(r){for(var o=r;o;)i.currentTarget=o,n.notifyTarget(i,"pointerupoutside"),"touch"===i.pointerType||("mouse"===i.pointerType||"pen"===i.pointerType)&&n.notifyTarget(i,2===i.button?"rightupoutside":"mouseupoutside"),rT.isNode(o)&&(o=o.parentNode);delete e.pressTargetsByButton[t.button]}n.freeEvent(i)},this.onWheel=function(t){var e=n.createWheelEvent(t);n.dispatchEvent(e),n.freeEvent(e)},this.onClick=function(t){if(n.context.config.useNativeClickEvent){var e=n.createPointerEvent(t);n.dispatchEvent(e),n.freeEvent(e)}},this.onPointerCancel=function(t){var e=n.createPointerEvent(t,void 0,void 0,n.context.config.alwaysTriggerPointerEventOnCanvas?n.rootTarget:void 0);n.dispatchEvent(e),n.freeEvent(e)}}return t.prototype.init=function(){this.rootTarget=this.context.renderingContext.root.parentNode,this.addEventMapping("pointerdown",this.onPointerDown),this.addEventMapping("pointerup",this.onPointerUp),this.addEventMapping("pointermove",this.onPointerMove),this.addEventMapping("pointerout",this.onPointerOut),this.addEventMapping("pointerleave",this.onPointerOut),this.addEventMapping("pointercancel",this.onPointerCancel),this.addEventMapping("pointerover",this.onPointerOver),this.addEventMapping("pointerupoutside",this.onPointerUpOutside),this.addEventMapping("wheel",this.onWheel),this.addEventMapping("click",this.onClick)},t.prototype.destroy=function(){this.emitter.removeAllListeners(),this.mappingTable={},this.mappingState={},this.eventPool.clear()},t.prototype.client2Viewport=function(t){var e=this.context.contextService.getBoundingClientRect();return new tz(t.x-((null==e?void 0:e.left)||0),t.y-((null==e?void 0:e.top)||0))},t.prototype.viewport2Client=function(t){var e=this.context.contextService.getBoundingClientRect();return new tz(t.x+((null==e?void 0:e.left)||0),t.y+((null==e?void 0:e.top)||0))},t.prototype.viewport2Canvas=function(t){var e=t.x,n=t.y,r=this.rootTarget.defaultView.getCamera(),i=this.context.config,o=i.width,a=i.height,s=r.getPerspectiveInverse(),l=r.getWorldTransform(),c=q.multiply(this.tmpMatrix,l,s),u=K.t8(this.tmpVec3,e/o*2-1,(1-n/a)*2-1,0);return K.fF(u,u,c),new tz(u[0],u[1])},t.prototype.canvas2Viewport=function(t){var e=this.rootTarget.defaultView.getCamera(),n=e.getPerspective(),r=e.getViewTransform(),i=q.multiply(this.tmpMatrix,n,r),o=K.t8(this.tmpVec3,t.x,t.y,0);K.fF(this.tmpVec3,this.tmpVec3,i);var a=this.context.config,s=a.width,l=a.height;return new tz((o[0]+1)/2*s,(1-(o[1]+1)/2)*l)},t.prototype.setPickHandler=function(t){this.pickHandler=t},t.prototype.addEventMapping=function(t,e){this.mappingTable[t]||(this.mappingTable[t]=[]),this.mappingTable[t].push({fn:e,priority:0}),this.mappingTable[t].sort(function(t,e){return t.priority-e.priority})},t.prototype.mapEvent=function(t){if(this.rootTarget){var e=this.mappingTable[t.type];if(e)for(var n=0,r=e.length;n=1;r--)if(t.currentTarget=n[r],this.notifyTarget(t,e),t.propagationStopped||t.propagationImmediatelyStopped)return;if(t.eventPhase=t.AT_TARGET,t.currentTarget=t.target,this.notifyTarget(t,e),!t.propagationStopped&&!t.propagationImmediatelyStopped){var i=n.indexOf(t.currentTarget);t.eventPhase=t.BUBBLING_PHASE;for(var r=i+1;ri||n>o?null:!a&&this.pickHandler(t)||this.rootTarget||null},t.prototype.isNativeEventFromCanvas=function(t){var e,n=this.context.contextService.getDomElement(),r=null===(e=t.nativeEvent)||void 0===e?void 0:e.target;if(r){if(r===n)return!0;if(n&&n.contains)return n.contains(r)}return!!t.nativeEvent.composedPath&&t.nativeEvent.composedPath().indexOf(n)>-1},t.prototype.getExistedHTML=function(t){var e,n;if(t.nativeEvent.composedPath)try{for(var r=(0,W.XA)(t.nativeEvent.composedPath()),i=r.next();!i.done;i=r.next()){var o=i.value,a=this.globalRuntime.nativeHTMLMap.get(o);if(a)return a}}catch(t){e={error:t}}finally{try{i&&!i.done&&(n=r.return)&&n.call(r)}finally{if(e)throw e.error}}return null},t.prototype.pickTarget=function(t){return this.hitTest({clientX:t.clientX,clientY:t.clientY,viewportX:t.viewportX,viewportY:t.viewportY,x:t.canvasX,y:t.canvasY})},t.prototype.createPointerEvent=function(t,e,n,r){var i=this.allocateEvent(rm);this.copyPointerData(t,i),this.copyMouseData(t,i),this.copyData(t,i),i.nativeEvent=t.nativeEvent,i.originalEvent=t;var o=this.getExistedHTML(i);return i.target=null!=n?n:o||this.isNativeEventFromCanvas(i)&&this.pickTarget(i)||r,"string"==typeof e&&(i.type=e),i},t.prototype.createWheelEvent=function(t){var e=this.allocateEvent(rE);this.copyWheelData(t,e),this.copyMouseData(t,e),this.copyData(t,e),e.nativeEvent=t.nativeEvent,e.originalEvent=t;var n=this.getExistedHTML(e);return e.target=n||this.isNativeEventFromCanvas(e)&&this.pickTarget(e),e},t.prototype.trackingData=function(t){return this.mappingState.trackingData[t]||(this.mappingState.trackingData[t]={pressTargetsByButton:{},clicksByButton:{},overTarget:null}),this.mappingState.trackingData[t]},t.prototype.cloneWheelEvent=function(t){var e=this.allocateEvent(rE);return e.nativeEvent=t.nativeEvent,e.originalEvent=t.originalEvent,this.copyWheelData(t,e),this.copyMouseData(t,e),this.copyData(t,e),e.target=t.target,e.path=t.composedPath().slice(),e.type=t.type,e},t.prototype.clonePointerEvent=function(t,e){var n=this.allocateEvent(rm);return n.nativeEvent=t.nativeEvent,n.originalEvent=t.originalEvent,this.copyPointerData(t,n),this.copyMouseData(t,n),this.copyData(t,n),n.target=t.target,n.path=t.composedPath().slice(),n.type=null!=e?e:n.type,n},t.prototype.copyPointerData=function(t,e){e.pointerId=t.pointerId,e.width=t.width,e.height=t.height,e.isPrimary=t.isPrimary,e.pointerType=t.pointerType,e.pressure=t.pressure,e.tangentialPressure=t.tangentialPressure,e.tiltX=t.tiltX,e.tiltY=t.tiltY,e.twist=t.twist},t.prototype.copyMouseData=function(t,e){e.altKey=t.altKey,e.button=t.button,e.buttons=t.buttons,e.ctrlKey=t.ctrlKey,e.metaKey=t.metaKey,e.shiftKey=t.shiftKey,e.client.copyFrom(t.client),e.movement.copyFrom(t.movement),e.canvas.copyFrom(t.canvas),e.screen.copyFrom(t.screen),e.global.copyFrom(t.global),e.offset.copyFrom(t.offset)},t.prototype.copyWheelData=function(t,e){e.deltaMode=t.deltaMode,e.deltaX=t.deltaX,e.deltaY=t.deltaY,e.deltaZ=t.deltaZ},t.prototype.copyData=function(t,e){e.isTrusted=t.isTrusted,e.timeStamp=nQ.now(),e.type=t.type,e.detail=t.detail,e.view=t.view,e.page.copyFrom(t.page),e.viewport.copyFrom(t.viewport)},t.prototype.allocateEvent=function(t){this.eventPool.has(t)||this.eventPool.set(t,[]);var e=this.eventPool.get(t).pop()||new t(this);return e.eventPhase=e.NONE,e.currentTarget=null,e.path=[],e.target=null,e},t.prototype.freeEvent=function(t){if(t.manager!==this)throw Error("It is illegal to free an event not managed by this EventBoundary!");var e=t.constructor;this.eventPool.has(e)||this.eventPool.set(e,[]),this.eventPool.get(e).push(t)},t.prototype.notifyTarget=function(t,e){e=null!=e?e:t.type;var n=t.eventPhase===t.CAPTURING_PHASE||t.eventPhase===t.AT_TARGET?"".concat(e,"capture"):e;this.notifyListeners(t,n),t.eventPhase===t.AT_TARGET&&this.notifyListeners(t,e)},t.prototype.notifyListeners=function(t,e){var n=t.currentTarget.emitter,r=n._events[e];if(r){if("fn"in r)r.once&&n.removeListener(e,r.fn,void 0,!0),r.fn.call(t.currentTarget||r.context,t);else for(var i=0;i=0;n--){var r=t[n];if(r===this.rootTarget||rT.isNode(r)&&r.parentNode===e)e=t[n];else break}return e},t.prototype.getCursor=function(t){for(var e=t;e;){var n=!!e.getAttribute&&e.getAttribute("cursor");if(n)return n;e=rT.isNode(e)&&e.parentNode}},t}(),rS=function(){function t(){}return t.prototype.getOrCreateCanvas=function(t,e){if(this.canvas)return this.canvas;if(t||r_.offscreenCanvas)this.canvas=t||r_.offscreenCanvas,this.context=this.canvas.getContext("2d",e);else try{this.canvas=new window.OffscreenCanvas(0,0),this.context=this.canvas.getContext("2d",e),this.context&&this.context.measureText||(this.canvas=document.createElement("canvas"),this.context=this.canvas.getContext("2d"))}catch(t){this.canvas=document.createElement("canvas"),this.context=this.canvas.getContext("2d",e)}return this.canvas.width=10,this.canvas.height=10,this.canvas},t.prototype.getOrCreateContext=function(t,e){return this.context||this.getOrCreateCanvas(t,e),this.context},t}();(E=X||(X={}))[E.CAMERA_CHANGED=0]="CAMERA_CHANGED",E[E.DISPLAY_OBJECT_CHANGED=1]="DISPLAY_OBJECT_CHANGED",E[E.NONE=2]="NONE";var rN=function(){function t(t,e){this.globalRuntime=t,this.context=e,this.inited=!1,this.stats={total:0,rendered:0},this.zIndexCounter=0,this.hooks={init:new rr,initAsync:new re,dirtycheck:new ri,cull:new ri,beginFrame:new rr,beforeRender:new rr,render:new rr,afterRender:new rr,endFrame:new rr,destroy:new rr,pick:new rn,pickSync:new ri,pointerDown:new rr,pointerUp:new rr,pointerMove:new rr,pointerOut:new rr,pointerOver:new rr,pointerWheel:new rr,pointerCancel:new rr,click:new rr}}return t.prototype.init=function(t){var e=this,n=(0,W.pi)((0,W.pi)({},this.globalRuntime),this.context);this.context.renderingPlugins.forEach(function(t){t.apply(n,e.globalRuntime)}),this.hooks.init.call(),0===this.hooks.initAsync.getCallbacksNum()?(this.inited=!0,t()):this.hooks.initAsync.promise().then(function(){e.inited=!0,t()})},t.prototype.getStats=function(){return this.stats},t.prototype.disableDirtyRectangleRendering=function(){return!this.context.config.renderer.getConfig().enableDirtyRectangleRendering||this.context.renderingContext.renderReasons.has(X.CAMERA_CHANGED)},t.prototype.render=function(t,e){var n=this;this.stats.total=0,this.stats.rendered=0,this.zIndexCounter=0;var r=this.context.renderingContext;if(this.globalRuntime.sceneGraphService.syncHierarchy(r.root),this.globalRuntime.sceneGraphService.triggerPendingEvents(),r.renderReasons.size&&this.inited){r.dirtyRectangleRenderingDisabled=this.disableDirtyRectangleRendering();var i=1===r.renderReasons.size&&r.renderReasons.has(X.CAMERA_CHANGED),o=!t.disableRenderHooks||!(t.disableRenderHooks&&i);o&&this.renderDisplayObject(r.root,t,r),this.hooks.beginFrame.call(),o&&r.renderListCurrentFrame.forEach(function(t){n.hooks.beforeRender.call(t),n.hooks.render.call(t),n.hooks.afterRender.call(t)}),this.hooks.endFrame.call(),r.renderListCurrentFrame=[],r.renderReasons.clear(),e()}},t.prototype.renderDisplayObject=function(t,e,n){var r=this,i=e.renderer.getConfig(),o=i.enableDirtyCheck,a=i.enableCulling;this.globalRuntime.enableCSSParsing&&this.globalRuntime.styleValueRegistry.recalc(t);var s=t.renderable,l=o?s.dirty||n.dirtyRectangleRenderingDisabled?t:null:t;if(l){var c=a?this.hooks.cull.call(l,this.context.camera):l;c&&(this.stats.rendered++,n.renderListCurrentFrame.push(c))}t.renderable.dirty=!1,t.sortable.renderOrder=this.zIndexCounter++,this.stats.total++;var u=t.sortable;u.dirty&&(this.sort(t,u),u.dirty=!1,u.dirtyChildren=[],u.dirtyReason=void 0),(u.sorted||t.childNodes).forEach(function(t){r.renderDisplayObject(t,e,n)})},t.prototype.sort=function(t,e){e.sorted&&e.dirtyReason!==V.Z_INDEX_CHANGED?e.dirtyChildren.forEach(function(n){if(-1===t.childNodes.indexOf(n)){var r=e.sorted.indexOf(n);r>=0&&e.sorted.splice(r,1)}else if(0===e.sorted.length)e.sorted.push(n);else{var i=function(t,e){for(var n=0,r=t.length;n>>1;0>nz(t[i],e)?n=i+1:r=i}return n}(e.sorted,n);e.sorted.splice(i,0,n)}}):e.sorted=t.childNodes.slice().sort(nz)},t.prototype.destroy=function(){this.inited=!1,this.hooks.destroy.call(),this.globalRuntime.sceneGraphService.clearPendingEvents()},t.prototype.dirtify=function(){this.context.renderingContext.renderReasons.add(X.DISPLAY_OBJECT_CHANGED)},t}(),rC=/\[\s*(.*)=(.*)\s*\]/,rw=function(){function t(){}return t.prototype.selectOne=function(t,e){var n=this;if(t.startsWith("."))return e.find(function(e){return((null==e?void 0:e.classList)||[]).indexOf(n.getIdOrClassname(t))>-1});if(t.startsWith("#"))return e.find(function(e){return e.id===n.getIdOrClassname(t)});if(!t.startsWith("["))return e.find(function(n){return e!==n&&n.nodeName===t});var r=this.getAttribute(t),i=r.name,o=r.value;return i?e.find(function(t){return e!==t&&("name"===i?t.name===o:n.attributeToString(t,i)===o)}):null},t.prototype.selectAll=function(t,e){var n=this;if(t.startsWith("."))return e.findAll(function(r){return e!==r&&((null==r?void 0:r.classList)||[]).indexOf(n.getIdOrClassname(t))>-1});if(t.startsWith("#"))return e.findAll(function(r){return e!==r&&r.id===n.getIdOrClassname(t)});if(!t.startsWith("["))return e.findAll(function(n){return e!==n&&n.nodeName===t});var r=this.getAttribute(t),i=r.name,o=r.value;return i?e.findAll(function(t){return e!==t&&("name"===i?t.name===o:n.attributeToString(t,i)===o)}):[]},t.prototype.is=function(t,e){if(t.startsWith("."))return e.className===this.getIdOrClassname(t);if(t.startsWith("#"))return e.id===this.getIdOrClassname(t);if(!t.startsWith("["))return e.nodeName===t;var n=this.getAttribute(t),r=n.name,i=n.value;return"name"===r?e.name===i:this.attributeToString(e,r)===i},t.prototype.getIdOrClassname=function(t){return t.substring(1)},t.prototype.getAttribute=function(t){var e=t.match(rC),n="",r="";return e&&e.length>2&&(n=e[1].replace(/"/g,""),r=e[2].replace(/"/g,"")),{name:n,value:r}},t.prototype.attributeToString=function(t,e){if(!t.getAttribute)return"";var n=t.getAttribute(e);return(0,tr.Z)(n)?"":n.toString?n.toString():""},t}(),rM=function(t){function e(e,n,r,i,o,a,s,l){var c=t.call(this,null)||this;return c.relatedNode=n,c.prevValue=r,c.newValue=i,c.attrName=o,c.attrChange=a,c.prevParsedValue=s,c.newParsedValue=l,c.type=e,c}return(0,W.ZT)(e,t),e.ADDITION=2,e.MODIFICATION=1,e.REMOVAL=3,e}(ry);function rk(t){var e=t.renderable;e&&(e.renderBoundsDirty=!0,e.boundsDirty=!0)}(x=H||(H={})).REPARENT="reparent",x.DESTROY="destroy",x.ATTR_MODIFIED="DOMAttrModified",x.INSERTED="DOMNodeInserted",x.REMOVED="removed",x.MOUNTED="DOMNodeInsertedIntoDocument",x.UNMOUNTED="DOMNodeRemovedFromDocument",x.BOUNDS_CHANGED="bounds-changed",x.CULLED="culled";var rR=new rM(H.REPARENT,null,"","","",0,"",""),rA=function(){function t(t){var e,n,r,i,o,a,s,l,c,u,h,p,f=this;this.runtime=t,this.pendingEvents=[],this.boundsChangedEvent=new rx(H.BOUNDS_CHANGED),this.rotate=(e=Q.Ue(),function(t,n,r,i){void 0===r&&(r=0),void 0===i&&(i=0),"number"==typeof n&&(n=K.al(n,r,i));var o=t.transformable;if(null!==t.parentNode&&t.parentNode.transformable){var a=Q.Ue();Q.Su(a,n[0],n[1],n[2]);var s=f.getRotation(t),l=f.getRotation(t.parentNode);Q.JG(e,l),Q.U_(e,e),Q.Jp(a,e,a),Q.Jp(o.localRotation,a,s),Q.Fv(o.localRotation,o.localRotation),f.dirtifyLocal(t,o)}else f.rotateLocal(t,n)}),this.rotateLocal=(n=Q.Ue(),function(t,e,r,i){void 0===r&&(r=0),void 0===i&&(i=0),"number"==typeof e&&(e=K.al(e,r,i));var o=t.transformable;Q.Su(n,e[0],e[1],e[2]),Q.dC(o.localRotation,o.localRotation,n),f.dirtifyLocal(t,o)}),this.setEulerAngles=(r=Q.Ue(),function(t,e,n,i){void 0===n&&(n=0),void 0===i&&(i=0),"number"==typeof e&&(e=K.al(e,n,i));var o=t.transformable;if(null!==t.parentNode&&t.parentNode.transformable){Q.Su(o.localRotation,e[0],e[1],e[2]);var a=f.getRotation(t.parentNode);Q.JG(r,Q.U_(Q.Ue(),a)),Q.dC(o.localRotation,o.localRotation,r),f.dirtifyLocal(t,o)}else f.setLocalEulerAngles(t,e)}),this.translateLocal=function(t,e,n,r){void 0===n&&(n=0),void 0===r&&(r=0),"number"==typeof e&&(e=K.al(e,n,r));var i=t.transformable;K.fS(e,K.Ue())||(K.VC(e,e,i.localRotation),K.IH(i.localPosition,i.localPosition,e),f.dirtifyLocal(t,i))},this.setPosition=(i=q.create(),o=K.Ue(),function(t,e){var n=t.transformable;if(o[0]=e[0],o[1]=e[1],o[2]=e[2]||0,!K.fS(f.getPosition(t),o)){if(K.JG(n.position,o),null!==t.parentNode&&t.parentNode.transformable){var r=t.parentNode.transformable;q.copy(i,r.worldTransform),q.invert(i,i),K.fF(n.localPosition,o,i)}else K.JG(n.localPosition,o);f.dirtifyLocal(t,n)}}),this.setLocalPosition=(a=K.Ue(),function(t,e){var n=t.transformable;a[0]=e[0],a[1]=e[1],a[2]=e[2]||0,K.fS(n.localPosition,a)||(K.JG(n.localPosition,a),f.dirtifyLocal(t,n))}),this.translate=(s=K.Ue(),l=K.Ue(),c=K.Ue(),function(t,e,n,r){void 0===n&&(n=0),void 0===r&&(r=0),"number"==typeof e&&(e=K.t8(l,e,n,r)),K.fS(e,s)||(K.IH(c,f.getPosition(t),e),f.setPosition(t,c))}),this.setRotation=function(){var t=Q.Ue();return function(e,n,r,i,o){var a=e.transformable;if("number"==typeof n&&(n=Q.al(n,r,i,o)),null!==e.parentNode&&e.parentNode.transformable){var s=f.getRotation(e.parentNode);Q.JG(t,s),Q.U_(t,t),Q.Jp(a.localRotation,t,n),Q.Fv(a.localRotation,a.localRotation),f.dirtifyLocal(e,a)}else f.setLocalRotation(e,n)}},this.displayObjectDependencyMap=new WeakMap,this.calcLocalTransform=(u=q.create(),h=K.Ue(),p=Q.al(0,0,0,1),function(t){if(0!==t.localSkew[0]||0!==t.localSkew[1]){if(q.fromRotationTranslationScaleOrigin(t.localTransform,t.localRotation,t.localPosition,K.al(1,1,1),t.origin),0!==t.localSkew[0]||0!==t.localSkew[1]){var e=q.identity(u);e[4]=Math.tan(t.localSkew[0]),e[1]=Math.tan(t.localSkew[1]),q.multiply(t.localTransform,t.localTransform,e)}var n=q.fromRotationTranslationScaleOrigin(u,p,h,t.localScale,t.origin);q.multiply(t.localTransform,t.localTransform,n)}else q.fromRotationTranslationScaleOrigin(t.localTransform,t.localRotation,t.localPosition,t.localScale,t.origin)})}return t.prototype.matches=function(t,e){return this.runtime.sceneGraphSelector.is(t,e)},t.prototype.querySelector=function(t,e){return this.runtime.sceneGraphSelector.selectOne(t,e)},t.prototype.querySelectorAll=function(t,e){return this.runtime.sceneGraphSelector.selectAll(t,e)},t.prototype.attach=function(t,e,n){var r,i,o=!1;t.parentNode&&(o=t.parentNode!==e,this.detach(t)),t.parentNode=e,(0,tr.Z)(n)?t.parentNode.childNodes.push(t):t.parentNode.childNodes.splice(n,0,t);var a=e.sortable;((null===(r=null==a?void 0:a.sorted)||void 0===r?void 0:r.length)||(null===(i=t.style)||void 0===i?void 0:i.zIndex))&&(-1===a.dirtyChildren.indexOf(t)&&a.dirtyChildren.push(t),a.dirty=!0,a.dirtyReason=V.ADDED);var s=t.transformable;s&&this.dirtifyWorld(t,s),s.frozen&&this.unfreezeParentToRoot(t),o&&t.dispatchEvent(rR)},t.prototype.detach=function(t){var e,n;if(t.parentNode){var r=t.transformable,i=t.parentNode.sortable;((null===(e=null==i?void 0:i.sorted)||void 0===e?void 0:e.length)||(null===(n=t.style)||void 0===n?void 0:n.zIndex))&&(-1===i.dirtyChildren.indexOf(t)&&i.dirtyChildren.push(t),i.dirty=!0,i.dirtyReason=V.REMOVED);var o=t.parentNode.childNodes.indexOf(t);o>-1&&t.parentNode.childNodes.splice(o,1),r&&this.dirtifyWorld(t,r),t.parentNode=null}},t.prototype.getOrigin=function(t){return t.transformable.origin},t.prototype.setOrigin=function(t,e,n,r){void 0===n&&(n=0),void 0===r&&(r=0),"number"==typeof e&&(e=[e,n,r]);var i=t.transformable;if(e[0]!==i.origin[0]||e[1]!==i.origin[1]||e[2]!==i.origin[2]){var o=i.origin;o[0]=e[0],o[1]=e[1],o[2]=e[2]||0,this.dirtifyLocal(t,i)}},t.prototype.setLocalEulerAngles=function(t,e,n,r){void 0===n&&(n=0),void 0===r&&(r=0),"number"==typeof e&&(e=K.al(e,n,r));var i=t.transformable;Q.Su(i.localRotation,e[0],e[1],e[2]),this.dirtifyLocal(t,i)},t.prototype.scaleLocal=function(t,e){var n=t.transformable;K.Jp(n.localScale,n.localScale,K.al(e[0],e[1],e[2]||1)),this.dirtifyLocal(t,n)},t.prototype.setLocalScale=function(t,e){var n=t.transformable,r=K.al(e[0],e[1],e[2]||n.localScale[2]);K.fS(r,n.localScale)||(K.JG(n.localScale,r),this.dirtifyLocal(t,n))},t.prototype.setLocalRotation=function(t,e,n,r,i){"number"==typeof e&&(e=Q.al(e,n,r,i));var o=t.transformable;Q.JG(o.localRotation,e),this.dirtifyLocal(t,o)},t.prototype.setLocalSkew=function(t,e,n){"number"==typeof e&&(e=tt.al(e,n));var r=t.transformable;tt.JG(r.localSkew,e),this.dirtifyLocal(t,r)},t.prototype.dirtifyLocal=function(t,e){e.localDirtyFlag||(e.localDirtyFlag=!0,e.dirtyFlag||this.dirtifyWorld(t,e))},t.prototype.dirtifyWorld=function(t,e){e.dirtyFlag||this.unfreezeParentToRoot(t),this.dirtifyWorldInternal(t,e),this.dirtifyToRoot(t,!0)},t.prototype.triggerPendingEvents=function(){var t=this,e=new Set,n=function(n,r){n.isConnected&&!e.has(n.entity)&&(t.boundsChangedEvent.detail=r,t.boundsChangedEvent.target=n,n.isMutationObserved?n.dispatchEvent(t.boundsChangedEvent):n.ownerDocument.defaultView.dispatchEvent(t.boundsChangedEvent,!0),e.add(n.entity))};this.pendingEvents.forEach(function(t){var e=(0,W.CR)(t,2),r=e[0],i=e[1];i.affectChildren?r.forEach(function(t){n(t,i)}):n(r,i)}),this.clearPendingEvents(),e.clear()},t.prototype.clearPendingEvents=function(){this.pendingEvents=[]},t.prototype.dirtifyToRoot=function(t,e){void 0===e&&(e=!1);var n=t;for(n.renderable&&(n.renderable.dirty=!0);n;)rk(n),n=n.parentNode;e&&t.forEach(function(t){rk(t)}),this.informDependentDisplayObjects(t),this.pendingEvents.push([t,{affectChildren:e}])},t.prototype.updateDisplayObjectDependency=function(t,e,n,r){if(e&&e!==n){var i=this.displayObjectDependencyMap.get(e);if(i&&i[t]){var o=i[t].indexOf(r);i[t].splice(o,1)}}if(n){var a=this.displayObjectDependencyMap.get(n);a||(this.displayObjectDependencyMap.set(n,{}),a=this.displayObjectDependencyMap.get(n)),a[t]||(a[t]=[]),a[t].push(r)}},t.prototype.informDependentDisplayObjects=function(t){var e=this,n=this.displayObjectDependencyMap.get(t);n&&Object.keys(n).forEach(function(t){n[t].forEach(function(n){e.dirtifyToRoot(n,!0),n.dispatchEvent(new rM(H.ATTR_MODIFIED,n,e,e,t,rM.MODIFICATION,e,e)),n.isCustomElement&&n.isConnected&&n.attributeChangedCallback&&n.attributeChangedCallback(t,e,e)})})},t.prototype.getPosition=function(t){var e=t.transformable;return q.getTranslation(e.position,this.getWorldTransform(t,e))},t.prototype.getRotation=function(t){var e=t.transformable;return q.getRotation(e.rotation,this.getWorldTransform(t,e))},t.prototype.getScale=function(t){var e=t.transformable;return q.getScaling(e.scaling,this.getWorldTransform(t,e))},t.prototype.getWorldTransform=function(t,e){return void 0===e&&(e=t.transformable),(e.localDirtyFlag||e.dirtyFlag)&&(t.parentNode&&t.parentNode.transformable&&this.getWorldTransform(t.parentNode),this.sync(t,e)),e.worldTransform},t.prototype.getLocalPosition=function(t){return t.transformable.localPosition},t.prototype.getLocalRotation=function(t){return t.transformable.localRotation},t.prototype.getLocalScale=function(t){return t.transformable.localScale},t.prototype.getLocalSkew=function(t){return t.transformable.localSkew},t.prototype.getLocalTransform=function(t){var e=t.transformable;return e.localDirtyFlag&&(this.calcLocalTransform(e),e.localDirtyFlag=!1),e.localTransform},t.prototype.setLocalTransform=function(t,e){var n=q.getTranslation(K.Ue(),e),r=q.getRotation(Q.Ue(),e),i=q.getScaling(K.Ue(),e);this.setLocalScale(t,i),this.setLocalPosition(t,n),this.setLocalRotation(t,r)},t.prototype.resetLocalTransform=function(t){this.setLocalScale(t,[1,1,1]),this.setLocalPosition(t,[0,0,0]),this.setLocalEulerAngles(t,[0,0,0]),this.setLocalSkew(t,[0,0])},t.prototype.getTransformedGeometryBounds=function(t,e,n){void 0===e&&(e=!1);var r=this.getGeometryBounds(t,e);if(tH.isEmpty(r))return null;var i=n||new tH;return i.setFromTransformedAABB(r,this.getWorldTransform(t)),i},t.prototype.getGeometryBounds=function(t,e){void 0===e&&(e=!1);var n=t.geometry;return(e?n.renderBounds:n.contentBounds||null)||new tH},t.prototype.getBounds=function(t,e){var n=this;void 0===e&&(e=!1);var r=t.renderable;if(!r.boundsDirty&&!e&&r.bounds)return r.bounds;if(!r.renderBoundsDirty&&e&&r.renderBounds)return r.renderBounds;var i=e?r.renderBounds:r.bounds,o=this.getTransformedGeometryBounds(t,e,i);if(t.childNodes.forEach(function(t){var 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e,n,r,i,o,a,s=this.parsedStyle,l=s.markerStart,c=s.markerEnd,u=s.markerStartOffset,h=s.markerEndOffset,p=s.points,f=s.defX,d=s.defY,v=(p||{}).points,y=t?l:c;if(y&&rH(y)&&v){var g=0;if(r=v[0][0]-f,i=v[0][1]-d,t)e=v[1][0]-v[0][0],n=v[1][1]-v[0][1],o=u||0,a=this.markerStartAngle;else{var m=v.length;this.parsedStyle.isClosed?(e=v[m-1][0]-v[0][0],n=v[m-1][1]-v[0][1]):(r=v[m-1][0]-f,i=v[m-1][1]-d,e=v[m-2][0]-v[m-1][0],n=v[m-2][1]-v[m-1][1]),o=h||0,a=this.markerEndAngle}g=Math.atan2(n,e),y.setLocalEulerAngles(180*g/Math.PI+a),y.setLocalPosition(r+Math.cos(g)*o,i+Math.sin(g)*o)}},e.prototype.placeMarkerMid=function(t){var e=this.parsedStyle,n=e.points,r=e.defX,i=e.defY,o=(n||{}).points;if(this.markerMidList.forEach(function(t){t.remove()}),this.markerMidList=[],t&&rH(t)&&o)for(var a=1;a<(this.parsedStyle.isClosed?o.length:o.length-1);a++){var s=o[a][0]-r,l=o[a][1]-i,c=1===a?t:t.cloneNode(!0);this.markerMidList.push(c),this.appendChild(c),c.setLocalPosition(s,l)}},e}(r$),r7=function(t){function 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h&&h[0]||this.documentElement},e.prototype.elementFromPoint=function(t,e){return(0,W.mG)(this,void 0,void 0,function(){var n,r,i,o,a,s,l,c,u,h;return(0,W.Jh)(this,function(p){switch(p.label){case 0:if(r=(n=this.defaultView.canvas2Viewport({x:t,y:e})).x,i=n.y,a=(o=this.defaultView.getConfig()).width,s=o.height,r<0||i<0||r>a||i>s)return[2,null];return c=(l=this.defaultView.viewport2Client({x:r,y:i})).x,u=l.y,[4,this.defaultView.getRenderingService().hooks.pick.promise({topmost:!0,position:{x:t,y:e,viewportX:r,viewportY:i,clientX:c,clientY:u},picked:[]})];case 1:return[2,(h=p.sent().picked)&&h[0]||this.documentElement]}})})},e.prototype.elementsFromPointSync=function(t,e){var n=this.defaultView.canvas2Viewport({x:t,y:e}),r=n.x,i=n.y,o=this.defaultView.getConfig(),a=o.width,s=o.height;if(r<0||i<0||r>a||i>s)return[];var 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Error(tJ)},e.prototype.insertBefore=function(t,e){throw Error(tJ)},e.prototype.removeChild=function(t,e){throw Error(tJ)},e.prototype.replaceChild=function(t,e,n){throw Error(tJ)},e.prototype.append=function(){throw Error(tJ)},e.prototype.prepend=function(){throw Error(tJ)},e.prototype.getElementById=function(t){return this.documentElement.getElementById(t)},e.prototype.getElementsByName=function(t){return this.documentElement.getElementsByName(t)},e.prototype.getElementsByTagName=function(t){return this.documentElement.getElementsByTagName(t)},e.prototype.getElementsByClassName=function(t){return this.documentElement.getElementsByClassName(t)},e.prototype.querySelector=function(t){return this.documentElement.querySelector(t)},e.prototype.querySelectorAll=function(t){return this.documentElement.querySelectorAll(t)},e.prototype.find=function(t){return this.documentElement.find(t)},e.prototype.findAll=function(t){return this.documentElement.findAll(t)},e}(rT),io=function(){function t(t){this.strategies=t}return t.prototype.apply=function(e){var n=e.camera,r=e.renderingService,i=e.renderingContext,o=this.strategies;r.hooks.cull.tap(t.tag,function(t){if(t){var e=t.cullable;return(0===o.length?e.visible=i.unculledEntities.indexOf(t.entity)>-1:e.visible=o.every(function(e){return e.isVisible(n,t)}),!t.isCulled()&&t.isVisible())?t:(t.dispatchEvent(new rx(H.CULLED)),null)}return t}),r.hooks.afterRender.tap(t.tag,function(t){t.cullable.visibilityPlaneMask=-1})},t.tag="Culling",t}(),ia=function(){function t(){var t=this;this.autoPreventDefault=!1,this.rootPointerEvent=new rm(null),this.rootWheelEvent=new rE(null),this.onPointerMove=function(e){var n,r,i,o,a=null===(o=null===(i=t.context.renderingContext.root)||void 0===i?void 0:i.ownerDocument)||void 0===o?void 0:o.defaultView;if(!a.supportsTouchEvents||"touch"!==e.pointerType){var s=t.normalizeToPointerEvent(e,a);try{for(var l=(0,W.XA)(s),c=l.next();!c.done;c=l.next()){var u=c.value,h=t.bootstrapEvent(t.rootPointerEvent,u,a,e);t.context.eventService.mapEvent(h)}}catch(t){n={error:t}}finally{try{c&&!c.done&&(r=l.return)&&r.call(l)}finally{if(n)throw n.error}}t.setCursor(t.context.eventService.cursor)}},this.onClick=function(e){var n,r,i,o,a=null===(o=null===(i=t.context.renderingContext.root)||void 0===i?void 0:i.ownerDocument)||void 0===o?void 0:o.defaultView,s=t.normalizeToPointerEvent(e,a);try{for(var l=(0,W.XA)(s),c=l.next();!c.done;c=l.next()){var u=c.value,h=t.bootstrapEvent(t.rootPointerEvent,u,a,e);t.context.eventService.mapEvent(h)}}catch(t){n={error:t}}finally{try{c&&!c.done&&(r=l.return)&&r.call(l)}finally{if(n)throw n.error}}t.setCursor(t.context.eventService.cursor)}}return t.prototype.apply=function(e){var n=this;this.context=e;var r=e.renderingService,i=this.context.renderingContext.root.ownerDocument.defaultView;this.context.eventService.setPickHandler(function(t){return n.context.renderingService.hooks.pickSync.call({position:t,picked:[],topmost:!0}).picked[0]||null}),r.hooks.pointerWheel.tap(t.tag,function(t){var e=n.normalizeWheelEvent(t);n.context.eventService.mapEvent(e)}),r.hooks.pointerDown.tap(t.tag,function(t){if(!i.supportsTouchEvents||"touch"!==t.pointerType){var e,r,o=n.normalizeToPointerEvent(t,i);n.autoPreventDefault&&o[0].isNormalized&&(t.cancelable||!("cancelable"in t))&&t.preventDefault();try{for(var a=(0,W.XA)(o),s=a.next();!s.done;s=a.next()){var l=s.value,c=n.bootstrapEvent(n.rootPointerEvent,l,i,t);n.context.eventService.mapEvent(c)}}catch(t){e={error:t}}finally{try{s&&!s.done&&(r=a.return)&&r.call(a)}finally{if(e)throw e.error}}n.setCursor(n.context.eventService.cursor)}}),r.hooks.pointerUp.tap(t.tag,function(t){if(!i.supportsTouchEvents||"touch"!==t.pointerType){var e,r,o=n.context.contextService.getDomElement(),a="outside";try{a=o&&t.target&&t.target!==o&&o.contains&&!o.contains(t.target)?"outside":""}catch(t){}var s=n.normalizeToPointerEvent(t,i);try{for(var l=(0,W.XA)(s),c=l.next();!c.done;c=l.next()){var u=c.value,h=n.bootstrapEvent(n.rootPointerEvent,u,i,t);h.type+=a,n.context.eventService.mapEvent(h)}}catch(t){e={error:t}}finally{try{c&&!c.done&&(r=l.return)&&r.call(l)}finally{if(e)throw e.error}}n.setCursor(n.context.eventService.cursor)}}),r.hooks.pointerMove.tap(t.tag,this.onPointerMove),r.hooks.pointerOver.tap(t.tag,this.onPointerMove),r.hooks.pointerOut.tap(t.tag,this.onPointerMove),r.hooks.click.tap(t.tag,this.onClick),r.hooks.pointerCancel.tap(t.tag,function(t){var e,r,o=n.normalizeToPointerEvent(t,i);try{for(var a=(0,W.XA)(o),s=a.next();!s.done;s=a.next()){var l=s.value,c=n.bootstrapEvent(n.rootPointerEvent,l,i,t);n.context.eventService.mapEvent(c)}}catch(t){e={error:t}}finally{try{s&&!s.done&&(r=a.return)&&r.call(a)}finally{if(e)throw e.error}}n.setCursor(n.context.eventService.cursor)})},t.prototype.getViewportXY=function(t){var e,n,r=t.offsetX,i=t.offsetY,o=t.clientX,a=t.clientY;if(!this.context.config.supportsCSSTransform||(0,tr.Z)(r)||(0,tr.Z)(i)){var s=this.context.eventService.client2Viewport(new tz(o,a));e=s.x,n=s.y}else e=r,n=i;return{x:e,y:n}},t.prototype.bootstrapEvent=function(t,e,n,r){t.view=n,t.originalEvent=null,t.nativeEvent=r,t.pointerId=e.pointerId,t.width=e.width,t.height=e.height,t.isPrimary=e.isPrimary,t.pointerType=e.pointerType,t.pressure=e.pressure,t.tangentialPressure=e.tangentialPressure,t.tiltX=e.tiltX,t.tiltY=e.tiltY,t.twist=e.twist,this.transferMouseData(t,e);var i=this.getViewportXY(e),o=i.x,a=i.y;t.viewport.x=o,t.viewport.y=a;var s=this.context.eventService.viewport2Canvas(t.viewport),l=s.x,c=s.y;return t.canvas.x=l,t.canvas.y=c,t.global.copyFrom(t.canvas),t.offset.copyFrom(t.canvas),t.isTrusted=r.isTrusted,"pointerleave"===t.type&&(t.type="pointerout"),t.type.startsWith("mouse")&&(t.type=t.type.replace("mouse","pointer")),t.type.startsWith("touch")&&(t.type=n$[t.type]||t.type),t},t.prototype.normalizeWheelEvent=function(t){var e=this.rootWheelEvent;this.transferMouseData(e,t),e.deltaMode=t.deltaMode,e.deltaX=t.deltaX,e.deltaY=t.deltaY,e.deltaZ=t.deltaZ;var n=this.getViewportXY(t),r=n.x,i=n.y;e.viewport.x=r,e.viewport.y=i;var o=this.context.eventService.viewport2Canvas(e.viewport),a=o.x,s=o.y;return e.canvas.x=a,e.canvas.y=s,e.global.copyFrom(e.canvas),e.offset.copyFrom(e.canvas),e.nativeEvent=t,e.type=t.type,e},t.prototype.transferMouseData=function(t,e){t.isTrusted=e.isTrusted,t.srcElement=e.srcElement,t.timeStamp=nQ.now(),t.type=e.type,t.altKey=e.altKey,t.metaKey=e.metaKey,t.shiftKey=e.shiftKey,t.ctrlKey=e.ctrlKey,t.button=e.button,t.buttons=e.buttons,t.client.x=e.clientX,t.client.y=e.clientY,t.movement.x=e.movementX,t.movement.y=e.movementY,t.page.x=e.pageX,t.page.y=e.pageY,t.screen.x=e.screenX,t.screen.y=e.screenY,t.relatedTarget=null},t.prototype.setCursor=function(t){this.context.contextService.applyCursorStyle(t||this.context.config.cursor||"default")},t.prototype.normalizeToPointerEvent=function(t,e){var n=[];if(e.isTouchEvent(t))for(var r=0;r-1,a=0,s=r.length;a=1?Math.ceil(M):1,C=l||("auto"===(n=nJ(a,"width"))?a.offsetWidth:parseFloat(n))||a.width/M,w=c||("auto"===(r=nJ(a,"height"))?a.offsetHeight:parseFloat(r))||a.height/M),s&&(r_.offscreenCanvas=s),i.devicePixelRatio=M,i.requestAnimationFrame=null!=v?v:n7.bind(r_.globalThis),i.cancelAnimationFrame=null!=y?y:rt.bind(r_.globalThis),i.supportsTouchEvents=null!=E?E:"ontouchstart"in r_.globalThis,i.supportsPointerEvents=null!=m?m:!!r_.globalThis.PointerEvent,i.isTouchEvent=null!=S?S:function(t){return i.supportsTouchEvents&&t instanceof r_.globalThis.TouchEvent},i.isMouseEvent=null!=N?N:function(t){return!r_.globalThis.MouseEvent||t instanceof r_.globalThis.MouseEvent&&(!i.supportsPointerEvents||!(t instanceof r_.globalThis.PointerEvent))},i.initRenderingContext({container:o,canvas:a,width:C,height:w,renderer:h,offscreenCanvas:s,devicePixelRatio:M,cursor:f||"default",background:p||"transparent",createImage:g,document:d,supportsCSSTransform:x,useNativeClickEvent:T,alwaysTriggerPointerEventOnCanvas:P}),i.initDefaultCamera(C,w,h.clipSpaceNearZ),i.initRenderer(h,!0),i}return(0,W.ZT)(e,t),e.prototype.initRenderingContext=function(t){this.context.config=t,this.context.renderingContext={root:this.document.documentElement,renderListCurrentFrame:[],unculledEntities:[],renderReasons:new Set,force:!1,dirty:!1}},e.prototype.initDefaultCamera=function(t,e,n){var r=this,i=new r_.CameraContribution;i.clipSpaceNearZ=n,i.setType(A.EXPLORING,O.DEFAULT).setPosition(t/2,e/2,500).setFocalPoint(t/2,e/2,0).setOrthographic(-(t/2),t/2,e/2,-(e/2),.1,1e3),i.canvas=this,i.eventEmitter.on(t$.UPDATED,function(){r.context.renderingContext.renderReasons.add(X.CAMERA_CHANGED)}),this.context.camera=i},e.prototype.getConfig=function(){return this.context.config},e.prototype.getRoot=function(){return this.document.documentElement},e.prototype.getCamera=function(){return this.context.camera},e.prototype.getContextService=function(){return this.context.contextService},e.prototype.getEventService=function(){return this.context.eventService},e.prototype.getRenderingService=function(){return this.context.renderingService},e.prototype.getRenderingContext=function(){return this.context.renderingContext},e.prototype.getStats=function(){return this.getRenderingService().getStats()},Object.defineProperty(e.prototype,"ready",{get:function(){var 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r=this.context.camera,i=r.getProjectionMode();r.setPosition(t/2,e/2,500).setFocalPoint(t/2,e/2,0),i===L.ORTHOGRAPHIC?r.setOrthographic(-(t/2),t/2,e/2,-(e/2),r.getNear(),r.getFar()):r.setAspect(t/e),this.dispatchEvent(new rx(j.RESIZE,{width:t,height:e}))},e.prototype.appendChild=function(t,e){return this.document.documentElement.appendChild(t,e)},e.prototype.insertBefore=function(t,e){return this.document.documentElement.insertBefore(t,e)},e.prototype.removeChild=function(t){return this.document.documentElement.removeChild(t)},e.prototype.removeChildren=function(){this.document.documentElement.removeChildren()},e.prototype.destroyChildren=function(){this.document.documentElement.destroyChildren()},e.prototype.render=function(){var t=this;this.dispatchEvent(ip),this.getRenderingService().render(this.getConfig(),function(){t.dispatchEvent(id)}),this.dispatchEvent(iv)},e.prototype.run=function(){var t=this,e=function(){t.render(),t.frameId=t.requestAnimationFrame(e)};e()},e.prototype.initRenderer=function(t,e){var n=this;if(void 0===e&&(e=!1),!t)throw Error("Renderer is required.");this.inited=!1,this.readyPromise=void 0,this.context.rBushRoot=new tC,this.context.renderingPlugins=[],this.context.renderingPlugins.push(new ia,new ic,new io([new il])),this.loadRendererContainerModule(t),this.context.contextService=new this.context.ContextService((0,W.pi)((0,W.pi)({},r_),this.context)),this.context.renderingService=new rN(r_,this.context),this.context.eventService=new rP(r_,this.context),this.context.eventService.init(),this.context.contextService.init?(this.context.contextService.init(),this.initRenderingService(t,e,!0)):this.context.contextService.initAsync().then(function(){n.initRenderingService(t,e)})},e.prototype.initRenderingService=function(t,e,n){var r=this;void 0===e&&(e=!1),void 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n=e.openElements.tmplCount>0;e.formElement&&!n||(e.openElements.hasInButtonScope(h.P)&&e._closePElement(),e._insertElement(t,b.HTML),n||(e.formElement=e.openElements.current))}(e,t):n===h.CODE||n===h.FONT?ep(e,t):n===h.NOBR?(e._reconstructActiveFormattingElements(),e.openElements.hasInScope(h.NOBR)&&(K(e,t),e._reconstructActiveFormattingElements()),e._insertElement(t,b.HTML),e.activeFormattingElements.pushElement(e.openElements.current,t)):n===h.AREA?eg(e,t):n===h.MATH?(e._reconstructActiveFormattingElements(),p.adjustTokenMathMLAttrs(t),p.adjustTokenXMLAttrs(t),t.selfClosing?e._appendElement(t,b.MATHML):e._insertElement(t,b.MATHML),t.ackSelfClosing=!0):n===h.MENU?(e.openElements.hasInButtonScope(h.P)&&e._closePElement(),e._insertElement(t,b.HTML)):n!==h.HEAD&&eT(e,t);break;case 5:n===h.STYLE||n===h.TITLE?er(e,t):n===h.ASIDE?eu(e,t):n===h.SMALL?ep(e,t):n===h.TABLE?(e.treeAdapter.getDocumentMode(e.document)!==f.DOCUMENT_MODE.QUIRKS&&e.openElements.hasInButtonScope(h.P)&&e._closePElement(),e._insertElement(t,b.HTML),e.framesetOk=!1,e.insertionMode=I):n===h.EMBED?eg(e,t):n===h.INPUT?function(e,t){e._reconstructActiveFormattingElements(),e._appendElement(t,b.HTML);let n=r.getTokenAttr(t,E.TYPE);n&&n.toLowerCase()===S||(e.framesetOk=!1),t.ackSelfClosing=!0}(e,t):n===h.PARAM||n===h.TRACK?ef(e,t):n===h.IMAGE?(t.tagName=h.IMG,eg(e,t)):n!==h.FRAME&&n!==h.TBODY&&n!==h.TFOOT&&n!==h.THEAD&&eT(e,t);break;case 6:n===h.SCRIPT?er(e,t):n===h.CENTER||n===h.FIGURE||n===h.FOOTER||n===h.HEADER||n===h.HGROUP||n===h.DIALOG?eu(e,t):n===h.BUTTON?(e.openElements.hasInScope(h.BUTTON)&&(e.openElements.generateImpliedEndTags(),e.openElements.popUntilTagNamePopped(h.BUTTON)),e._reconstructActiveFormattingElements(),e._insertElement(t,b.HTML),e.framesetOk=!1):n===h.STRIKE||n===h.STRONG?ep(e,t):n===h.APPLET||n===h.OBJECT?em(e,t):n===h.KEYGEN?eg(e,t):n===h.SOURCE?ef(e,t):n===h.IFRAME?(e.framesetOk=!1,e._switchToTextParsing(t,r.MODE.RAWTEXT)):n===h.SELECT?(e._reconstructActiveFormattingElements(),e._insertElement(t,b.HTML),e.framesetOk=!1,e.insertionMode===I||e.insertionMode===w||e.insertionMode===L||e.insertionMode===D||e.insertionMode===P?e.insertionMode=F:e.insertionMode=M):n===h.OPTION?eb(e,t):eT(e,t);break;case 7:n===h.BGSOUND?er(e,t):n===h.DETAILS||n===h.ADDRESS||n===h.ARTICLE||n===h.SECTION||n===h.SUMMARY?eu(e,t):n===h.LISTING?ed(e,t):n===h.MARQUEE?em(e,t):n===h.NOEMBED?eh(e,t):n!==h.CAPTION&&eT(e,t);break;case 8:n===h.BASEFONT?er(e,t):n===h.FRAMESET?function(e,t){let n=e.openElements.tryPeekProperlyNestedBodyElement();e.framesetOk&&n&&(e.treeAdapter.detachNode(n),e.openElements.popAllUpToHtmlElement(),e._insertElement(t,b.HTML),e.insertionMode=H)}(e,t):n===h.FIELDSET?eu(e,t):n===h.TEXTAREA?(e._insertElement(t,b.HTML),e.skipNextNewLine=!0,e.tokenizer.state=r.MODE.RCDATA,e.originalInsertionMode=e.insertionMode,e.framesetOk=!1,e.insertionMode=R):n===h.TEMPLATE?er(e,t):n===h.NOSCRIPT?e.options.scriptingEnabled?eh(e,t):eT(e,t):n===h.OPTGROUP?eb(e,t):n!==h.COLGROUP&&eT(e,t);break;case 9:n===h.PLAINTEXT?(e.openElements.hasInButtonScope(h.P)&&e._closePElement(),e._insertElement(t,b.HTML),e.tokenizer.state=r.MODE.PLAINTEXT):eT(e,t);break;case 10:n===h.BLOCKQUOTE||n===h.FIGCAPTION?eu(e,t):eT(e,t);break;default:eT(e,t)}}function ey(e,t){let n=t.tagName;e.openElements.hasInScope(n)&&(e.openElements.generateImpliedEndTags(),e.openElements.popUntilTagNamePopped(n))}function eA(e,t){let n=t.tagName;e.openElements.hasInScope(n)&&(e.openElements.generateImpliedEndTags(),e.openElements.popUntilTagNamePopped(n),e.activeFormattingElements.clearToLastMarker())}function e_(e,t){let n=t.tagName;for(let t=e.openElements.stackTop;t>0;t--){let r=e.openElements.items[t];if(e.treeAdapter.getTagName(r)===n){e.openElements.generateImpliedEndTagsWithExclusion(n),e.openElements.popUntilElementPopped(r);break}if(e._isSpecialElement(r))break}}function ek(e,t){let n=t.tagName;switch(n.length){case 1:n===h.A||n===h.B||n===h.I||n===h.S||n===h.U?K(e,t):n===h.P?(e.openElements.hasInButtonScope(h.P)||e._insertFakeElement(h.P),e._closePElement()):e_(e,t);break;case 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t=e.openElements.tmplCount>0,n=e.formElement;t||(e.formElement=null),(n||t)&&e.openElements.hasInScope(h.FORM)&&(e.openElements.generateImpliedEndTags(),t?e.openElements.popUntilTagNamePopped(h.FORM):e.openElements.remove(n))}(e,t):n===h.CODE||n===h.FONT||n===h.NOBR?K(e,t):n===h.MAIN||n===h.MENU?ey(e,t):e_(e,t);break;case 5:n===h.ASIDE?ey(e,t):n===h.SMALL?K(e,t):e_(e,t);break;case 6:n===h.CENTER||n===h.FIGURE||n===h.FOOTER||n===h.HEADER||n===h.HGROUP||n===h.DIALOG?ey(e,t):n===h.APPLET||n===h.OBJECT?eA(e,t):n===h.STRIKE||n===h.STRONG?K(e,t):e_(e,t);break;case 7:n===h.ADDRESS||n===h.ARTICLE||n===h.DETAILS||n===h.SECTION||n===h.SUMMARY||n===h.LISTING?ey(e,t):n===h.MARQUEE?eA(e,t):e_(e,t);break;case 8:n===h.FIELDSET?ey(e,t):n===h.TEMPLATE?ea(e,t):e_(e,t);break;case 10:n===h.BLOCKQUOTE||n===h.FIGCAPTION?ey(e,t):e_(e,t);break;default:e_(e,t)}}function ev(e,t){e.tmplInsertionModeStackTop>-1?eD(e,t):e.stopped=!0}function eN(e,t){let n=e.openElements.currentTagName;n===h.TABLE||n===h.TBODY||n===h.TFOOT||n===h.THEAD||n===h.TR?(e.pendingCharacterTokens=[],e.hasNonWhitespacePendingCharacterToken=!1,e.originalInsertionMode=e.insertionMode,e.insertionMode=O,e._processToken(t)):eI(e,t)}function eC(e,t){let n=t.tagName;switch(n.length){case 2:n===h.TD||n===h.TH||n===h.TR?(e.openElements.clearBackToTableContext(),e._insertFakeElement(h.TBODY),e.insertionMode=L,e._processToken(t)):eI(e,t);break;case 3:n===h.COL?(e.openElements.clearBackToTableContext(),e._insertFakeElement(h.COLGROUP),e.insertionMode=x,e._processToken(t)):eI(e,t);break;case 4:n===h.FORM?e.formElement||0!==e.openElements.tmplCount||(e._insertElement(t,b.HTML),e.formElement=e.openElements.current,e.openElements.pop()):eI(e,t);break;case 5:n===h.TABLE?e.openElements.hasInTableScope(h.TABLE)&&(e.openElements.popUntilTagNamePopped(h.TABLE),e._resetInsertionMode(),e._processToken(t)):n===h.STYLE?er(e,t):n===h.TBODY||n===h.TFOOT||n===h.THEAD?(e.openElements.clearBackToTableContext(),e._insertElement(t,b.HTML),e.insertionMode=L):n===h.INPUT?function(e,t){let n=r.getTokenAttr(t,E.TYPE);n&&n.toLowerCase()===S?e._appendElement(t,b.HTML):eI(e,t),t.ackSelfClosing=!0}(e,t):eI(e,t);break;case 6:n===h.SCRIPT?er(e,t):eI(e,t);break;case 7:n===h.CAPTION?(e.openElements.clearBackToTableContext(),e.activeFormattingElements.insertMarker(),e._insertElement(t,b.HTML),e.insertionMode=w):eI(e,t);break;case 8:n===h.COLGROUP?(e.openElements.clearBackToTableContext(),e._insertElement(t,b.HTML),e.insertionMode=x):n===h.TEMPLATE?er(e,t):eI(e,t);break;default:eI(e,t)}}function eR(e,t){let n=t.tagName;n===h.TABLE?e.openElements.hasInTableScope(h.TABLE)&&(e.openElements.popUntilTagNamePopped(h.TABLE),e._resetInsertionMode()):n===h.TEMPLATE?ea(e,t):n!==h.BODY&&n!==h.CAPTION&&n!==h.COL&&n!==h.COLGROUP&&n!==h.HTML&&n!==h.TBODY&&n!==h.TD&&n!==h.TFOOT&&n!==h.TH&&n!==h.THEAD&&n!==h.TR&&eI(e,t)}function eI(e,t){let n=e.fosterParentingEnabled;e.fosterParentingEnabled=!0,e._processTokenInBodyMode(t),e.fosterParentingEnabled=n}function eO(e,t){let n=0;if(e.hasNonWhitespacePendingCharacterToken)for(;n0?(e.openElements.popUntilTagNamePopped(h.TEMPLATE),e.activeFormattingElements.clearToLastMarker(),e._popTmplInsertionMode(),e._resetInsertionMode(),e._processToken(t)):e.stopped=!0}function eP(e,t){e.insertionMode=C,e._processToken(t)}function eM(e,t){e.insertionMode=C,e._processToken(t)}e.exports=class{constructor(e){this.options=u(T,e),this.treeAdapter=this.options.treeAdapter,this.pendingScript=null,this.options.sourceCodeLocationInfo&&l.install(this,o),this.options.onParseError&&l.install(this,s,{onParseError:this.options.onParseError})}parse(e){let t=this.treeAdapter.createDocument();return this._bootstrap(t,null),this.tokenizer.write(e,!0),this._runParsingLoop(null),t}parseFragment(e,t){t||(t=this.treeAdapter.createElement(h.TEMPLATE,b.HTML,[]));let n=this.treeAdapter.createElement("documentmock",b.HTML,[]);this._bootstrap(n,t),this.treeAdapter.getTagName(t)===h.TEMPLATE&&this._pushTmplInsertionMode(U),this._initTokenizerForFragmentParsing(),this._insertFakeRootElement(),this._resetInsertionMode(),this._findFormInFragmentContext(),this.tokenizer.write(e,!0),this._runParsingLoop(null);let r=this.treeAdapter.getFirstChild(n),a=this.treeAdapter.createDocumentFragment();return this._adoptNodes(r,a),a}_bootstrap(e,t){this.tokenizer=new r(this.options),this.stopped=!1,this.insertionMode=y,this.originalInsertionMode="",this.document=e,this.fragmentContext=t,this.headElement=null,this.formElement=null,this.openElements=new a(this.document,this.treeAdapter),this.activeFormattingElements=new i(this.treeAdapter),this.tmplInsertionModeStack=[],this.tmplInsertionModeStackTop=-1,this.currentTmplInsertionMode=null,this.pendingCharacterTokens=[],this.hasNonWhitespacePendingCharacterToken=!1,this.framesetOk=!0,this.skipNextNewLine=!1,this.fosterParentingEnabled=!1}_err(){}_runParsingLoop(e){for(;!this.stopped;){this._setupTokenizerCDATAMode();let t=this.tokenizer.getNextToken();if(t.type===r.HIBERNATION_TOKEN)break;if(this.skipNextNewLine&&(this.skipNextNewLine=!1,t.type===r.WHITESPACE_CHARACTER_TOKEN&&"\n"===t.chars[0])){if(1===t.chars.length)continue;t.chars=t.chars.substr(1)}if(this._processInputToken(t),e&&this.pendingScript)break}}runParsingLoopForCurrentChunk(e,t){if(this._runParsingLoop(t),t&&this.pendingScript){let e=this.pendingScript;this.pendingScript=null,t(e);return}e&&e()}_setupTokenizerCDATAMode(){let e=this._getAdjustedCurrentElement();this.tokenizer.allowCDATA=e&&e!==this.document&&this.treeAdapter.getNamespaceURI(e)!==b.HTML&&!this._isIntegrationPoint(e)}_switchToTextParsing(e,t){this._insertElement(e,b.HTML),this.tokenizer.state=t,this.originalInsertionMode=this.insertionMode,this.insertionMode=R}switchToPlaintextParsing(){this.insertionMode=R,this.originalInsertionMode=C,this.tokenizer.state=r.MODE.PLAINTEXT}_getAdjustedCurrentElement(){return 0===this.openElements.stackTop&&this.fragmentContext?this.fragmentContext:this.openElements.current}_findFormInFragmentContext(){let e=this.fragmentContext;do{if(this.treeAdapter.getTagName(e)===h.FORM){this.formElement=e;break}e=this.treeAdapter.getParentNode(e)}while(e)}_initTokenizerForFragmentParsing(){if(this.treeAdapter.getNamespaceURI(this.fragmentContext)===b.HTML){let e=this.treeAdapter.getTagName(this.fragmentContext);e===h.TITLE||e===h.TEXTAREA?this.tokenizer.state=r.MODE.RCDATA:e===h.STYLE||e===h.XMP||e===h.IFRAME||e===h.NOEMBED||e===h.NOFRAMES||e===h.NOSCRIPT?this.tokenizer.state=r.MODE.RAWTEXT:e===h.SCRIPT?this.tokenizer.state=r.MODE.SCRIPT_DATA:e===h.PLAINTEXT&&(this.tokenizer.state=r.MODE.PLAINTEXT)}}_setDocumentType(e){let t=e.name||"",n=e.publicId||"",r=e.systemId||"";this.treeAdapter.setDocumentType(this.document,t,n,r)}_attachElementToTree(e){if(this._shouldFosterParentOnInsertion())this._fosterParentElement(e);else{let t=this.openElements.currentTmplContent||this.openElements.current;this.treeAdapter.appendChild(t,e)}}_appendElement(e,t){let n=this.treeAdapter.createElement(e.tagName,t,e.attrs);this._attachElementToTree(n)}_insertElement(e,t){let n=this.treeAdapter.createElement(e.tagName,t,e.attrs);this._attachElementToTree(n),this.openElements.push(n)}_insertFakeElement(e){let t=this.treeAdapter.createElement(e,b.HTML,[]);this._attachElementToTree(t),this.openElements.push(t)}_insertTemplate(e){let t=this.treeAdapter.createElement(e.tagName,b.HTML,e.attrs),n=this.treeAdapter.createDocumentFragment();this.treeAdapter.setTemplateContent(t,n),this._attachElementToTree(t),this.openElements.push(t)}_insertFakeRootElement(){let e=this.treeAdapter.createElement(h.HTML,b.HTML,[]);this.treeAdapter.appendChild(this.openElements.current,e),this.openElements.push(e)}_appendCommentNode(e,t){let n=this.treeAdapter.createCommentNode(e.data);this.treeAdapter.appendChild(t,n)}_insertCharacters(e){if(this._shouldFosterParentOnInsertion())this._fosterParentText(e.chars);else{let t=this.openElements.currentTmplContent||this.openElements.current;this.treeAdapter.insertText(t,e.chars)}}_adoptNodes(e,t){for(let n=this.treeAdapter.getFirstChild(e);n;n=this.treeAdapter.getFirstChild(e))this.treeAdapter.detachNode(n),this.treeAdapter.appendChild(t,n)}_shouldProcessTokenInForeignContent(e){let t=this._getAdjustedCurrentElement();if(!t||t===this.document)return!1;let n=this.treeAdapter.getNamespaceURI(t);if(n===b.HTML||this.treeAdapter.getTagName(t)===h.ANNOTATION_XML&&n===b.MATHML&&e.type===r.START_TAG_TOKEN&&e.tagName===h.SVG)return!1;let a=e.type===r.CHARACTER_TOKEN||e.type===r.NULL_CHARACTER_TOKEN||e.type===r.WHITESPACE_CHARACTER_TOKEN,i=e.type===r.START_TAG_TOKEN&&e.tagName!==h.MGLYPH&&e.tagName!==h.MALIGNMARK;return!((i||a)&&this._isIntegrationPoint(t,b.MATHML)||(e.type===r.START_TAG_TOKEN||a)&&this._isIntegrationPoint(t,b.HTML))&&e.type!==r.EOF_TOKEN}_processToken(e){W[this.insertionMode][e.type](this,e)}_processTokenInBodyMode(e){W[C][e.type](this,e)}_processTokenInForeignContent(e){e.type===r.CHARACTER_TOKEN?(this._insertCharacters(e),this.framesetOk=!1):e.type===r.NULL_CHARACTER_TOKEN?(e.chars=g.REPLACEMENT_CHARACTER,this._insertCharacters(e)):e.type===r.WHITESPACE_CHARACTER_TOKEN?Q(this,e):e.type===r.COMMENT_TOKEN?q(this,e):e.type===r.START_TAG_TOKEN?function(e,t){if(p.causesExit(t)&&!e.fragmentContext){for(;e.treeAdapter.getNamespaceURI(e.openElements.current)!==b.HTML&&!e._isIntegrationPoint(e.openElements.current);)e.openElements.pop();e._processToken(t)}else{let n=e._getAdjustedCurrentElement(),r=e.treeAdapter.getNamespaceURI(n);r===b.MATHML?p.adjustTokenMathMLAttrs(t):r===b.SVG&&(p.adjustTokenSVGTagName(t),p.adjustTokenSVGAttrs(t)),p.adjustTokenXMLAttrs(t),t.selfClosing?e._appendElement(t,r):e._insertElement(t,r),t.ackSelfClosing=!0}}(this,e):e.type===r.END_TAG_TOKEN&&function(e,t){for(let n=e.openElements.stackTop;n>0;n--){let r=e.openElements.items[n];if(e.treeAdapter.getNamespaceURI(r)===b.HTML){e._processToken(t);break}if(e.treeAdapter.getTagName(r).toLowerCase()===t.tagName){e.openElements.popUntilElementPopped(r);break}}}(this,e)}_processInputToken(e){this._shouldProcessTokenInForeignContent(e)?this._processTokenInForeignContent(e):this._processToken(e),e.type===r.START_TAG_TOKEN&&e.selfClosing&&!e.ackSelfClosing&&this._err(m.nonVoidHtmlElementStartTagWithTrailingSolidus)}_isIntegrationPoint(e,t){let n=this.treeAdapter.getTagName(e),r=this.treeAdapter.getNamespaceURI(e),a=this.treeAdapter.getAttrList(e);return p.isIntegrationPoint(n,r,a,t)}_reconstructActiveFormattingElements(){let e=this.activeFormattingElements.length;if(e){let t=e,n=null;do if(t--,(n=this.activeFormattingElements.entries[t]).type===i.MARKER_ENTRY||this.openElements.contains(n.element)){t++;break}while(t>0);for(let r=t;r=0;e--){let n=this.openElements.items[e];0===e&&(t=!0,this.fragmentContext&&(n=this.fragmentContext));let r=this.treeAdapter.getTagName(n),a=j[r];if(a){this.insertionMode=a;break}if(t||r!==h.TD&&r!==h.TH){if(t||r!==h.HEAD){if(r===h.SELECT){this._resetInsertionModeForSelect(e);break}if(r===h.TEMPLATE){this.insertionMode=this.currentTmplInsertionMode;break}if(r===h.HTML){this.insertionMode=this.headElement?N:_;break}else if(t){this.insertionMode=C;break}}else{this.insertionMode=k;break}}else{this.insertionMode=P;break}}}_resetInsertionModeForSelect(e){if(e>0)for(let t=e-1;t>0;t--){let e=this.openElements.items[t],n=this.treeAdapter.getTagName(e);if(n===h.TEMPLATE)break;if(n===h.TABLE){this.insertionMode=F;return}}this.insertionMode=M}_pushTmplInsertionMode(e){this.tmplInsertionModeStack.push(e),this.tmplInsertionModeStackTop++,this.currentTmplInsertionMode=e}_popTmplInsertionMode(){this.tmplInsertionModeStack.pop(),this.tmplInsertionModeStackTop--,this.currentTmplInsertionMode=this.tmplInsertionModeStack[this.tmplInsertionModeStackTop]}_isElementCausesFosterParenting(e){let t=this.treeAdapter.getTagName(e);return t===h.TABLE||t===h.TBODY||t===h.TFOOT||t===h.THEAD||t===h.TR}_shouldFosterParentOnInsertion(){return this.fosterParentingEnabled&&this._isElementCausesFosterParenting(this.openElements.current)}_findFosterParentingLocation(){let e={parent:null,beforeElement:null};for(let t=this.openElements.stackTop;t>=0;t--){let n=this.openElements.items[t],r=this.treeAdapter.getTagName(n),a=this.treeAdapter.getNamespaceURI(n);if(r===h.TEMPLATE&&a===b.HTML){e.parent=this.treeAdapter.getTemplateContent(n);break}if(r===h.TABLE){e.parent=this.treeAdapter.getParentNode(n),e.parent?e.beforeElement=n:e.parent=this.openElements.items[t-1];break}}return e.parent||(e.parent=this.openElements.items[0]),e}_fosterParentElement(e){let t=this._findFosterParentingLocation();t.beforeElement?this.treeAdapter.insertBefore(t.parent,e,t.beforeElement):this.treeAdapter.appendChild(t.parent,e)}_fosterParentText(e){let t=this._findFosterParentingLocation();t.beforeElement?this.treeAdapter.insertTextBefore(t.parent,e,t.beforeElement):this.treeAdapter.insertText(t.parent,e)}_isSpecialElement(e){let t=this.treeAdapter.getTagName(e),n=this.treeAdapter.getNamespaceURI(e);return f.SPECIAL_ELEMENTS[n][t]}}},46519:function(e,t,n){"use strict";let r=n(16152),a=r.TAG_NAMES,i=r.NAMESPACES;function o(e){switch(e.length){case 1:return e===a.P;case 2:return e===a.RB||e===a.RP||e===a.RT||e===a.DD||e===a.DT||e===a.LI;case 3:return e===a.RTC;case 6:return e===a.OPTION;case 8:return e===a.OPTGROUP}return!1}function s(e,t){switch(e.length){case 2:if(e===a.TD||e===a.TH)return t===i.HTML;if(e===a.MI||e===a.MO||e===a.MN||e===a.MS)return t===i.MATHML;break;case 4:if(e===a.HTML)return t===i.HTML;if(e===a.DESC)return t===i.SVG;break;case 5:if(e===a.TABLE)return t===i.HTML;if(e===a.MTEXT)return t===i.MATHML;if(e===a.TITLE)return t===i.SVG;break;case 6:return(e===a.APPLET||e===a.OBJECT)&&t===i.HTML;case 7:return(e===a.CAPTION||e===a.MARQUEE)&&t===i.HTML;case 8:return e===a.TEMPLATE&&t===i.HTML;case 13:return e===a.FOREIGN_OBJECT&&t===i.SVG;case 14:return e===a.ANNOTATION_XML&&t===i.MATHML}return!1}e.exports=class{constructor(e,t){this.stackTop=-1,this.items=[],this.current=e,this.currentTagName=null,this.currentTmplContent=null,this.tmplCount=0,this.treeAdapter=t}_indexOf(e){let t=-1;for(let n=this.stackTop;n>=0;n--)if(this.items[n]===e){t=n;break}return t}_isInTemplate(){return this.currentTagName===a.TEMPLATE&&this.treeAdapter.getNamespaceURI(this.current)===i.HTML}_updateCurrentElement(){this.current=this.items[this.stackTop],this.currentTagName=this.current&&this.treeAdapter.getTagName(this.current),this.currentTmplContent=this._isInTemplate()?this.treeAdapter.getTemplateContent(this.current):null}push(e){this.items[++this.stackTop]=e,this._updateCurrentElement(),this._isInTemplate()&&this.tmplCount++}pop(){this.stackTop--,this.tmplCount>0&&this._isInTemplate()&&this.tmplCount--,this._updateCurrentElement()}replace(e,t){let n=this._indexOf(e);this.items[n]=t,n===this.stackTop&&this._updateCurrentElement()}insertAfter(e,t){let n=this._indexOf(e)+1;this.items.splice(n,0,t),n===++this.stackTop&&this._updateCurrentElement()}popUntilTagNamePopped(e){for(;this.stackTop>-1;){let 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t=this.stackTop;t>=0;t--)if(this.items[t]===e){this.items.splice(t,1),this.stackTop--,this._updateCurrentElement();break}}tryPeekProperlyNestedBodyElement(){let e=this.items[1];return e&&this.treeAdapter.getTagName(e)===a.BODY?e:null}contains(e){return this._indexOf(e)>-1}getCommonAncestor(e){let t=this._indexOf(e);return--t>=0?this.items[t]:null}isRootHtmlElementCurrent(){return 0===this.stackTop&&this.currentTagName===a.HTML}hasInScope(e){for(let t=this.stackTop;t>=0;t--){let n=this.treeAdapter.getTagName(this.items[t]),r=this.treeAdapter.getNamespaceURI(this.items[t]);if(n===e&&r===i.HTML)break;if(s(n,r))return!1}return!0}hasNumberedHeaderInScope(){for(let e=this.stackTop;e>=0;e--){let t=this.treeAdapter.getTagName(this.items[e]),n=this.treeAdapter.getNamespaceURI(this.items[e]);if((t===a.H1||t===a.H2||t===a.H3||t===a.H4||t===a.H5||t===a.H6)&&n===i.HTML)break;if(s(t,n))return!1}return!0}hasInListItemScope(e){for(let t=this.stackTop;t>=0;t--){let n=this.treeAdapter.getTagName(this.items[t]),r=this.treeAdapter.getNamespaceURI(this.items[t]);if(n===e&&r===i.HTML)break;if((n===a.UL||n===a.OL)&&r===i.HTML||s(n,r))return!1}return!0}hasInButtonScope(e){for(let t=this.stackTop;t>=0;t--){let n=this.treeAdapter.getTagName(this.items[t]),r=this.treeAdapter.getNamespaceURI(this.items[t]);if(n===e&&r===i.HTML)break;if(n===a.BUTTON&&r===i.HTML||s(n,r))return!1}return!0}hasInTableScope(e){for(let t=this.stackTop;t>=0;t--){let n=this.treeAdapter.getTagName(this.items[t]),r=this.treeAdapter.getNamespaceURI(this.items[t]);if(r===i.HTML){if(n===e)break;if(n===a.TABLE||n===a.TEMPLATE||n===a.HTML)return!1}}return!0}hasTableBodyContextInTableScope(){for(let e=this.stackTop;e>=0;e--){let t=this.treeAdapter.getTagName(this.items[e]),n=this.treeAdapter.getNamespaceURI(this.items[e]);if(n===i.HTML){if(t===a.TBODY||t===a.THEAD||t===a.TFOOT)break;if(t===a.TABLE||t===a.HTML)return!1}}return!0}hasInSelectScope(e){for(let t=this.stackTop;t>=0;t--){let n=this.treeAdapter.getTagName(this.items[t]),r=this.treeAdapter.getNamespaceURI(this.items[t]);if(r===i.HTML){if(n===e)break;if(n!==a.OPTION&&n!==a.OPTGROUP)return!1}}return!0}generateImpliedEndTags(){for(;o(this.currentTagName);)this.pop()}generateImpliedEndTagsThoroughly(){for(;function(e){switch(e.length){case 1:return e===a.P;case 2:return e===a.RB||e===a.RP||e===a.RT||e===a.DD||e===a.DT||e===a.LI||e===a.TD||e===a.TH||e===a.TR;case 3:return e===a.RTC;case 5:return e===a.TBODY||e===a.TFOOT||e===a.THEAD;case 6:return e===a.OPTION;case 7:return e===a.CAPTION;case 8:return e===a.OPTGROUP||e===a.COLGROUP}return!1}(this.currentTagName);)this.pop()}generateImpliedEndTagsWithExclusion(e){for(;o(this.currentTagName)&&this.currentTagName!==e;)this.pop()}}},55763:function(e,t,n){"use strict";let r=n(77118),a=n(54284),i=n(5482),o=n(41734),s=a.CODE_POINTS,l=a.CODE_POINT_SEQUENCES,c={128:8364,130:8218,131:402,132:8222,133:8230,134:8224,135:8225,136:710,137:8240,138:352,139:8249,140:338,142:381,145:8216,146:8217,147:8220,148:8221,149:8226,150:8211,151:8212,152:732,153:8482,154:353,155:8250,156:339,158:382,159:376},u="DATA_STATE",d="RCDATA_STATE",p="RAWTEXT_STATE",m="SCRIPT_DATA_STATE",g="PLAINTEXT_STATE",f="TAG_OPEN_STATE",h="END_TAG_OPEN_STATE",b="TAG_NAME_STATE",E="RCDATA_LESS_THAN_SIGN_STATE",T="RCDATA_END_TAG_OPEN_STATE",S="RCDATA_END_TAG_NAME_STATE",y="RAWTEXT_LESS_THAN_SIGN_STATE",A="RAWTEXT_END_TAG_OPEN_STATE",_="RAWTEXT_END_TAG_NAME_STATE",k="SCRIPT_DATA_LESS_THAN_SIGN_STATE",v="SCRIPT_DATA_END_TAG_OPEN_STATE",N="SCRIPT_DATA_END_TAG_NAME_STATE",C="SCRIPT_DATA_ESCAPE_START_STATE",R="SCRIPT_DATA_ESCAPE_START_DASH_STATE",I="SCRIPT_DATA_ESCAPED_STATE",O="SCRIPT_DATA_ESCAPED_DASH_STATE",w="SCRIPT_DATA_ESCAPED_DASH_DASH_STATE",x="SCRIPT_DATA_ESCAPED_LESS_THAN_SIGN_STATE",L="SCRIPT_DATA_ESCAPED_END_TAG_OPEN_STATE",D="SCRIPT_DATA_ESCAPED_END_TAG_NAME_STATE",P="SCRIPT_DATA_DOUBLE_ESCAPE_START_STATE",M="SCRIPT_DATA_DOUBLE_ESCAPED_STATE",F="SCRIPT_DATA_DOUBLE_ESCAPED_DASH_STATE",U="SCRIPT_DATA_DOUBLE_ESCAPED_DASH_DASH_STATE",B="SCRIPT_DATA_DOUBLE_ESCAPED_LESS_THAN_SIGN_STATE",H="SCRIPT_DATA_DOUBLE_ESCAPE_END_STATE",G="BEFORE_ATTRIBUTE_NAME_STATE",z="ATTRIBUTE_NAME_STATE",$="AFTER_ATTRIBUTE_NAME_STATE",j="BEFORE_ATTRIBUTE_VALUE_STATE",V="ATTRIBUTE_VALUE_DOUBLE_QUOTED_STATE",W="ATTRIBUTE_VALUE_SINGLE_QUOTED_STATE",K="ATTRIBUTE_VALUE_UNQUOTED_STATE",Z="AFTER_ATTRIBUTE_VALUE_QUOTED_STATE",Y="SELF_CLOSING_START_TAG_STATE",q="BOGUS_COMMENT_STATE",X="MARKUP_DECLARATION_OPEN_STATE",Q="COMMENT_START_STATE",J="COMMENT_START_DASH_STATE",ee="COMMENT_STATE",et="COMMENT_LESS_THAN_SIGN_STATE",en="COMMENT_LESS_THAN_SIGN_BANG_STATE",er="COMMENT_LESS_THAN_SIGN_BANG_DASH_STATE",ea="COMMENT_LESS_THAN_SIGN_BANG_DASH_DASH_STATE",ei="COMMENT_END_DASH_STATE",eo="COMMENT_END_STATE",es="COMMENT_END_BANG_STATE",el="DOCTYPE_STATE",ec="BEFORE_DOCTYPE_NAME_STATE",eu="DOCTYPE_NAME_STATE",ed="AFTER_DOCTYPE_NAME_STATE",ep="AFTER_DOCTYPE_PUBLIC_KEYWORD_STATE",em="BEFORE_DOCTYPE_PUBLIC_IDENTIFIER_STATE",eg="DOCTYPE_PUBLIC_IDENTIFIER_DOUBLE_QUOTED_STATE",ef="DOCTYPE_PUBLIC_IDENTIFIER_SINGLE_QUOTED_STATE",eh="AFTER_DOCTYPE_PUBLIC_IDENTIFIER_STATE",eb="BETWEEN_DOCTYPE_PUBLIC_AND_SYSTEM_IDENTIFIERS_STATE",eE="AFTER_DOCTYPE_SYSTEM_KEYWORD_STATE",eT="BEFORE_DOCTYPE_SYSTEM_IDENTIFIER_STATE",eS="DOCTYPE_SYSTEM_IDENTIFIER_DOUBLE_QUOTED_STATE",ey="DOCTYPE_SYSTEM_IDENTIFIER_SINGLE_QUOTED_STATE",eA="AFTER_DOCTYPE_SYSTEM_IDENTIFIER_STATE",e_="BOGUS_DOCTYPE_STATE",ek="CDATA_SECTION_STATE",ev="CDATA_SECTION_BRACKET_STATE",eN="CDATA_SECTION_END_STATE",eC="CHARACTER_REFERENCE_STATE",eR="NAMED_CHARACTER_REFERENCE_STATE",eI="AMBIGUOS_AMPERSAND_STATE",eO="NUMERIC_CHARACTER_REFERENCE_STATE",ew="HEXADEMICAL_CHARACTER_REFERENCE_START_STATE",ex="DECIMAL_CHARACTER_REFERENCE_START_STATE",eL="HEXADEMICAL_CHARACTER_REFERENCE_STATE",eD="DECIMAL_CHARACTER_REFERENCE_STATE",eP="NUMERIC_CHARACTER_REFERENCE_END_STATE";function eM(e){return e===s.SPACE||e===s.LINE_FEED||e===s.TABULATION||e===s.FORM_FEED}function eF(e){return e>=s.DIGIT_0&&e<=s.DIGIT_9}function eU(e){return e>=s.LATIN_CAPITAL_A&&e<=s.LATIN_CAPITAL_Z}function eB(e){return e>=s.LATIN_SMALL_A&&e<=s.LATIN_SMALL_Z}function eH(e){return eB(e)||eU(e)}function eG(e){return eH(e)||eF(e)}function ez(e){return e>=s.LATIN_CAPITAL_A&&e<=s.LATIN_CAPITAL_F}function e$(e){return e>=s.LATIN_SMALL_A&&e<=s.LATIN_SMALL_F}function ej(e){return e<=65535?String.fromCharCode(e):String.fromCharCode((e-=65536)>>>10&1023|55296)+String.fromCharCode(56320|1023&e)}function eV(e){return String.fromCharCode(e+32)}function eW(e,t){let n=i[++e],r=++e,a=r+n-1;for(;r<=a;){let e=r+a>>>1,o=i[e];if(ot))return i[e+n];a=e-1}}return -1}class eK{constructor(){this.preprocessor=new 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t}_isCharacterReferenceInAttribute(){return this.returnState===V||this.returnState===W||this.returnState===K}_isCharacterReferenceAttributeQuirk(e){if(!e&&this._isCharacterReferenceInAttribute()){let e=this._consume();return this._unconsume(),e===s.EQUALS_SIGN||eG(e)}return!1}_flushCodePointsConsumedAsCharacterReference(){if(this._isCharacterReferenceInAttribute())for(let e=0;e")):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.state=I,this._emitChars(a.REPLACEMENT_CHARACTER)):e===s.EOF?(this._err(o.eofInScriptHtmlCommentLikeText),this._emitEOFToken()):(this.state=I,this._emitCodePoint(e))}[x](e){e===s.SOLIDUS?(this.tempBuff=[],this.state=L):eH(e)?(this.tempBuff=[],this._emitChars("<"),this._reconsumeInState(P)):(this._emitChars("<"),this._reconsumeInState(I))}[L](e){eH(e)?(this._createEndTagToken(),this._reconsumeInState(D)):(this._emitChars("")):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.state=M,this._emitChars(a.REPLACEMENT_CHARACTER)):e===s.EOF?(this._err(o.eofInScriptHtmlCommentLikeText),this._emitEOFToken()):(this.state=M,this._emitCodePoint(e))}[B](e){e===s.SOLIDUS?(this.tempBuff=[],this.state=H,this._emitChars("/")):this._reconsumeInState(M)}[H](e){eM(e)||e===s.SOLIDUS||e===s.GREATER_THAN_SIGN?(this.state=this._isTempBufferEqualToScriptString()?I:M,this._emitCodePoint(e)):eU(e)?(this.tempBuff.push(e+32),this._emitCodePoint(e)):eB(e)?(this.tempBuff.push(e),this._emitCodePoint(e)):this._reconsumeInState(M)}[G](e){eM(e)||(e===s.SOLIDUS||e===s.GREATER_THAN_SIGN||e===s.EOF?this._reconsumeInState($):e===s.EQUALS_SIGN?(this._err(o.unexpectedEqualsSignBeforeAttributeName),this._createAttr("="),this.state=z):(this._createAttr(""),this._reconsumeInState(z)))}[z](e){eM(e)||e===s.SOLIDUS||e===s.GREATER_THAN_SIGN||e===s.EOF?(this._leaveAttrName($),this._unconsume()):e===s.EQUALS_SIGN?this._leaveAttrName(j):eU(e)?this.currentAttr.name+=eV(e):e===s.QUOTATION_MARK||e===s.APOSTROPHE||e===s.LESS_THAN_SIGN?(this._err(o.unexpectedCharacterInAttributeName),this.currentAttr.name+=ej(e)):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentAttr.name+=a.REPLACEMENT_CHARACTER):this.currentAttr.name+=ej(e)}[$](e){eM(e)||(e===s.SOLIDUS?this.state=Y:e===s.EQUALS_SIGN?this.state=j:e===s.GREATER_THAN_SIGN?(this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInTag),this._emitEOFToken()):(this._createAttr(""),this._reconsumeInState(z)))}[j](e){eM(e)||(e===s.QUOTATION_MARK?this.state=V:e===s.APOSTROPHE?this.state=W:e===s.GREATER_THAN_SIGN?(this._err(o.missingAttributeValue),this.state=u,this._emitCurrentToken()):this._reconsumeInState(K))}[V](e){e===s.QUOTATION_MARK?this.state=Z:e===s.AMPERSAND?(this.returnState=V,this.state=eC):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentAttr.value+=a.REPLACEMENT_CHARACTER):e===s.EOF?(this._err(o.eofInTag),this._emitEOFToken()):this.currentAttr.value+=ej(e)}[W](e){e===s.APOSTROPHE?this.state=Z:e===s.AMPERSAND?(this.returnState=W,this.state=eC):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentAttr.value+=a.REPLACEMENT_CHARACTER):e===s.EOF?(this._err(o.eofInTag),this._emitEOFToken()):this.currentAttr.value+=ej(e)}[K](e){eM(e)?this._leaveAttrValue(G):e===s.AMPERSAND?(this.returnState=K,this.state=eC):e===s.GREATER_THAN_SIGN?(this._leaveAttrValue(u),this._emitCurrentToken()):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentAttr.value+=a.REPLACEMENT_CHARACTER):e===s.QUOTATION_MARK||e===s.APOSTROPHE||e===s.LESS_THAN_SIGN||e===s.EQUALS_SIGN||e===s.GRAVE_ACCENT?(this._err(o.unexpectedCharacterInUnquotedAttributeValue),this.currentAttr.value+=ej(e)):e===s.EOF?(this._err(o.eofInTag),this._emitEOFToken()):this.currentAttr.value+=ej(e)}[Z](e){eM(e)?this._leaveAttrValue(G):e===s.SOLIDUS?this._leaveAttrValue(Y):e===s.GREATER_THAN_SIGN?(this._leaveAttrValue(u),this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInTag),this._emitEOFToken()):(this._err(o.missingWhitespaceBetweenAttributes),this._reconsumeInState(G))}[Y](e){e===s.GREATER_THAN_SIGN?(this.currentToken.selfClosing=!0,this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInTag),this._emitEOFToken()):(this._err(o.unexpectedSolidusInTag),this._reconsumeInState(G))}[q](e){e===s.GREATER_THAN_SIGN?(this.state=u,this._emitCurrentToken()):e===s.EOF?(this._emitCurrentToken(),this._emitEOFToken()):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentToken.data+=a.REPLACEMENT_CHARACTER):this.currentToken.data+=ej(e)}[X](e){this._consumeSequenceIfMatch(l.DASH_DASH_STRING,e,!0)?(this._createCommentToken(),this.state=Q):this._consumeSequenceIfMatch(l.DOCTYPE_STRING,e,!1)?this.state=el:this._consumeSequenceIfMatch(l.CDATA_START_STRING,e,!0)?this.allowCDATA?this.state=ek:(this._err(o.cdataInHtmlContent),this._createCommentToken(),this.currentToken.data="[CDATA[",this.state=q):this._ensureHibernation()||(this._err(o.incorrectlyOpenedComment),this._createCommentToken(),this._reconsumeInState(q))}[Q](e){e===s.HYPHEN_MINUS?this.state=J:e===s.GREATER_THAN_SIGN?(this._err(o.abruptClosingOfEmptyComment),this.state=u,this._emitCurrentToken()):this._reconsumeInState(ee)}[J](e){e===s.HYPHEN_MINUS?this.state=eo:e===s.GREATER_THAN_SIGN?(this._err(o.abruptClosingOfEmptyComment),this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInComment),this._emitCurrentToken(),this._emitEOFToken()):(this.currentToken.data+="-",this._reconsumeInState(ee))}[ee](e){e===s.HYPHEN_MINUS?this.state=ei:e===s.LESS_THAN_SIGN?(this.currentToken.data+="<",this.state=et):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentToken.data+=a.REPLACEMENT_CHARACTER):e===s.EOF?(this._err(o.eofInComment),this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.data+=ej(e)}[et](e){e===s.EXCLAMATION_MARK?(this.currentToken.data+="!",this.state=en):e===s.LESS_THAN_SIGN?this.currentToken.data+="!":this._reconsumeInState(ee)}[en](e){e===s.HYPHEN_MINUS?this.state=er:this._reconsumeInState(ee)}[er](e){e===s.HYPHEN_MINUS?this.state=ea:this._reconsumeInState(ei)}[ea](e){e!==s.GREATER_THAN_SIGN&&e!==s.EOF&&this._err(o.nestedComment),this._reconsumeInState(eo)}[ei](e){e===s.HYPHEN_MINUS?this.state=eo:e===s.EOF?(this._err(o.eofInComment),this._emitCurrentToken(),this._emitEOFToken()):(this.currentToken.data+="-",this._reconsumeInState(ee))}[eo](e){e===s.GREATER_THAN_SIGN?(this.state=u,this._emitCurrentToken()):e===s.EXCLAMATION_MARK?this.state=es:e===s.HYPHEN_MINUS?this.currentToken.data+="-":e===s.EOF?(this._err(o.eofInComment),this._emitCurrentToken(),this._emitEOFToken()):(this.currentToken.data+="--",this._reconsumeInState(ee))}[es](e){e===s.HYPHEN_MINUS?(this.currentToken.data+="--!",this.state=ei):e===s.GREATER_THAN_SIGN?(this._err(o.incorrectlyClosedComment),this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInComment),this._emitCurrentToken(),this._emitEOFToken()):(this.currentToken.data+="--!",this._reconsumeInState(ee))}[el](e){eM(e)?this.state=ec:e===s.GREATER_THAN_SIGN?this._reconsumeInState(ec):e===s.EOF?(this._err(o.eofInDoctype),this._createDoctypeToken(null),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.missingWhitespaceBeforeDoctypeName),this._reconsumeInState(ec))}[ec](e){eM(e)||(eU(e)?(this._createDoctypeToken(eV(e)),this.state=eu):e===s.NULL?(this._err(o.unexpectedNullCharacter),this._createDoctypeToken(a.REPLACEMENT_CHARACTER),this.state=eu):e===s.GREATER_THAN_SIGN?(this._err(o.missingDoctypeName),this._createDoctypeToken(null),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this.state=u):e===s.EOF?(this._err(o.eofInDoctype),this._createDoctypeToken(null),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._createDoctypeToken(ej(e)),this.state=eu))}[eu](e){eM(e)?this.state=ed:e===s.GREATER_THAN_SIGN?(this.state=u,this._emitCurrentToken()):eU(e)?this.currentToken.name+=eV(e):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentToken.name+=a.REPLACEMENT_CHARACTER):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.name+=ej(e)}[ed](e){!eM(e)&&(e===s.GREATER_THAN_SIGN?(this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this._consumeSequenceIfMatch(l.PUBLIC_STRING,e,!1)?this.state=ep:this._consumeSequenceIfMatch(l.SYSTEM_STRING,e,!1)?this.state=eE:this._ensureHibernation()||(this._err(o.invalidCharacterSequenceAfterDoctypeName),this.currentToken.forceQuirks=!0,this._reconsumeInState(e_)))}[ep](e){eM(e)?this.state=em:e===s.QUOTATION_MARK?(this._err(o.missingWhitespaceAfterDoctypePublicKeyword),this.currentToken.publicId="",this.state=eg):e===s.APOSTROPHE?(this._err(o.missingWhitespaceAfterDoctypePublicKeyword),this.currentToken.publicId="",this.state=ef):e===s.GREATER_THAN_SIGN?(this._err(o.missingDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.missingQuoteBeforeDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(e_))}[em](e){eM(e)||(e===s.QUOTATION_MARK?(this.currentToken.publicId="",this.state=eg):e===s.APOSTROPHE?(this.currentToken.publicId="",this.state=ef):e===s.GREATER_THAN_SIGN?(this._err(o.missingDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.missingQuoteBeforeDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(e_)))}[eg](e){e===s.QUOTATION_MARK?this.state=eh:e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentToken.publicId+=a.REPLACEMENT_CHARACTER):e===s.GREATER_THAN_SIGN?(this._err(o.abruptDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this.state=u):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.publicId+=ej(e)}[ef](e){e===s.APOSTROPHE?this.state=eh:e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentToken.publicId+=a.REPLACEMENT_CHARACTER):e===s.GREATER_THAN_SIGN?(this._err(o.abruptDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this.state=u):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.publicId+=ej(e)}[eh](e){eM(e)?this.state=eb:e===s.GREATER_THAN_SIGN?(this.state=u,this._emitCurrentToken()):e===s.QUOTATION_MARK?(this._err(o.missingWhitespaceBetweenDoctypePublicAndSystemIdentifiers),this.currentToken.systemId="",this.state=eS):e===s.APOSTROPHE?(this._err(o.missingWhitespaceBetweenDoctypePublicAndSystemIdentifiers),this.currentToken.systemId="",this.state=ey):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.missingQuoteBeforeDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(e_))}[eb](e){eM(e)||(e===s.GREATER_THAN_SIGN?(this._emitCurrentToken(),this.state=u):e===s.QUOTATION_MARK?(this.currentToken.systemId="",this.state=eS):e===s.APOSTROPHE?(this.currentToken.systemId="",this.state=ey):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.missingQuoteBeforeDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(e_)))}[eE](e){eM(e)?this.state=eT:e===s.QUOTATION_MARK?(this._err(o.missingWhitespaceAfterDoctypeSystemKeyword),this.currentToken.systemId="",this.state=eS):e===s.APOSTROPHE?(this._err(o.missingWhitespaceAfterDoctypeSystemKeyword),this.currentToken.systemId="",this.state=ey):e===s.GREATER_THAN_SIGN?(this._err(o.missingDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.missingQuoteBeforeDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(e_))}[eT](e){eM(e)||(e===s.QUOTATION_MARK?(this.currentToken.systemId="",this.state=eS):e===s.APOSTROPHE?(this.currentToken.systemId="",this.state=ey):e===s.GREATER_THAN_SIGN?(this._err(o.missingDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.missingQuoteBeforeDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(e_)))}[eS](e){e===s.QUOTATION_MARK?this.state=eA:e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentToken.systemId+=a.REPLACEMENT_CHARACTER):e===s.GREATER_THAN_SIGN?(this._err(o.abruptDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this.state=u):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.systemId+=ej(e)}[ey](e){e===s.APOSTROPHE?this.state=eA:e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentToken.systemId+=a.REPLACEMENT_CHARACTER):e===s.GREATER_THAN_SIGN?(this._err(o.abruptDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this.state=u):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.systemId+=ej(e)}[eA](e){eM(e)||(e===s.GREATER_THAN_SIGN?(this._emitCurrentToken(),this.state=u):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.unexpectedCharacterAfterDoctypeSystemIdentifier),this._reconsumeInState(e_)))}[e_](e){e===s.GREATER_THAN_SIGN?(this._emitCurrentToken(),this.state=u):e===s.NULL?this._err(o.unexpectedNullCharacter):e===s.EOF&&(this._emitCurrentToken(),this._emitEOFToken())}[ek](e){e===s.RIGHT_SQUARE_BRACKET?this.state=ev:e===s.EOF?(this._err(o.eofInCdata),this._emitEOFToken()):this._emitCodePoint(e)}[ev](e){e===s.RIGHT_SQUARE_BRACKET?this.state=eN:(this._emitChars("]"),this._reconsumeInState(ek))}[eN](e){e===s.GREATER_THAN_SIGN?this.state=u:e===s.RIGHT_SQUARE_BRACKET?this._emitChars("]"):(this._emitChars("]]"),this._reconsumeInState(ek))}[eC](e){this.tempBuff=[s.AMPERSAND],e===s.NUMBER_SIGN?(this.tempBuff.push(e),this.state=eO):eG(e)?this._reconsumeInState(eR):(this._flushCodePointsConsumedAsCharacterReference(),this._reconsumeInState(this.returnState))}[eR](e){let t=this._matchNamedCharacterReference(e);if(this._ensureHibernation())this.tempBuff=[s.AMPERSAND];else if(t){let e=this.tempBuff[this.tempBuff.length-1]===s.SEMICOLON;this._isCharacterReferenceAttributeQuirk(e)||(e||this._errOnNextCodePoint(o.missingSemicolonAfterCharacterReference),this.tempBuff=t),this._flushCodePointsConsumedAsCharacterReference(),this.state=this.returnState}else this._flushCodePointsConsumedAsCharacterReference(),this.state=eI}[eI](e){eG(e)?this._isCharacterReferenceInAttribute()?this.currentAttr.value+=ej(e):this._emitCodePoint(e):(e===s.SEMICOLON&&this._err(o.unknownNamedCharacterReference),this._reconsumeInState(this.returnState))}[eO](e){this.charRefCode=0,e===s.LATIN_SMALL_X||e===s.LATIN_CAPITAL_X?(this.tempBuff.push(e),this.state=ew):this._reconsumeInState(ex)}[ew](e){eF(e)||ez(e)||e$(e)?this._reconsumeInState(eL):(this._err(o.absenceOfDigitsInNumericCharacterReference),this._flushCodePointsConsumedAsCharacterReference(),this._reconsumeInState(this.returnState))}[ex](e){eF(e)?this._reconsumeInState(eD):(this._err(o.absenceOfDigitsInNumericCharacterReference),this._flushCodePointsConsumedAsCharacterReference(),this._reconsumeInState(this.returnState))}[eL](e){ez(e)?this.charRefCode=16*this.charRefCode+e-55:e$(e)?this.charRefCode=16*this.charRefCode+e-87:eF(e)?this.charRefCode=16*this.charRefCode+e-48:e===s.SEMICOLON?this.state=eP:(this._err(o.missingSemicolonAfterCharacterReference),this._reconsumeInState(eP))}[eD](e){eF(e)?this.charRefCode=10*this.charRefCode+e-48:e===s.SEMICOLON?this.state=eP:(this._err(o.missingSemicolonAfterCharacterReference),this._reconsumeInState(eP))}[eP](){if(this.charRefCode===s.NULL)this._err(o.nullCharacterReference),this.charRefCode=s.REPLACEMENT_CHARACTER;else if(this.charRefCode>1114111)this._err(o.characterReferenceOutsideUnicodeRange),this.charRefCode=s.REPLACEMENT_CHARACTER;else if(a.isSurrogate(this.charRefCode))this._err(o.surrogateCharacterReference),this.charRefCode=s.REPLACEMENT_CHARACTER;else if(a.isUndefinedCodePoint(this.charRefCode))this._err(o.noncharacterCharacterReference);else if(a.isControlCodePoint(this.charRefCode)||this.charRefCode===s.CARRIAGE_RETURN){this._err(o.controlCharacterReference);let e=c[this.charRefCode];e&&(this.charRefCode=e)}this.tempBuff=[this.charRefCode],this._flushCodePointsConsumedAsCharacterReference(),this._reconsumeInState(this.returnState)}}eK.CHARACTER_TOKEN="CHARACTER_TOKEN",eK.NULL_CHARACTER_TOKEN="NULL_CHARACTER_TOKEN",eK.WHITESPACE_CHARACTER_TOKEN="WHITESPACE_CHARACTER_TOKEN",eK.START_TAG_TOKEN="START_TAG_TOKEN",eK.END_TAG_TOKEN="END_TAG_TOKEN",eK.COMMENT_TOKEN="COMMENT_TOKEN",eK.DOCTYPE_TOKEN="DOCTYPE_TOKEN",eK.EOF_TOKEN="EOF_TOKEN",eK.HIBERNATION_TOKEN="HIBERNATION_TOKEN",eK.MODE={DATA:u,RCDATA:d,RAWTEXT:p,SCRIPT_DATA:m,PLAINTEXT:g},eK.getTokenAttr=function(e,t){for(let n=e.attrs.length-1;n>=0;n--)if(e.attrs[n].name===t)return e.attrs[n].value;return null},e.exports=eK},5482:function(e){"use strict";e.exports=new 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this._adoptNodes(r,a),a}_bootstrap(e,t){this.tokenizer=new r(this.options),this.stopped=!1,this.insertionMode=y,this.originalInsertionMode="",this.document=e,this.fragmentContext=t,this.headElement=null,this.formElement=null,this.openElements=new a(this.document,this.treeAdapter),this.activeFormattingElements=new i(this.treeAdapter),this.tmplInsertionModeStack=[],this.tmplInsertionModeStackTop=-1,this.currentTmplInsertionMode=null,this.pendingCharacterTokens=[],this.hasNonWhitespacePendingCharacterToken=!1,this.framesetOk=!0,this.skipNextNewLine=!1,this.fosterParentingEnabled=!1}_err(){}_runParsingLoop(e){for(;!this.stopped;){this._setupTokenizerCDATAMode();let t=this.tokenizer.getNextToken();if(t.type===r.HIBERNATION_TOKEN)break;if(this.skipNextNewLine&&(this.skipNextNewLine=!1,t.type===r.WHITESPACE_CHARACTER_TOKEN&&"\n"===t.chars[0])){if(1===t.chars.length)continue;t.chars=t.chars.substr(1)}if(this._processInputToken(t),e&&this.pendingScript)break}}runParsingLoopForCurrentChunk(e,t){if(this._runParsingLoop(t),t&&this.pendingScript){let e=this.pendingScript;this.pendingScript=null,t(e);return}e&&e()}_setupTokenizerCDATAMode(){let e=this._getAdjustedCurrentElement();this.tokenizer.allowCDATA=e&&e!==this.document&&this.treeAdapter.getNamespaceURI(e)!==b.HTML&&!this._isIntegrationPoint(e)}_switchToTextParsing(e,t){this._insertElement(e,b.HTML),this.tokenizer.state=t,this.originalInsertionMode=this.insertionMode,this.insertionMode=R}switchToPlaintextParsing(){this.insertionMode=R,this.originalInsertionMode=C,this.tokenizer.state=r.MODE.PLAINTEXT}_getAdjustedCurrentElement(){return 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t=this.openElements.currentTmplContent||this.openElements.current;this.treeAdapter.appendChild(t,e)}}_appendElement(e,t){let n=this.treeAdapter.createElement(e.tagName,t,e.attrs);this._attachElementToTree(n)}_insertElement(e,t){let n=this.treeAdapter.createElement(e.tagName,t,e.attrs);this._attachElementToTree(n),this.openElements.push(n)}_insertFakeElement(e){let t=this.treeAdapter.createElement(e,b.HTML,[]);this._attachElementToTree(t),this.openElements.push(t)}_insertTemplate(e){let t=this.treeAdapter.createElement(e.tagName,b.HTML,e.attrs),n=this.treeAdapter.createDocumentFragment();this.treeAdapter.setTemplateContent(t,n),this._attachElementToTree(t),this.openElements.push(t)}_insertFakeRootElement(){let e=this.treeAdapter.createElement(h.HTML,b.HTML,[]);this.treeAdapter.appendChild(this.openElements.current,e),this.openElements.push(e)}_appendCommentNode(e,t){let n=this.treeAdapter.createCommentNode(e.data);this.treeAdapter.appendChild(t,n)}_insertCharacters(e){if(this._shouldFosterParentOnInsertion())this._fosterParentText(e.chars);else{let t=this.openElements.currentTmplContent||this.openElements.current;this.treeAdapter.insertText(t,e.chars)}}_adoptNodes(e,t){for(let n=this.treeAdapter.getFirstChild(e);n;n=this.treeAdapter.getFirstChild(e))this.treeAdapter.detachNode(n),this.treeAdapter.appendChild(t,n)}_shouldProcessTokenInForeignContent(e){let t=this._getAdjustedCurrentElement();if(!t||t===this.document)return!1;let n=this.treeAdapter.getNamespaceURI(t);if(n===b.HTML||this.treeAdapter.getTagName(t)===h.ANNOTATION_XML&&n===b.MATHML&&e.type===r.START_TAG_TOKEN&&e.tagName===h.SVG)return!1;let a=e.type===r.CHARACTER_TOKEN||e.type===r.NULL_CHARACTER_TOKEN||e.type===r.WHITESPACE_CHARACTER_TOKEN,i=e.type===r.START_TAG_TOKEN&&e.tagName!==h.MGLYPH&&e.tagName!==h.MALIGNMARK;return!((i||a)&&this._isIntegrationPoint(t,b.MATHML)||(e.type===r.START_TAG_TOKEN||a)&&this._isIntegrationPoint(t,b.HTML))&&e.type!==r.EOF_TOKEN}_processToken(e){W[this.insertionMode][e.type](this,e)}_processTokenInBodyMode(e){W[C][e.type](this,e)}_processTokenInForeignContent(e){e.type===r.CHARACTER_TOKEN?(this._insertCharacters(e),this.framesetOk=!1):e.type===r.NULL_CHARACTER_TOKEN?(e.chars=g.REPLACEMENT_CHARACTER,this._insertCharacters(e)):e.type===r.WHITESPACE_CHARACTER_TOKEN?Q(this,e):e.type===r.COMMENT_TOKEN?q(this,e):e.type===r.START_TAG_TOKEN?function(e,t){if(p.causesExit(t)&&!e.fragmentContext){for(;e.treeAdapter.getNamespaceURI(e.openElements.current)!==b.HTML&&!e._isIntegrationPoint(e.openElements.current);)e.openElements.pop();e._processToken(t)}else{let n=e._getAdjustedCurrentElement(),r=e.treeAdapter.getNamespaceURI(n);r===b.MATHML?p.adjustTokenMathMLAttrs(t):r===b.SVG&&(p.adjustTokenSVGTagName(t),p.adjustTokenSVGAttrs(t)),p.adjustTokenXMLAttrs(t),t.selfClosing?e._appendElement(t,r):e._insertElement(t,r),t.ackSelfClosing=!0}}(this,e):e.type===r.END_TAG_TOKEN&&function(e,t){for(let n=e.openElements.stackTop;n>0;n--){let r=e.openElements.items[n];if(e.treeAdapter.getNamespaceURI(r)===b.HTML){e._processToken(t);break}if(e.treeAdapter.getTagName(r).toLowerCase()===t.tagName){e.openElements.popUntilElementPopped(r);break}}}(this,e)}_processInputToken(e){this._shouldProcessTokenInForeignContent(e)?this._processTokenInForeignContent(e):this._processToken(e),e.type===r.START_TAG_TOKEN&&e.selfClosing&&!e.ackSelfClosing&&this._err(m.nonVoidHtmlElementStartTagWithTrailingSolidus)}_isIntegrationPoint(e,t){let n=this.treeAdapter.getTagName(e),r=this.treeAdapter.getNamespaceURI(e),a=this.treeAdapter.getAttrList(e);return p.isIntegrationPoint(n,r,a,t)}_reconstructActiveFormattingElements(){let e=this.activeFormattingElements.length;if(e){let t=e,n=null;do if(t--,(n=this.activeFormattingElements.entries[t]).type===i.MARKER_ENTRY||this.openElements.contains(n.element)){t++;break}while(t>0);for(let r=t;r=0;e--){let n=this.openElements.items[e];0===e&&(t=!0,this.fragmentContext&&(n=this.fragmentContext));let r=this.treeAdapter.getTagName(n),a=j[r];if(a){this.insertionMode=a;break}if(t||r!==h.TD&&r!==h.TH){if(t||r!==h.HEAD){if(r===h.SELECT){this._resetInsertionModeForSelect(e);break}if(r===h.TEMPLATE){this.insertionMode=this.currentTmplInsertionMode;break}if(r===h.HTML){this.insertionMode=this.headElement?N:_;break}else if(t){this.insertionMode=C;break}}else{this.insertionMode=k;break}}else{this.insertionMode=P;break}}}_resetInsertionModeForSelect(e){if(e>0)for(let t=e-1;t>0;t--){let e=this.openElements.items[t],n=this.treeAdapter.getTagName(e);if(n===h.TEMPLATE)break;if(n===h.TABLE){this.insertionMode=F;return}}this.insertionMode=M}_pushTmplInsertionMode(e){this.tmplInsertionModeStack.push(e),this.tmplInsertionModeStackTop++,this.currentTmplInsertionMode=e}_popTmplInsertionMode(){this.tmplInsertionModeStack.pop(),this.tmplInsertionModeStackTop--,this.currentTmplInsertionMode=this.tmplInsertionModeStack[this.tmplInsertionModeStackTop]}_isElementCausesFosterParenting(e){let t=this.treeAdapter.getTagName(e);return t===h.TABLE||t===h.TBODY||t===h.TFOOT||t===h.THEAD||t===h.TR}_shouldFosterParentOnInsertion(){return this.fosterParentingEnabled&&this._isElementCausesFosterParenting(this.openElements.current)}_findFosterParentingLocation(){let e={parent:null,beforeElement:null};for(let t=this.openElements.stackTop;t>=0;t--){let n=this.openElements.items[t],r=this.treeAdapter.getTagName(n),a=this.treeAdapter.getNamespaceURI(n);if(r===h.TEMPLATE&&a===b.HTML){e.parent=this.treeAdapter.getTemplateContent(n);break}if(r===h.TABLE){e.parent=this.treeAdapter.getParentNode(n),e.parent?e.beforeElement=n:e.parent=this.openElements.items[t-1];break}}return e.parent||(e.parent=this.openElements.items[0]),e}_fosterParentElement(e){let t=this._findFosterParentingLocation();t.beforeElement?this.treeAdapter.insertBefore(t.parent,e,t.beforeElement):this.treeAdapter.appendChild(t.parent,e)}_fosterParentText(e){let t=this._findFosterParentingLocation();t.beforeElement?this.treeAdapter.insertTextBefore(t.parent,e,t.beforeElement):this.treeAdapter.insertText(t.parent,e)}_isSpecialElement(e){let t=this.treeAdapter.getTagName(e),n=this.treeAdapter.getNamespaceURI(e);return f.SPECIAL_ELEMENTS[n][t]}}},46519:function(e,t,n){"use strict";let r=n(16152),a=r.TAG_NAMES,i=r.NAMESPACES;function o(e){switch(e.length){case 1:return e===a.P;case 2:return e===a.RB||e===a.RP||e===a.RT||e===a.DD||e===a.DT||e===a.LI;case 3:return e===a.RTC;case 6:return e===a.OPTION;case 8:return e===a.OPTGROUP}return!1}function s(e,t){switch(e.length){case 2:if(e===a.TD||e===a.TH)return t===i.HTML;if(e===a.MI||e===a.MO||e===a.MN||e===a.MS)return t===i.MATHML;break;case 4:if(e===a.HTML)return t===i.HTML;if(e===a.DESC)return t===i.SVG;break;case 5:if(e===a.TABLE)return t===i.HTML;if(e===a.MTEXT)return t===i.MATHML;if(e===a.TITLE)return t===i.SVG;break;case 6:return(e===a.APPLET||e===a.OBJECT)&&t===i.HTML;case 7:return(e===a.CAPTION||e===a.MARQUEE)&&t===i.HTML;case 8:return e===a.TEMPLATE&&t===i.HTML;case 13:return e===a.FOREIGN_OBJECT&&t===i.SVG;case 14:return e===a.ANNOTATION_XML&&t===i.MATHML}return!1}e.exports=class{constructor(e,t){this.stackTop=-1,this.items=[],this.current=e,this.currentTagName=null,this.currentTmplContent=null,this.tmplCount=0,this.treeAdapter=t}_indexOf(e){let t=-1;for(let n=this.stackTop;n>=0;n--)if(this.items[n]===e){t=n;break}return t}_isInTemplate(){return this.currentTagName===a.TEMPLATE&&this.treeAdapter.getNamespaceURI(this.current)===i.HTML}_updateCurrentElement(){this.current=this.items[this.stackTop],this.currentTagName=this.current&&this.treeAdapter.getTagName(this.current),this.currentTmplContent=this._isInTemplate()?this.treeAdapter.getTemplateContent(this.current):null}push(e){this.items[++this.stackTop]=e,this._updateCurrentElement(),this._isInTemplate()&&this.tmplCount++}pop(){this.stackTop--,this.tmplCount>0&&this._isInTemplate()&&this.tmplCount--,this._updateCurrentElement()}replace(e,t){let n=this._indexOf(e);this.items[n]=t,n===this.stackTop&&this._updateCurrentElement()}insertAfter(e,t){let n=this._indexOf(e)+1;this.items.splice(n,0,t),n===++this.stackTop&&this._updateCurrentElement()}popUntilTagNamePopped(e){for(;this.stackTop>-1;){let t=this.currentTagName,n=this.treeAdapter.getNamespaceURI(this.current);if(this.pop(),t===e&&n===i.HTML)break}}popUntilElementPopped(e){for(;this.stackTop>-1;){let t=this.current;if(this.pop(),t===e)break}}popUntilNumberedHeaderPopped(){for(;this.stackTop>-1;){let e=this.currentTagName,t=this.treeAdapter.getNamespaceURI(this.current);if(this.pop(),e===a.H1||e===a.H2||e===a.H3||e===a.H4||e===a.H5||e===a.H6&&t===i.HTML)break}}popUntilTableCellPopped(){for(;this.stackTop>-1;){let e=this.currentTagName,t=this.treeAdapter.getNamespaceURI(this.current);if(this.pop(),e===a.TD||e===a.TH&&t===i.HTML)break}}popAllUpToHtmlElement(){this.stackTop=0,this._updateCurrentElement()}clearBackToTableContext(){for(;this.currentTagName!==a.TABLE&&this.currentTagName!==a.TEMPLATE&&this.currentTagName!==a.HTML||this.treeAdapter.getNamespaceURI(this.current)!==i.HTML;)this.pop()}clearBackToTableBodyContext(){for(;this.currentTagName!==a.TBODY&&this.currentTagName!==a.TFOOT&&this.currentTagName!==a.THEAD&&this.currentTagName!==a.TEMPLATE&&this.currentTagName!==a.HTML||this.treeAdapter.getNamespaceURI(this.current)!==i.HTML;)this.pop()}clearBackToTableRowContext(){for(;this.currentTagName!==a.TR&&this.currentTagName!==a.TEMPLATE&&this.currentTagName!==a.HTML||this.treeAdapter.getNamespaceURI(this.current)!==i.HTML;)this.pop()}remove(e){for(let t=this.stackTop;t>=0;t--)if(this.items[t]===e){this.items.splice(t,1),this.stackTop--,this._updateCurrentElement();break}}tryPeekProperlyNestedBodyElement(){let e=this.items[1];return e&&this.treeAdapter.getTagName(e)===a.BODY?e:null}contains(e){return this._indexOf(e)>-1}getCommonAncestor(e){let t=this._indexOf(e);return--t>=0?this.items[t]:null}isRootHtmlElementCurrent(){return 0===this.stackTop&&this.currentTagName===a.HTML}hasInScope(e){for(let t=this.stackTop;t>=0;t--){let n=this.treeAdapter.getTagName(this.items[t]),r=this.treeAdapter.getNamespaceURI(this.items[t]);if(n===e&&r===i.HTML)break;if(s(n,r))return!1}return!0}hasNumberedHeaderInScope(){for(let e=this.stackTop;e>=0;e--){let t=this.treeAdapter.getTagName(this.items[e]),n=this.treeAdapter.getNamespaceURI(this.items[e]);if((t===a.H1||t===a.H2||t===a.H3||t===a.H4||t===a.H5||t===a.H6)&&n===i.HTML)break;if(s(t,n))return!1}return!0}hasInListItemScope(e){for(let t=this.stackTop;t>=0;t--){let n=this.treeAdapter.getTagName(this.items[t]),r=this.treeAdapter.getNamespaceURI(this.items[t]);if(n===e&&r===i.HTML)break;if((n===a.UL||n===a.OL)&&r===i.HTML||s(n,r))return!1}return!0}hasInButtonScope(e){for(let t=this.stackTop;t>=0;t--){let n=this.treeAdapter.getTagName(this.items[t]),r=this.treeAdapter.getNamespaceURI(this.items[t]);if(n===e&&r===i.HTML)break;if(n===a.BUTTON&&r===i.HTML||s(n,r))return!1}return!0}hasInTableScope(e){for(let t=this.stackTop;t>=0;t--){let n=this.treeAdapter.getTagName(this.items[t]),r=this.treeAdapter.getNamespaceURI(this.items[t]);if(r===i.HTML){if(n===e)break;if(n===a.TABLE||n===a.TEMPLATE||n===a.HTML)return!1}}return!0}hasTableBodyContextInTableScope(){for(let e=this.stackTop;e>=0;e--){let t=this.treeAdapter.getTagName(this.items[e]),n=this.treeAdapter.getNamespaceURI(this.items[e]);if(n===i.HTML){if(t===a.TBODY||t===a.THEAD||t===a.TFOOT)break;if(t===a.TABLE||t===a.HTML)return!1}}return!0}hasInSelectScope(e){for(let t=this.stackTop;t>=0;t--){let n=this.treeAdapter.getTagName(this.items[t]),r=this.treeAdapter.getNamespaceURI(this.items[t]);if(r===i.HTML){if(n===e)break;if(n!==a.OPTION&&n!==a.OPTGROUP)return!1}}return!0}generateImpliedEndTags(){for(;o(this.currentTagName);)this.pop()}generateImpliedEndTagsThoroughly(){for(;function(e){switch(e.length){case 1:return e===a.P;case 2:return e===a.RB||e===a.RP||e===a.RT||e===a.DD||e===a.DT||e===a.LI||e===a.TD||e===a.TH||e===a.TR;case 3:return e===a.RTC;case 5:return e===a.TBODY||e===a.TFOOT||e===a.THEAD;case 6:return e===a.OPTION;case 7:return e===a.CAPTION;case 8:return e===a.OPTGROUP||e===a.COLGROUP}return!1}(this.currentTagName);)this.pop()}generateImpliedEndTagsWithExclusion(e){for(;o(this.currentTagName)&&this.currentTagName!==e;)this.pop()}}},55763:function(e,t,n){"use strict";let r=n(77118),a=n(54284),i=n(5482),o=n(41734),s=a.CODE_POINTS,l=a.CODE_POINT_SEQUENCES,c={128:8364,130:8218,131:402,132:8222,133:8230,134:8224,135:8225,136:710,137:8240,138:352,139:8249,140:338,142:381,145:8216,146:8217,147:8220,148:8221,149:8226,150:8211,151:8212,152:732,153:8482,154:353,155:8250,156:339,158:382,159:376},u="DATA_STATE",d="RCDATA_STATE",p="RAWTEXT_STATE",m="SCRIPT_DATA_STATE",g="PLAINTEXT_STATE",f="TAG_OPEN_STATE",h="END_TAG_OPEN_STATE",b="TAG_NAME_STATE",E="RCDATA_LESS_THAN_SIGN_STATE",T="RCDATA_END_TAG_OPEN_STATE",S="RCDATA_END_TAG_NAME_STATE",y="RAWTEXT_LESS_THAN_SIGN_STATE",A="RAWTEXT_END_TAG_OPEN_STATE",_="RAWTEXT_END_TAG_NAME_STATE",k="SCRIPT_DATA_LESS_THAN_SIGN_STATE",v="SCRIPT_DATA_END_TAG_OPEN_STATE",N="SCRIPT_DATA_END_TAG_NAME_STATE",C="SCRIPT_DATA_ESCAPE_START_STATE",R="SCRIPT_DATA_ESCAPE_START_DASH_STATE",I="SCRIPT_DATA_ESCAPED_STATE",O="SCRIPT_DATA_ESCAPED_DASH_STATE",w="SCRIPT_DATA_ESCAPED_DASH_DASH_STATE",x="SCRIPT_DATA_ESCAPED_LESS_THAN_SIGN_STATE",L="SCRIPT_DATA_ESCAPED_END_TAG_OPEN_STATE",D="SCRIPT_DATA_ESCAPED_END_TAG_NAME_STATE",P="SCRIPT_DATA_DOUBLE_ESCAPE_START_STATE",M="SCRIPT_DATA_DOUBLE_ESCAPED_STATE",F="SCRIPT_DATA_DOUBLE_ESCAPED_DASH_STATE",U="SCRIPT_DATA_DOUBLE_ESCAPED_DASH_DASH_STATE",B="SCRIPT_DATA_DOUBLE_ESCAPED_LESS_THAN_SIGN_STATE",H="SCRIPT_DATA_DOUBLE_ESCAPE_END_STATE",G="BEFORE_ATTRIBUTE_NAME_STATE",z="ATTRIBUTE_NAME_STATE",$="AFTER_ATTRIBUTE_NAME_STATE",j="BEFORE_ATTRIBUTE_VALUE_STATE",V="ATTRIBUTE_VALUE_DOUBLE_QUOTED_STATE",W="ATTRIBUTE_VALUE_SINGLE_QUOTED_STATE",K="ATTRIBUTE_VALUE_UNQUOTED_STATE",Z="AFTER_ATTRIBUTE_VALUE_QUOTED_STATE",Y="SELF_CLOSING_START_TAG_STATE",q="BOGUS_COMMENT_STATE",X="MARKUP_DECLARATION_OPEN_STATE",Q="COMMENT_START_STATE",J="COMMENT_START_DASH_STATE",ee="COMMENT_STATE",et="COMMENT_LESS_THAN_SIGN_STATE",en="COMMENT_LESS_THAN_SIGN_BANG_STATE",er="COMMENT_LESS_THAN_SIGN_BANG_DASH_STATE",ea="COMMENT_LESS_THAN_SIGN_BANG_DASH_DASH_STATE",ei="COMMENT_END_DASH_STATE",eo="COMMENT_END_STATE",es="COMMENT_END_BANG_STATE",el="DOCTYPE_STATE",ec="BEFORE_DOCTYPE_NAME_STATE",eu="DOCTYPE_NAME_STATE",ed="AFTER_DOCTYPE_NAME_STATE",ep="AFTER_DOCTYPE_PUBLIC_KEYWORD_STATE",em="BEFORE_DOCTYPE_PUBLIC_IDENTIFIER_STATE",eg="DOCTYPE_PUBLIC_IDENTIFIER_DOUBLE_QUOTED_STATE",ef="DOCTYPE_PUBLIC_IDENTIFIER_SINGLE_QUOTED_STATE",eh="AFTER_DOCTYPE_PUBLIC_IDENTIFIER_STATE",eb="BETWEEN_DOCTYPE_PUBLIC_AND_SYSTEM_IDENTIFIERS_STATE",eE="AFTER_DOCTYPE_SYSTEM_KEYWORD_STATE",eT="BEFORE_DOCTYPE_SYSTEM_IDENTIFIER_STATE",eS="DOCTYPE_SYSTEM_IDENTIFIER_DOUBLE_QUOTED_STATE",ey="DOCTYPE_SYSTEM_IDENTIFIER_SINGLE_QUOTED_STATE",eA="AFTER_DOCTYPE_SYSTEM_IDENTIFIER_STATE",e_="BOGUS_DOCTYPE_STATE",ek="CDATA_SECTION_STATE",ev="CDATA_SECTION_BRACKET_STATE",eN="CDATA_SECTION_END_STATE",eC="CHARACTER_REFERENCE_STATE",eR="NAMED_CHARACTER_REFERENCE_STATE",eI="AMBIGUOS_AMPERSAND_STATE",eO="NUMERIC_CHARACTER_REFERENCE_STATE",ew="HEXADEMICAL_CHARACTER_REFERENCE_START_STATE",ex="DECIMAL_CHARACTER_REFERENCE_START_STATE",eL="HEXADEMICAL_CHARACTER_REFERENCE_STATE",eD="DECIMAL_CHARACTER_REFERENCE_STATE",eP="NUMERIC_CHARACTER_REFERENCE_END_STATE";function eM(e){return e===s.SPACE||e===s.LINE_FEED||e===s.TABULATION||e===s.FORM_FEED}function eF(e){return e>=s.DIGIT_0&&e<=s.DIGIT_9}function eU(e){return e>=s.LATIN_CAPITAL_A&&e<=s.LATIN_CAPITAL_Z}function eB(e){return e>=s.LATIN_SMALL_A&&e<=s.LATIN_SMALL_Z}function eH(e){return eB(e)||eU(e)}function eG(e){return eH(e)||eF(e)}function ez(e){return e>=s.LATIN_CAPITAL_A&&e<=s.LATIN_CAPITAL_F}function e$(e){return e>=s.LATIN_SMALL_A&&e<=s.LATIN_SMALL_F}function ej(e){return e<=65535?String.fromCharCode(e):String.fromCharCode((e-=65536)>>>10&1023|55296)+String.fromCharCode(56320|1023&e)}function eV(e){return String.fromCharCode(e+32)}function eW(e,t){let n=i[++e],r=++e,a=r+n-1;for(;r<=a;){let e=r+a>>>1,o=i[e];if(ot))return i[e+n];a=e-1}}return -1}class eK{constructor(){this.preprocessor=new r,this.tokenQueue=[],this.allowCDATA=!1,this.state=u,this.returnState="",this.charRefCode=-1,this.tempBuff=[],this.lastStartTagName="",this.consumedAfterSnapshot=-1,this.active=!1,this.currentCharacterToken=null,this.currentToken=null,this.currentAttr=null}_err(){}_errOnNextCodePoint(e){this._consume(),this._err(e),this._unconsume()}getNextToken(){for(;!this.tokenQueue.length&&this.active;){this.consumedAfterSnapshot=0;let e=this._consume();this._ensureHibernation()||this[this.state](e)}return this.tokenQueue.shift()}write(e,t){this.active=!0,this.preprocessor.write(e,t)}insertHtmlAtCurrentPos(e){this.active=!0,this.preprocessor.insertHtmlAtCurrentPos(e)}_ensureHibernation(){if(this.preprocessor.endOfChunkHit){for(;this.consumedAfterSnapshot>0;this.consumedAfterSnapshot--)this.preprocessor.retreat();return this.active=!1,this.tokenQueue.push({type:eK.HIBERNATION_TOKEN}),!0}return!1}_consume(){return this.consumedAfterSnapshot++,this.preprocessor.advance()}_unconsume(){this.consumedAfterSnapshot--,this.preprocessor.retreat()}_reconsumeInState(e){this.state=e,this._unconsume()}_consumeSequenceIfMatch(e,t,n){let r,a=0,i=!0,o=e.length,l=0,c=t;for(;l0&&(c=this._consume(),a++),c===s.EOF||c!==(r=e[l])&&(n||c!==r+32)){i=!1;break}if(!i)for(;a--;)this._unconsume();return i}_isTempBufferEqualToScriptString(){if(this.tempBuff.length!==l.SCRIPT_STRING.length)return!1;for(let e=0;e0&&this._err(o.endTagWithAttributes),e.selfClosing&&this._err(o.endTagWithTrailingSolidus)),this.tokenQueue.push(e)}_emitCurrentCharacterToken(){this.currentCharacterToken&&(this.tokenQueue.push(this.currentCharacterToken),this.currentCharacterToken=null)}_emitEOFToken(){this._createEOFToken(),this._emitCurrentToken()}_appendCharToCurrentCharacterToken(e,t){this.currentCharacterToken&&this.currentCharacterToken.type!==e&&this._emitCurrentCharacterToken(),this.currentCharacterToken?this.currentCharacterToken.chars+=t:this._createCharacterToken(e,t)}_emitCodePoint(e){let t=eK.CHARACTER_TOKEN;eM(e)?t=eK.WHITESPACE_CHARACTER_TOKEN:e===s.NULL&&(t=eK.NULL_CHARACTER_TOKEN),this._appendCharToCurrentCharacterToken(t,ej(e))}_emitSeveralCodePoints(e){for(let t=0;t-1;){let e=i[r],a=e<7,o=a&&1&e;o&&(t=2&e?[i[++r],i[++r]]:[i[++r]],n=0);let l=this._consume();if(this.tempBuff.push(l),n++,l===s.EOF)break;r=a?4&e?eW(r,l):-1:l===e?++r:-1}for(;n--;)this.tempBuff.pop(),this._unconsume();return t}_isCharacterReferenceInAttribute(){return this.returnState===V||this.returnState===W||this.returnState===K}_isCharacterReferenceAttributeQuirk(e){if(!e&&this._isCharacterReferenceInAttribute()){let e=this._consume();return this._unconsume(),e===s.EQUALS_SIGN||eG(e)}return!1}_flushCodePointsConsumedAsCharacterReference(){if(this._isCharacterReferenceInAttribute())for(let e=0;e")):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.state=I,this._emitChars(a.REPLACEMENT_CHARACTER)):e===s.EOF?(this._err(o.eofInScriptHtmlCommentLikeText),this._emitEOFToken()):(this.state=I,this._emitCodePoint(e))}[x](e){e===s.SOLIDUS?(this.tempBuff=[],this.state=L):eH(e)?(this.tempBuff=[],this._emitChars("<"),this._reconsumeInState(P)):(this._emitChars("<"),this._reconsumeInState(I))}[L](e){eH(e)?(this._createEndTagToken(),this._reconsumeInState(D)):(this._emitChars("")):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.state=M,this._emitChars(a.REPLACEMENT_CHARACTER)):e===s.EOF?(this._err(o.eofInScriptHtmlCommentLikeText),this._emitEOFToken()):(this.state=M,this._emitCodePoint(e))}[B](e){e===s.SOLIDUS?(this.tempBuff=[],this.state=H,this._emitChars("/")):this._reconsumeInState(M)}[H](e){eM(e)||e===s.SOLIDUS||e===s.GREATER_THAN_SIGN?(this.state=this._isTempBufferEqualToScriptString()?I:M,this._emitCodePoint(e)):eU(e)?(this.tempBuff.push(e+32),this._emitCodePoint(e)):eB(e)?(this.tempBuff.push(e),this._emitCodePoint(e)):this._reconsumeInState(M)}[G](e){eM(e)||(e===s.SOLIDUS||e===s.GREATER_THAN_SIGN||e===s.EOF?this._reconsumeInState($):e===s.EQUALS_SIGN?(this._err(o.unexpectedEqualsSignBeforeAttributeName),this._createAttr("="),this.state=z):(this._createAttr(""),this._reconsumeInState(z)))}[z](e){eM(e)||e===s.SOLIDUS||e===s.GREATER_THAN_SIGN||e===s.EOF?(this._leaveAttrName($),this._unconsume()):e===s.EQUALS_SIGN?this._leaveAttrName(j):eU(e)?this.currentAttr.name+=eV(e):e===s.QUOTATION_MARK||e===s.APOSTROPHE||e===s.LESS_THAN_SIGN?(this._err(o.unexpectedCharacterInAttributeName),this.currentAttr.name+=ej(e)):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentAttr.name+=a.REPLACEMENT_CHARACTER):this.currentAttr.name+=ej(e)}[$](e){eM(e)||(e===s.SOLIDUS?this.state=Y:e===s.EQUALS_SIGN?this.state=j:e===s.GREATER_THAN_SIGN?(this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInTag),this._emitEOFToken()):(this._createAttr(""),this._reconsumeInState(z)))}[j](e){eM(e)||(e===s.QUOTATION_MARK?this.state=V:e===s.APOSTROPHE?this.state=W:e===s.GREATER_THAN_SIGN?(this._err(o.missingAttributeValue),this.state=u,this._emitCurrentToken()):this._reconsumeInState(K))}[V](e){e===s.QUOTATION_MARK?this.state=Z:e===s.AMPERSAND?(this.returnState=V,this.state=eC):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentAttr.value+=a.REPLACEMENT_CHARACTER):e===s.EOF?(this._err(o.eofInTag),this._emitEOFToken()):this.currentAttr.value+=ej(e)}[W](e){e===s.APOSTROPHE?this.state=Z:e===s.AMPERSAND?(this.returnState=W,this.state=eC):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentAttr.value+=a.REPLACEMENT_CHARACTER):e===s.EOF?(this._err(o.eofInTag),this._emitEOFToken()):this.currentAttr.value+=ej(e)}[K](e){eM(e)?this._leaveAttrValue(G):e===s.AMPERSAND?(this.returnState=K,this.state=eC):e===s.GREATER_THAN_SIGN?(this._leaveAttrValue(u),this._emitCurrentToken()):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentAttr.value+=a.REPLACEMENT_CHARACTER):e===s.QUOTATION_MARK||e===s.APOSTROPHE||e===s.LESS_THAN_SIGN||e===s.EQUALS_SIGN||e===s.GRAVE_ACCENT?(this._err(o.unexpectedCharacterInUnquotedAttributeValue),this.currentAttr.value+=ej(e)):e===s.EOF?(this._err(o.eofInTag),this._emitEOFToken()):this.currentAttr.value+=ej(e)}[Z](e){eM(e)?this._leaveAttrValue(G):e===s.SOLIDUS?this._leaveAttrValue(Y):e===s.GREATER_THAN_SIGN?(this._leaveAttrValue(u),this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInTag),this._emitEOFToken()):(this._err(o.missingWhitespaceBetweenAttributes),this._reconsumeInState(G))}[Y](e){e===s.GREATER_THAN_SIGN?(this.currentToken.selfClosing=!0,this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInTag),this._emitEOFToken()):(this._err(o.unexpectedSolidusInTag),this._reconsumeInState(G))}[q](e){e===s.GREATER_THAN_SIGN?(this.state=u,this._emitCurrentToken()):e===s.EOF?(this._emitCurrentToken(),this._emitEOFToken()):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentToken.data+=a.REPLACEMENT_CHARACTER):this.currentToken.data+=ej(e)}[X](e){this._consumeSequenceIfMatch(l.DASH_DASH_STRING,e,!0)?(this._createCommentToken(),this.state=Q):this._consumeSequenceIfMatch(l.DOCTYPE_STRING,e,!1)?this.state=el:this._consumeSequenceIfMatch(l.CDATA_START_STRING,e,!0)?this.allowCDATA?this.state=ek:(this._err(o.cdataInHtmlContent),this._createCommentToken(),this.currentToken.data="[CDATA[",this.state=q):this._ensureHibernation()||(this._err(o.incorrectlyOpenedComment),this._createCommentToken(),this._reconsumeInState(q))}[Q](e){e===s.HYPHEN_MINUS?this.state=J:e===s.GREATER_THAN_SIGN?(this._err(o.abruptClosingOfEmptyComment),this.state=u,this._emitCurrentToken()):this._reconsumeInState(ee)}[J](e){e===s.HYPHEN_MINUS?this.state=eo:e===s.GREATER_THAN_SIGN?(this._err(o.abruptClosingOfEmptyComment),this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInComment),this._emitCurrentToken(),this._emitEOFToken()):(this.currentToken.data+="-",this._reconsumeInState(ee))}[ee](e){e===s.HYPHEN_MINUS?this.state=ei:e===s.LESS_THAN_SIGN?(this.currentToken.data+="<",this.state=et):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentToken.data+=a.REPLACEMENT_CHARACTER):e===s.EOF?(this._err(o.eofInComment),this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.data+=ej(e)}[et](e){e===s.EXCLAMATION_MARK?(this.currentToken.data+="!",this.state=en):e===s.LESS_THAN_SIGN?this.currentToken.data+="!":this._reconsumeInState(ee)}[en](e){e===s.HYPHEN_MINUS?this.state=er:this._reconsumeInState(ee)}[er](e){e===s.HYPHEN_MINUS?this.state=ea:this._reconsumeInState(ei)}[ea](e){e!==s.GREATER_THAN_SIGN&&e!==s.EOF&&this._err(o.nestedComment),this._reconsumeInState(eo)}[ei](e){e===s.HYPHEN_MINUS?this.state=eo:e===s.EOF?(this._err(o.eofInComment),this._emitCurrentToken(),this._emitEOFToken()):(this.currentToken.data+="-",this._reconsumeInState(ee))}[eo](e){e===s.GREATER_THAN_SIGN?(this.state=u,this._emitCurrentToken()):e===s.EXCLAMATION_MARK?this.state=es:e===s.HYPHEN_MINUS?this.currentToken.data+="-":e===s.EOF?(this._err(o.eofInComment),this._emitCurrentToken(),this._emitEOFToken()):(this.currentToken.data+="--",this._reconsumeInState(ee))}[es](e){e===s.HYPHEN_MINUS?(this.currentToken.data+="--!",this.state=ei):e===s.GREATER_THAN_SIGN?(this._err(o.incorrectlyClosedComment),this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInComment),this._emitCurrentToken(),this._emitEOFToken()):(this.currentToken.data+="--!",this._reconsumeInState(ee))}[el](e){eM(e)?this.state=ec:e===s.GREATER_THAN_SIGN?this._reconsumeInState(ec):e===s.EOF?(this._err(o.eofInDoctype),this._createDoctypeToken(null),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.missingWhitespaceBeforeDoctypeName),this._reconsumeInState(ec))}[ec](e){eM(e)||(eU(e)?(this._createDoctypeToken(eV(e)),this.state=eu):e===s.NULL?(this._err(o.unexpectedNullCharacter),this._createDoctypeToken(a.REPLACEMENT_CHARACTER),this.state=eu):e===s.GREATER_THAN_SIGN?(this._err(o.missingDoctypeName),this._createDoctypeToken(null),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this.state=u):e===s.EOF?(this._err(o.eofInDoctype),this._createDoctypeToken(null),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._createDoctypeToken(ej(e)),this.state=eu))}[eu](e){eM(e)?this.state=ed:e===s.GREATER_THAN_SIGN?(this.state=u,this._emitCurrentToken()):eU(e)?this.currentToken.name+=eV(e):e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentToken.name+=a.REPLACEMENT_CHARACTER):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.name+=ej(e)}[ed](e){!eM(e)&&(e===s.GREATER_THAN_SIGN?(this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this._consumeSequenceIfMatch(l.PUBLIC_STRING,e,!1)?this.state=ep:this._consumeSequenceIfMatch(l.SYSTEM_STRING,e,!1)?this.state=eE:this._ensureHibernation()||(this._err(o.invalidCharacterSequenceAfterDoctypeName),this.currentToken.forceQuirks=!0,this._reconsumeInState(e_)))}[ep](e){eM(e)?this.state=em:e===s.QUOTATION_MARK?(this._err(o.missingWhitespaceAfterDoctypePublicKeyword),this.currentToken.publicId="",this.state=eg):e===s.APOSTROPHE?(this._err(o.missingWhitespaceAfterDoctypePublicKeyword),this.currentToken.publicId="",this.state=ef):e===s.GREATER_THAN_SIGN?(this._err(o.missingDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.missingQuoteBeforeDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(e_))}[em](e){eM(e)||(e===s.QUOTATION_MARK?(this.currentToken.publicId="",this.state=eg):e===s.APOSTROPHE?(this.currentToken.publicId="",this.state=ef):e===s.GREATER_THAN_SIGN?(this._err(o.missingDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.missingQuoteBeforeDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(e_)))}[eg](e){e===s.QUOTATION_MARK?this.state=eh:e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentToken.publicId+=a.REPLACEMENT_CHARACTER):e===s.GREATER_THAN_SIGN?(this._err(o.abruptDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this.state=u):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.publicId+=ej(e)}[ef](e){e===s.APOSTROPHE?this.state=eh:e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentToken.publicId+=a.REPLACEMENT_CHARACTER):e===s.GREATER_THAN_SIGN?(this._err(o.abruptDoctypePublicIdentifier),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this.state=u):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.publicId+=ej(e)}[eh](e){eM(e)?this.state=eb:e===s.GREATER_THAN_SIGN?(this.state=u,this._emitCurrentToken()):e===s.QUOTATION_MARK?(this._err(o.missingWhitespaceBetweenDoctypePublicAndSystemIdentifiers),this.currentToken.systemId="",this.state=eS):e===s.APOSTROPHE?(this._err(o.missingWhitespaceBetweenDoctypePublicAndSystemIdentifiers),this.currentToken.systemId="",this.state=ey):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.missingQuoteBeforeDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(e_))}[eb](e){eM(e)||(e===s.GREATER_THAN_SIGN?(this._emitCurrentToken(),this.state=u):e===s.QUOTATION_MARK?(this.currentToken.systemId="",this.state=eS):e===s.APOSTROPHE?(this.currentToken.systemId="",this.state=ey):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.missingQuoteBeforeDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(e_)))}[eE](e){eM(e)?this.state=eT:e===s.QUOTATION_MARK?(this._err(o.missingWhitespaceAfterDoctypeSystemKeyword),this.currentToken.systemId="",this.state=eS):e===s.APOSTROPHE?(this._err(o.missingWhitespaceAfterDoctypeSystemKeyword),this.currentToken.systemId="",this.state=ey):e===s.GREATER_THAN_SIGN?(this._err(o.missingDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.missingQuoteBeforeDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(e_))}[eT](e){eM(e)||(e===s.QUOTATION_MARK?(this.currentToken.systemId="",this.state=eS):e===s.APOSTROPHE?(this.currentToken.systemId="",this.state=ey):e===s.GREATER_THAN_SIGN?(this._err(o.missingDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this.state=u,this._emitCurrentToken()):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.missingQuoteBeforeDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._reconsumeInState(e_)))}[eS](e){e===s.QUOTATION_MARK?this.state=eA:e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentToken.systemId+=a.REPLACEMENT_CHARACTER):e===s.GREATER_THAN_SIGN?(this._err(o.abruptDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this.state=u):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.systemId+=ej(e)}[ey](e){e===s.APOSTROPHE?this.state=eA:e===s.NULL?(this._err(o.unexpectedNullCharacter),this.currentToken.systemId+=a.REPLACEMENT_CHARACTER):e===s.GREATER_THAN_SIGN?(this._err(o.abruptDoctypeSystemIdentifier),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this.state=u):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):this.currentToken.systemId+=ej(e)}[eA](e){eM(e)||(e===s.GREATER_THAN_SIGN?(this._emitCurrentToken(),this.state=u):e===s.EOF?(this._err(o.eofInDoctype),this.currentToken.forceQuirks=!0,this._emitCurrentToken(),this._emitEOFToken()):(this._err(o.unexpectedCharacterAfterDoctypeSystemIdentifier),this._reconsumeInState(e_)))}[e_](e){e===s.GREATER_THAN_SIGN?(this._emitCurrentToken(),this.state=u):e===s.NULL?this._err(o.unexpectedNullCharacter):e===s.EOF&&(this._emitCurrentToken(),this._emitEOFToken())}[ek](e){e===s.RIGHT_SQUARE_BRACKET?this.state=ev:e===s.EOF?(this._err(o.eofInCdata),this._emitEOFToken()):this._emitCodePoint(e)}[ev](e){e===s.RIGHT_SQUARE_BRACKET?this.state=eN:(this._emitChars("]"),this._reconsumeInState(ek))}[eN](e){e===s.GREATER_THAN_SIGN?this.state=u:e===s.RIGHT_SQUARE_BRACKET?this._emitChars("]"):(this._emitChars("]]"),this._reconsumeInState(ek))}[eC](e){this.tempBuff=[s.AMPERSAND],e===s.NUMBER_SIGN?(this.tempBuff.push(e),this.state=eO):eG(e)?this._reconsumeInState(eR):(this._flushCodePointsConsumedAsCharacterReference(),this._reconsumeInState(this.returnState))}[eR](e){let t=this._matchNamedCharacterReference(e);if(this._ensureHibernation())this.tempBuff=[s.AMPERSAND];else if(t){let e=this.tempBuff[this.tempBuff.length-1]===s.SEMICOLON;this._isCharacterReferenceAttributeQuirk(e)||(e||this._errOnNextCodePoint(o.missingSemicolonAfterCharacterReference),this.tempBuff=t),this._flushCodePointsConsumedAsCharacterReference(),this.state=this.returnState}else this._flushCodePointsConsumedAsCharacterReference(),this.state=eI}[eI](e){eG(e)?this._isCharacterReferenceInAttribute()?this.currentAttr.value+=ej(e):this._emitCodePoint(e):(e===s.SEMICOLON&&this._err(o.unknownNamedCharacterReference),this._reconsumeInState(this.returnState))}[eO](e){this.charRefCode=0,e===s.LATIN_SMALL_X||e===s.LATIN_CAPITAL_X?(this.tempBuff.push(e),this.state=ew):this._reconsumeInState(ex)}[ew](e){eF(e)||ez(e)||e$(e)?this._reconsumeInState(eL):(this._err(o.absenceOfDigitsInNumericCharacterReference),this._flushCodePointsConsumedAsCharacterReference(),this._reconsumeInState(this.returnState))}[ex](e){eF(e)?this._reconsumeInState(eD):(this._err(o.absenceOfDigitsInNumericCharacterReference),this._flushCodePointsConsumedAsCharacterReference(),this._reconsumeInState(this.returnState))}[eL](e){ez(e)?this.charRefCode=16*this.charRefCode+e-55:e$(e)?this.charRefCode=16*this.charRefCode+e-87:eF(e)?this.charRefCode=16*this.charRefCode+e-48:e===s.SEMICOLON?this.state=eP:(this._err(o.missingSemicolonAfterCharacterReference),this._reconsumeInState(eP))}[eD](e){eF(e)?this.charRefCode=10*this.charRefCode+e-48:e===s.SEMICOLON?this.state=eP:(this._err(o.missingSemicolonAfterCharacterReference),this._reconsumeInState(eP))}[eP](){if(this.charRefCode===s.NULL)this._err(o.nullCharacterReference),this.charRefCode=s.REPLACEMENT_CHARACTER;else if(this.charRefCode>1114111)this._err(o.characterReferenceOutsideUnicodeRange),this.charRefCode=s.REPLACEMENT_CHARACTER;else if(a.isSurrogate(this.charRefCode))this._err(o.surrogateCharacterReference),this.charRefCode=s.REPLACEMENT_CHARACTER;else if(a.isUndefinedCodePoint(this.charRefCode))this._err(o.noncharacterCharacterReference);else if(a.isControlCodePoint(this.charRefCode)||this.charRefCode===s.CARRIAGE_RETURN){this._err(o.controlCharacterReference);let e=c[this.charRefCode];e&&(this.charRefCode=e)}this.tempBuff=[this.charRefCode],this._flushCodePointsConsumedAsCharacterReference(),this._reconsumeInState(this.returnState)}}eK.CHARACTER_TOKEN="CHARACTER_TOKEN",eK.NULL_CHARACTER_TOKEN="NULL_CHARACTER_TOKEN",eK.WHITESPACE_CHARACTER_TOKEN="WHITESPACE_CHARACTER_TOKEN",eK.START_TAG_TOKEN="START_TAG_TOKEN",eK.END_TAG_TOKEN="END_TAG_TOKEN",eK.COMMENT_TOKEN="COMMENT_TOKEN",eK.DOCTYPE_TOKEN="DOCTYPE_TOKEN",eK.EOF_TOKEN="EOF_TOKEN",eK.HIBERNATION_TOKEN="HIBERNATION_TOKEN",eK.MODE={DATA:u,RCDATA:d,RAWTEXT:p,SCRIPT_DATA:m,PLAINTEXT:g},eK.getTokenAttr=function(e,t){for(let n=e.attrs.length-1;n>=0;n--)if(e.attrs[n].name===t)return e.attrs[n].value;return null},e.exports=eK},5482:function(e){"use strict";e.exports=new 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t;return(0,em.vQ)(e[0],0)?t="center":e[0]>0?t="start":e[0]<0&&(t="end"),t},t.prototype.getTextBaseline=function(e){var t;return(0,em.vQ)(e[1],0)?t="middle":e[1]>0?t="top":e[1]<0&&(t="bottom"),t},t.prototype.processOverlap=function(e){},t.prototype.drawLine=function(e){var t=this.getLinePath(),i=this.get("line");this.addShape(e,{type:"path",id:this.getElementId("line"),name:"axis-line",attrs:(0,em.CD)({path:t},i.style)})},t.prototype.getTickLineItems=function(e){var t=this,i=[],n=this.get("tickLine"),r=n.alignTick,o=n.length,s=1;return e.length>=2&&(s=e[1].value-e[0].value),(0,em.S6)(e,function(e){var n=e.point;r||(n=t.getTickPoint(e.value-s/2));var a=t.getSidePoint(n,o);i.push({startPoint:n,tickValue:e.value,endPoint:a,tickId:e.id,id:"tickline-"+e.id})}),i},t.prototype.getSubTickLineItems=function(e){var t=[],i=this.get("subTickLine"),n=i.count,r=e.length;if(r>=2)for(var o=0;o0){var i=(0,em.dp)(t);if(i>e.threshold){var n=Math.ceil(i/e.threshold),r=t.filter(function(e,t){return t%n==0});this.set("ticks",r),this.set("originalTicks",t)}}},t.prototype.getLabelAttrs=function(e,t,i){var n=this.get("label"),r=n.offset,o=n.offsetX,s=n.offsetY,a=n.rotate,l=n.formatter,h=this.getSidePoint(e.point,r),u=this.getSideVector(r,h),d=l?l(e.name,e,t):e.name,c=n.style;c=(0,em.mf)(c)?(0,em.U2)(this.get("theme"),["label","style"],{}):c;var g=(0,em.CD)({x:h.x+o,y:h.y+s,text:d,textAlign:this.getTextAnchor(u),textBaseline:this.getTextBaseline(u)},c);return a&&(g.matrix=ic(h,a)),g},t.prototype.drawLabels=function(e){var t=this,i=this.get("ticks"),n=this.addGroup(e,{name:"axis-label-group",id:this.getElementId("label-group")});(0,em.S6)(i,function(e,r){t.addShape(n,{type:"text",name:"axis-label",id:t.getElementId("label-"+e.id),attrs:t.getLabelAttrs(e,r,i),delegateObject:{tick:e,item:e,index:r}})}),this.processOverlap(n);var r=n.getChildren(),o=(0,em.U2)(this.get("theme"),["label","style"],{}),s=this.get("label"),a=s.style,l=s.formatter;if((0,em.mf)(a)){var h=r.map(function(e){return(0,em.U2)(e.get("delegateObject"),"tick")});(0,em.S6)(r,function(e,t){var i=e.get("delegateObject").tick,n=l?l(i.name,i,t):i.name,r=(0,em.CD)({},o,a(n,t,h));e.attr(r)})}},t.prototype.getTitleAttrs=function(){var e=this.get("title"),t=e.style,i=e.position,n=e.offset,r=e.spacing,o=void 0===r?0:r,s=e.autoRotate,a=t.fontSize,l=.5;"start"===i?l=0:"end"===i&&(l=1);var h=this.getTickPoint(l),u=this.getSidePoint(h,n||o+a/2),d=(0,em.CD)({x:u.x,y:u.y,text:e.text},t),c=e.rotate,g=c;if((0,em.UM)(c)&&s){var p=this.getAxisVector(h);g=it.Dg(p,[1,0],!0)}if(g){var f=ic(u,g);d.matrix=f}return d},t.prototype.drawTitle=function(e){var t,i=this.getTitleAttrs(),n=this.addShape(e,{type:"text",id:this.getElementId("title"),name:"axis-title",attrs:i});(null===(t=this.get("title"))||void 0===t?void 0:t.description)&&this.drawDescriptionIcon(e,n,i.matrix)},t.prototype.drawDescriptionIcon=function(e,t,i){var n=this.addGroup(e,{name:"axis-description",id:this.getElementById("description")}),r=t.getBBox(),o=r.maxX,s=r.maxY,a=r.height,l=this.get("title").iconStyle,h=a/2,u=h/6,d=o+4,c=s-a/2,g=[d+h,c-h],p=g[0],f=g[1],m=[p+h,f+h],v=m[0],E=m[1],_=[p,E+h],C=_[0],S=_[1],y=[d,f+h],T=y[0],b=y[1],A=[d+h,c-a/4],R=A[0],L=A[1],N=[R,L+u],I=N[0],w=N[1],O=[I,w+u],x=O[0],D=O[1],M=[x,D+3*h/4],k=M[0],P=M[1];this.addShape(n,{type:"path",id:this.getElementId("title-description-icon"),name:"axis-title-description-icon",attrs:(0,ef.pi)({path:[["M",p,f],["A",h,h,0,0,1,v,E],["A",h,h,0,0,1,C,S],["A",h,h,0,0,1,T,b],["A",h,h,0,0,1,p,f],["M",R,L],["L",I,w],["M",x,D],["L",k,P]],lineWidth:u,matrix:i},l)}),this.addShape(n,{type:"rect",id:this.getElementId("title-description-rect"),name:"axis-title-description-rect",attrs:{x:d,y:c-a/2,width:a,height:a,stroke:"#000",fill:"#000",opacity:0,matrix:i,cursor:"pointer"}})},t.prototype.applyTickStates=function(e,t){if(this.getItemStates(e).length){var i=this.get("tickStates"),n=this.getElementId("label-"+e.id),r=t.findById(n);if(r){var o=ij(e,"label",i);o&&r.attr(o)}var s=this.getElementId("tickline-"+e.id),a=t.findById(s);if(a){var l=ij(e,"tickLine",i);l&&a.attr(l)}}},t.prototype.updateTickStates=function(e){var t=this.getItemStates(e),i=this.get("tickStates"),n=this.get("label"),r=this.getElementByLocalId("label-"+e.id),o=this.get("tickLine"),s=this.getElementByLocalId("tickline-"+e.id);if(t.length){if(r){var a=ij(e,"label",i);a&&r.attr(a)}if(s){var l=ij(e,"tickLine",i);l&&s.attr(l)}}else r&&r.attr(n.style),s&&s.attr(o.style)},t}(iO);function iZ(e,t,i,n){var r=t.getChildren(),o=!1;return(0,em.S6)(r,function(t){var r=iM(e,t,i,n);o=o||r}),o}function iJ(){return i0}function iQ(e,t,i){return iZ(e,t,i,"head")}function i0(e,t,i){return iZ(e,t,i,"tail")}function i1(e,t,i){return iZ(e,t,i,"middle")}function i2(e){var t,i;return((t=e.attr("matrix"))&&1!==t[0]?(t8(i=[0,0,0],[1,0,0],e.attr("matrix")),Math.atan2(i[1],i[0])):0)%360}function 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t=this.get("start"),i=this.get("end"),n=e.prototype.getInnerLayoutBBox.call(this),r=Math.min(t.x,i.x,n.x),o=Math.min(t.y,i.y,n.y),s=Math.max(t.x,i.x,n.maxX),a=Math.max(t.y,i.y,n.maxY);return{x:r,y:o,minX:r,minY:o,maxX:s,maxY:a,width:s-r,height:a-o}},t.prototype.isVertical=function(){var e=this.get("start"),t=this.get("end");return(0,em.vQ)(e.x,t.x)},t.prototype.isHorizontal=function(){var e=this.get("start"),t=this.get("end");return(0,em.vQ)(e.y,t.y)},t.prototype.getTickPoint=function(e){var t=this.get("start"),i=this.get("end"),n=i.x-t.x,r=i.y-t.y;return{x:t.x+n*e,y:t.y+r*e}},t.prototype.getSideVector=function(e){var t=this.getAxisVector(),i=io.Fv([0,0],t),n=this.get("verticalFactor"),r=[i[1],-1*i[0]];return io.bA([0,0],r,e*n)},t.prototype.getAxisVector=function(){var e=this.get("start"),t=this.get("end");return[t.x-e.x,t.y-e.y]},t.prototype.processOverlap=function(e){var t=this,i=this.isVertical(),n=this.isHorizontal();if(i||n){var r=this.get("label"),o=this.get("title"),s=this.get("verticalLimitLength"),a=r.offset,l=s,h=0,u=0;o&&(h=o.style.fontSize,u=o.spacing),l&&(l=l-a-u-h);var d=this.get("overlapOrder");if((0,em.S6)(d,function(i){r[i]&&t.canProcessOverlap(i)&&t.autoProcessOverlap(i,r[i],e,l)}),o&&(0,em.UM)(o.offset)){var c=e.getCanvasBBox(),g=i?c.width:c.height;o.offset=a+g+u+h/2}}},t.prototype.canProcessOverlap=function(e){var t=this.get("label");return"autoRotate"!==e||(0,em.UM)(t.rotate)},t.prototype.autoProcessOverlap=function(e,t,i,n){var r=this,o=this.isVertical(),s=!1,a=ea[e];if(!0===t?(this.get("label"),s=a.getDefault()(o,i,n)):(0,em.mf)(t)?s=t(o,i,n):(0,em.Kn)(t)?a[t.type]&&(s=a[t.type](o,i,n,t.cfg)):a[t]&&(s=a[t](o,i,n)),"autoRotate"===e){if(s){var l=i.getChildren(),h=this.get("verticalFactor");(0,em.S6)(l,function(e){"center"===e.attr("textAlign")&&e.attr("textAlign",h>0?"end":"start")})}}else if("autoHide"===e){var u=i.getChildren().slice(0);(0,em.S6)(u,function(e){e.get("visible")||(r.get("isRegister")&&r.unregisterElement(e),e.remove())})}},t}(iq),na=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return(0,ef.ZT)(t,e),t.prototype.getDefaultCfg=function(){var t=e.prototype.getDefaultCfg.call(this);return(0,ef.pi)((0,ef.pi)({},t),{type:"circle",locationType:"circle",center:null,radius:null,startAngle:-Math.PI/2,endAngle:3*Math.PI/2})},t.prototype.getLinePath=function(){var e=this.get("center"),t=e.x,i=e.y,n=this.get("radius"),r=this.get("startAngle"),o=this.get("endAngle"),s=[];if(Math.abs(o-r)===2*Math.PI)s=[["M",t,i-n],["A",n,n,0,1,1,t,i+n],["A",n,n,0,1,1,t,i-n],["Z"]];else{var a=this.getCirclePoint(r),l=this.getCirclePoint(o),h=Math.abs(o-r)>Math.PI?1:0,u=r>o?0:1;s=[["M",t,i],["L",a.x,a.y],["A",n,n,0,h,u,l.x,l.y],["L",t,i]]}return s},t.prototype.getTickPoint=function(e){var t=this.get("startAngle"),i=this.get("endAngle");return this.getCirclePoint(t+(i-t)*e)},t.prototype.getSideVector=function(e,t){var i=this.get("center"),n=[t.x-i.x,t.y-i.y],r=this.get("verticalFactor"),o=io.kE(n);return io.bA(n,n,r*e/o),n},t.prototype.getAxisVector=function(e){var t=this.get("center"),i=[e.x-t.x,e.y-t.y];return[i[1],-1*i[0]]},t.prototype.getCirclePoint=function(e,t){var i=this.get("center");return t=t||this.get("radius"),{x:i.x+Math.cos(e)*t,y:i.y+Math.sin(e)*t}},t.prototype.canProcessOverlap=function(e){var t=this.get("label");return"autoRotate"!==e||(0,em.UM)(t.rotate)},t.prototype.processOverlap=function(e){var t=this,i=this.get("label"),n=this.get("title"),r=this.get("verticalLimitLength"),o=i.offset,s=r,a=0,l=0;n&&(a=n.style.fontSize,l=n.spacing),s&&(s=s-o-l-a);var h=this.get("overlapOrder");if((0,em.S6)(h,function(n){i[n]&&t.canProcessOverlap(n)&&t.autoProcessOverlap(n,i[n],e,s)}),n&&(0,em.UM)(n.offset)){var u=e.getCanvasBBox().height;n.offset=o+u+l+a/2}},t.prototype.autoProcessOverlap=function(e,t,i,n){var r=this,o=!1,s=ea[e];if(n>0&&(!0===t?o=s.getDefault()(!1,i,n):(0,em.mf)(t)?o=t(!1,i,n):(0,em.Kn)(t)?s[t.type]&&(o=s[t.type](!1,i,n,t.cfg)):s[t]&&(o=s[t](!1,i,n))),"autoRotate"===e){if(o){var a=i.getChildren(),l=this.get("verticalFactor");(0,em.S6)(a,function(e){"center"===e.attr("textAlign")&&e.attr("textAlign",l>0?"end":"start")})}}else if("autoHide"===e){var h=i.getChildren().slice(0);(0,em.S6)(h,function(e){e.get("visible")||(r.get("isRegister")&&r.unregisterElement(e),e.remove())})}},t}(iq),nl=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return(0,ef.ZT)(t,e),t.prototype.getDefaultCfg=function(){var t=e.prototype.getDefaultCfg.call(this);return(0,ef.pi)((0,ef.pi)({},t),{name:"crosshair",type:"base",line:{},text:null,textBackground:{},capture:!1,defaultCfg:{line:{style:{lineWidth:1,stroke:iP.lineColor}},text:{position:"start",offset:10,autoRotate:!1,content:null,style:{fill:iP.textColor,textAlign:"center",textBaseline:"middle",fontFamily:iP.fontFamily}},textBackground:{padding:5,style:{stroke:iP.lineColor}}}})},t.prototype.renderInner=function(e){this.get("line")&&this.renderLine(e),this.get("text")&&(this.renderText(e),this.renderBackground(e))},t.prototype.renderText=function(e){var t=this.get("text"),i=t.style,n=t.autoRotate,r=t.content;if(!(0,em.UM)(r)){var o=this.getTextPoint(),s=null;n&&(s=ic(o,this.getRotateAngle())),this.addShape(e,{type:"text",name:"crosshair-text",id:this.getElementId("text"),attrs:(0,ef.pi)((0,ef.pi)((0,ef.pi)({},o),{text:r,matrix:s}),i)})}},t.prototype.renderLine=function(e){var t=this.getLinePath(),i=this.get("line").style;this.addShape(e,{type:"path",name:"crosshair-line",id:this.getElementId("line"),attrs:(0,ef.pi)({path:t},i)})},t.prototype.renderBackground=function(e){var t=this.getElementId("text"),i=e.findById(t),n=this.get("textBackground");if(n&&i){var r=i.getBBox(),o=iE(n.padding),s=n.style;this.addShape(e,{type:"rect",name:"crosshair-text-background",id:this.getElementId("text-background"),attrs:(0,ef.pi)({x:r.x-o[3],y:r.y-o[0],width:r.width+o[1]+o[3],height:r.height+o[0]+o[2],matrix:i.attr("matrix")},s)}).toBack()}},t}(iO),nh=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return(0,ef.ZT)(t,e),t.prototype.getDefaultCfg=function(){var t=e.prototype.getDefaultCfg.call(this);return(0,ef.pi)((0,ef.pi)({},t),{type:"line",locationType:"region",start:null,end:null})},t.prototype.getRotateAngle=function(){var e=this.getLocation(),t=e.start,i=e.end,n=this.get("text").position,r=Math.atan2(i.y-t.y,i.x-t.x);return"start"===n?r-Math.PI/2:r+Math.PI/2},t.prototype.getTextPoint=function(){var e=this.getLocation(),t=e.start,i=e.end,n=this.get("text");return iA(t,i,n.position,n.offset)},t.prototype.getLinePath=function(){var e=this.getLocation(),t=e.start,i=e.end;return[["M",t.x,t.y],["L",i.x,i.y]]},t}(nl),nu=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return(0,ef.ZT)(t,e),t.prototype.getDefaultCfg=function(){var t=e.prototype.getDefaultCfg.call(this);return(0,ef.pi)((0,ef.pi)({},t),{type:"circle",locationType:"circle",center:null,radius:100,startAngle:-Math.PI/2,endAngle:3*Math.PI/2})},t.prototype.getRotateAngle=function(){var e=this.getLocation(),t=e.startAngle,i=e.endAngle;return"start"===this.get("text").position?t+Math.PI/2:i-Math.PI/2},t.prototype.getTextPoint=function(){var e=this.get("text"),t=e.position,i=e.offset,n=this.getLocation(),r=n.center,o=n.radius,s=n.startAngle,a=n.endAngle,l=this.getRotateAngle()-Math.PI,h=iy(r,o,"start"===t?s:a),u=Math.cos(l)*i,d=Math.sin(l)*i;return{x:h.x+u,y:h.y+d}},t.prototype.getLinePath=function(){var e=this.getLocation(),t=e.center,i=e.radius,n=e.startAngle,r=e.endAngle,o=null;if(r-n==2*Math.PI){var s=t.x,a=t.y;o=[["M",s,a-i],["A",i,i,0,1,1,s,a+i],["A",i,i,0,1,1,s,a-i],["Z"]]}else{var l=iy(t,i,n),h=iy(t,i,r),u=Math.abs(r-n)>Math.PI?1:0,d=n>r?0:1;o=[["M",l.x,l.y],["A",i,i,0,u,d,h.x,h.y]]}return o},t}(nl),nd="g2-crosshair",nc=nd+"-line",ng=nd+"-text",np=((G={})[""+nd]={position:"relative"},G[""+nc]={position:"absolute",backgroundColor:"rgba(0, 0, 0, 0.25)"},G[""+ng]={position:"absolute",color:iP.textColor,fontFamily:iP.fontFamily},G),nf=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return(0,ef.ZT)(t,e),t.prototype.getDefaultCfg=function(){var t=e.prototype.getDefaultCfg.call(this);return(0,ef.pi)((0,ef.pi)({},t),{name:"crosshair",type:"html",locationType:"region",start:{x:0,y:0},end:{x:0,y:0},capture:!1,text:null,containerTpl:'
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p=this._dodgeText([d,c],t,i),f=p[0],m=p[1];this.minHandler&&(this.minHandler.update({x:d-g/2}),this.get("updateAutoRender")||this.minHandler.render()),(0,em.S6)(f,function(e,i){return t.attr(i,e)}),this.maxHandler&&(this.maxHandler.update({x:c-g/2}),this.get("updateAutoRender")||this.maxHandler.render()),(0,em.S6)(m,function(e,t){return i.attr(t,e)})},t.prototype.bindEvents=function(){var e=this.get("group");e.on("handler-min:mousedown",this.onMouseDown("minHandler")),e.on("handler-min:touchstart",this.onMouseDown("minHandler")),e.on("handler-max:mousedown",this.onMouseDown("maxHandler")),e.on("handler-max:touchstart",this.onMouseDown("maxHandler"));var t=e.findById(this.getElementId("foreground"));t.on("mousedown",this.onMouseDown("foreground")),t.on("touchstart",this.onMouseDown("foreground"))},t.prototype.adjustOffsetRange=function(e){var t=this.cfg,i=t.start,n=t.end;switch(this.currentTarget){case"minHandler":var r=0-i,o=1-i;return Math.min(o,Math.max(r,e));case"maxHandler":var 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e=t.getContainerDOM(),i=[];i=t.isMobile?[nZ(e,"touchmove",t.onMouseMove),nZ(e,"touchend",t.onMouseUp),nZ(e,"touchcancel",t.onMouseUp)]:[nZ(e,"mousemove",t.onMouseMove),nZ(e,"mouseup",t.onMouseUp),nZ(e,"mouseleave",t.onMouseUp)],t.clearEvents=function(){i.forEach(function(e){e.remove()})}},t.onMouseMove=function(e){var i=t.cfg,n=i.isHorizontal,r=i.thumbOffset;e.preventDefault();var o=t.isMobile?(0,em.U2)(e,"touches.0.clientX"):e.clientX,s=t.isMobile?(0,em.U2)(e,"touches.0.clientY"):e.clientY,a=n?o:s,l=a-t.startPos;t.startPos=a,t.updateThumbOffset(r+l)},t.onMouseUp=function(e){e.preventDefault(),t.clearEvents()},t.onTrackClick=function(e){var i=t.cfg,n=i.isHorizontal,r=i.x,o=i.y,s=i.thumbLen,a=t.getContainerDOM().getBoundingClientRect(),l=e.clientX,h=e.clientY,u=n?l-a.left-r-s/2:h-a.top-o-s/2,d=t.validateRange(u);t.updateThumbOffset(d)},t.onThumbMouseOver=function(){var 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t=e.prototype.getDefaultCfg.call(this);return(0,ef.pi)((0,ef.pi)({},t),{name:"scrollbar",isHorizontal:!0,minThumbLen:20,thumbOffset:0,theme:nJ})},t.prototype.renderInner=function(e){this.renderTrackShape(e),this.renderThumbShape(e)},t.prototype.applyOffset=function(){this.moveElementTo(this.get("group"),{x:this.get("x"),y:this.get("y")})},t.prototype.initEvent=function(){this.bindEvents()},t.prototype.renderTrackShape=function(e){var t=this.cfg,i=t.trackLen,n=t.theme,r=(0,em.b$)({},nJ,void 0===n?{default:{}}:n).default,o=r.lineCap,s=r.trackColor,a=r.size,l=(0,em.U2)(this.cfg,"size",a),h=this.get("isHorizontal")?{x1:0+l/2,y1:l/2,x2:i-l/2,y2:l/2,lineWidth:l,stroke:s,lineCap:o}:{x1:l/2,y1:0+l/2,x2:l/2,y2:i-l/2,lineWidth:l,stroke:s,lineCap:o};return this.addShape(e,{id:this.getElementId("track"),name:"track",type:"line",attrs:h})},t.prototype.renderThumbShape=function(e){var 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c=(0,em.U2)(e.get("animateCfg"),i?"enter":"appear");c&&oP(e,c,{toAttrs:(0,ef.pi)({},e.attr()),coordinate:e.get("coordinate")})}delete l[t]}}),(0,em.S6)(l,function(e){var t=(0,em.U2)(e.get("animateCfg"),"leave");t?oP(e,t,{toAttrs:null,coordinate:e.get("coordinate")}):e.remove(!0)}),this.lastShapesMap=h,o.destroy()},e.prototype.clear=function(){this.container.clear(),this.shapesMap={},this.lastShapesMap={}},e.prototype.destroy=function(){this.container.destroy(),this.shapesMap=null,this.lastShapesMap=null},e.prototype.renderLabel=function(e,t){var i,n=e.id,r=e.elementId,o=e.data,s=e.mappingData,a=e.coordinate,l=e.animate,h=e.content,u={id:n,elementId:r,data:o,origin:(0,ef.pi)((0,ef.pi)({},s),{data:s[e_]}),coordinate:a},d=t.addGroup((0,ef.pi)({name:"label",animateCfg:!1!==this.animate&&null!==l&&!1!==l&&(0,em.b$)({},this.animate,l)},u));if(h.isGroup&&h.isGroup()||h.isShape&&h.isShape()){var c=h.getCanvasBBox(),g=c.width,p=c.height,f=(0,em.U2)(e,"textAlign","left"),m=e.x,v=e.y-p/2;"center"===f?m-=g/2:("right"===f||"end"===f)&&(m-=g),o0(h,m,v),i=h,d.add(h)}else{var E=(0,em.U2)(e,["style","fill"]);i=d.addShape("text",(0,ef.pi)({attrs:(0,ef.pi)((0,ef.pi)({x:e.x,y:e.y,textAlign:e.textAlign,textBaseline:(0,em.U2)(e,"textBaseline","middle"),text:e.content},e.style),{fill:(0,em.Ft)(E)?e.color:E})},u))}e.rotate&&o1(i,e.rotate),this.shapesMap[n]=d},e.prototype.doLayout=function(e,t){var i=this;if(this.layout){var n=(0,em.kJ)(this.layout)?this.layout:[this.layout];(0,em.S6)(n,function(n){var r=oH[(0,em.U2)(n,"type","").toLowerCase()];if(r){var o=[],s=[];(0,em.S6)(i.shapesMap,function(e,i){o.push(e),s.push(t[e.get("elementId")])}),r(e,o,s,i.region,n.cfg)}})}},e.prototype.renderLabelLine=function(e){var t=this;(0,em.S6)(e,function(e){var i=(0,em.U2)(e,"coordinate");if(e&&i){var n=i.getCenter(),r=i.getRadius();if(e.labelLine){var o=(0,em.U2)(e,"labelLine",{}),s=e.id,a=o.path;if(!a){var l=n2(n.x,n.y,r,e.angle);a=[["M",l.x,l.y],["L",e.x,e.y]]}var h=t.shapesMap[s];h.destroyed||h.addShape("path",{capture:!1,attrs:(0,ef.pi)({path:a,stroke:e.color?e.color:(0,em.U2)(e,["style","fill"],"#000"),fill:null},o.style),id:s,origin:e.mappingData,data:e.data,coordinate:e.coordinate})}}})},e.prototype.renderLabelBackground=function(e){var t=this;(0,em.S6)(e,function(e){var i=(0,em.U2)(e,"coordinate"),n=(0,em.U2)(e,"background");if(n&&i){var r=e.id,o=t.shapesMap[r];if(!o.destroyed){var s=o.getChildren()[0];if(s){var a=o4(o,e,n.padding),l=a.rotation,h=(0,ef._T)(a,["rotation"]),u=o.addShape("rect",{attrs:(0,ef.pi)((0,ef.pi)({},h),n.style||{}),id:r,origin:e.mappingData,data:e.data,coordinate:e.coordinate});if(u.setZIndex(-1),l){var d=s.getMatrix();u.setMatrix(d)}}}}})},e.prototype.createOffscreenGroup=function(){return new(this.container.getGroupBase())({})},e.prototype.adjustLabel=function(e){var t=this;(0,em.S6)(e,function(e){if(e){var i=e.id,n=t.shapesMap[i];if(!n.destroyed){var 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t.addShape("path",{attrs:(0,ef.pi)((0,ef.pi)({},n),{path:r}),name:"interval"})},getMarker:function(e){return{symbol:function(e,t,i){return[["M",e,t-i],["L",e,t+i]]},style:{r:5,stroke:e.color}}}}),o$("interval","pyramid",{getPoints:function(e){return e.size=2*e.size,a3(e)},draw:function(e,t){var i=aX(e,!1,!0),n=this.parsePath(le(e.points,e.nextPoints,!0));return t.addShape("path",{attrs:(0,ef.pi)((0,ef.pi)({},i),{path:n}),name:"interval"})},getMarker:function(e){return{symbol:"square",style:{r:4,fill:e.color}}}}),o$("interval","tick",{getPoints:function(e){var t,i,n,r,o,s,a,l;return n=e.x,r=e.y,o=e.y0,s=e.size,(0,em.kJ)(r)?(t=r[0],i=r[1]):(t=o,i=r),a=n+s/2,l=n-s/2,[{x:n,y:t},{x:n,y:i},{x:l,y:t},{x:a,y:t},{x:l,y:i},{x:a,y:i}]},draw:function(e,t){var i,n=aX(e,!0,!1),r=this.parsePath([["M",(i=e.points)[0].x,i[0].y],["L",i[1].x,i[1].y],["M",i[2].x,i[2].y],["L",i[3].x,i[3].y],["M",i[4].x,i[4].y],["L",i[5].x,i[5].y]]);return t.addShape("path",{attrs:(0,ef.pi)((0,ef.pi)({},n),{path:r}),name:"interval"})},getMarker:function(e){return{symbol:function(e,t,i){return[["M",e-i/2,t-i],["L",e+i/2,t-i],["M",e,t-i],["L",e,t+i],["M",e-i/2,t+i],["L",e+i/2,t+i]]},style:{r:5,stroke:e.color}}}});var lg=function(e,t,i){var n,r=e.x,o=e.y,s=t.x,a=t.y;switch(i){case"hv":n=[{x:s,y:o}];break;case"vh":n=[{x:r,y:a}];break;case"hvh":var l=(s+r)/2;n=[{x:l,y:o},{x:l,y:a}];break;case"vhv":var h=(o+a)/2;n=[{x:r,y:h},{x:s,y:h}]}return n};function lp(e){var t=(0,em.kJ)(e)?e:[e],i=t[0],n=t[t.length-1],r=t.length>1?t[1]:i,o=t.length>3?t[3]:n,s=t.length>2?t[2]:r;return{min:i,max:n,min1:r,max1:o,median:s}}function lf(e,t,i){var n,r=i/2;if((0,em.kJ)(t)){var o=lp(t),s=o.min,a=o.max,l=o.median,h=o.min1,u=o.max1,d=e-r,c=e+r;n=[[d,a],[c,a],[e,a],[e,u],[d,h],[d,u],[c,u],[c,h],[e,h],[e,s],[d,s],[c,s],[d,l],[c,l]]}else{t=(0,em.UM)(t)?.5:t;var 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n,r=1,o=e.content[i];this.isToMiddle(t)?n=this.getMiddlePoint(t.points):(1===e.content.length&&0===i?i=1:0===i&&(r=-1),n=this.getArcPoint(t,i));var s=e.offset*r,a=this.getPointAngle(n),l=e.labelEmit,h=this.getCirclePoint(a,s,n,l);return 0===h.r?h.content="":(h.content=o,h.angle=a,h.color=t.color),h.rotate=e.autoRotate?this.getLabelRotate(a,s,l):e.rotate,h.start={x:n.x,y:n.y},h},t.prototype.getArcPoint=function(e,t){return(void 0===t&&(t=0),(0,em.kJ)(e.x)||(0,em.kJ)(e.y))?{x:(0,em.kJ)(e.x)?e.x[t]:e.x,y:(0,em.kJ)(e.y)?e.y[t]:e.y}:{x:e.x,y:e.y}},t.prototype.getPointAngle=function(e){return rr(this.getCoordinate(),e)},t.prototype.getCirclePoint=function(e,t,i,n){var r=this.getCoordinate(),o=r.getCenter(),s=ri(r,i);if(0===s)return(0,ef.pi)((0,ef.pi)({},o),{r:s});var a=e;return r.isTransposed&&s>t&&!n?a=e+2*Math.asin(t/(2*s)):s+=t,{x:o.x+s*Math.cos(a),y:o.y+s*Math.sin(a),r:s}},t.prototype.getLabelRotate=function(e,t,i){var n=e+lE;return 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D=(0,em.b$)({},i,(0,ef.pi)((0,ef.pi)({},s),{top:t.top,style:t.style,offsetX:t.offsetX,offsetY:t.offsetY}));return"html"!==e&&(D.container=this.getComponentContainer(D)),D.animate=this.view.getOptions().animate&&D.animate&&(0,em.U2)(t,"animate",D.animate),D.animateOption=(0,em.b$)({},ox,D.animateOption,t.animateOption),D},t.prototype.isTop=function(e){return(0,em.U2)(e,"top",!0)},t.prototype.getComponentContainer=function(e){return this.isTop(e)?this.foregroundContainer:this.backgroundContainer},t.prototype.getAnnotationTheme=function(e){return(0,em.U2)(this.view.getTheme(),["components","annotation",e],{})},t.prototype.updateOrCreate=function(e){var t=this.cache.get(this.getCacheKey(e));if(t){var i=e.type,n=this.getAnnotationTheme(i),r=this.getAnnotationCfg(i,e,n);n7(r,["container"]),t.component.update(r),(0,em.q9)(lQ,e.type)&&t.component.render()}else(t=this.createAnnotation(e))&&(t.component.init(),(0,em.q9)(lQ,e.type)&&t.component.render());return 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Map;this.cache.forEach(function(i,n){e.has(n)?t.set(n,i):i.component.destroy()}),this.cache=t},t.prototype.clear=function(){e.prototype.clear.call(this),this.cache.clear(),this.gridContainer.clear(),this.gridForeContainer.clear(),this.axisContainer.clear(),this.axisForeContainer.clear()},t.prototype.destroy=function(){e.prototype.destroy.call(this),this.gridContainer.remove(!0),this.gridForeContainer.remove(!0),this.axisContainer.remove(!0),this.axisForeContainer.remove(!0)},t.prototype.getComponents=function(){var e=[];return this.cache.forEach(function(t){e.push(t)}),e},t.prototype.updateXAxes=function(e){var t=this.view.getXScale();if(t&&!t.isIdentity){var i=rf(this.option,t.field);if(!1!==i){var n=rm(i,x.BOTTOM),r=O.BG,o=this.view.getCoordinate(),s=this.getId("axis",t.field),a=this.getId("grid",t.field);if(o.isRect){var l=this.cache.get(s);if(l){var h=this.getLineAxisCfg(t,i,n);n7(h,l6),l.component.update(h),e.set(s,l)}else l=this.createLineAxis(t,i,r,n,"x"),this.cache.set(s,l),e.set(s,l);var u=this.cache.get(a);if(u){var h=this.getLineGridCfg(t,i,n,"x");n7(h,l6),u.component.update(h),e.set(a,u)}else(u=this.createLineGrid(t,i,r,n,"x"))&&(this.cache.set(a,u),e.set(a,u))}else if(o.isPolar){var l=this.cache.get(s);if(l){var h=o.isTransposed?this.getLineAxisCfg(t,i,x.RADIUS):this.getCircleAxisCfg(t,i,n);n7(h,l6),l.component.update(h),e.set(s,l)}else{if(o.isTransposed){if((0,em.o8)(i))return;l=this.createLineAxis(t,i,r,x.RADIUS,"x")}else l=this.createCircleAxis(t,i,r,n,"x");this.cache.set(s,l),e.set(s,l)}var u=this.cache.get(a);if(u){var h=o.isTransposed?this.getCircleGridCfg(t,i,x.RADIUS,"x"):this.getLineGridCfg(t,i,x.CIRCLE,"x");n7(h,l6),u.component.update(h),e.set(a,u)}else{if(o.isTransposed){if((0,em.o8)(i))return;u=this.createCircleGrid(t,i,r,x.RADIUS,"x")}else u=this.createLineGrid(t,i,r,x.CIRCLE,"x");u&&(this.cache.set(a,u),e.set(a,u))}}}}},t.prototype.updateYAxes=function(e){var t=this,i=this.view.getYScales();(0,em.S6)(i,function(i,n){if(i&&!i.isIdentity){var r=i.field,o=rf(t.option,r);if(!1!==o){var s=O.BG,a=t.getId("axis",r),l=t.getId("grid",r),h=t.view.getCoordinate();if(h.isRect){var u=rm(o,0===n?x.LEFT:x.RIGHT),d=t.cache.get(a);if(d){var c=t.getLineAxisCfg(i,o,u);n7(c,l6),d.component.update(c),e.set(a,d)}else d=t.createLineAxis(i,o,s,u,"y"),t.cache.set(a,d),e.set(a,d);var g=t.cache.get(l);if(g){var c=t.getLineGridCfg(i,o,u,"y");n7(c,l6),g.component.update(c),e.set(l,g)}else(g=t.createLineGrid(i,o,s,u,"y"))&&(t.cache.set(l,g),e.set(l,g))}else if(h.isPolar){var d=t.cache.get(a);if(d){var c=h.isTransposed?t.getCircleAxisCfg(i,o,x.CIRCLE):t.getLineAxisCfg(i,o,x.RADIUS);n7(c,l6),d.component.update(c),e.set(a,d)}else{if(h.isTransposed){if((0,em.o8)(o))return;d=t.createCircleAxis(i,o,s,x.CIRCLE,"y")}else d=t.createLineAxis(i,o,s,x.RADIUS,"y");t.cache.set(a,d),e.set(a,d)}var g=t.cache.get(l);if(g){var c=h.isTransposed?t.getLineGridCfg(i,o,x.CIRCLE,"y"):t.getCircleGridCfg(i,o,x.RADIUS,"y");n7(c,l6),g.component.update(c),e.set(l,g)}else{if(h.isTransposed){if((0,em.o8)(o))return;g=t.createLineGrid(i,o,s,x.CIRCLE,"y")}else g=t.createCircleGrid(i,o,s,x.RADIUS,"y");g&&(t.cache.set(l,g),e.set(l,g))}}}}})},t.prototype.createLineAxis=function(e,t,i,n,r){var o={component:new ns(this.getLineAxisCfg(e,t,n)),layer:i,direction:n===x.RADIUS?x.NONE:n,type:D.AXIS,extra:{dim:r,scale:e}};return o.component.set("field",e.field),o.component.init(),o},t.prototype.createLineGrid=function(e,t,i,n,r){var o=this.getLineGridCfg(e,t,n,r);if(o){var s={component:new nE(o),layer:i,direction:x.NONE,type:D.GRID,extra:{dim:r,scale:e,alignTick:(0,em.U2)(o,"alignTick",!0)}};return s.component.init(),s}},t.prototype.createCircleAxis=function(e,t,i,n,r){var o={component:new na(this.getCircleAxisCfg(e,t,n)),layer:i,direction:n,type:D.AXIS,extra:{dim:r,scale:e}};return o.component.set("field",e.field),o.component.init(),o},t.prototype.createCircleGrid=function(e,t,i,n,r){var o=this.getCircleGridCfg(e,t,n,r);if(o){var s={component:new nv(o),layer:i,direction:x.NONE,type:D.GRID,extra:{dim:r,scale:e,alignTick:(0,em.U2)(o,"alignTick",!0)}};return s.component.init(),s}},t.prototype.getLineAxisCfg=function(e,t,i){var n=(0,em.U2)(t,["top"])?this.axisForeContainer:this.axisContainer,r=this.view.getCoordinate(),o=rh(r,i),s=rv(e,t),a=rc(this.view.getTheme(),i),l=(0,em.U2)(t,["title"])?(0,em.b$)({title:{style:{text:s}}},{title:rg(this.view.getTheme(),i,t.title)},t):t,h=(0,em.b$)((0,ef.pi)((0,ef.pi)({container:n},o),{ticks:e.getTicks().map(function(e){return{id:""+e.tickValue,name:e.text,value:e.value}}),verticalFactor:r.isPolar?-1*rd(o,r.getCenter()):rd(o,r.getCenter()),theme:a}),a,l),u=this.getAnimateCfg(h),d=u.animate,c=u.animateOption;h.animateOption=c,h.animate=d;var g=ru(o),p=(0,em.U2)(h,"verticalLimitLength",g?1/3:.5);if(p<=1){var f=this.view.getCanvas().get("width"),m=this.view.getCanvas().get("height");h.verticalLimitLength=p*(g?f:m)}return h},t.prototype.getLineGridCfg=function(e,t,i,n){if(l5(rc(this.view.getTheme(),i),t)){var r=l1(this.view.getTheme(),i),o=(0,em.b$)({container:(0,em.U2)(t,["top"])?this.gridForeContainer:this.gridContainer},r,(0,em.U2)(t,"grid"),this.getAnimateCfg(t));return o.items=l2(this.view.getCoordinate(),e,n,(0,em.U2)(o,"alignTick",!0)),o}},t.prototype.getCircleAxisCfg=function(e,t,i){var n=(0,em.U2)(t,["top"])?this.axisForeContainer:this.axisContainer,r=this.view.getCoordinate(),o=e.getTicks().map(function(e){return{id:""+e.tickValue,name:e.text,value:e.value}});e.isCategory||Math.abs(r.endAngle-r.startAngle)!==2*Math.PI||o.pop();var s=rv(e,t),a=rc(this.view.getTheme(),x.CIRCLE),l=(0,em.U2)(t,["title"])?(0,em.b$)({title:{style:{text:s}}},{title:rg(this.view.getTheme(),i,t.title)},t):t,h=(0,em.b$)((0,ef.pi)((0,ef.pi)({container:n},rp(this.view.getCoordinate())),{ticks:o,verticalFactor:1,theme:a}),a,l),u=this.getAnimateCfg(h),d=u.animate,c=u.animateOption;return h.animate=d,h.animateOption=c,h},t.prototype.getCircleGridCfg=function(e,t,i,n){if(l5(rc(this.view.getTheme(),i),t)){var r=l1(this.view.getTheme(),x.RADIUS),o=(0,em.b$)({container:(0,em.U2)(t,["top"])?this.gridForeContainer:this.gridContainer,center:this.view.getCoordinate().getCenter()},r,(0,em.U2)(t,"grid"),this.getAnimateCfg(t)),s=(0,em.U2)(o,"alignTick",!0),a="x"===n?this.view.getYScales()[0]:this.view.getXScale();return o.items=l4(this.view.getCoordinate(),a,e,s,n),o}},t.prototype.getId=function(e,t){return e+"-"+t+"-"+this.view.getCoordinate().type},t.prototype.getAnimateCfg=function(e){return{animate:this.view.getOptions().animate&&(0,em.U2)(e,"animate"),animateOption:e&&e.animateOption?(0,em.b$)({},l3,e.animateOption):l3}},t}(oL);function l7(e,t,i){return i===x.TOP?[e.minX+e.width/2-t.width/2,e.minY]:i===x.BOTTOM?[e.minX+e.width/2-t.width/2,e.maxY-t.height]:i===x.LEFT?[e.minX,e.minY+e.height/2-t.height/2]:i===x.RIGHT?[e.maxX-t.width,e.minY+e.height/2-t.height/2]:i===x.TOP_LEFT||i===x.LEFT_TOP?[e.tl.x,e.tl.y]:i===x.TOP_RIGHT||i===x.RIGHT_TOP?[e.tr.x-t.width,e.tr.y]:i===x.BOTTOM_LEFT||i===x.LEFT_BOTTOM?[e.bl.x,e.bl.y-t.height]:i===x.BOTTOM_RIGHT||i===x.RIGHT_BOTTOM?[e.br.x-t.width,e.br.y-t.height]:[0,0]}function l8(e,t){return(0,em.jn)(e)?!1!==e&&{}:(0,em.U2)(e,[t],e)}function he(e){return(0,em.U2)(e,"position",x.BOTTOM)}var ht=function(e){function t(t){var i=e.call(this,t)||this;return i.container=i.view.getLayer(O.FORE).addGroup(),i}return(0,ef.ZT)(t,e),Object.defineProperty(t.prototype,"name",{get:function(){return"legend"},enumerable:!1,configurable:!0}),t.prototype.init=function(){},t.prototype.render=function(){this.update()},t.prototype.layout=function(){var e=this;this.layoutBBox=this.view.viewBBox,(0,em.S6)(this.components,function(t){var i=t.component,n=t.direction,r=st(n),o=i.get("maxWidthRatio"),s=i.get("maxHeightRatio"),a=e.getCategoryLegendSizeCfg(r,o,s),l=i.get("maxWidth"),h=i.get("maxHeight");i.update({maxWidth:Math.min(a.maxWidth,l||0),maxHeight:Math.min(a.maxHeight,h||0)});var u=i.get("padding"),d=i.getLayoutBBox(),c=new re(d.x,d.y,d.width,d.height).expand(u),g=l7(e.view.viewBBox,c,n),p=g[0],f=g[1],m=l7(e.layoutBBox,c,n),v=m[0],E=m[1],_=0,C=0;n.startsWith("top")||n.startsWith("bottom")?(_=p,C=E):(_=v,C=f),i.setLocation({x:_+u[3],y:C+u[0]}),e.layoutBBox=e.layoutBBox.cut(c,n)})},t.prototype.update=function(){var e=this;this.option=this.view.getOptions().legends;var t={};if((0,em.U2)(this.option,"custom")){var i="global-custom",n=this.getComponentById(i);if(n){var r=this.getCategoryCfg(void 0,void 0,void 0,this.option,!0);n7(r,["container"]),n.component.update(r),t[i]=!0}else{var o=this.createCustomLegend(void 0,void 0,void 0,this.option);if(o){o.init();var s=O.FORE,a=he(this.option);this.components.push({id:i,component:o,layer:s,direction:a,type:D.LEGEND,extra:void 0}),t[i]=!0}}}else this.loopLegends(function(i,n,r){var o=e.getId(r.field),s=e.getComponentById(o);if(s){var a=void 0,l=l8(e.option,r.field);!1!==l&&((0,em.U2)(l,"custom")?a=e.getCategoryCfg(i,n,r,l,!0):r.isLinear?a=e.getContinuousCfg(i,n,r,l):r.isCategory&&(a=e.getCategoryCfg(i,n,r,l))),a&&(n7(a,["container"]),s.direction=he(l),s.component.update(a),t[o]=!0)}else{var h=e.createFieldLegend(i,n,r);h&&(h.component.init(),e.components.push(h),t[o]=!0)}});var l=[];(0,em.S6)(this.getComponents(),function(e){t[e.id]?l.push(e):e.component.destroy()}),this.components=l},t.prototype.clear=function(){e.prototype.clear.call(this),this.container.clear()},t.prototype.destroy=function(){e.prototype.destroy.call(this),this.container.remove(!0)},t.prototype.getGeometries=function(e){var t=this,i=e.geometries;return(0,em.S6)(e.views,function(e){i=i.concat(t.getGeometries(e))}),i},t.prototype.loopLegends=function(e){if(this.view.getRootView()===this.view){var t=this.getGeometries(this.view),i={};(0,em.S6)(t,function(t){var n=t.getGroupAttributes();(0,em.S6)(n,function(n){var r=n.getScale(n.type);r&&"identity"!==r.type&&!i[r.field]&&(e(t,n,r),i[r.field]=!0)})})}},t.prototype.createFieldLegend=function(e,t,i){var n,r=l8(this.option,i.field),o=O.FORE,s=he(r);if(!1!==r&&((0,em.U2)(r,"custom")?n=this.createCustomLegend(e,t,i,r):i.isLinear?n=this.createContinuousLegend(e,t,i,r):i.isCategory&&(n=this.createCategoryLegend(e,t,i,r))),n)return n.set("field",i.field),{id:this.getId(i.field),component:n,layer:o,direction:s,type:D.LEGEND,extra:{scale:i}}},t.prototype.createCustomLegend=function(e,t,i,n){var r=this.getCategoryCfg(e,t,i,n,!0);return new nA(r)},t.prototype.createContinuousLegend=function(e,t,i,n){var r=this.getContinuousCfg(e,t,i,n7(n,["value"]));return new nR(r)},t.prototype.createCategoryLegend=function(e,t,i,n){var r=this.getCategoryCfg(e,t,i,n);return new nA(r)},t.prototype.getContinuousCfg=function(e,t,i,n){var r=i.getTicks(),o=(0,em.sE)(r,function(e){return 0===e.value}),s=(0,em.sE)(r,function(e){return 1===e.value}),a=r.map(function(e){var n=e.value,r=e.tickValue,o=t.mapping(i.invert(n)).join("");return{value:r,attrValue:o,color:o,scaleValue:n}});o||a.push({value:i.min,attrValue:t.mapping(i.invert(0)).join(""),color:t.mapping(i.invert(0)).join(""),scaleValue:0}),s||a.push({value:i.max,attrValue:t.mapping(i.invert(1)).join(""),color:t.mapping(i.invert(1)).join(""),scaleValue:1}),a.sort(function(e,t){return e.value-t.value});var l={min:(0,em.YM)(a).value,max:(0,em.Z$)(a).value,colors:[],rail:{type:t.type},track:{}};"size"===t.type&&(l.track={style:{fill:"size"===t.type?this.view.getTheme().defaultColor:void 0}}),"color"===t.type&&(l.colors=a.map(function(e){return e.attrValue}));var h=this.container,u=st(he(n)),d=(0,em.U2)(n,"title");return d&&(d=(0,em.b$)({text:ra(i)},d)),l.container=h,l.layout=u,l.title=d,l.animateOption=ox,this.mergeLegendCfg(l,n,"continuous")},t.prototype.getCategoryCfg=function(e,t,i,n,r){var o=this.container,s=(0,em.U2)(n,"position",x.BOTTOM),a=sn(this.view.getTheme(),s),l=(0,em.U2)(a,["marker"]),h=(0,em.U2)(n,"marker"),u=st(s),d=(0,em.U2)(a,["pageNavigator"]),c=(0,em.U2)(n,"pageNavigator"),g=r?n.items.map(function(e,t){var i=h;(0,em.mf)(i)&&(i=i(e.name,t,(0,em.b$)({},l,e)));var n=(0,em.mf)(e.marker)?e.marker(e.name,t,(0,em.b$)({},l,e)):e.marker,r=(0,em.b$)({},l,i,n);return se(r),e.marker=r,e}):si(this.view,e,t,l,h),p=(0,em.U2)(n,"title");p&&(p=(0,em.b$)({text:i?ra(i):""},p));var f=(0,em.U2)(n,"maxWidthRatio"),m=(0,em.U2)(n,"maxHeightRatio"),v=this.getCategoryLegendSizeCfg(u,f,m);v.container=o,v.layout=u,v.items=g,v.title=p,v.animateOption=ox,v.pageNavigator=(0,em.b$)({},d,c);var E=this.mergeLegendCfg(v,n,s);E.reversed&&E.items.reverse();var _=(0,em.U2)(E,"maxItemWidth");return _&&_<=1&&(E.maxItemWidth=this.view.viewBBox.width*_),E},t.prototype.mergeLegendCfg=function(e,t,i){var n=i.split("-")[0],r=sn(this.view.getTheme(),n);return(0,em.b$)({},r,e,t)},t.prototype.getId=function(e){return this.name+"-"+e},t.prototype.getComponentById=function(e){return(0,em.sE)(this.components,function(t){return t.id===e})},t.prototype.getCategoryLegendSizeCfg=function(e,t,i){void 0===t&&(t=.25),void 0===i&&(i=.25);var n=this.view.viewBBox,r=n.width,o=n.height;return"vertical"===e?{maxWidth:r*t,maxHeight:o}:{maxWidth:r,maxHeight:o*i}},t}(oL),hi=function(e){function t(t){var i=e.call(this,t)||this;return i.onChangeFn=em.ZT,i.resetMeasure=function(){i.clear()},i.onValueChange=function(e){var t=e[0],n=e[1];i.start=t,i.end=n,i.changeViewData(t,n)},i.container=i.view.getLayer(O.FORE).addGroup(),i.onChangeFn=(0,em.P2)(i.onValueChange,20,{leading:!0}),i.width=0,i.view.on(M.BEFORE_CHANGE_DATA,i.resetMeasure),i.view.on(M.BEFORE_CHANGE_SIZE,i.resetMeasure),i}return(0,ef.ZT)(t,e),Object.defineProperty(t.prototype,"name",{get:function(){return"slider"},enumerable:!1,configurable:!0}),t.prototype.destroy=function(){e.prototype.destroy.call(this),this.view.off(M.BEFORE_CHANGE_DATA,this.resetMeasure),this.view.off(M.BEFORE_CHANGE_SIZE,this.resetMeasure)},t.prototype.init=function(){},t.prototype.render=function(){this.option=this.view.getOptions().slider;var e=this.getSliderCfg(),t=e.start,i=e.end;(0,em.UM)(this.start)&&(this.start=t,this.end=i);var n=this.view.getOptions().data;this.option&&!(0,em.xb)(n)?this.slider?this.slider=this.updateSlider():(this.slider=this.createSlider(),this.slider.component.on("sliderchange",this.onChangeFn)):this.slider&&(this.slider.component.destroy(),this.slider=void 0)},t.prototype.layout=function(){var e=this;if(this.option&&!this.width&&(this.measureSlider(),setTimeout(function(){e.view.destroyed||e.changeViewData(e.start,e.end)},0)),this.slider){var t=this.view.coordinateBBox.width,i=this.slider.component.get("padding"),n=i[0],r=(i[1],i[2],i[3]),o=this.slider.component.getLayoutBBox(),s=new re(o.x,o.y,Math.min(o.width,t),o.height).expand(i),a=this.getMinMaxText(this.start,this.end),l=a.minText,h=a.maxText,u=l7(this.view.viewBBox,s,x.BOTTOM),d=(u[0],u[1]),c=l7(this.view.coordinateBBox,s,x.BOTTOM),g=c[0];c[1],this.slider.component.update((0,ef.pi)((0,ef.pi)({},this.getSliderCfg()),{x:g+r,y:d+n,width:this.width,start:this.start,end:this.end,minText:l,maxText:h})),this.view.viewBBox=this.view.viewBBox.cut(s,x.BOTTOM)}},t.prototype.update=function(){this.render()},t.prototype.createSlider=function(){var e=this.getSliderCfg(),t=new nq((0,ef.pi)({container:this.container},e));return t.init(),{component:t,layer:O.FORE,direction:x.BOTTOM,type:D.SLIDER}},t.prototype.updateSlider=function(){var e=this.getSliderCfg();if(this.width){var t=this.getMinMaxText(this.start,this.end),i=t.minText,n=t.maxText;e=(0,ef.pi)((0,ef.pi)({},e),{width:this.width,start:this.start,end:this.end,minText:i,maxText:n})}return this.slider.component.update(e),this.slider},t.prototype.measureSlider=function(){var e=this.getSliderCfg().width;this.width=e},t.prototype.getSliderCfg=function(){var e={height:16,start:0,end:1,minText:"",maxText:"",x:0,y:0,width:this.view.coordinateBBox.width};if((0,em.Kn)(this.option)){var t=(0,ef.pi)({data:this.getData()},(0,em.U2)(this.option,"trendCfg",{}));e=(0,em.b$)({},e,this.getThemeOptions(),this.option),e=(0,ef.pi)((0,ef.pi)({},e),{trendCfg:t})}return e.start=(0,em.uZ)(Math.min((0,em.UM)(e.start)?0:e.start,(0,em.UM)(e.end)?1:e.end),0,1),e.end=(0,em.uZ)(Math.max((0,em.UM)(e.start)?0:e.start,(0,em.UM)(e.end)?1:e.end),0,1),e},t.prototype.getData=function(){var e=this.view.getOptions().data,t=this.view.getYScales()[0],i=this.view.getGroupScales();if(i.length){var n=i[0],r=n.field,o=n.ticks;return e.reduce(function(e,i){return i[r]===o[0]&&e.push(i[t.field]),e},[])}return e.map(function(e){return e[t.field]||0})},t.prototype.getThemeOptions=function(){var e=this.view.getTheme();return(0,em.U2)(e,["components","slider","common"],{})},t.prototype.getMinMaxText=function(e,t){var i=this.view.getOptions().data,n=this.view.getXScale(),r=(0,em.I)(i,n.field),o=(0,em.dp)(i);if(!n||!o)return{};var s=(0,em.dp)(r),a=Math.floor(e*(s-1)),l=Math.floor(t*(s-1)),h=(0,em.U2)(r,[a]),u=(0,em.U2)(r,[l]),d=this.getSliderCfg().formatter;return d&&(h=d(h,i[a],a),u=d(u,i[l],l)),{minText:h,maxText:u}},t.prototype.changeViewData=function(e,t){var i=this.view.getOptions().data,n=this.view.getXScale(),r=(0,em.dp)(i);if(n&&r){var o=(0,em.I)(i,n.field),s=(0,em.dp)(o),a=Math.floor(e*(s-1)),l=Math.floor(t*(s-1));this.view.filter(n.field,function(e,t){var i=o.indexOf(e);return!(i>-1)||n9(i,a,l)}),this.view.render(!0)}},t.prototype.getComponents=function(){return this.slider?[this.slider]:[]},t.prototype.clear=function(){this.slider&&(this.slider.component.destroy(),this.slider=void 0),this.width=0,this.start=void 0,this.end=void 0},t}(oL),hn=function(e){function t(t){var i=e.call(this,t)||this;return i.onChangeFn=em.ZT,i.resetMeasure=function(){i.clear()},i.onValueChange=function(e){var t=e.ratio,n=i.getValidScrollbarCfg().animate;i.ratio=(0,em.uZ)(t,0,1);var 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p=l7(this.view.viewBBox,r,x.RIGHT),h=p[0],u=p[1],f=l7(this.view.viewBBox,r,x.RIGHT),c=f[0],g=f[1];s=c,a=u}s+=i[3],a+=i[0],this.trackLen?this.scrollbar.component.update((0,ef.pi)((0,ef.pi)({},o),{x:s,y:a,trackLen:this.trackLen,thumbLen:this.thumbLen,thumbOffset:(this.trackLen-this.thumbLen)*this.ratio})):this.scrollbar.component.update((0,ef.pi)((0,ef.pi)({},o),{x:s,y:a})),this.view.viewBBox=this.view.viewBBox.cut(r,o.isHorizontal?x.BOTTOM:x.RIGHT)}},t.prototype.update=function(){this.render()},t.prototype.getComponents=function(){return this.scrollbar?[this.scrollbar]:[]},t.prototype.clear=function(){this.scrollbar&&(this.scrollbar.component.destroy(),this.scrollbar=void 0),this.trackLen=0,this.thumbLen=0,this.ratio=0,this.cnt=0,this.step=0,this.data=void 0,this.xScaleCfg=void 0,this.yScalesCfg=[]},t.prototype.setValue=function(e){this.onValueChange({ratio:e})},t.prototype.getValue=function(){return this.ratio},t.prototype.getThemeOptions=function(){var 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e.toString}constructor(e,t,i,n,r,o=!1){"object"==typeof e?(this.scheme=e.scheme||"",this.authority=e.authority||"",this.path=e.path||"",this.query=e.query||"",this.fragment=e.fragment||""):(this.scheme=e||o?e:"file",this.authority=t||"",this.path=function(e,t){switch(e){case"https":case"http":case"file":t?"/"!==t[0]&&(t="/"+t):t="/"}return t}(this.scheme,i||""),this.query=n||"",this.fragment=r||"",function(e,t){if(!e.scheme&&t)throw Error(`[UriError]: Scheme is missing: {scheme: "", authority: "${e.authority}", path: "${e.path}", query: "${e.query}", fragment: "${e.fragment}"}`);if(e.scheme&&!rr.test(e.scheme))throw Error("[UriError]: Scheme contains illegal characters.");if(e.path){if(e.authority){if(!ro.test(e.path))throw Error('[UriError]: If a URI contains an authority component, then the path component must either be empty or begin with a slash ("/") character')}else if(rs.test(e.path))throw Error('[UriError]: If a URI does not contain an authority component, then the path cannot 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Error("[UriError]: cannot call joinPath on URI without path");return i=nY.ED&&"file"===e.scheme?rl.file(n5.join(rp(e,!0),...t)).path:n3.join(e.path,...t),e.with({path:i})}toString(e=!1){return rf(this,e)}toJSON(){return this}static revive(e){var t,i;if(!e||e instanceof rl)return e;{let n=new ru(e);return n._formatted=null!==(t=e.external)&&void 0!==t?t:null,n._fsPath=e._sep===rh&&null!==(i=e.fsPath)&&void 0!==i?i:null,n}}}let rh=nY.ED?1:void 0;class ru extends rl{constructor(){super(...arguments),this._formatted=null,this._fsPath=null}get fsPath(){return this._fsPath||(this._fsPath=rp(this,!1)),this._fsPath}toString(e=!1){return e?rf(this,!0):(this._formatted||(this._formatted=rf(this,!1)),this._formatted)}toJSON(){let e={$mid:1};return this._fsPath&&(e.fsPath=this._fsPath,e._sep=rh),this._formatted&&(e.external=this._formatted),this.path&&(e.path=this.path),this.scheme&&(e.scheme=this.scheme),this.authority&&(e.authority=this.authority),this.query&&(e.query=this.query),this.fragment&&(e.fragment=this.fragment),e}}let rd={58:"%3A",47:"%2F",63:"%3F",35:"%23",91:"%5B",93:"%5D",64:"%40",33:"%21",36:"%24",38:"%26",39:"%27",40:"%28",41:"%29",42:"%2A",43:"%2B",44:"%2C",59:"%3B",61:"%3D",32:"%20"};function rc(e,t,i){let n;let r=-1;for(let o=0;o=97&&s<=122||s>=65&&s<=90||s>=48&&s<=57||45===s||46===s||95===s||126===s||t&&47===s||i&&91===s||i&&93===s||i&&58===s)-1!==r&&(n+=encodeURIComponent(e.substring(r,o)),r=-1),void 0!==n&&(n+=e.charAt(o));else{void 0===n&&(n=e.substr(0,o));let t=rd[s];void 0!==t?(-1!==r&&(n+=encodeURIComponent(e.substring(r,o)),r=-1),n+=t):-1===r&&(r=o)}}return -1!==r&&(n+=encodeURIComponent(e.substring(r))),void 0!==n?n:e}function rg(e){let t;for(let i=0;i1&&"file"===e.scheme?`//${e.authority}${e.path}`:47===e.path.charCodeAt(0)&&(e.path.charCodeAt(1)>=65&&90>=e.path.charCodeAt(1)||e.path.charCodeAt(1)>=97&&122>=e.path.charCodeAt(1))&&58===e.path.charCodeAt(2)?t?e.path.substr(1):e.path[1].toLowerCase()+e.path.substr(2):e.path,nY.ED&&(i=i.replace(/\//g,"\\")),i}function rf(e,t){let i=t?rg:rc,n="",{scheme:r,authority:o,path:s,query:a,fragment:l}=e;if(r&&(n+=r+":"),(o||"file"===r)&&(n+="//"),o){let e=o.indexOf("@");if(-1!==e){let t=o.substr(0,e);o=o.substr(e+1),-1===(e=t.lastIndexOf(":"))?n+=i(t,!1,!1):n+=i(t.substr(0,e),!1,!1)+":"+i(t.substr(e+1),!1,!0),n+="@"}-1===(e=(o=o.toLowerCase()).lastIndexOf(":"))?n+=i(o,!1,!0):n+=i(o.substr(0,e),!1,!0)+o.substr(e)}if(s){if(s.length>=3&&47===s.charCodeAt(0)&&58===s.charCodeAt(2)){let e=s.charCodeAt(1);e>=65&&e<=90&&(s=`/${String.fromCharCode(e+32)}:${s.substr(3)}`)}else if(s.length>=2&&58===s.charCodeAt(1)){let e=s.charCodeAt(0);e>=65&&e<=90&&(s=`${String.fromCharCode(e+32)}:${s.substr(2)}`)}n+=i(s,!0,!1)}return a&&(n+="?"+i(a,!1,!1)),l&&(n+="#"+(t?l:rc(l,!1,!1))),n}let rm=/(%[0-9A-Za-z][0-9A-Za-z])+/g;function rv(e){return e.match(rm)?e.replace(rm,e=>(function e(t){try{return decodeURIComponent(t)}catch(i){if(t.length>3)return t.substr(0,3)+e(t.substr(3));return t}})(e)):e}class rE{constructor(e,t){this.lineNumber=e,this.column=t}with(e=this.lineNumber,t=this.column){return e===this.lineNumber&&t===this.column?this:new rE(e,t)}delta(e=0,t=0){return this.with(this.lineNumber+e,this.column+t)}equals(e){return rE.equals(this,e)}static equals(e,t){return!e&&!t||!!e&&!!t&&e.lineNumber===t.lineNumber&&e.column===t.column}isBefore(e){return rE.isBefore(this,e)}static isBefore(e,t){return e.lineNumberi||e===i&&t>n?(this.startLineNumber=i,this.startColumn=n,this.endLineNumber=e,this.endColumn=t):(this.startLineNumber=e,this.startColumn=t,this.endLineNumber=i,this.endColumn=n)}isEmpty(){return r_.isEmpty(this)}static isEmpty(e){return e.startLineNumber===e.endLineNumber&&e.startColumn===e.endColumn}containsPosition(e){return r_.containsPosition(this,e)}static containsPosition(e,t){return!(t.lineNumbere.endLineNumber)&&(t.lineNumber!==e.startLineNumber||!(t.columne.endColumn))}static strictContainsPosition(e,t){return!(t.lineNumbere.endLineNumber)&&(t.lineNumber!==e.startLineNumber||!(t.column<=e.startColumn))&&(t.lineNumber!==e.endLineNumber||!(t.column>=e.endColumn))}containsRange(e){return r_.containsRange(this,e)}static containsRange(e,t){return!(t.startLineNumbere.endLineNumber)&&!(t.endLineNumber>e.endLineNumber)&&(t.startLineNumber!==e.startLineNumber||!(t.startColumne.endColumn))}strictContainsRange(e){return r_.strictContainsRange(this,e)}static strictContainsRange(e,t){return!(t.startLineNumbere.endLineNumber)&&!(t.endLineNumber>e.endLineNumber)&&(t.startLineNumber!==e.startLineNumber||!(t.startColumn<=e.startColumn))&&(t.endLineNumber!==e.endLineNumber||!(t.endColumn>=e.endColumn))}plusRange(e){return r_.plusRange(this,e)}static plusRange(e,t){let i,n,r,o;return t.startLineNumbere.endLineNumber?(r=t.endLineNumber,o=t.endColumn):t.endLineNumber===e.endLineNumber?(r=t.endLineNumber,o=Math.max(t.endColumn,e.endColumn)):(r=e.endLineNumber,o=e.endColumn),new r_(i,n,r,o)}intersectRanges(e){return r_.intersectRanges(this,e)}static intersectRanges(e,t){let i=e.startLineNumber,n=e.startColumn,r=e.endLineNumber,o=e.endColumn,s=t.startLineNumber,a=t.startColumn,l=t.endLineNumber,h=t.endColumn;return(il?(r=l,o=h):r===l&&(o=Math.min(o,h)),i>r||i===r&&n>o)?null:new r_(i,n,r,o)}equalsRange(e){return r_.equalsRange(this,e)}static equalsRange(e,t){return!e&&!t||!!e&&!!t&&e.startLineNumber===t.startLineNumber&&e.startColumn===t.startColumn&&e.endLineNumber===t.endLineNumber&&e.endColumn===t.endColumn}getEndPosition(){return r_.getEndPosition(this)}static getEndPosition(e){return new rE(e.endLineNumber,e.endColumn)}getStartPosition(){return r_.getStartPosition(this)}static getStartPosition(e){return new rE(e.startLineNumber,e.startColumn)}toString(){return"["+this.startLineNumber+","+this.startColumn+" -> "+this.endLineNumber+","+this.endColumn+"]"}setEndPosition(e,t){return new r_(this.startLineNumber,this.startColumn,e,t)}setStartPosition(e,t){return new r_(e,t,this.endLineNumber,this.endColumn)}collapseToStart(){return r_.collapseToStart(this)}static collapseToStart(e){return new r_(e.startLineNumber,e.startColumn,e.startLineNumber,e.startColumn)}collapseToEnd(){return r_.collapseToEnd(this)}static collapseToEnd(e){return new r_(e.endLineNumber,e.endColumn,e.endLineNumber,e.endColumn)}delta(e){return new r_(this.startLineNumber+e,this.startColumn,this.endLineNumber+e,this.endColumn)}static fromPositions(e,t=e){return new r_(e.lineNumber,e.column,t.lineNumber,t.column)}static lift(e){return e?new r_(e.startLineNumber,e.startColumn,e.endLineNumber,e.endColumn):null}static isIRange(e){return e&&"number"==typeof e.startLineNumber&&"number"==typeof e.startColumn&&"number"==typeof e.endLineNumber&&"number"==typeof e.endColumn}static areIntersectingOrTouching(e,t){return!(e.endLineNumbere.startLineNumber}toJSON(){return this}}class rC extends r_{constructor(e,t,i,n){super(e,t,i,n),this.selectionStartLineNumber=e,this.selectionStartColumn=t,this.positionLineNumber=i,this.positionColumn=n}toString(){return"["+this.selectionStartLineNumber+","+this.selectionStartColumn+" -> "+this.positionLineNumber+","+this.positionColumn+"]"}equalsSelection(e){return rC.selectionsEqual(this,e)}static selectionsEqual(e,t){return e.selectionStartLineNumber===t.selectionStartLineNumber&&e.selectionStartColumn===t.selectionStartColumn&&e.positionLineNumber===t.positionLineNumber&&e.positionColumn===t.positionColumn}getDirection(){return this.selectionStartLineNumber===this.startLineNumber&&this.selectionStartColumn===this.startColumn?0:1}setEndPosition(e,t){return 0===this.getDirection()?new rC(this.startLineNumber,this.startColumn,e,t):new rC(e,t,this.startLineNumber,this.startColumn)}getPosition(){return new rE(this.positionLineNumber,this.positionColumn)}getSelectionStart(){return new rE(this.selectionStartLineNumber,this.selectionStartColumn)}setStartPosition(e,t){return 0===this.getDirection()?new rC(e,t,this.endLineNumber,this.endColumn):new rC(this.endLineNumber,this.endColumn,e,t)}static fromPositions(e,t=e){return new rC(e.lineNumber,e.column,t.lineNumber,t.column)}static fromRange(e,t){return 0===t?new rC(e.startLineNumber,e.startColumn,e.endLineNumber,e.endColumn):new rC(e.endLineNumber,e.endColumn,e.startLineNumber,e.startColumn)}static liftSelection(e){return new rC(e.selectionStartLineNumber,e.selectionStartColumn,e.positionLineNumber,e.positionColumn)}static selectionsArrEqual(e,t){if(e&&!t||!e&&t)return!1;if(!e&&!t)return!0;if(e.length!==t.length)return!1;for(let i=0,n=e.length;i{e&&e.dispose()})}get tokenizationSupport(){return this._tokenizationSupport||(this._tokenizationSupport=this.createSupport()),this._tokenizationSupport}}let rD=new class{constructor(){this._tokenizationSupports=new Map,this._factories=new Map,this._onDidChange=new nT,this.onDidChange=this._onDidChange.event,this._colorMap=null}handleChange(e){this._onDidChange.fire({changedLanguages:e,changedColorMap:!1})}register(e,t){return this._tokenizationSupports.set(e,t),this.handleChange([e]),nu(()=>{this._tokenizationSupports.get(e)===t&&(this._tokenizationSupports.delete(e),this.handleChange([e]))})}get(e){return this._tokenizationSupports.get(e)||null}registerFactory(e,t){var i;null===(i=this._factories.get(e))||void 0===i||i.dispose();let n=new rR(this,e,t);return this._factories.set(e,n),nu(()=>{let t=this._factories.get(e);t&&t===n&&(this._factories.delete(e),t.dispose())})}getOrCreate(e){return rA(this,void 0,void 0,function*(){let t=this.get(e);if(t)return t;let i=this._factories.get(e);return!i||i.isResolved?null:(yield i.resolve(),this.get(e))})}isResolved(e){let t=this.get(e);if(t)return!0;let i=this._factories.get(e);return!i||!!i.isResolved}setColorMap(e){this._colorMap=e,this._onDidChange.fire({changedLanguages:Array.from(this._tokenizationSupports.keys()),changedColorMap:!0})}getColorMap(){return this._colorMap}getDefaultBackground(){return this._colorMap&&this._colorMap.length>2?this._colorMap[2]:null}};(N=tl||(tl={}))[N.Unknown=0]="Unknown",N[N.Disabled=1]="Disabled",N[N.Enabled=2]="Enabled",(I=th||(th={}))[I.Invoke=1]="Invoke",I[I.Auto=2]="Auto",(w=tu||(tu={}))[w.None=0]="None",w[w.KeepWhitespace=1]="KeepWhitespace",w[w.InsertAsSnippet=4]="InsertAsSnippet",(O=td||(td={}))[O.Method=0]="Method",O[O.Function=1]="Function",O[O.Constructor=2]="Constructor",O[O.Field=3]="Field",O[O.Variable=4]="Variable",O[O.Class=5]="Class",O[O.Struct=6]="Struct",O[O.Interface=7]="Interface",O[O.Module=8]="Module",O[O.Property=9]="Property",O[O.Event=10]="Event",O[O.Operator=11]="Operator",O[O.Unit=12]="Unit",O[O.Value=13]="Value",O[O.Constant=14]="Constant",O[O.Enum=15]="Enum",O[O.EnumMember=16]="EnumMember",O[O.Keyword=17]="Keyword",O[O.Text=18]="Text",O[O.Color=19]="Color",O[O.File=20]="File",O[O.Reference=21]="Reference",O[O.Customcolor=22]="Customcolor",O[O.Folder=23]="Folder",O[O.TypeParameter=24]="TypeParameter",O[O.User=25]="User",O[O.Issue=26]="Issue",O[O.Snippet=27]="Snippet",(x=tc||(tc={}))[x.Deprecated=1]="Deprecated",(D=tg||(tg={}))[D.Invoke=0]="Invoke",D[D.TriggerCharacter=1]="TriggerCharacter",D[D.TriggerForIncompleteCompletions=2]="TriggerForIncompleteCompletions",(M=tp||(tp={}))[M.EXACT=0]="EXACT",M[M.ABOVE=1]="ABOVE",M[M.BELOW=2]="BELOW",(k=tf||(tf={}))[k.NotSet=0]="NotSet",k[k.ContentFlush=1]="ContentFlush",k[k.RecoverFromMarkers=2]="RecoverFromMarkers",k[k.Explicit=3]="Explicit",k[k.Paste=4]="Paste",k[k.Undo=5]="Undo",k[k.Redo=6]="Redo",(P=tm||(tm={}))[P.LF=1]="LF",P[P.CRLF=2]="CRLF",(F=tv||(tv={}))[F.Text=0]="Text",F[F.Read=1]="Read",F[F.Write=2]="Write",(B=tE||(tE={}))[B.None=0]="None",B[B.Keep=1]="Keep",B[B.Brackets=2]="Brackets",B[B.Advanced=3]="Advanced",B[B.Full=4]="Full",(U=t_||(t_={}))[U.acceptSuggestionOnCommitCharacter=0]="acceptSuggestionOnCommitCharacter",U[U.acceptSuggestionOnEnter=1]="acceptSuggestionOnEnter",U[U.accessibilitySupport=2]="accessibilitySupport",U[U.accessibilityPageSize=3]="accessibilityPageSize",U[U.ariaLabel=4]="ariaLabel",U[U.ariaRequired=5]="ariaRequired",U[U.autoClosingBrackets=6]="autoClosingBrackets",U[U.screenReaderAnnounceInlineSuggestion=7]="screenReaderAnnounceInlineSuggestion",U[U.autoClosingDelete=8]="autoClosingDelete",U[U.autoClosingOvertype=9]="autoClosingOvertype",U[U.autoClosingQuotes=10]="autoClosingQuotes",U[U.autoIndent=11]="autoIndent",U[U.automaticLayout=12]="automaticLayout",U[U.autoSurround=13]="autoSurround",U[U.bracketPairColorization=14]="bracketPairColorization",U[U.guides=15]="guides",U[U.codeLens=16]="codeLens",U[U.codeLensFontFamily=17]="codeLensFontFamily",U[U.codeLensFontSize=18]="codeLensFontSize",U[U.colorDecorators=19]="colorDecorators",U[U.colorDecoratorsLimit=20]="colorDecoratorsLimit",U[U.columnSelection=21]="columnSelection",U[U.comments=22]="comments",U[U.contextmenu=23]="contextmenu",U[U.copyWithSyntaxHighlighting=24]="copyWithSyntaxHighlighting",U[U.cursorBlinking=25]="cursorBlinking",U[U.cursorSmoothCaretAnimation=26]="cursorSmoothCaretAnimation",U[U.cursorStyle=27]="cursorStyle",U[U.cursorSurroundingLines=28]="cursorSurroundingLines",U[U.cursorSurroundingLinesStyle=29]="cursorSurroundingLinesStyle",U[U.cursorWidth=30]="cursorWidth",U[U.disableLayerHinting=31]="disableLayerHinting",U[U.disableMonospaceOptimizations=32]="disableMonospaceOptimizations",U[U.domReadOnly=33]="domReadOnly",U[U.dragAndDrop=34]="dragAndDrop",U[U.dropIntoEditor=35]="dropIntoEditor",U[U.emptySelectionClipboard=36]="emptySelectionClipboard",U[U.experimentalWhitespaceRendering=37]="experimentalWhitespaceRendering",U[U.extraEditorClassName=38]="extraEditorClassName",U[U.fastScrollSensitivity=39]="fastScrollSensitivity",U[U.find=40]="find",U[U.fixedOverflowWidgets=41]="fixedOverflowWidgets",U[U.folding=42]="folding",U[U.foldingStrategy=43]="foldingStrategy",U[U.foldingHighlight=44]="foldingHighlight",U[U.foldingImportsByDefault=45]="foldingImportsByDefault",U[U.foldingMaximumRegions=46]="foldingMaximumRegions",U[U.unfoldOnClickAfterEndOfLine=47]="unfoldOnClickAfterEndOfLine",U[U.fontFamily=48]="fontFamily",U[U.fontInfo=49]="fontInfo",U[U.fontLigatures=50]="fontLigatures",U[U.fontSize=51]="fontSize",U[U.fontWeight=52]="fontWeight",U[U.fontVariations=53]="fontVariations",U[U.formatOnPaste=54]="formatOnPaste",U[U.formatOnType=55]="formatOnType",U[U.glyphMargin=56]="glyphMargin",U[U.gotoLocation=57]="gotoLocation",U[U.hideCursorInOverviewRuler=58]="hideCursorInOverviewRuler",U[U.hover=59]="hover",U[U.inDiffEditor=60]="inDiffEditor",U[U.inlineSuggest=61]="inlineSuggest",U[U.letterSpacing=62]="letterSpacing",U[U.lightbulb=63]="lightbulb",U[U.lineDecorationsWidth=64]="lineDecorationsWidth",U[U.lineHeight=65]="lineHeight",U[U.lineNumbers=66]="lineNumbers",U[U.lineNumbersMinChars=67]="lineNumbersMinChars",U[U.linkedEditing=68]="linkedEditing",U[U.links=69]="links",U[U.matchBrackets=70]="matchBrackets",U[U.minimap=71]="minimap",U[U.mouseStyle=72]="mouseStyle",U[U.mouseWheelScrollSensitivity=73]="mouseWheelScro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10===e?3:13===e?2:4;if(e<127)return 0;let t=this._data,i=t.length/3,n=1;for(;n<=i;)if(et[3*n+1]))return t[3*n+2];n=2*n+1}return 0}}op._INSTANCE=null;class of{static getInstance(e){return of.cache.get(Array.from(e))}static getLocales(){return of._locales.value}constructor(e){this.confusableDictionary=e}isAmbiguous(e){return this.confusableDictionary.has(e)}getPrimaryConfusable(e){return this.confusableDictionary.get(e)}getConfusableCodePoints(){return new Set(this.confusableDictionary.keys())}}of.ambiguousCharacterData=new 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i=0;i1&&"file"===e.scheme?`//${e.authority}${e.path}`:47===e.path.charCodeAt(0)&&(e.path.charCodeAt(1)>=65&&90>=e.path.charCodeAt(1)||e.path.charCodeAt(1)>=97&&122>=e.path.charCodeAt(1))&&58===e.path.charCodeAt(2)?t?e.path.substr(1):e.path[1].toLowerCase()+e.path.substr(2):e.path,nz.ED&&(i=i.replace(/\//g,"\\")),i}function rp(e,t){let i=t?rf:rc,n="",{scheme:r,authority:o,path:s,query:a,fragment:l}=e;if(r&&(n+=r+":"),(o||"file"===r)&&(n+="//"),o){let e=o.indexOf("@");if(-1!==e){let t=o.substr(0,e);o=o.substr(e+1),-1===(e=t.lastIndexOf(":"))?n+=i(t,!1,!1):n+=i(t.substr(0,e),!1,!1)+":"+i(t.substr(e+1),!1,!0),n+="@"}-1===(e=(o=o.toLowerCase()).lastIndexOf(":"))?n+=i(o,!1,!0):n+=i(o.substr(0,e),!1,!0)+o.substr(e)}if(s){if(s.length>=3&&47===s.charCodeAt(0)&&58===s.charCodeAt(2)){let e=s.charCodeAt(1);e>=65&&e<=90&&(s=`/${String.fromCharCode(e+32)}:${s.substr(3)}`)}else if(s.length>=2&&58===s.charCodeAt(1)){let e=s.charCodeAt(0);e>=65&&e<=90&&(s=`${String.fromCharCode(e+32)}:${s.substr(2)}`)}n+=i(s,!0,!1)}return a&&(n+="?"+i(a,!1,!1)),l&&(n+="#"+(t?l:rc(l,!1,!1))),n}let rm=/(%[0-9A-Za-z][0-9A-Za-z])+/g;function r_(e){return e.match(rm)?e.replace(rm,e=>(function e(t){try{return decodeURIComponent(t)}catch(i){if(t.length>3)return t.substr(0,3)+e(t.substr(3));return t}})(e)):e}class rE{constructor(e,t){this.lineNumber=e,this.column=t}with(e=this.lineNumber,t=this.column){return e===this.lineNumber&&t===this.column?this:new rE(e,t)}delta(e=0,t=0){return this.with(this.lineNumber+e,this.column+t)}equals(e){return rE.equals(this,e)}static equals(e,t){return!e&&!t||!!e&&!!t&&e.lineNumber===t.lineNumber&&e.column===t.column}isBefore(e){return rE.isBefore(this,e)}static isBefore(e,t){return e.lineNumberi||e===i&&t>n?(this.startLineNumber=i,this.startColumn=n,this.endLineNumber=e,this.endColumn=t):(this.startLineNumber=e,this.startColumn=t,this.endLineNumber=i,this.endColumn=n)}isEmpty(){return rv.isEmpty(this)}static isEmpty(e){return e.startLineNumber===e.endLineNumber&&e.startColumn===e.endColumn}containsPosition(e){return rv.containsPosition(this,e)}static containsPosition(e,t){return!(t.lineNumbere.endLineNumber)&&(t.lineNumber!==e.startLineNumber||!(t.columne.endColumn))}static strictContainsPosition(e,t){return!(t.lineNumbere.endLineNumber)&&(t.lineNumber!==e.startLineNumber||!(t.column<=e.startColumn))&&(t.lineNumber!==e.endLineNumber||!(t.column>=e.endColumn))}containsRange(e){return rv.containsRange(this,e)}static containsRange(e,t){return!(t.startLineNumbere.endLineNumber)&&!(t.endLineNumber>e.endLineNumber)&&(t.startLineNumber!==e.startLineNumber||!(t.startColumne.endColumn))}strictContainsRange(e){return rv.strictContainsRange(this,e)}static strictContainsRange(e,t){return!(t.startLineNumbere.endLineNumber)&&!(t.endLineNumber>e.endLineNumber)&&(t.startLineNumber!==e.startLineNumber||!(t.startColumn<=e.startColumn))&&(t.endLineNumber!==e.endLineNumber||!(t.endColumn>=e.endColumn))}plusRange(e){return rv.plusRange(this,e)}static plusRange(e,t){let i,n,r,o;return t.startLineNumbere.endLineNumber?(r=t.endLineNumber,o=t.endColumn):t.endLineNumber===e.endLineNumber?(r=t.endLineNumber,o=Math.max(t.endColumn,e.endColumn)):(r=e.endLineNumber,o=e.endColumn),new rv(i,n,r,o)}intersectRanges(e){return rv.intersectRanges(this,e)}static intersectRanges(e,t){let i=e.startLineNumber,n=e.startColumn,r=e.endLineNumber,o=e.endColumn,s=t.startLineNumber,a=t.startColumn,l=t.endLineNumber,h=t.endColumn;return(il?(r=l,o=h):r===l&&(o=Math.min(o,h)),i>r||i===r&&n>o)?null:new rv(i,n,r,o)}equalsRange(e){return rv.equalsRange(this,e)}static equalsRange(e,t){return!e&&!t||!!e&&!!t&&e.startLineNumber===t.startLineNumber&&e.startColumn===t.startColumn&&e.endLineNumber===t.endLineNumber&&e.endColumn===t.endColumn}getEndPosition(){return rv.getEndPosition(this)}static getEndPosition(e){return new rE(e.endLineNumber,e.endColumn)}getStartPosition(){return rv.getStartPosition(this)}static getStartPosition(e){return new rE(e.startLineNumber,e.startColumn)}toString(){return"["+this.startLineNumber+","+this.startColumn+" -> "+this.endLineNumber+","+this.endColumn+"]"}setEndPosition(e,t){return new rv(this.startLineNumber,this.startColumn,e,t)}setStartPosition(e,t){return new rv(e,t,this.endLineNumber,this.endColumn)}collapseToStart(){return rv.collapseToStart(this)}static collapseToStart(e){return new rv(e.startLineNumber,e.startColumn,e.startLineNumber,e.startColumn)}collapseToEnd(){return rv.collapseToEnd(this)}static collapseToEnd(e){return new rv(e.endLineNumber,e.endColumn,e.endLineNumber,e.endColumn)}delta(e){return new rv(this.startLineNumber+e,this.startColumn,this.endLineNumber+e,this.endColumn)}static fromPositions(e,t=e){return new rv(e.lineNumber,e.column,t.lineNumber,t.column)}static lift(e){return e?new rv(e.startLineNumber,e.startColumn,e.endLineNumber,e.endColumn):null}static isIRange(e){return e&&"number"==typeof e.startLineNumber&&"number"==typeof e.startColumn&&"number"==typeof e.endLineNumber&&"number"==typeof e.endColumn}static areIntersectingOrTouching(e,t){return!(e.endLineNumbere.startLineNumber}toJSON(){return this}}class rb extends rv{constructor(e,t,i,n){super(e,t,i,n),this.selectionStartLineNumber=e,this.selectionStartColumn=t,this.positionLineNumber=i,this.positionColumn=n}toString(){return"["+this.selectionStartLineNumber+","+this.selectionStartColumn+" -> "+this.positionLineNumber+","+this.positionColumn+"]"}equalsSelection(e){return rb.selectionsEqual(this,e)}static selectionsEqual(e,t){return e.selectionStartLineNumber===t.selectionStartLineNumber&&e.selectionStartColumn===t.selectionStartColumn&&e.positionLineNumber===t.positionLineNumber&&e.positionColumn===t.positionColumn}getDirection(){return this.selectionStartLineNumber===this.startLineNumber&&this.selectionStartColumn===this.startColumn?0:1}setEndPosition(e,t){return 0===this.getDirection()?new rb(this.startLineNumber,this.startColumn,e,t):new rb(e,t,this.startLineNumber,this.startColumn)}getPosition(){return new rE(this.positionLineNumber,this.positionColumn)}getSelectionStart(){return new rE(this.selectionStartLineNumber,this.selectionStartColumn)}setStartPosition(e,t){return 0===this.getDirection()?new rb(e,t,this.endLineNumber,this.endColumn):new rb(this.endLineNumber,this.endColumn,e,t)}static fromPositions(e,t=e){return new rb(e.lineNumber,e.column,t.lineNumber,t.column)}static fromRange(e,t){return 0===t?new rb(e.startLineNumber,e.startColumn,e.endLineNumber,e.endColumn):new rb(e.endLineNumber,e.endColumn,e.startLineNumber,e.startColumn)}static liftSelection(e){return new rb(e.selectionStartLineNumber,e.selectionStartColumn,e.positionLineNumber,e.positionColumn)}static selectionsArrEqual(e,t){if(e&&!t||!e&&t)return!1;if(!e&&!t)return!0;if(e.length!==t.length)return!1;for(let i=0,n=e.length;i{e&&e.dispose()})}get tokenizationSupport(){return this._tokenizationSupport||(this._tokenizationSupport=this.createSupport()),this._tokenizationSupport}}let rx=new class{constructor(){this._tokenizationSupports=new Map,this._factories=new Map,this._onDidChange=new ny,this.onDidChange=this._onDidChange.event,this._colorMap=null}handleChange(e){this._onDidChange.fire({changedLanguages:e,changedColorMap:!1})}register(e,t){return this._tokenizationSupports.set(e,t),this.handleChange([e]),nu(()=>{this._tokenizationSupports.get(e)===t&&(this._tokenizationSupports.delete(e),this.handleChange([e]))})}get(e){return this._tokenizationSupports.get(e)||null}registerFactory(e,t){var i;null===(i=this._factories.get(e))||void 0===i||i.dispose();let n=new rA(this,e,t);return this._factories.set(e,n),nu(()=>{let t=this._factories.get(e);t&&t===n&&(this._factories.delete(e),t.dispose())})}getOrCreate(e){return rR(this,void 0,void 0,function*(){let t=this.get(e);if(t)return t;let i=this._factories.get(e);return!i||i.isResolved?null:(yield i.resolve(),this.get(e))})}isResolved(e){let t=this.get(e);if(t)return!0;let i=this._factories.get(e);return!i||!!i.isResolved}setColorMap(e){this._colorMap=e,this._onDidChange.fire({changedLanguages:Array.from(this._tokenizationSupports.keys()),changedColorMap:!0})}getColorMap(){return this._colorMap}getDefaultBackground(){return this._colorMap&&this._colorMap.length>2?this._colorMap[2]:null}};(O=tl||(tl={}))[O.Unknown=0]="Unknown",O[O.Disabled=1]="Disabled",O[O.Enabled=2]="Enabled",(L=th||(th={}))[L.Invoke=1]="Invoke",L[L.Auto=2]="Auto",(I=tu||(tu={}))[I.None=0]="None",I[I.KeepWhitespace=1]="KeepWhitespace",I[I.InsertAsSnippet=4]="InsertAsSnippet",(w=td||(td={}))[w.Method=0]="Method",w[w.Function=1]="Function",w[w.Constructor=2]="Constructor",w[w.Field=3]="Field",w[w.Variable=4]="Variable",w[w.Class=5]="Class",w[w.Struct=6]="Struct",w[w.Interface=7]="Interface",w[w.Module=8]="Module",w[w.Property=9]="Property",w[w.Event=10]="Event",w[w.Operator=11]="Operator",w[w.Unit=12]="Unit",w[w.Value=13]="Value",w[w.Constant=14]="Constant",w[w.Enum=15]="Enum",w[w.EnumMember=16]="EnumMember",w[w.Keyword=17]="Keyword",w[w.Text=18]="Text",w[w.Color=19]="Color",w[w.File=20]="File",w[w.Reference=21]="Reference",w[w.Customcolor=22]="Customcolor",w[w.Folder=23]="Folder",w[w.TypeParameter=24]="TypeParameter",w[w.User=25]="User",w[w.Issue=26]="Issue",w[w.Snippet=27]="Snippet",(D=tc||(tc={}))[D.Deprecated=1]="Deprecated",(x=tf||(tf={}))[x.Invoke=0]="Invoke",x[x.TriggerCharacter=1]="TriggerCharacter",x[x.TriggerForIncompleteCompletions=2]="TriggerForIncompleteCompletions",(M=tg||(tg={}))[M.EXACT=0]="EXACT",M[M.ABOVE=1]="ABOVE",M[M.BELOW=2]="BELOW",(k=tp||(tp={}))[k.NotSet=0]="NotSet",k[k.ContentFlush=1]="ContentFlush",k[k.RecoverFromMarkers=2]="RecoverFromMarkers",k[k.Explicit=3]="Explicit",k[k.Paste=4]="Paste",k[k.Undo=5]="Undo",k[k.Redo=6]="Redo",(P=tm||(tm={}))[P.LF=1]="LF",P[P.CRLF=2]="CRLF",(F=t_||(t_={}))[F.Text=0]="Text",F[F.Read=1]="Read",F[F.Write=2]="Write",(B=tE||(tE={}))[B.None=0]="None",B[B.Keep=1]="Keep",B[B.Brackets=2]="Brackets",B[B.Advanced=3]="Advanced",B[B.Full=4]="Full",(U=tv||(tv={}))[U.acceptSuggestionOnCommitCharacter=0]="acceptSuggestionOnCommitCharacter",U[U.acceptSuggestionOnEnter=1]="acceptSuggestionOnEnter",U[U.accessibilitySupport=2]="accessibilitySupport",U[U.accessibilityPageSize=3]="accessibilityPageSize",U[U.ariaLabel=4]="ariaLabel",U[U.ariaRequired=5]="ariaRequired",U[U.autoClosingBrackets=6]="autoClosingBrackets",U[U.screenReaderAnnounceInlineSuggestion=7]="screenReaderAnnounceInlineSuggestion",U[U.autoClosingDelete=8]="autoClosingDelete",U[U.autoClosingOvertype=9]="autoClosingOvertype",U[U.autoClosingQuotes=10]="autoClosingQuotes",U[U.autoIndent=11]="autoIndent",U[U.automaticLayout=12]="automaticLayout",U[U.autoSurround=13]="autoSurround",U[U.bracketPairColorization=14]="bracketPairColorization",U[U.guides=15]="guides",U[U.codeLens=16]="codeLens",U[U.codeLensFontFamily=17]="codeLensFontFamily",U[U.codeLensFontSize=18]="codeLensFontSize",U[U.colorDecorators=19]="colorDecorators",U[U.colorDecoratorsLimit=20]="colorDecoratorsLimit",U[U.columnSelection=21]="columnSelection",U[U.comments=22]="comments",U[U.contextmenu=23]="contextmenu",U[U.copyWithSyntaxHighlighting=24]="copyWithSyntaxHighlighting",U[U.cursorBlinking=25]="cursorBlinking",U[U.cursorSmoothCaretAnimation=26]="cursorSmoothCaretAnimation",U[U.cursorStyle=27]="cursorStyle",U[U.cursorSurroundingLines=28]="cursorSurroundingLines",U[U.cursorSurroundingLinesStyle=29]="cursorSurroundingLinesStyle",U[U.cursorWidth=30]="cursorWidth",U[U.disableLayerHinting=31]="disableLayerHinting",U[U.disableMonospaceOptimizations=32]="disableMonospaceOptimizations",U[U.domReadOnly=33]="domReadOnly",U[U.dragAndDrop=34]="dragAndDrop",U[U.dropIntoEditor=35]="dropIntoEditor",U[U.emptySelectionClipboard=36]="emptySelectionClipboard",U[U.experimentalWhitespaceRendering=37]="experimentalWhitespaceRendering",U[U.extraEditorClassName=38]="extraEditorClassName",U[U.fastScrollSensitivity=39]="fastScrollSensitivity",U[U.find=40]="find",U[U.fixedOverflowWidgets=41]="fixedOverflowWidgets",U[U.folding=42]="folding",U[U.foldingStrategy=43]="foldingStrategy",U[U.foldingHighlight=44]="foldingHighlight",U[U.foldingImportsByDefault=45]="foldingImportsByDefault",U[U.foldingMaximumRegions=46]="foldingMaximumRegions",U[U.unfoldOnClickAfterEndOfLine=47]="unfoldOnClickAfterEndOfLine",U[U.fontFamily=48]="fontFamily",U[U.fontInfo=49]="fontInfo",U[U.fontLigatures=50]="fontLigatures",U[U.fontSize=51]="fontSize",U[U.fontWeight=52]="fontWeight",U[U.fontVariations=53]="fontVariations",U[U.formatOnPaste=54]="formatOnPaste",U[U.formatOnType=55]="formatOnType",U[U.glyphMargin=56]="glyphMargin",U[U.gotoLocation=57]="gotoLocation",U[U.hideCursorInOverviewRuler=58]="hideCursorInOverviewRuler",U[U.hover=59]="hover",U[U.inDiffEditor=60]="inDiffEditor",U[U.inlineSuggest=61]="inlineSuggest",U[U.letterSpacing=62]="letterSpacing",U[U.lightbulb=63]="lightbulb",U[U.lineDecorationsWidth=64]="lineDecorationsWidth",U[U.lineHeight=65]="lineHeight",U[U.lineNumbers=66]="lineNumbers",U[U.lineNumbersMinChars=67]="lineNumbersMinChars",U[U.linkedEditing=68]="linkedEditing",U[U.links=69]="links",U[U.matchBrackets=70]="matchBrackets",U[U.minimap=71]="minimap",U[U.mouseStyle=72]="mouseStyle",U[U.mouseWheelScrollSensitivity=73]="mouseWheelScro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10===e?3:13===e?2:4;if(e<127)return 0;let t=this._data,i=t.length/3,n=1;for(;n<=i;)if(et[3*n+1]))return t[3*n+2];n=2*n+1}return 0}}og._INSTANCE=null;class op{static getInstance(e){return op.cache.get(Array.from(e))}static getLocales(){return op._locales.value}constructor(e){this.confusableDictionary=e}isAmbiguous(e){return this.confusableDictionary.has(e)}getPrimaryConfusable(e){return this.confusableDictionary.get(e)}getConfusableCodePoints(){return new Set(this.confusableDictionary.keys())}}op.ambiguousCharacterData=new 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oO=oS.indexOf("Android")>=0,oL=!1;if(window.matchMedia){let e=window.matchMedia("(display-mode: standalone) or (display-mode: window-controls-overlay)"),t=window.matchMedia("(display-mode: fullscreen)");oL=e.matches,ob(e,({matches:e})=>{oL&&t.matches||(oL=e)})}class oI{constructor(e){this.domNode=e,this._maxWidth="",this._width="",this._height="",this._top="",this._left="",this._bottom="",this._right="",this._paddingLeft="",this._fontFamily="",this._fontWeight="",this._fontSize="",this._fontStyle="",this._fontFeatureSettings="",this._fontVariationSettings="",this._textDecoration="",this._lineHeight="",this._letterSpacing="",this._className="",this._display="",this._position="",this._visibility="",this._color="",this._backgroundColor="",this._layerHint=!1,this._contain="none",this._boxShadow=""}setMaxWidth(e){let t=ow(e);this._maxWidth!==t&&(this._maxWidth=t,this.domNode.style.maxWidth=this._maxWidth)}setWidth(e){let 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t=ow(e);this._letterSpacing!==t&&(this._letterSpacing=t,this.domNode.style.letterSpacing=this._letterSpacing)}setClassName(e){this._className!==e&&(this._className=e,this.domNode.className=this._className)}toggleClassName(e,t){this.domNode.classList.toggle(e,t),this._className=this.domNode.className}setDisplay(e){this._display!==e&&(this._display=e,this.domNode.style.display=this._display)}setPosition(e){this._position!==e&&(this._position=e,this.domNode.style.position=this._position)}setVisibility(e){this._visibility!==e&&(this._visibility=e,this.domNode.style.visibility=this._visibility)}setColor(e){this._color!==e&&(this._color=e,this.domNode.style.color=this._color)}setBackgroundColor(e){this._backgroundColor!==e&&(this._backgroundColor=e,this.domNode.style.backgroundColor=this._backgroundColor)}setLayerHinting(e){this._layerHint!==e&&(this._layerHint=e,this.domNode.style.transform=this._layerHint?"translate3d(0px, 0px, 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ny),this.onDidChange=this._onDidChange.event,this._cache=new oH,this._evictUntrustedReadingsTimeout=-1}dispose(){-1!==this._evictUntrustedReadingsTimeout&&(window.clearTimeout(this._evictUntrustedReadingsTimeout),this._evictUntrustedReadingsTimeout=-1),super.dispose()}clearAllFontInfos(){this._cache=new oH,this._onDidChange.fire()}_writeToCache(e,t){this._cache.put(e,t),t.isTrusted||-1!==this._evictUntrustedReadingsTimeout||(this._evictUntrustedReadingsTimeout=window.setTimeout(()=>{this._evictUntrustedReadingsTimeout=-1,this._evictUntrustedReadings()},5e3))}_evictUntrustedReadings(){let e=this._cache.getValues(),t=!1;for(let i of e)i.isTrusted||(t=!0,this._cache.remove(i));t&&this._onDidChange.fire()}readFontInfo(e){if(!this._cache.has(e)){let t=this._actualReadFontInfo(e);(t.typicalHalfwidthCharacterWidth<=2||t.typicalFullwidthCharacterWidth<=2||t.spaceWidth<=2||t.maxDigitWidth<=2)&&(t=new oU({pixelRatio:oC.value,fontFamily:t.fontFamily,fontWeight:t.fontWeight,fontSize:t.fontSize,fontFeatureSettings:t.fontFeatureSettings,fontVariationSettings:t.fontVariationSettings,lineHeight:t.lineHeight,letterSpacing:t.letterSpacing,isMonospace:t.isMonospace,typicalHalfwidthCharacterWidth:Math.max(t.typicalHalfwidthCharacterWidth,5),typicalFullwidthCharacterWidth:Math.max(t.typicalFullwidthCharacterWidth,5),canUseHalfwidthRightwardsArrow:t.canUseHalfwidthRightwardsArrow,spaceWidth:Math.max(t.spaceWidth,5),middotWidth:Math.max(t.middotWidth,5),wsmiddotWidth:Math.max(t.wsmiddotWidth,5),maxDigitWidth:Math.max(t.maxDigitWidth,5)},!1)),this._writeToCache(e,t)}return this._cache.get(e)}_createRequest(e,t,i,n){let r=new oM(e,t);return i.push(r),null==n||n.push(r),r}_actualReadFontInfo(e){let t=[],i=[],n=this._createRequest("n",0,t,i),r=this._createRequest("ｍ",0,t,null),o=this._createRequest(" 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oU({pixelRatio:oC.value,fontFamily:e.fontFamily,fontWeight:e.fontWeight,fontSize:e.fontSize,fontFeatureSettings:e.fontFeatureSettings,fontVariationSettings:e.fontVariationSettings,lineHeight:e.lineHeight,letterSpacing:e.letterSpacing,isMonospace:S,typicalHalfwidthCharacterWidth:n.width,typicalFullwidthCharacterWidth:r.width,canUseHalfwidthRightwardsArrow:T,spaceWidth:o.width,middotWidth:E.width,wsmiddotWidth:v.width,maxDigitWidth:C},!0)}};(ef=tY||(tY={})).serviceIds=new Map,ef.DI_TARGET="$di$target",ef.DI_DEPENDENCIES="$di$dependencies",ef.getServiceDependencies=function(e){return e[ef.DI_DEPENDENCIES]||[]};let oW=oG("instantiationService");function oG(e){if(tY.serviceIds.has(e))return tY.serviceIds.get(e);let t=function(e,i,n){if(3!=arguments.length)throw Error("@IServiceName-decorator can only be used to decorate a parameter");e[tY.DI_TARGET]===e?e[tY.DI_DEPENDENCIES].push({id:t,index:n}):(e[tY.DI_DEPENDENCIES]=[{id:t,index:n}],e[tY.DI_TARGET]=e)};return t.toString=()=>e,tY.serviceIds.set(e,t),t}let oj=oG("codeEditorService");function oz(e,t){if(!e)throw Error(t?`Assertion failed (${t})`:"Assertion Failed")}function oK(e,t="Unreachable"){throw Error(t)}function oY(e){e()||(e(),i1(new nt("Assertion Failed")))}function o$(e,t){let i=0;for(;i\-\./\\:\*\?\+\[\]\^,#@;"%\$\p{L}-]+/uy}static getLexeme(e){switch(e.type){case 0:return"(";case 1:return")";case 2:return"!";case 3:return e.isTripleEq?"===":"==";case 4:return e.isTripleEq?"!==":"!=";case 5:return"<";case 6:return"<=";case 7:case 8:return">=";case 9:return"=~";case 10:case 17:case 18:case 19:return e.lexeme;case 11:return"true";case 12:return"false";case 13:return"in";case 14:return"not";case 15:return"&&";case 16:return"||";case 20:return"EOF";default:throw i8(`unhandled token type: ${JSON.stringify(e)}; have you forgotten to add a case?`)}}reset(e){return this._input=e,this._start=0,this._current=0,this._tokens=[],this._errors=[],this}scan(){for(;!this._isAtEnd();){this._start=this._current;let e=this._advance();switch(e){case 40:this._addToken(0);break;case 41:this._addToken(1);break;case 33:if(this._match(61)){let e=this._match(61);this._tokens.push({type:4,offset:this._start,isTripleEq:e})}else this._addToken(2);break;case 39:this._quotedString();break;case 47:this._regex();break;case 61:if(this._match(61)){let e=this._match(61);this._tokens.push({type:3,offset:this._start,isTripleEq:e})}else this._match(126)?this._addToken(9):this._error(o0("==","=~"));break;case 60:this._addToken(this._match(61)?6:5);break;case 62:this._addToken(this._match(61)?8:7);break;case 38:this._match(38)?this._addToken(15):this._error(o0("&&"));break;case 124:this._match(124)?this._addToken(16):this._error(o0("||"));break;case 32:case 13:case 9:case 10:case 160:break;default:this._string()}}return this._start=this._current,this._addToken(20),Array.from(this._tokens)}_match(e){return!this._isAtEnd()&&this._input.charCodeAt(this._current)===e&&(this._current++,!0)}_advance(){return this._input.charCodeAt(this._current++)}_peek(){return this._isAtEnd()?0:this._input.charCodeAt(this._current)}_addToken(e){this._tokens.push({type:e,offset:this._start})}_error(e){let t=this._start,i=this._input.substring(this._start,this._current),n={type:19,offset:this._start,lexeme:i};this._errors.push({offset:t,lexeme:i,additionalInfo:e}),this._tokens.push(n)}_string(){this.stringRe.lastIndex=this._start;let e=this.stringRe.exec(this._input);if(e){this._current=this._start+e[0].length;let t=this._input.substring(this._start,this._current),i=o5._keywords.get(t);i?this._addToken(i):this._tokens.push({type:17,lexeme:t,offset:this._start})}}_quotedString(){for(;39!==this._peek()&&!this._isAtEnd();)this._advance();if(this._isAtEnd()){this._error(o1);return}this._advance(),this._tokens.push({type:18,lexeme:this._input.substring(this._start+1,this._current-1),offset:this._start+1})}_regex(){let e=this._current,t=!1,i=!1;for(;;){if(e>=this._input.length){this._current=e,this._error(o2);return}let n=this._input.charCodeAt(e);if(t)t=!1;else if(47!==n||i)91===n?i=!0:92===n?t=!0:93===n&&(i=!1);else{e++;break}e++}for(;e=this._input.length}}o5._regexFlags=new Set(["i","g","s","m","y","u"].map(e=>e.charCodeAt(0))),o5._keywords=new Map([["not",14],["in",13],["false",12],["true",11]]);let o4=new Map;o4.set("false",!1),o4.set("true",!0),o4.set("isMac",nz.dz),o4.set("isLinux",nz.IJ),o4.set("isWindows",nz.ED),o4.set("isWeb",nz.$L),o4.set("isMacNative",nz.dz&&!nz.$L),o4.set("isEdge",nz.un),o4.set("isFirefox",nz.vU),o4.set("isChrome",nz.i7),o4.set("isSafari",nz.G6);let o3=Object.prototype.hasOwnProperty,o6={regexParsingWithErrorRecovery:!0},o9=(0,rN.NC)("contextkey.parser.error.emptyString","Empty context key expression"),o8=(0,rN.NC)("contextkey.parser.error.emptyString.hint","Did you forget to write an expression? 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e=this._peek(),t=17===e.type?si:void 0;throw this._parsingErrors.push({message:st,offset:e.offset,lexeme:o5.getLexeme(e),additionalInfo:t}),so._parseError}return e}catch(e){if(e!==so._parseError)throw e;return}}_expr(){return this._or()}_or(){let e=[this._and()];for(;this._matchOne(16);){let t=this._and();e.push(t)}return 1===e.length?e[0]:ss.or(...e)}_and(){let e=[this._term()];for(;this._matchOne(15);){let t=this._term();e.push(t)}return 1===e.length?e[0]:ss.and(...e)}_term(){if(this._matchOne(2)){let e=this._peek();switch(e.type){case 11:return this._advance(),sl.INSTANCE;case 12:return this._advance(),sh.INSTANCE;case 0:{this._advance();let e=this._expr();return this._consume(1,se),null==e?void 0:e.negate()}case 17:return this._advance(),sp.create(e.lexeme);default:throw this._errExpectedButGot("KEY | true | false | '(' expression ')'",e)}}return this._primary()}_primary(){let e=this._peek();switch(e.type){case 11:return this._advance(),ss.true();case 12:return this._advance(),ss.false();case 0:{this._advance();let e=this._expr();return this._consume(1,se),e}case 17:{let t=e.lexeme;if(this._advance(),this._matchOne(9)){let e=this._peek();if(!this._config.regexParsingWithErrorRecovery){let i;if(this._advance(),10!==e.type)throw this._errExpectedButGot("REGEX",e);let n=e.lexeme,r=n.lastIndexOf("/"),o=r===n.length-1?void 0:this._removeFlagsGY(n.substring(r+1));try{i=new RegExp(n.substring(1,r),o)}catch(t){throw this._errExpectedButGot("REGEX",e)}return sC.create(t,i)}switch(e.type){case 10:case 19:{let i;let n=[e.lexeme];this._advance();let r=this._peek(),o=0;for(let t=0;t=0){let o=i.slice(t+1,r),s="i"===i[r+1]?"i":"";try{n=new RegExp(o,s)}catch(t){throw this._errExpectedButGot("REGEX",e)}}}if(null===n)throw this._errExpectedButGot("REGEX",e);return sC.create(t,n)}default:throw this._errExpectedButGot("REGEX",this._peek())}}if(this._matchOne(14)){this._consume(13,o7);let e=this._value();return ss.notIn(t,e)}let i=this._peek().type;switch(i){case 3:{this._advance();let e=this._value();if(18===this._previous().type)return ss.equals(t,e);switch(e){case"true":return ss.has(t);case"false":return ss.not(t);default:return ss.equals(t,e)}}case 4:{this._advance();let e=this._value();if(18===this._previous().type)return ss.notEquals(t,e);switch(e){case"true":return ss.not(t);case"false":return ss.has(t);default:return ss.notEquals(t,e)}}case 5:return this._advance(),sv.create(t,this._value());case 6:return this._advance(),sb.create(t,this._value());case 7:return this._advance(),s_.create(t,this._value());case 8:return this._advance(),sE.create(t,this._value());case 13:return this._advance(),ss.in(t,this._value());default:return ss.has(t)}}case 20:throw this._parsingErrors.push({message:sn,offset:e.offset,lexeme:"",additionalInfo:sr}),so._parseError;default:throw this._errExpectedButGot(`true | false | KEY | KEY '=~' REGEX - | KEY ('==' | '!=' | '<' | '<=' | '>' | '>=' | 'in' | 'not' 'in') value`,this._peek())}}_value(){let e=this._peek();switch(e.type){case 17:case 18:return this._advance(),e.lexeme;case 11:return this._advance(),"true";case 12:return this._advance(),"false";case 13:return this._advance(),"in";default:return""}}_removeFlagsGY(e){return e.replaceAll(this._flagsGYRe,"")}_previous(){return this._tokens[this._current-1]}_matchOne(e){return!!this._check(e)&&(this._advance(),!0)}_advance(){return!this._isAtEnd()&&this._current++,this._previous()}_consume(e,t){if(this._check(e))return this._advance();throw this._errExpectedButGot(t,this._peek())}_errExpectedButGot(e,t,i){let n=(0,rL.NC)("contextkey.parser.error.expectedButGot","Expected: {0}\nReceived: '{1}'.",e,o4.getLexeme(t)),r=t.offset,o=o4.getLexeme(t);return this._parsingErrors.push({message:n,offset:r,lexeme:o,additionalInfo:i}),so._parseError}_check(e){return this._peek().type===e}_peek(){return this._tokens[this._current]}_isAtEnd(){return 20===this._peek().type}}so._parseError=Error();class ss{static false(){return sl.INSTANCE}static 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e.type!==this.type?this.type-e.type:sN(this.key,this.value,e.key,e.value)}equals(e){return e.type===this.type&&this.key===e.key&&this.value===e.value}substituteConstants(){let e=o5.get(this.key);return"boolean"==typeof e?this.value===(e?"true":"false")?sh.INSTANCE:sl.INSTANCE:this}evaluate(e){return e.getValue(this.key)==this.value}serialize(){return`${this.key} == '${this.value}'`}keys(){return[this.key]}negate(){return this.negated||(this.negated=sp.create(this.key,this.value,this)),this.negated}}class sc{static create(e,t){return new sc(e,t)}constructor(e,t){this.key=e,this.valueKey=t,this.type=10,this.negated=null}cmp(e){return e.type!==this.type?this.type-e.type:sN(this.key,this.valueKey,e.key,e.valueKey)}equals(e){return e.type===this.type&&this.key===e.key&&this.valueKey===e.valueKey}substituteConstants(){return this}evaluate(e){let t=e.getValue(this.valueKey),i=e.getValue(this.key);return Array.isArray(t)?t.includes(i):"string"==typeof i&&"object"==typeof 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e?e?sl.INSTANCE:sh.INSTANCE:this}evaluate(e){return!e.getValue(this.key)}serialize(){return`!${this.key}`}keys(){return[this.key]}negate(){return this.negated||(this.negated=su.create(this.key,this)),this.negated}}function sm(e,t){if("string"==typeof e){let t=parseFloat(e);isNaN(t)||(e=t)}return"string"==typeof e||"number"==typeof e?t(e):sl.INSTANCE}class sv{static create(e,t,i=null){return sm(t,t=>new sv(e,t,i))}constructor(e,t,i){this.key=e,this.value=t,this.negated=i,this.type=12}cmp(e){return e.type!==this.type?this.type-e.type:sN(this.key,this.value,e.key,e.value)}equals(e){return e.type===this.type&&this.key===e.key&&this.value===e.value}substituteConstants(){return this}evaluate(e){return"string"!=typeof this.value&&parseFloat(e.getValue(this.key))>this.value}serialize(){return`${this.key} > ${this.value}`}keys(){return[this.key]}negate(){return this.negated||(this.negated=sC.create(this.key,this.value,this)),this.negated}}class sE{static create(e,t,i=null){return sm(t,t=>new sE(e,t,i))}constructor(e,t,i){this.key=e,this.value=t,this.negated=i,this.type=13}cmp(e){return e.type!==this.type?this.type-e.type:sN(this.key,this.value,e.key,e.value)}equals(e){return e.type===this.type&&this.key===e.key&&this.value===e.value}substituteConstants(){return this}evaluate(e){return"string"!=typeof this.value&&parseFloat(e.getValue(this.key))>=this.value}serialize(){return`${this.key} >= ${this.value}`}keys(){return[this.key]}negate(){return this.negated||(this.negated=s_.create(this.key,this.value,this)),this.negated}}class s_{static create(e,t,i=null){return sm(t,t=>new s_(e,t,i))}constructor(e,t,i){this.key=e,this.value=t,this.negated=i,this.type=14}cmp(e){return e.type!==this.type?this.type-e.type:sN(this.key,this.value,e.key,e.value)}equals(e){return e.type===this.type&&this.key===e.key&&this.value===e.value}substituteConstants(){return this}evaluate(e){return"string"!=typeof this.value&&parseFloat(e.getValue(this.key))new sC(e,t,i))}constructor(e,t,i){this.key=e,this.value=t,this.negated=i,this.type=15}cmp(e){return e.type!==this.type?this.type-e.type:sN(this.key,this.value,e.key,e.value)}equals(e){return e.type===this.type&&this.key===e.key&&this.value===e.value}substituteConstants(){return this}evaluate(e){return"string"!=typeof this.value&&parseFloat(e.getValue(this.key))<=this.value}serialize(){return`${this.key} <= ${this.value}`}keys(){return[this.key]}negate(){return this.negated||(this.negated=sv.create(this.key,this.value,this)),this.negated}}class sS{static create(e,t){return new sS(e,t)}constructor(e,t){this.key=e,this.regexp=t,this.type=7,this.negated=null}cmp(e){if(e.type!==this.type)return this.type-e.type;if(this.keye.key)return 1;let t=this.regexp?this.regexp.source:"",i=e.regexp?e.regexp.source:"";return ti?1:0}equals(e){if(e.type===this.type){let t=this.regexp?this.regexp.source:"",i=e.regexp?e.regexp.source:"";return this.key===e.key&&t===i}return!1}substituteConstants(){return this}evaluate(e){let t=e.getValue(this.key);return!!this.regexp&&this.regexp.test(t)}serialize(){let e=this.regexp?`/${this.regexp.source}/${this.regexp.flags}`:"/invalid/";return`${this.key} =~ ${e}`}keys(){return[this.key]}negate(){return this.negated||(this.negated=sy.create(this)),this.negated}}class sy{static create(e){return new sy(e)}constructor(e){this._actual=e,this.type=8}cmp(e){return e.type!==this.type?this.type-e.type:this._actual.cmp(e._actual)}equals(e){return e.type===this.type&&this._actual.equals(e._actual)}substituteConstants(){return this}evaluate(e){return!this._actual.evaluate(e)}serialize(){return`!(${this._actual.serialize()})`}keys(){return this._actual.keys()}negate(){return this._actual}}function sT(e){let t=null;for(let i=0,n=e.length;ie.expr.length)return 1;for(let t=0,i=this.expr.length;t1;){let e=n[n.length-1];if(9!==e.type)break;n.pop();let t=n.pop(),r=0===n.length,o=sA.create(e.expr.map(e=>sb.create([e,t],null,i)),null,r);o&&(n.push(o),n.sort(sa))}if(1===n.length)return n[0];if(i){for(let e=0;ee.serialize()).join(" && ")}keys(){let e=[];for(let t of this.expr)e.push(...t.keys());return e}negate(){if(!this.negated){let e=[];for(let t of this.expr)e.push(t.negate());this.negated=sA.create(e,this,!0)}return this.negated}}class sA{static create(e,t,i){return sA._normalizeArr(e,t,i)}constructor(e,t){this.expr=e,this.negated=t,this.type=9}cmp(e){if(e.type!==this.type)return this.type-e.type;if(this.expr.lengthe.expr.length)return 1;for(let t=0,i=this.expr.length;te.serialize()).join(" || ")}keys(){let e=[];for(let t of this.expr)e.push(...t.keys());return e}negate(){if(!this.negated){let e=[];for(let t of this.expr)e.push(t.negate());for(;e.length>1;){let t=e.shift(),i=e.shift(),n=[];for(let e of sw(t))for(let t of sw(i))n.push(sb.create([e,t],null,!1));e.unshift(sA.create(n,null,!1))}this.negated=sA.create(e,this,!0)}return this.negated}}class sR extends su{static all(){return sR._info.values()}constructor(e,t,i){super(e,null),this._defaultValue=t,"object"==typeof i?sR._info.push(Object.assign(Object.assign({},i),{key:e})):!0!==i&&sR._info.push({key:e,description:i,type:null!=t?typeof t:void 0})}bindTo(e){return e.createKey(this.key,this._defaultValue)}getValue(e){return e.getContextKeyValue(this.key)}toNegated(){return this.negate()}isEqualTo(e){return sd.create(this.key,e)}}sR._info=[];let sL=oG("contextKeyService");function sN(e,t,i,n){return ei?1:tn?1:0}function sI(e,t){let i=0,n=0;for(;ithis._onDiffUpdated())),this._options.followsCaret&&this._register(this._editor.getModifiedEditor().onDidChangeCursorPosition(e=>{this.ignoreSelectionChange||(this._updateAccessibilityState(e.position.lineNumber),this.nextIdx=-1)})),this._init()}_init(){let e=this._editor.getLineChanges();if(!e)return}_onDiffUpdated(){this._init(),this._compute(this._editor.getLineChanges()),this.revealFirst&&null!==this._editor.getLineChanges()&&(this.revealFirst=!1,this.nextIdx=-1,this.next(1))}_compute(e){this.ranges=[],e&&e.forEach(e=>{!this._options.ignoreCharChanges&&e.charChanges?e.charChanges.forEach(e=>{this.ranges.push({rhs:!0,range:new r_(e.modifiedStartLineNumber,e.modifiedStartColumn,e.modifiedEndLineNumber,e.modifiedEndColumn)})}):0===e.modifiedEndLineNumber?this.ranges.push({rhs:!0,range:new r_(e.modifiedStartLineNumber,1,e.modifiedStartLineNumber+1,1)}):this.ranges.push({rhs:!0,range:new r_(e.modifiedStartLineNumber,1,e.modifiedEndLineNumber+1,1)})}),this.ranges.sort((e,t)=>r_.compareRangesUsingStarts(e.range,t.range)),this._onDidUpdate.fire(this)}_initIdx(e){let t=!1,i=this._editor.getPosition();if(!i){this.nextIdx=0;return}for(let n=0,r=this.ranges.length;n=this.ranges.length&&(this.nextIdx=0)):(this.nextIdx-=1,this.nextIdx<0&&(this.nextIdx=this.ranges.length-1));let i=this.ranges[this.nextIdx];this.ignoreSelectionChange=!0;try{let e=i.range.getStartPosition();this._editor.setPosition(e),this._editor.revealRangeInCenter(i.range,t),this._updateAccessibilityState(e.lineNumber,!0)}finally{this.ignoreSelectionChange=!1}}_updateAccessibilityState(e,t){var i;let n=null===(i=this._editor.getModel())||void 0===i?void 0:i.modified;if(!n)return;let r=n.getLineDecorations(e).find(e=>"line-insert"===e.options.className);if(r)this._audioCueService.playAudioCue(oQ.diffLineModified,!0);else{if(!t)return;this._audioCueService.playAudioCue(oQ.diffLineDeleted,!0)}let 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n=e.length-1;n>=0;n--)t[i++]=e.charCodeAt(n);return s4().decode(t)}(_=e)),C});class an{static _findPrevBracketInText(e,t,i,n){let r=i.match(e);if(!r)return null;let o=i.length-(r.index||0),s=r[0].length,a=n+o;return new r_(t,a-s+1,t,a+1)}static findPrevBracketInRange(e,t,i,n,r){let o=ai(i),s=o.substring(i.length-r,i.length-n);return this._findPrevBracketInText(e,t,s,n)}static findNextBracketInText(e,t,i,n){let r=i.match(e);if(!r)return null;let o=r.index||0,s=r[0].length;if(0===s)return null;let a=n+o;return new r_(t,a+1,t,a+1+s)}static findNextBracketInRange(e,t,i,n,r){let o=i.substring(n,r);return this.findNextBracketInText(e,t,o,n)}}class ar{constructor(e){this._richEditBrackets=e}getElectricCharacters(){let e=[];if(this._richEditBrackets)for(let t of this._richEditBrackets.brackets)for(let i of t.close){let t=i.charAt(i.length-1);e.push(t)}return(0,sq.EB)(e)}onElectricCharacter(e,t,i){if(!this._richEditBrackets||0===this._richEditBrackets.brackets.length)return null;let n=t.findTokenIndexAtOffset(i-1);if(sX(t.getStandardTokenType(n)))return null;let r=this._richEditBrackets.reversedRegex,o=t.getLineContent().substring(0,i-1)+e,s=an.findPrevBracketInRange(r,1,o,0,o.length);if(!s)return null;let a=o.substring(s.startColumn-1,s.endColumn-1).toLowerCase(),l=this._richEditBrackets.textIsOpenBracket[a];if(l)return null;let h=t.getActualLineContentBefore(s.startColumn-1);return/^\s*$/.test(h)?{matchOpenBracket:a}:null}}function ao(e){return e.global&&(e.lastIndex=0),!0}class as{constructor(e){this._indentationRules=e}shouldIncrease(e){return!!(this._indentationRules&&this._indentationRules.increaseIndentPattern&&ao(this._indentationRules.increaseIndentPattern)&&this._indentationRules.increaseIndentPattern.test(e))}shouldDecrease(e){return!!(this._indentationRules&&this._indentationRules.decreaseIndentPattern&&ao(this._indentationRules.decreaseIndentPattern)&&this._indentationRules.decreaseIndentPattern.test(e))}shouldIndentNextLine(e){return!!(this._indentationRules&&this._indentationRules.indentNextLinePattern&&ao(this._indentationRules.indentNextLinePattern)&&this._indentationRules.indentNextLinePattern.test(e))}shouldIgnore(e){return!!(this._indentationRules&&this._indentationRules.unIndentedLinePattern&&ao(this._indentationRules.unIndentedLinePattern)&&this._indentationRules.unIndentedLinePattern.test(e))}getIndentMetadata(e){let t=0;return this.shouldIncrease(e)&&(t+=1),this.shouldDecrease(e)&&(t+=2),this.shouldIndentNextLine(e)&&(t+=4),this.shouldIgnore(e)&&(t+=8),t}}class aa{constructor(e){(e=e||{}).brackets=e.brackets||[["(",")"],["{","}"],["[","]"]],this._brackets=[],e.brackets.forEach(e=>{let t=aa._createOpenBracketRegExp(e[0]),i=aa._createCloseBracketRegExp(e[1]);t&&i&&this._brackets.push({open:e[0],openRegExp:t,close:e[1],closeRegExp:i})}),this._regExpRules=e.onEnterRules||[]}onEnter(e,t,i,n){if(e>=3)for(let e=0,r=this._regExpRules.length;e!e.reg||(e.reg.lastIndex=0,e.reg.test(e.text)));if(o)return r.action}if(e>=2&&i.length>0&&n.length>0)for(let e=0,t=this._brackets.length;e=2&&i.length>0)for(let e=0,t=this._brackets.length;e0&&"#"===e.charAt(e.length-1)?e.substring(0,e.length-1):e]=t,this._onDidChangeSchema.fire(e)}notifySchemaChanged(e){this._onDidChangeSchema.fire(e)}};am.add(aE.JSONContribution,a_);let aC={Configuration:"base.contributions.configuration"},aS={properties:{},patternProperties:{}},ay={properties:{},patternProperties:{}},aT={properties:{},patternProperties:{}},ab={properties:{},patternProperties:{}},aA={properties:{},patternProperties:{}},aR={properties:{},patternProperties:{}},aL="vscode://schemas/settings/resourceLanguage",aN=am.as(aE.JSONContribution),aI="\\[([^\\]]+)\\]",aw=RegExp(aI,"g"),aO=`^(${aI})+$`,ax=new RegExp(aO);function aD(e){let t=[];if(ax.test(e)){let i=aw.exec(e);for(;null==i?void 0:i.length;){let n=i[1].trim();n&&t.push(n),i=aw.exec(e)}}return(0,sq.EB)(t)}let aM=new class{constructor(){this.overrideIdentifiers=new Set,this._onDidSchemaChange=new nT,this._onDidUpdateConfiguration=new nT,this.configurationDefaultsOverrides=new Map,this.defaultLanguageConfigurationOverridesNode={id:"defaultOverrides",title:rL.NC("defaultLanguageConfigurationOverrides.title","Default Language Configuration Overrides"),properties:{}},this.configurationContributors=[this.defaultLanguageConfigurationOverridesNode],this.resourceLanguageSettingsSchema={properties:{},patternProperties:{},additionalProperties:!0,allowTrailingCommas:!0,allowComments:!0},this.configurationProperties={},this.policyConfigurations=new Map,this.excludedConfigurationProperties={},aN.registerSchema(aL,this.resourceLanguageSettingsSchema),this.registerOverridePropertyPatternKey()}registerConfiguration(e,t=!0){this.registerConfigurations([e],t)}registerConfigurations(e,t=!0){let i=new Set;this.doRegisterConfigurations(e,t,i),aN.registerSchema(aL,this.resourceLanguageSettingsSchema),this._onDidSchemaChange.fire(),this._onDidUpdateConfiguration.fire({properties:i})}registerDefaultConfigurations(e){let t=new Set;this.doRegisterDefaultConfigurations(e,t),this._onDidSchemaChange.fire(),this._onDidUpdateConfiguration.fire({properties:t,defaultsOverrides:!0})}doRegisterDefaultConfigurations(e,t){var i;let n=[];for(let{overrides:r,source:o}of e)for(let e in r)if(t.add(e),ax.test(e)){let t=this.configurationDefaultsOverrides.get(e),s=null!==(i=null==t?void 0:t.valuesSources)&&void 0!==i?i:new Map;if(o)for(let t of Object.keys(r[e]))s.set(t,o);let a=Object.assign(Object.assign({},(null==t?void 0:t.value)||{}),r[e]);this.configurationDefaultsOverrides.set(e,{source:o,value:a,valuesSources:s});let l=e.replace(/[\[\]]/g,""),h={type:"object",default:a,description:rL.NC("defaultLanguageConfiguration.description","Configure settings to be overridden for the {0} language.",l),$ref:aL,defaultDefaultValue:a,source:rS.HD(o)?void 0:o,defaultValueSource:o};n.push(...aD(e)),this.configurationProperties[e]=h,this.defaultLanguageConfigurationOverridesNode.properties[e]=h}else{this.configurationDefaultsOverrides.set(e,{value:r[e],source:o});let t=this.configurationProperties[e];t&&(this.updatePropertyDefaultValue(e,t),this.updateSchema(e,t))}this.doRegisterOverrideIdentifiers(n)}registerOverrideIdentifiers(e){this.doRegisterOverrideIdentifiers(e),this._onDidSchemaChange.fire()}doRegisterOverrideIdentifiers(e){for(let t of e)this.overrideIdentifiers.add(t);this.updateOverridePropertyPatternKey()}doRegisterConfigurations(e,t,i){e.forEach(e=>{this.validateAndRegisterProperties(e,t,e.extensionInfo,e.restrictedProperties,void 0,i),this.configurationContributors.push(e),this.registerJSONConfiguration(e)})}validateAndRegisterProperties(e,t=!0,i,n,r=3,o){var s;r=rS.Jp(e.scope)?r:e.scope;let a=e.properties;if(a)for(let e in a){let l=a[e];if(t&&function(e,t){var i,n,r,o;return e.trim()?ax.test(e)?rL.NC("config.property.languageDefault","Cannot register '{0}'. This matches property pattern '\\\\[.*\\\\]$' for describing language specific editor settings. Use 'configurationDefaults' contribution.",e):void 0!==aM.getConfigurationProperties()[e]?rL.NC("config.property.duplicate","Cannot register '{0}'. This property is already registered.",e):(null===(i=t.policy)||void 0===i?void 0:i.name)&&void 0!==aM.getPolicyConfigurations().get(null===(n=t.policy)||void 0===n?void 0:n.name)?rL.NC("config.policy.duplicate","Cannot register '{0}'. The associated policy {1} is already registered with {2}.",e,null===(r=t.policy)||void 0===r?void 0:r.name,aM.getPolicyConfigurations().get(null===(o=t.policy)||void 0===o?void 0:o.name)):null:rL.NC("config.property.empty","Cannot register an empty property")}(e,l)){delete a[e];continue}if(l.source=i,l.defaultDefaultValue=a[e].default,this.updatePropertyDefaultValue(e,l),ax.test(e)?l.scope=void 0:(l.scope=rS.Jp(l.scope)?r:l.scope,l.restricted=rS.Jp(l.restricted)?!!(null==n?void 0:n.includes(e)):l.restricted),a[e].hasOwnProperty("included")&&!a[e].included){this.excludedConfigurationProperties[e]=a[e],delete a[e];continue}this.configurationProperties[e]=a[e],(null===(s=a[e].policy)||void 0===s?void 0:s.name)&&this.policyConfigurations.set(a[e].policy.name,e),!a[e].deprecationMessage&&a[e].markdownDeprecationMessage&&(a[e].deprecationMessage=a[e].markdownDeprecationMessage),o.add(e)}let l=e.allOf;if(l)for(let e of l)this.validateAndRegisterProperties(e,t,i,n,r,o)}getConfigurationProperties(){return this.configurationProperties}getPolicyConfigurations(){return this.policyConfigurations}registerJSONConfiguration(e){let t=e=>{let i=e.properties;if(i)for(let e in i)this.updateSchema(e,i[e]);let n=e.allOf;null==n||n.forEach(t)};t(e)}updateSchema(e,t){switch(aS.properties[e]=t,t.scope){case 1:ay.properties[e]=t;break;case 2:aT.properties[e]=t;break;case 6:ab.properties[e]=t;break;case 3:aA.properties[e]=t;break;case 4:aR.properties[e]=t;break;case 5:aR.properties[e]=t,this.resourceLanguageSettingsSchema.properties[e]=t}}updateOverridePropertyPatternKey(){for(let e of this.overrideIdentifiers.values()){let t=`[${e}]`,i={type:"object",description:rL.NC("overrideSettings.defaultDescription","Configure editor settings to be overridden for a language."),errorMessage:rL.NC("overrideSettings.errorMessage","This setting does not support per-language configuration."),$ref:aL};this.updatePropertyDefaultValue(t,i),aS.properties[t]=i,ay.properties[t]=i,aT.properties[t]=i,ab.properties[t]=i,aA.properties[t]=i,aR.properties[t]=i}}registerOverridePropertyPatternKey(){let e={type:"object",description:rL.NC("overrideSettings.defaultDescription","Configure editor settings to be overridden for a language."),errorMessage:rL.NC("overrideSettings.errorMessage","This setting does not support per-language configuration."),$ref:aL};aS.patternProperties[aO]=e,ay.patternProperties[aO]=e,aT.patternProperties[aO]=e,ab.patternProperties[aO]=e,aA.patternProperties[aO]=e,aR.patternProperties[aO]=e,this._onDidSchemaChange.fire()}updatePropertyDefaultValue(e,t){let i=this.configurationDefaultsOverrides.get(e),n=null==i?void 0:i.value,r=null==i?void 0:i.source;rS.o8(n)&&(n=t.defaultDefaultValue,r=void 0),rS.o8(n)&&(n=function(e){let t=Array.isArray(e)?e[0]:e;switch(t){case"boolean":return!1;case"integer":case"number":return 0;case"string":return"";case"array":return[];case"object":return{};default:return null}}(t.type)),t.default=n,t.defaultValueSource=r}};am.add(aC.Configuration,aM);let ak=new class{constructor(){this._onDidChangeLanguages=new nT,this.onDidChangeLanguages=this._onDidChangeLanguages.event,this._languages=[]}registerLanguage(e){return this._languages.push(e),this._onDidChangeLanguages.fire(void 0),{dispose:()=>{for(let t=0,i=this._languages.length;t{let t=new Set;return{info:new aH(this,e,t),closing:t}}),r=new rP(e=>{let t=new Set,i=new Set;return{info:new aV(this,e,t,i),opening:t,openingColorized:i}});for(let[e,t]of i){let i=n.get(e),o=r.get(t);i.closing.add(o.info),o.opening.add(i.info)}let o=t.colorizedBracketPairs?aB(t.colorizedBracketPairs):i.filter(e=>!("<"===e[0]&&">"===e[1]));for(let[e,t]of o){let i=n.get(e),o=r.get(t);i.closing.add(o.info),o.openingColorized.add(i.info),o.opening.add(i.info)}this._openingBrackets=new Map([...n.cachedValues].map(([e,t])=>[e,t.info])),this._closingBrackets=new Map([...r.cachedValues].map(([e,t])=>[e,t.info]))}get openingBrackets(){return[...this._openingBrackets.values()]}get closingBrackets(){return[...this._closingBrackets.values()]}getOpeningBracketInfo(e){return this._openingBrackets.get(e)}getClosingBracketInfo(e){return this._closingBrackets.get(e)}getBracketInfo(e){return this.getOpeningBracketInfo(e)||this.getClosingBracketInfo(e)}}function aB(e){return e.filter(([e,t])=>""!==e&&""!==t)}class aU{constructor(e,t){this.config=e,this.bracketText=t}get languageId(){return this.config.languageId}}class aH extends aU{constructor(e,t,i){super(e,t),this.openedBrackets=i,this.isOpeningBracket=!0}}class aV extends aU{constructor(e,t,i,n){super(e,t),this.openingBrackets=i,this.openingColorizedBrackets=n,this.isOpeningBracket=!1}closes(e){return e.config===this.config&&this.openingBrackets.has(e)}closesColorized(e){return e.config===this.config&&this.openingColorizedBrackets.has(e)}getOpeningBrackets(){return[...this.openingBrackets]}}var aW=function(e,t){return function(i,n){t(i,n,e)}};class aG{constructor(e){this.languageId=e}affects(e){return!this.languageId||this.languageId===e}}let az=oG("languageConfigurationService"),aY=class extends nc{constructor(e,t){super(),this.configurationService=e,this.languageService=t,this._registry=this._register(new a0),this.onDidChangeEmitter=this._register(new nT),this.onDidChange=this.onDidChangeEmitter.event,this.configurations=new Map;let i=new Set(Object.values(aK));this._register(this.configurationService.onDidChangeConfiguration(e=>{let t=e.change.keys.some(e=>i.has(e)),n=e.change.overrides.filter(([e,t])=>t.some(e=>i.has(e))).map(([e])=>e);if(t)this.configurations.clear(),this.onDidChangeEmitter.fire(new aG(void 0));else for(let e of n)this.languageService.isRegisteredLanguageId(e)&&(this.configurations.delete(e),this.onDidChangeEmitter.fire(new aG(e)))})),this._register(this._registry.onDidChange(e=>{this.configurations.delete(e.languageId),this.onDidChangeEmitter.fire(new aG(e.languageId))}))}register(e,t,i){return this._registry.register(e,t,i)}getLanguageConfiguration(e){let t=this.configurations.get(e);return t||(t=function(e,t,i,n){let r=t.getLanguageConfiguration(e);if(!r){if(!n.isRegisteredLanguageId(e))return new a1(e,{});r=new a1(e,{})}let o=function(e,t){let i=t.getValue(aK.brackets,{overrideIdentifier:e}),n=t.getValue(aK.colorizedBracketPairs,{overrideIdentifier:e});return{brackets:a$(i),colorizedBracketPairs:a$(n)}}(r.languageId,i),s=aZ([r.underlyingConfig,o]),a=new a1(r.languageId,s);return a}(e,this._registry,this.configurationService,this.languageService),this.configurations.set(e,t)),t}};aY=function(e,t,i,n){var r,o=arguments.length,s=o<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,i):n;if("object"==typeof Reflect&&"function"==typeof Reflect.decorate)s=Reflect.decorate(e,t,i,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(o<3?r(s):o>3?r(t,i,s):r(t,i))||s);return o>3&&s&&Object.defineProperty(t,i,s),s}([aW(0,al),aW(1,ac)],aY);let aK={brackets:"editor.language.brackets",colorizedBracketPairs:"editor.language.colorizedBracketPairs"};function a$(e){if(Array.isArray(e))return e.map(e=>{if(Array.isArray(e)&&2===e.length)return[e[0],e[1]]}).filter(e=>!!e)}function aX(e,t,i){let n=e.getLineContent(t),r=r$(n);return r.length>i-1&&(r=r.substring(0,i-1)),r}function aj(e,t,i){e.tokenization.forceTokenization(t);let n=e.tokenization.getLineTokens(t),r=void 0===i?e.getLineMaxColumn(t)-1:i-1;return sK(n,r)}class aq{constructor(e){this.languageId=e,this._resolved=null,this._entries=[],this._order=0,this._resolved=null}register(e,t){let i=new aJ(e,t,++this._order);return this._entries.push(i),this._resolved=null,nu(()=>{for(let e=0;ee.configuration)))}}function aZ(e){let t={comments:void 0,brackets:void 0,wordPattern:void 0,indentationRules:void 0,onEnterRules:void 0,autoClosingPairs:void 0,surroundingPairs:void 0,autoCloseBefore:void 0,folding:void 0,colorizedBracketPairs:void 0,__electricCharacterSupport:void 0};for(let i of e)t={comments:i.comments||t.comments,brackets:i.brackets||t.brackets,wordPattern:i.wordPattern||t.wordPattern,indentationRules:i.indentationRules||t.indentationRules,onEnterRules:i.onEnterRules||t.onEnterRules,autoClosingPairs:i.autoClosingPairs||t.autoClosingPairs,surroundingPairs:i.surroundingPairs||t.surroundingPairs,autoCloseBefore:i.autoCloseBefore||t.autoCloseBefore,folding:i.folding||t.folding,colorizedBracketPairs:i.colorizedBracketPairs||t.colorizedBracketPairs,__electricCharacterSupport:i.__electricCharacterSupport||t.__electricCharacterSupport};return t}class aJ{constructor(e,t,i){this.configuration=e,this.priority=t,this.order=i}static cmp(e,t){return e.priority===t.priority?e.order-t.order:e.priority-t.priority}}class aQ{constructor(e){this.languageId=e}}class a0 extends nc{constructor(){super(),this._entries=new Map,this._onDidChange=this._register(new nT),this.onDidChange=this._onDidChange.event,this._register(this.register(aP,{brackets:[["(",")"],["[","]"],["{","}"]],surroundingPairs:[{open:"{",close:"}"},{open:"[",close:"]"},{open:"(",close:")"},{open:"<",close:">"},{open:'"',close:'"'},{open:"'",close:"'"},{open:"`",close:"`"}],colorizedBracketPairs:[],folding:{offSide:!0}},0))}register(e,t,i=0){let n=this._entries.get(e);n||(n=new aq(e),this._entries.set(e,n));let r=n.register(t,i);return this._onDidChange.fire(new aQ(e)),nu(()=>{r.dispose(),this._onDidChange.fire(new aQ(e))})}getLanguageConfiguration(e){let t=this._entries.get(e);return(null==t?void 0:t.getResolvedConfiguration())||null}}class a1{constructor(e,t){this.languageId=e,this.underlyingConfig=t,this._brackets=null,this._electricCharacter=null,this._onEnterSupport=this.underlyingConfig.brackets||this.underlyingConfig.indentationRules||this.underlyingConfig.onEnterRules?new aa(this.underlyingConfig):null,this.comments=a1._handleComments(this.underlyingConfig),this.characterPair=new sj(this.underlyingConfig),this.wordDefinition=this.underlyingConfig.wordPattern||sW.Af,this.indentationRules=this.underlyingConfig.indentationRules,this.underlyingConfig.indentationRules?this.indentRulesSupport=new as(this.underlyingConfig.indentationRules):this.indentRulesSupport=null,this.foldingRules=this.underlyingConfig.folding||{},this.bracketsNew=new aF(e,this.underlyingConfig)}getWordDefinition(){return(0,sW.eq)(this.wordDefinition)}get brackets(){return!this._brackets&&this.underlyingConfig.brackets&&(this._brackets=new s3(this.languageId,this.underlyingConfig.brackets)),this._brackets}get electricCharacter(){return this._electricCharacter||(this._electricCharacter=new ar(this.brackets)),this._electricCharacter}onEnter(e,t,i,n){return this._onEnterSupport?this._onEnterSupport.onEnter(e,t,i,n):null}getAutoClosingPairs(){return new 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lI(this.m_originalStart,this.m_originalCount,this.m_modifiedStart,this.m_modifiedCount)),this.m_originalCount=0,this.m_modifiedCount=0,this.m_originalStart=1073741824,this.m_modifiedStart=1073741824}AddOriginalElement(e,t){this.m_originalStart=Math.min(this.m_originalStart,e),this.m_modifiedStart=Math.min(this.m_modifiedStart,t),this.m_originalCount++}AddModifiedElement(e,t){this.m_originalStart=Math.min(this.m_originalStart,e),this.m_modifiedStart=Math.min(this.m_modifiedStart,t),this.m_modifiedCount++}getChanges(){return(this.m_originalCount>0||this.m_modifiedCount>0)&&this.MarkNextChange(),this.m_changes}getReverseChanges(){return(this.m_originalCount>0||this.m_modifiedCount>0)&&this.MarkNextChange(),this.m_changes.reverse(),this.m_changes}}class lH{constructor(e,t,i=null){this.ContinueProcessingPredicate=i,this._originalSequence=e,this._modifiedSequence=t;let[n,r,o]=lH._getElements(e),[s,a,l]=lH._getElements(t);this._hasStrings=o&&l,this._originalStringElements=n,this._originalElementsOrHash=r,this._modifiedStringElements=s,this._modifiedElementsOrHash=a,this.m_forwardHistory=[],this.m_reverseHistory=[]}static _isStringArray(e){return e.length>0&&"string"==typeof e[0]}static _getElements(e){let t=e.getElements();if(lH._isStringArray(t)){let e=new Int32Array(t.length);for(let i=0,n=t.length;i=e&&n>=i&&this.ElementsAreEqual(t,n);)t--,n--;if(e>t||i>n){let r;return i<=n?(lF.Assert(e===t+1,"originalStart should only be one more than originalEnd"),r=[new lI(e,0,i,n-i+1)]):e<=t?(lF.Assert(i===n+1,"modifiedStart should only be one more than modifiedEnd"),r=[new lI(e,t-e+1,i,0)]):(lF.Assert(e===t+1,"originalStart should only be one more than originalEnd"),lF.Assert(i===n+1,"modifiedStart should only be one more than modifiedEnd"),r=[]),r}let o=[0],s=[0],a=this.ComputeRecursionPoint(e,t,i,n,o,s,r),l=o[0],h=s[0];if(null!==a)return a;if(!r[0]){let o=this.ComputeDiffRecursive(e,l,i,h,r),s=[];return s=r[0]?[new lI(l+1,t-(l+1)+1,h+1,n-(h+1)+1)]:this.ComputeDiffRecursive(l+1,t,h+1,n,r),this.ConcatenateChanges(o,s)}return[new lI(e,t-e+1,i,n-i+1)]}WALKTRACE(e,t,i,n,r,o,s,a,l,h,u,d,c,g,p,f,m,v){let E=null,_=null,C=new lU,S=t,y=i,T=c[0]-f[0]-n,b=-1073741824,A=this.m_forwardHistory.length-1;do{let t=T+e;t===S||t=0&&(e=(l=this.m_forwardHistory[A])[0],S=1,y=l.length-1)}while(--A>=-1);if(E=C.getReverseChanges(),v[0]){let e=c[0]+1,t=f[0]+1;if(null!==E&&E.length>0){let i=E[E.length-1];e=Math.max(e,i.getOriginalEnd()),t=Math.max(t,i.getModifiedEnd())}_=[new lI(e,d-e+1,t,p-t+1)]}else{C=new lU,S=o,y=s,T=c[0]-f[0]-a,b=1073741824,A=m?this.m_reverseHistory.length-1:this.m_reverseHistory.length-2;do{let e=T+r;e===S||e=h[e+1]?(g=(u=h[e+1]-1)-T-a,u>b&&C.MarkNextChange(),b=u+1,C.AddOriginalElement(u+1,g+1),T=e+1-r):(g=(u=h[e-1])-T-a,u>b&&C.MarkNextChange(),b=u,C.AddModifiedElement(u+1,g+1),T=e-1-r),A>=0&&(r=(h=this.m_reverseHistory[A])[0],S=1,y=h.length-1)}while(--A>=-1);_=C.getChanges()}return this.ConcatenateChanges(E,_)}ComputeRecursionPoint(e,t,i,n,r,o,s){let a=0,l=0,h=0,u=0,d=0,c=0;e--,i--,r[0]=0,o[0]=0,this.m_forwardHistory=[],this.m_reverseHistory=[];let g=t-e+(n-i),p=g+1,f=new Int32Array(p),m=new Int32Array(p),v=n-i,E=t-e,_=e-i,C=t-n,S=E-v,y=S%2==0;f[v]=e,m[E]=t,s[0]=!1;for(let S=1;S<=g/2+1;S++){let g=0,T=0;h=this.ClipDiagonalBound(v-S,S,v,p),u=this.ClipDiagonalBound(v+S,S,v,p);for(let e=h;e<=u;e+=2){l=(a=e===h||eg+T&&(g=a,T=l),!y&&Math.abs(e-E)<=S-1&&a>=m[e]){if(r[0]=a,o[0]=l,i<=m[e]&&S<=1448)return this.WALKTRACE(v,h,u,_,E,d,c,C,f,m,a,t,r,l,n,o,y,s);return null}}let b=(g-e+(T-i)-S)/2;if(null!==this.ContinueProcessingPredicate&&!this.ContinueProcessingPredicate(g,b)){if(s[0]=!0,r[0]=g,o[0]=T,!(b>0)||!(S<=1448))return e++,i++,[new lI(e,t-e+1,i,n-i+1)];break}d=this.ClipDiagonalBound(E-S,S,E,p),c=this.ClipDiagonalBound(E+S,S,E,p);for(let g=d;g<=c;g+=2){l=(a=g===d||g=m[g+1]?m[g+1]-1:m[g-1])-(g-E)-C;let p=a;for(;a>e&&l>i&&this.ElementsAreEqual(a,l);)a--,l--;if(m[g]=a,y&&Math.abs(g-v)<=S&&a<=f[g]){if(r[0]=a,o[0]=l,p>=f[g]&&S<=1448)return this.WALKTRACE(v,h,u,_,E,d,c,C,f,m,a,t,r,l,n,o,y,s);return null}}if(S<=1447){let e=new Int32Array(u-h+2);e[0]=v-h+1,lB.Copy2(f,h,e,1,u-h+1),this.m_forwardHistory.push(e),(e=new Int32Array(c-d+2))[0]=E-d+1,lB.Copy2(m,d,e,1,c-d+1),this.m_reverseHistory.push(e)}}return this.WALKTRACE(v,h,u,_,E,d,c,C,f,m,a,t,r,l,n,o,y,s)}PrettifyChanges(e){for(let t=0;t0,s=i.modifiedLength>0;for(;i.originalStart+i.originalLength=0;t--){let i=e[t],n=0,r=0;if(t>0){let i=e[t-1];n=i.originalStart+i.originalLength,r=i.modifiedStart+i.modifiedLength}let o=i.originalLength>0,s=i.modifiedLength>0,a=0,l=this._boundaryScore(i.originalStart,i.originalLength,i.modifiedStart,i.modifiedLength);for(let e=1;;e++){let t=i.originalStart-e,h=i.modifiedStart-e;if(tl&&(l=d,a=e)}i.originalStart-=a,i.modifiedStart-=a;let h=[null];if(t>0&&this.ChangesOverlap(e[t-1],e[t],h)){e[t-1]=h[0],e.splice(t,1),t++;continue}}if(this._hasStrings)for(let t=1,i=e.length;t0&&i>a&&(a=i,l=t,h=e)}return a>0?[l,h]:null}_contiguousSequenceScore(e,t,i){let n=0;for(let r=0;r=this._originalElementsOrHash.length-1||this._hasStrings&&/^\s*$/.test(this._originalStringElements[e])}_OriginalRegionIsBoundary(e,t){if(this._OriginalIsBoundary(e)||this._OriginalIsBoundary(e-1))return!0;if(t>0){let i=e+t;if(this._OriginalIsBoundary(i-1)||this._OriginalIsBoundary(i))return!0}return!1}_ModifiedIsBoundary(e){return e<=0||e>=this._modifiedElementsOrHash.length-1||this._hasStrings&&/^\s*$/.test(this._modifiedStringElements[e])}_ModifiedRegionIsBoundary(e,t){if(this._ModifiedIsBoundary(e)||this._ModifiedIsBoundary(e-1))return!0;if(t>0){let i=e+t;if(this._ModifiedIsBoundary(i-1)||this._ModifiedIsBoundary(i))return!0}return!1}_boundaryScore(e,t,i,n){let r=this._OriginalRegionIsBoundary(e,t)?1:0,o=this._ModifiedRegionIsBoundary(i,n)?1:0;return r+o}ConcatenateChanges(e,t){let i=[];if(0===e.length||0===t.length)return t.length>0?t:e;if(this.ChangesOverlap(e[e.length-1],t[0],i)){let n=Array(e.length+t.length-1);return lB.Copy(e,0,n,0,e.length-1),n[e.length-1]=i[0],lB.Copy(t,1,n,e.length,t.length-1),n}{let i=Array(e.length+t.length);return lB.Copy(e,0,i,0,e.length),lB.Copy(t,0,i,e.length,t.length),i}}ChangesOverlap(e,t,i){if(lF.Assert(e.originalStart<=t.originalStart,"Left change is not less than or equal to right change"),lF.Assert(e.modifiedStart<=t.modifiedStart,"Left change is not less than or equal to right change"),!(e.originalStart+e.originalLength>=t.originalStart)&&!(e.modifiedStart+e.modifiedLength>=t.modifiedStart))return i[0]=null,!1;{let n=e.originalStart,r=e.originalLength,o=e.modifiedStart,s=e.modifiedLength;return e.originalStart+e.originalLength>=t.originalStart&&(r=t.originalStart+t.originalLength-e.originalStart),e.modifiedStart+e.modifiedLength>=t.modifiedStart&&(s=t.modifiedStart+t.modifiedLength-e.modifiedStart),i[0]=new lI(n,r,o,s),!0}}ClipDiagonalBound(e,t,i,n){if(e>=0&&e255?255:0|e}function lW(e){return e<0?0:e>4294967295?4294967295:0|e}class lG{constructor(e){this.values=e,this.prefixSum=new Uint32Array(e.length),this.prefixSumValidIndex=new Int32Array(1),this.prefixSumValidIndex[0]=-1}insertValues(e,t){e=lW(e);let i=this.values,n=this.prefixSum,r=t.length;return 0!==r&&(this.values=new Uint32Array(i.length+r),this.values.set(i.subarray(0,e),0),this.values.set(i.subarray(e),e+r),this.values.set(t,e),e-1=0&&this.prefixSum.set(n.subarray(0,this.prefixSumValidIndex[0]+1)),!0)}setValue(e,t){return e=lW(e),t=lW(t),this.values[e]!==t&&(this.values[e]=t,e-1=i.length)return!1;let r=i.length-e;return t>=r&&(t=r),0!==t&&(this.values=new Uint32Array(i.length-t),this.values.set(i.subarray(0,e),0),this.values.set(i.subarray(e+t),e),this.prefixSum=new Uint32Array(this.values.length),e-1=0&&this.prefixSum.set(n.subarray(0,this.prefixSumValidIndex[0]+1)),!0)}getTotalSum(){return 0===this.values.length?0:this._getPrefixSum(this.values.length-1)}getPrefixSum(e){return e<0?0:(e=lW(e),this._getPrefixSum(e))}_getPrefixSum(e){if(e<=this.prefixSumValidIndex[0])return this.prefixSum[e];let t=this.prefixSumValidIndex[0]+1;0===t&&(this.prefixSum[0]=this.values[0],t++),e>=this.values.length&&(e=this.values.length-1);for(let i=t;i<=e;i++)this.prefixSum[i]=this.prefixSum[i-1]+this.values[i];return this.prefixSumValidIndex[0]=Math.max(this.prefixSumValidIndex[0],e),this.prefixSum[e]}getIndexOf(e){e=Math.floor(e),this.getTotalSum();let t=0,i=this.values.length-1,n=0,r=0,o=0;for(;t<=i;)if(n=t+(i-t)/2|0,e<(o=(r=this.prefixSum[n])-this.values[n]))i=n-1;else if(e>=r)t=n+1;else break;return new lY(n,e-o)}}class lz{constructor(e){this._values=e,this._isValid=!1,this._validEndIndex=-1,this._prefixSum=[],this._indexBySum=[]}getTotalSum(){return this._ensureValid(),this._indexBySum.length}getPrefixSum(e){return(this._ensureValid(),0===e)?0:this._prefixSum[e-1]}getIndexOf(e){this._ensureValid();let t=this._indexBySum[e],i=t>0?this._prefixSum[t-1]:0;return new lY(t,e-i)}removeValues(e,t){this._values.splice(e,t),this._invalidate(e)}insertValues(e,t){this._values=(0,sq.Zv)(this._values,e,t),this._invalidate(e)}_invalidate(e){this._isValid=!1,this._validEndIndex=Math.min(this._validEndIndex,e-1)}_ensureValid(){if(!this._isValid){for(let e=this._validEndIndex+1,t=this._values.length;e0?this._prefixSum[e-1]:0;this._prefixSum[e]=i+t;for(let n=0;n=0&&e<256?this._asciiMap[e]=i:this._map.set(e,i)}get(e){return e>=0&&e<256?this._asciiMap[e]:this._map.get(e)||this._defaultValue}clear(){this._asciiMap.fill(this._defaultValue),this._map.clear()}}class lX{constructor(e,t,i){let n=new Uint8Array(e*t);for(let r=0,o=e*t;rt&&(t=o),r>i&&(i=r),s>i&&(i=s)}t++,i++;let n=new lX(i,t,0);for(let t=0,i=e.length;t=this._maxCharCode?0:this._states.get(e,t)}}let lq=null;function lZ(){return null===lq&&(lq=new lj([[1,104,2],[1,72,2],[1,102,6],[1,70,6],[2,116,3],[2,84,3],[3,116,4],[3,84,4],[4,112,5],[4,80,5],[5,115,9],[5,83,9],[5,58,10],[6,105,7],[6,73,7],[7,108,8],[7,76,8],[8,101,9],[8,69,9],[9,58,10],[10,47,11],[11,47,12]])),lq}let lJ=null;class lQ{static _createLink(e,t,i,n,r){let o=r-1;do{let i=t.charCodeAt(o),n=e.get(i);if(2!==n)break;o--}while(o>n);if(n>0){let e=t.charCodeAt(n-1),i=t.charCodeAt(o);(40===e&&41===i||91===e&&93===i||123===e&&125===i)&&o--}return{range:{startLineNumber:i,startColumn:n+1,endLineNumber:i,endColumn:o+2},url:t.substring(n,o+1)}}static computeLinks(e,t=lZ()){let i=function(){if(null===lJ){lJ=new l$(0);let e=" <>'\"、。｡､，．：；‘〈「『〔（［｛｢｣｝］）〕』」〉’｀～…";for(let t=0;t=0?((n+=i?1:-1)<0?n=e.length-1:n%=e.length,e[n]):null}}l0.INSTANCE=new l0;class l1 extends l${constructor(e){super(0);for(let t=0,i=e.length;t(t.hasOwnProperty(i)||(t[i]=e(i)),t[i])}(e=>new l1(e));class l4{constructor(e,t,i,n){this.searchString=e,this.isRegex=t,this.matchCase=i,this.wordSeparators=n}parseSearchRequest(){let e;if(""===this.searchString)return null;e=this.isRegex?function(e){if(!e||0===e.length)return!1;for(let t=0,i=e.length;t=i)break;let n=e.charCodeAt(t);if(110===n||114===n||87===n)return!0}}return!1}(this.searchString):this.searchString.indexOf("\n")>=0;let t=null;try{t=rG(this.searchString,this.isRegex,{matchCase:this.matchCase,wholeWord:!1,multiline:e,global:!0,unicode:!0})}catch(e){return null}if(!t)return null;let i=!this.isRegex&&!e;return i&&this.searchString.toLowerCase()!==this.searchString.toUpperCase()&&(i=this.matchCase),new sH(t,this.wordSeparators?l2(this.wordSeparators):null,i?this.searchString:null)}}function l5(e,t,i){if(!i)return new sB(e,null);let n=[];for(let e=0,i=t.length;e>0);t[r]>=e?n=r-1:t[r+1]>=e?(i=r,n=r):i=r+1}return i+1}}class l3{static findMatches(e,t,i,n,r){let o=t.parseSearchRequest();return o?o.regex.multiline?this._doFindMatchesMultiline(e,i,new l7(o.wordSeparators,o.regex),n,r):this._doFindMatchesLineByLine(e,i,o,n,r):[]}static _getMultilineMatchRange(e,t,i,n,r,o){let s,a;let l=0;if(n?(l=n.findLineFeedCountBeforeOffset(r),s=t+r+l):s=t+r,n){let e=n.findLineFeedCountBeforeOffset(r+o.length),t=e-l;a=s+o.length+t}else a=s+o.length;let h=e.getPositionAt(s),u=e.getPositionAt(a);return new r_(h.lineNumber,h.column,u.lineNumber,u.column)}static _doFindMatchesMultiline(e,t,i,n,r){let o;let s=e.getOffsetAt(t.getStartPosition()),a=e.getValueInRange(t,1),l="\r\n"===e.getEOL()?new l6(a):null,h=[],u=0;for(i.reset(0);(o=i.next(a))&&(h[u++]=l5(this._getMultilineMatchRange(e,s,a,l,o.index,o[0]),o,n),!(u>=r)););return h}static _doFindMatchesLineByLine(e,t,i,n,r){let o=[],s=0;if(t.startLineNumber===t.endLineNumber){let a=e.getLineContent(t.startLineNumber).substring(t.startColumn-1,t.endColumn-1);return s=this._findMatchesInLine(i,a,t.startLineNumber,t.startColumn-1,s,o,n,r),o}let a=e.getLineContent(t.startLineNumber).substring(t.startColumn-1);s=this._findMatchesInLine(i,a,t.startLineNumber,t.startColumn-1,s,o,n,r);for(let a=t.startLineNumber+1;a=a))););return r}let u=new l7(e.wordSeparators,e.regex);u.reset(0);do if((l=u.next(t))&&(o[r++]=l5(new r_(i,l.index+1+n,i,l.index+1+l[0].length+n),l,s),r>=a))break;while(l);return r}static findNextMatch(e,t,i,n){let r=t.parseSearchRequest();if(!r)return null;let o=new l7(r.wordSeparators,r.regex);return r.regex.multiline?this._doFindNextMatchMultiline(e,i,o,n):this._doFindNextMatchLineByLine(e,i,o,n)}static _doFindNextMatchMultiline(e,t,i,n){let r=new rE(t.lineNumber,1),o=e.getOffsetAt(r),s=e.getLineCount(),a=e.getValueInRange(new r_(r.lineNumber,r.column,s,e.getLineMaxColumn(s)),1),l="\r\n"===e.getEOL()?new l6(a):null;i.reset(t.column-1);let h=i.next(a);return h?l5(this._getMultilineMatchRange(e,o,a,l,h.index,h[0]),h,n):1!==t.lineNumber||1!==t.column?this._doFindNextMatchMultiline(e,new rE(1,1),i,n):null}static _doFindNextMatchLineByLine(e,t,i,n){let r=e.getLineCount(),o=t.lineNumber,s=e.getLineContent(o),a=this._findFirstMatchInLine(i,s,o,t.column,n);if(a)return a;for(let t=1;t<=r;t++){let s=(o+t-1)%r,a=e.getLineContent(s+1),l=this._findFirstMatchInLine(i,a,s+1,1,n);if(l)return l}return null}static _findFirstMatchInLine(e,t,i,n,r){e.reset(n-1);let o=e.next(t);return o?l5(new r_(i,o.index+1,i,o.index+1+o[0].length),o,r):null}static findPreviousMatch(e,t,i,n){let r=t.parseSearchRequest();if(!r)return null;let o=new l7(r.wordSeparators,r.regex);return r.regex.multiline?this._doFindPreviousMatchMultiline(e,i,o,n):this._doFindPreviousMatchLineByLine(e,i,o,n)}static _doFindPreviousMatchMultiline(e,t,i,n){let r=this._doFindMatchesMultiline(e,new r_(1,1,t.lineNumber,t.column),i,n,9990);if(r.length>0)return r[r.length-1];let o=e.getLineCount();return t.lineNumber!==o||t.column!==e.getLineMaxColumn(o)?this._doFindPreviousMatchMultiline(e,new rE(o,e.getLineMaxColumn(o)),i,n):null}static _doFindPreviousMatchLineByLine(e,t,i,n){let r=e.getLineCount(),o=t.lineNumber,s=e.getLineContent(o).substring(0,t.column-1),a=this._findLastMatchInLine(i,s,o,n);if(a)return a;for(let t=1;t<=r;t++){let s=(r+o-t-1)%r,a=e.getLineContent(s+1),l=this._findLastMatchInLine(i,a,s+1,n);if(l)return l}return null}static _findLastMatchInLine(e,t,i,n){let r,o=null;for(e.reset(0);r=e.next(t);)o=l5(new r_(i,r.index+1,i,r.index+1+r[0].length),r,n);return o}}function l9(e,t,i,n,r){return function(e,t,i,n,r){if(0===n)return!0;let o=t.charCodeAt(n-1);if(0!==e.get(o)||13===o||10===o)return!0;if(r>0){let i=t.charCodeAt(n);if(0!==e.get(i))return!0}return!1}(e,t,0,n,r)&&function(e,t,i,n,r){if(n+r===i)return!0;let o=t.charCodeAt(n+r);if(0!==e.get(o)||13===o||10===o)return!0;if(r>0){let i=t.charCodeAt(n+r-1);if(0!==e.get(i))return!0}return!1}(e,t,i,n,r)}class l7{constructor(e,t){this._wordSeparators=e,this._searchRegex=t,this._prevMatchStartIndex=-1,this._prevMatchLength=0}reset(e){this._searchRegex.lastIndex=e,this._prevMatchStartIndex=-1,this._prevMatchLength=0}next(e){let t;let i=e.length;do{if(this._prevMatchStartIndex+this._prevMatchLength===i||!(t=this._searchRegex.exec(e)))break;let n=t.index,r=t[0].length;if(n===this._prevMatchStartIndex&&r===this._prevMatchLength){if(0===r){r8(e,i,this._searchRegex.lastIndex)>65535?this._searchRegex.lastIndex+=2:this._searchRegex.lastIndex+=1;continue}break}if(this._prevMatchStartIndex=n,this._prevMatchLength=r,!this._wordSeparators||l9(this._wordSeparators,e,i,n,r))return t}while(t);return null}}class l8{static computeUnicodeHighlights(e,t,i){let n,r;let o=i?i.startLineNumber:1,s=i?i.endLineNumber:e.getLineCount(),a=new he(t),l=a.getCandidateCodePoints();n="allNonBasicAscii"===l?RegExp("[^\\t\\n\\r\\x20-\\x7E]","g"):RegExp(`${function(e,t){let i=`[${rV(e.map(e=>String.fromCodePoint(e)).join(""))}]`;return i}(Array.from(l))}`,"g");let h=new l7(null,n),u=[],d=!1,c=0,g=0,p=0;t:for(let t=o;t<=s;t++){let i=e.getLineContent(t),n=i.length;h.reset(0);do if(r=h.next(i)){let e=r.index,o=r.index+r[0].length;if(e>0){let t=i.charCodeAt(e-1);r3(t)&&e--}if(o+1=1e3){d=!0;break t}u.push(new r_(t,e+1,t,o+1))}}while(r)}return{ranges:u,hasMore:d,ambiguousCharacterCount:c,invisibleCharacterCount:g,nonBasicAsciiCharacterCount:p}}static computeUnicodeHighlightReason(e,t){let i=new he(t),n=i.shouldHighlightNonBasicASCII(e,null);switch(n){case 0:return null;case 2:return{kind:1};case 3:{let n=e.codePointAt(0),r=i.ambiguousCharacters.getPrimaryConfusable(n),o=of.getLocales().filter(e=>!of.getInstance(new Set([...t.allowedLocales,e])).isAmbiguous(n));return{kind:0,confusableWith:String.fromCodePoint(r),notAmbiguousInLocales:o}}case 1:return{kind:2}}}}class he{constructor(e){this.options=e,this.allowedCodePoints=new Set(e.allowedCodePoints),this.ambiguousCharacters=of.getInstance(new Set(e.allowedLocales))}getCandidateCodePoints(){if(this.options.nonBasicASCII)return"allNonBasicAscii";let e=new Set;if(this.options.invisibleCharacters)for(let t of om.codePoints)ht(String.fromCodePoint(t))||e.add(t);if(this.options.ambiguousCharacters)for(let t of this.ambiguousCharacters.getConfusableCodePoints())e.add(t);for(let t of this.allowedCodePoints)e.delete(t);return e}shouldHighlightNonBasicASCII(e,t){let i=e.codePointAt(0);if(this.allowedCodePoints.has(i))return 0;if(this.options.nonBasicASCII)return 1;let n=!1,r=!1;if(t)for(let e of t){let t=e.codePointAt(0),i=os(e);n=n||i,i||this.ambiguousCharacters.isAmbiguous(t)||om.isInvisibleCharacter(t)||(r=!0)}return!n&&r?0:this.options.invisibleCharacters&&!ht(e)&&om.isInvisibleCharacter(i)?2:this.options.ambiguousCharacters&&this.ambiguousCharacters.isAmbiguous(i)?3:0}}function ht(e){return" "===e||"\n"===e||" "===e}class hi{static fromRange(e){return new hi(e.startLineNumber,e.endLineNumber)}static subtract(e,t){return t?e.startLineNumber=s.startLineNumber?o=new hi(o.startLineNumber,Math.max(o.endLineNumberExclusive,s.endLineNumberExclusive)):(i.push(o),o=s)}return null!==o&&i.push(o),i}static ofLength(e,t){return new hi(e,e+t)}static deserialize(e){return new hi(e[0],e[1])}constructor(e,t){if(e>t)throw new nt(`startLineNumber ${e} cannot be after endLineNumberExclusive ${t}`);this.startLineNumber=e,this.endLineNumberExclusive=t}contains(e){return this.startLineNumber<=e&&e${this.modifiedRange.toString()}}`}get changedLineCount(){return Math.max(this.originalRange.length,this.modifiedRange.length)}flip(){var e;return new hr(this.modifiedRange,this.originalRange,null===(e=this.innerChanges)||void 0===e?void 0:e.map(e=>e.flip()))}}class ho{constructor(e,t){this.originalRange=e,this.modifiedRange=t}toString(){return`{${this.originalRange.toString()}->${this.modifiedRange.toString()}}`}flip(){return new ho(this.modifiedRange,this.originalRange)}}class hs{constructor(e,t){this.original=e,this.modified=t}toString(){return`{${this.original.toString()}->${this.modified.toString()}}`}flip(){return new hs(this.modified,this.original)}}class ha{constructor(e,t){this.lineRangeMapping=e,this.changes=t}flip(){return new ha(this.lineRangeMapping.flip(),this.changes.map(e=>e.flip()))}}class hl{computeDiff(e,t,i){var n;let r=new hp(e,t,{maxComputationTime:i.maxComputationTimeMs,shouldIgnoreTrimWhitespace:i.ignoreTrimWhitespace,shouldComputeCharChanges:!0,shouldMakePrettyDiff:!0,shouldPostProcessCharChanges:!0}),o=r.computeDiff(),s=[],a=null;for(let e of o.changes){let t,i;t=0===e.originalEndLineNumber?new hi(e.originalStartLineNumber+1,e.originalStartLineNumber+1):new hi(e.originalStartLineNumber,e.originalEndLineNumber+1),i=0===e.modifiedEndLineNumber?new hi(e.modifiedStartLineNumber+1,e.modifiedStartLineNumber+1):new hi(e.modifiedStartLineNumber,e.modifiedEndLineNumber+1);let r=new hr(t,i,null===(n=e.charChanges)||void 0===n?void 0:n.map(e=>new ho(new r_(e.originalStartLineNumber,e.originalStartColumn,e.originalEndLineNumber,e.originalEndColumn),new r_(e.modifiedStartLineNumber,e.modifiedStartColumn,e.modifiedEndLineNumber,e.modifiedEndColumn))));a&&(a.modifiedRange.endLineNumberExclusive===r.modifiedRange.startLineNumber||a.originalRange.endLineNumberExclusive===r.originalRange.startLineNumber)&&(r=new hr(a.originalRange.join(r.originalRange),a.modifiedRange.join(r.modifiedRange),a.innerChanges&&r.innerChanges?a.innerChanges.concat(r.innerChanges):void 0),s.pop()),s.push(r),a=r}return o$(()=>oX(s,(e,t)=>t.originalRange.startLineNumber-e.originalRange.endLineNumberExclusive==t.modifiedRange.startLineNumber-e.modifiedRange.endLineNumberExclusive&&e.originalRange.endLineNumberExclusive(10===e?"\\n":String.fromCharCode(e))+`-(${this._lineNumbers[t]},${this._columns[t]})`).join(", ")+"]"}_assertIndex(e,t){if(e<0||e>=t.length)throw Error("Illegal index")}getElements(){return this._charCodes}getStartLineNumber(e){return e>0&&e===this._lineNumbers.length?this.getEndLineNumber(e-1):(this._assertIndex(e,this._lineNumbers),this._lineNumbers[e])}getEndLineNumber(e){return -1===e?this.getStartLineNumber(e+1):(this._assertIndex(e,this._lineNumbers),10===this._charCodes[e])?this._lineNumbers[e]+1:this._lineNumbers[e]}getStartColumn(e){return e>0&&e===this._columns.length?this.getEndColumn(e-1):(this._assertIndex(e,this._columns),this._columns[e])}getEndColumn(e){return -1===e?this.getStartColumn(e+1):(this._assertIndex(e,this._columns),10===this._charCodes[e])?1:this._columns[e]+1}}class hc{constructor(e,t,i,n,r,o,s,a){this.originalStartLineNumber=e,this.originalStartColumn=t,this.originalEndLineNumber=i,this.originalEndColumn=n,this.modifiedStartLineNumber=r,this.modifiedStartColumn=o,this.modifiedEndLineNumber=s,this.modifiedEndColumn=a}static createFromDiffChange(e,t,i){let n=t.getStartLineNumber(e.originalStart),r=t.getStartColumn(e.originalStart),o=t.getEndLineNumber(e.originalStart+e.originalLength-1),s=t.getEndColumn(e.originalStart+e.originalLength-1),a=i.getStartLineNumber(e.modifiedStart),l=i.getStartColumn(e.modifiedStart),h=i.getEndLineNumber(e.modifiedStart+e.modifiedLength-1),u=i.getEndColumn(e.modifiedStart+e.modifiedLength-1);return new hc(n,r,o,s,a,l,h,u)}}class hg{constructor(e,t,i,n,r){this.originalStartLineNumber=e,this.originalEndLineNumber=t,this.modifiedStartLineNumber=i,this.modifiedEndLineNumber=n,this.charChanges=r}static createFromDiffResult(e,t,i,n,r,o,s){let a,l,h,u,d;if(0===t.originalLength?(a=i.getStartLineNumber(t.originalStart)-1,l=0):(a=i.getStartLineNumber(t.originalStart),l=i.getEndLineNumber(t.originalStart+t.originalLength-1)),0===t.modifiedLength?(h=n.getStartLineNumber(t.modifiedStart)-1,u=0):(h=n.getStartLineNumber(t.modifiedStart),u=n.getEndLineNumber(t.modifiedStart+t.modifiedLength-1)),o&&t.originalLength>0&&t.originalLength<20&&t.modifiedLength>0&&t.modifiedLength<20&&r()){let o=i.createCharSequence(e,t.originalStart,t.originalStart+t.originalLength-1),a=n.createCharSequence(e,t.modifiedStart,t.modifiedStart+t.modifiedLength-1);if(o.getElements().length>0&&a.getElements().length>0){let e=hh(o,a,r,!0).changes;s&&(e=function(e){if(e.length<=1)return e;let t=[e[0]],i=t[0];for(let n=1,r=e.length;n1&&s>1;){let n=e.charCodeAt(i-2),r=t.charCodeAt(s-2);if(n!==r)break;i--,s--}(i>1||s>1)&&this._pushTrimWhitespaceCharChange(n,r+1,1,i,o+1,1,s)}{let i=hm(e,1),s=hm(t,1),a=e.length+1,l=t.length+1;for(;i!0;let t=Date.now();return()=>Date.now()-tt))return new hE(e,t)}constructor(e,t){if(this.start=e,this.endExclusive=t,e>t)throw new nt(`Invalid range: ${this.toString()}`)}get isEmpty(){return this.start===this.endExclusive}delta(e){return new hE(this.start+e,this.endExclusive+e)}get length(){return this.endExclusive-this.start}toString(){return`[${this.start}, ${this.endExclusive})`}equals(e){return this.start===e.start&&this.endExclusive===e.endExclusive}containsRange(e){return this.start<=e.start&&e.endExclusive<=this.endExclusive}contains(e){return this.start<=e&&e ${this.seq2Range}`}join(e){return new hC(this.seq1Range.join(e.seq1Range),this.seq2Range.join(e.seq2Range))}delta(e){return 0===e?this:new hC(this.seq1Range.delta(e),this.seq2Range.delta(e))}}class hS{isValid(){return!0}}hS.instance=new hS;class hy{constructor(e){if(this.timeout=e,this.startTime=Date.now(),this.valid=!0,e<=0)throw new nt("timeout must be positive")}isValid(){let e=Date.now()-this.startTime0&&l>0&&3===o.get(a-1,l-1)&&(h+=s.get(a-1,l-1)),h+=n?n(a,l):1):h=-1;let c=Math.max(u,d,h);if(c===h){let e=a>0&&l>0?s.get(a-1,l-1):0;s.set(a,l,e+1),o.set(a,l,3)}else c===u?(s.set(a,l,0),o.set(a,l,1)):c===d&&(s.set(a,l,0),o.set(a,l,2));r.set(a,l,c)}let a=[],l=e.length,h=t.length;function u(e,t){(e+1!==l||t+1!==h)&&a.push(new hC(new hE(e+1,l),new hE(t+1,h))),l=e,h=t}let d=e.length-1,c=t.length-1;for(;d>=0&&c>=0;)3===o.get(d,c)?(u(d,c),d--,c--):1===o.get(d,c)?d--:c--;return u(-1,-1),a.reverse(),new h_(a,!1)}}function hA(e,t,i){let n=i;return n=function(e,t,i){if(0===i.length)return i;let n=[];n.push(i[0]);for(let r=1;r0&&(s=s.delta(r))}r.push(s)}return n.length>0&&r.push(n[n.length-1]),r}(e,t,n),n=function(e,t,i){if(!e.getBoundaryScore||!t.getBoundaryScore)return i;for(let n=0;n0?i[n-1]:void 0,o=i[n],s=n+1=n.start&&e.seq2Range.start-o>=r.start&&i.getElement(e.seq2Range.start-o)===i.getElement(e.seq2Range.endExclusive-o)&&o<100;)o++;o--;let s=0;for(;e.seq1Range.start+sl&&(l=h,a=n)}return e.delta(a)}class hL{compute(e,t,i=hS.instance){if(0===e.length||0===t.length)return h_.trivial(e,t);function n(i,n){for(;ie.length||d>t.length)continue;let c=n(u,d);o.set(a,c);let g=u===i?s.get(a+1):s.get(a-1);if(s.set(a,c!==u?new hN(g,u,d,c-u):g),o.get(a)===e.length&&o.get(a)-a===t.length)break i}}let l=s.get(a),h=[],u=e.length,d=t.length;for(;;){let e=l?l.x+l.length:0,t=l?l.y+l.length:0;if((e!==u||t!==d)&&h.push(new hC(new hE(e,u),new hE(t,d))),!l)break;u=l.x,d=l.y,l=l.prev}return h.reverse(),new h_(h,!1)}}class hN{constructor(e,t,i,n){this.prev=e,this.x=t,this.y=i,this.length=n}}class hI{constructor(){this.positiveArr=new Int32Array(10),this.negativeArr=new Int32Array(10)}get(e){return e<0?(e=-e-1,this.negativeArr[e]):this.positiveArr[e]}set(e,t){if(e<0){if((e=-e-1)>=this.negativeArr.length){let e=this.negativeArr;this.negativeArr=new Int32Array(2*e.length),this.negativeArr.set(e)}this.negativeArr[e]=t}else{if(e>=this.positiveArr.length){let e=this.positiveArr;this.positiveArr=new Int32Array(2*e.length),this.positiveArr.set(e)}this.positiveArr[e]=t}}}class hw{constructor(){this.positiveArr=[],this.negativeArr=[]}get(e){return e<0?(e=-e-1,this.negativeArr[e]):this.positiveArr[e]}set(e,t){e<0?(e=-e-1,this.negativeArr[e]=t):this.positiveArr[e]=t}}class hO{constructor(){this.dynamicProgrammingDiffing=new hb,this.myersDiffingAlgorithm=new hL}computeDiff(e,t,i){if(1===e.length&&0===e[0].length||1===t.length&&0===t[0].length)return{changes:[new hr(new hi(1,e.length+1),new hi(1,t.length+1),[new ho(new r_(1,1,e.length,e[0].length+1),new r_(1,1,t.length,t[0].length+1))])],hitTimeout:!1,moves:[]};let n=0===i.maxComputationTimeMs?hS.instance:new hy(i.maxComputationTimeMs),r=!i.ignoreTrimWhitespace,o=new Map;function s(e){let t=o.get(e);return void 0===t&&(t=o.size,o.set(e,t)),t}let a=e.map(e=>s(e.trim())),l=t.map(e=>s(e.trim())),h=new hD(a,e),u=new hD(l,t),d=h.length+u.length<1500?this.dynamicProgrammingDiffing.compute(h,u,n,(i,n)=>e[i]===t[n]?0===t[n].length?.1:1+Math.log(1+t[n].length):.99):this.myersDiffingAlgorithm.compute(h,u),c=d.diffs,g=d.hitTimeout;c=hA(h,u,c);let p=[],f=i=>{if(r)for(let o=0;oi.seq1Range.start-m==i.seq2Range.start-v);let o=i.seq1Range.start-m;f(o),m=i.seq1Range.endExclusive,v=i.seq2Range.endExclusive;let s=this.refineDiff(e,t,i,n,r);for(let e of(s.hitTimeout&&(g=!0),s.mappings))p.push(e)}f(e.length-m);let E=hx(p,e,t),_=[];if(i.computeMoves){let i=E.filter(e=>e.modifiedRange.isEmpty&&e.originalRange.length>=3).map(t=>new hV(t.originalRange,e)),o=new Set(E.filter(e=>e.originalRange.isEmpty&&e.modifiedRange.length>=3).map(e=>new hV(e.modifiedRange,t)));for(let s of i){let i,a=-1;for(let e of o){let t=s.computeSimilarity(e);t>a&&(a=t,i=e)}if(a>.9&&i){let a=this.refineDiff(e,t,new hC(new hE(s.range.startLineNumber-1,s.range.endLineNumberExclusive-1),new hE(i.range.startLineNumber-1,i.range.endLineNumberExclusive-1)),n,r),l=hx(a.mappings,e,t,!0);o.delete(i),_.push(new ha(new hs(s.range,i.range),l))}}}return o$(()=>{function i(e,t){if(e.lineNumber<1||e.lineNumber>t.length)return!1;let i=t[e.lineNumber-1];return!(e.column<1)&&!(e.column>i.length+1)}function n(e,t){return!(e.startLineNumber<1)&&!(e.startLineNumber>t.length+1)&&!(e.endLineNumberExclusive<1)&&!(e.endLineNumberExclusive>t.length+1)}for(let r of E){if(!r.innerChanges)return!1;for(let n of r.innerChanges){let r=i(n.modifiedRange.getStartPosition(),t)&&i(n.modifiedRange.getEndPosition(),t)&&i(n.originalRange.getStartPosition(),e)&&i(n.originalRange.getEndPosition(),e);if(!r)return!1}if(!n(r.modifiedRange,t)||!n(r.originalRange,e))return!1}return!0}),new hn(E,_,g)}refineDiff(e,t,i,n,r){let o=new hk(e,i.seq1Range,r),s=new hk(t,i.seq2Range,r),a=o.length+s.length<500?this.dynamicProgrammingDiffing.compute(o,s,n):this.myersDiffingAlgorithm.compute(o,s,n),l=a.diffs;l=hA(o,s,l),l=function(e,t,i){let n=[];for(let e of i){let t=n[n.length-1];if(!t){n.push(e);continue}e.seq1Range.start-t.seq1Range.endExclusive<=2||e.seq2Range.start-t.seq2Range.endExclusive<=2?n[n.length-1]=new hC(t.seq1Range.join(e.seq1Range),t.seq2Range.join(e.seq2Range)):n.push(e)}return n}(0,0,l=function(e,t,i){let n;let r=[];function o(){if(!n)return;let e=n.s1Range.length-n.deleted;n.s2Range.length,n.added,Math.max(n.deleted,n.added)+(n.count-1)>e&&r.push(new hC(n.s1Range,n.s2Range)),n=void 0}for(let r of i){function s(e,t){var i,s,a,l;if(!n||!n.s1Range.containsRange(e)||!n.s2Range.containsRange(t)){if(n&&!(n.s1Range.endExclusive0||t.length>0;){let n;let r=e[0],o=t[0];n=r&&(!o||r.seq1Range.start0&&i[i.length-1].seq1Range.endExclusive>=n.seq1Range.start?i[i.length-1]=i[i.length-1].join(n):i.push(n)}return i}(i,r);return a}(o,s,l)),l=function(e,t,i){let n,r=i;if(0===r.length)return r;let o=0;do{n=!1;let i=[r[0]];for(let o=1;o5||r.length>500)return!1;let l=e.getText(r).trim();if(l.length>20||l.split(/\r\n|\r|\n/).length>1)return!1;let h=e.countLinesIn(i.seq1Range),u=i.seq1Range.length,d=t.countLinesIn(i.seq2Range),c=i.seq2Range.length,g=e.countLinesIn(n.seq1Range),p=n.seq1Range.length,f=t.countLinesIn(n.seq2Range),m=n.seq2Range.length;function v(e){return Math.min(e,130)}return Math.pow(Math.pow(v(40*h+u),1.5)+Math.pow(v(40*d+c),1.5),1.5)+Math.pow(Math.pow(v(40*g+p),1.5)+Math.pow(v(40*f+m),1.5),1.5)>74184.96480721243}(a,s);l?(n=!0,i[i.length-1]=i[i.length-1].join(s)):i.push(s)}r=i}while(o++<10&&n);return r}(o,s,l);let h=l.map(e=>new ho(o.translateRange(e.seq1Range),s.translateRange(e.seq2Range)));return{mappings:h,hitTimeout:a.hitTimeout}}}function hx(e,t,i,n=!1){let r=[];for(let n of function*(e,t){let i,n;for(let r of e)void 0!==n&&t(n,r)?i.push(r):(i&&(yield i),i=[r]),n=r;i&&(yield i)}(e.map(e=>(function(e,t,i){let n=0,r=0;1===e.modifiedRange.endColumn&&1===e.originalRange.endColumn&&e.originalRange.startLineNumber+n<=e.originalRange.endLineNumber&&e.modifiedRange.startLineNumber+n<=e.modifiedRange.endLineNumber&&(r=-1),e.modifiedRange.startColumn-1>=i[e.modifiedRange.startLineNumber-1].length&&e.originalRange.startColumn-1>=t[e.originalRange.startLineNumber-1].length&&e.originalRange.startLineNumber<=e.originalRange.endLineNumber+r&&e.modifiedRange.startLineNumber<=e.modifiedRange.endLineNumber+r&&(n=1);let o=new hi(e.originalRange.startLineNumber+n,e.originalRange.endLineNumber+1+r),s=new hi(e.modifiedRange.startLineNumber+n,e.modifiedRange.endLineNumber+1+r);return new hr(o,s,[e])})(e,t,i)),(e,t)=>e.originalRange.overlapOrTouch(t.originalRange)||e.modifiedRange.overlapOrTouch(t.modifiedRange))){let e=n[0],t=n[n.length-1];r.push(new hr(e.originalRange.join(t.originalRange),e.modifiedRange.join(t.modifiedRange),n.map(e=>e.innerChanges[0])))}return o$(()=>(!!n||!(r.length>0)||r[0].originalRange.startLineNumber===r[0].modifiedRange.startLineNumber)&&oX(r,(e,t)=>t.originalRange.startLineNumber-e.originalRange.endLineNumberExclusive==t.modifiedRange.startLineNumber-e.modifiedRange.endLineNumberExclusive&&e.originalRange.endLineNumberExclusive0&&t.endExclusive>=e.length&&(t=new hE(t.start-1,t.endExclusive),n=!0),this.lineRange=t;for(let t=this.lineRange.start;tString.fromCharCode(e)).join("")}getElement(e){return this.elements[e]}get length(){return this.elements.length}getBoundaryScore(e){let t=hB(e>0?this.elements[e-1]:-1),i=hB(ee?i=n:t=n+1}let n=0===t?0:this.firstCharOffsetByLineMinusOne[t-1];return new rE(this.lineRange.start+t+1,e-n+1+this.offsetByLine[t])}translateRange(e){return r_.fromPositions(this.translateOffset(e.start),this.translateOffset(e.endExclusive))}findWordContaining(e){if(e<0||e>=this.elements.length||!hP(this.elements[e]))return;let t=e;for(;t>0&&hP(this.elements[t-1]);)t--;let i=e;for(;i=97&&e<=122||e>=65&&e<=90||e>=48&&e<=57}let hF={0:0,1:0,2:0,3:10,4:2,5:3,6:10,7:10};function hB(e){if(10===e)return 7;if(13===e)return 6;if(32===e||9===e)return 5;if(e>=97&&e<=122)return 0;if(e>=65&&e<=90)return 1;if(e>=48&&e<=57)return 2;if(-1===e)return 3;else return 4}let hU=new Map;function hH(e){let t=hU.get(e);return void 0===t&&(t=hU.size,hU.set(e,t)),t}class hV{constructor(e,t){this.range=e,this.lines=t,this.histogram=[];let i=0;for(let n=e.startLineNumber-1;nnew hl,getAdvanced:()=>new hO};function hG(e,t){let i=Math.pow(10,t);return Math.round(e*i)/i}class hz{constructor(e,t,i,n=1){this._rgbaBrand=void 0,this.r=0|Math.min(255,Math.max(0,e)),this.g=0|Math.min(255,Math.max(0,t)),this.b=0|Math.min(255,Math.max(0,i)),this.a=hG(Math.max(Math.min(1,n),0),3)}static equals(e,t){return e.r===t.r&&e.g===t.g&&e.b===t.b&&e.a===t.a}}class hY{constructor(e,t,i,n){this._hslaBrand=void 0,this.h=0|Math.max(Math.min(360,e),0),this.s=hG(Math.max(Math.min(1,t),0),3),this.l=hG(Math.max(Math.min(1,i),0),3),this.a=hG(Math.max(Math.min(1,n),0),3)}static equals(e,t){return e.h===t.h&&e.s===t.s&&e.l===t.l&&e.a===t.a}static fromRGBA(e){let t=e.r/255,i=e.g/255,n=e.b/255,r=e.a,o=Math.max(t,i,n),s=Math.min(t,i,n),a=0,l=0,h=(s+o)/2,u=o-s;if(u>0){switch(l=Math.min(h<=.5?u/(2*h):u/(2-2*h),1),o){case t:a=(i-n)/u+(i1&&(i-=1),i<1/6)?e+(t-e)*6*i:i<.5?t:i<2/3?e+(t-e)*(2/3-i)*6:e}static toRGBA(e){let t,i,n;let r=e.h/360,{s:o,l:s,a}=e;if(0===o)t=i=n=s;else{let e=s<.5?s*(1+o):s+o-s*o,a=2*s-e;t=hY._hue2rgb(a,e,r+1/3),i=hY._hue2rgb(a,e,r),n=hY._hue2rgb(a,e,r-1/3)}return new hz(Math.round(255*t),Math.round(255*i),Math.round(255*n),a)}}class hK{constructor(e,t,i,n){this._hsvaBrand=void 0,this.h=0|Math.max(Math.min(360,e),0),this.s=hG(Math.max(Math.min(1,t),0),3),this.v=hG(Math.max(Math.min(1,i),0),3),this.a=hG(Math.max(Math.min(1,n),0),3)}static equals(e,t){return e.h===t.h&&e.s===t.s&&e.v===t.v&&e.a===t.a}static fromRGBA(e){let t;let i=e.r/255,n=e.g/255,r=e.b/255,o=Math.max(i,n,r),s=Math.min(i,n,r),a=o-s,l=0===o?0:a/o;return t=0===a?0:o===i?((n-r)/a%6+6)%6:o===n?(r-i)/a+2:(i-n)/a+4,new hK(Math.round(60*t),l,o,e.a)}static toRGBA(e){let{h:t,s:i,v:n,a:r}=e,o=n*i,s=o*(1-Math.abs(t/60%2-1)),a=n-o,[l,h,u]=[0,0,0];return t<60?(l=o,h=s):t<120?(l=s,h=o):t<180?(h=o,u=s):t<240?(h=s,u=o):t<300?(l=s,u=o):t<=360&&(l=o,u=s),l=Math.round((l+a)*255),h=Math.round((h+a)*255),u=Math.round((u+a)*255),new hz(l,h,u,r)}}class h${static fromHex(e){return h$.Format.CSS.parseHex(e)||h$.red}static equals(e,t){return!e&&!t||!!e&&!!t&&e.equals(t)}get hsla(){return this._hsla?this._hsla:hY.fromRGBA(this.rgba)}get hsva(){return this._hsva?this._hsva:hK.fromRGBA(this.rgba)}constructor(e){if(e){if(e instanceof hz)this.rgba=e;else if(e instanceof hY)this._hsla=e,this.rgba=hY.toRGBA(e);else if(e instanceof hK)this._hsva=e,this.rgba=hK.toRGBA(e);else throw Error("Invalid color ctor argument")}else throw Error("Color needs a value")}equals(e){return!!e&&hz.equals(this.rgba,e.rgba)&&hY.equals(this.hsla,e.hsla)&&hK.equals(this.hsva,e.hsva)}getRelativeLuminance(){let e=h$._relativeLuminanceForComponent(this.rgba.r),t=h$._relativeLuminanceForComponent(this.rgba.g),i=h$._relativeLuminanceForComponent(this.rgba.b);return hG(.2126*e+.7152*t+.0722*i,4)}static _relativeLuminanceForComponent(e){let t=e/255;return t<=.03928?t/12.92:Math.pow((t+.055)/1.055,2.4)}isLighter(){let e=(299*this.rgba.r+587*this.rgba.g+114*this.rgba.b)/1e3;return e>=128}isLighterThan(e){let t=this.getRelativeLuminance(),i=e.getRelativeLuminance();return t>i}isDarkerThan(e){let t=this.getRelativeLuminance(),i=e.getRelativeLuminance();return tthis._lines.length)t=this._lines.length,i=this._lines[t-1].length+1,n=!0;else{let e=this._lines[t-1].length+1;i<1?(i=1,n=!0):i>e&&(i=e,n=!0)}return n?{lineNumber:t,column:i}:e}}class h2{constructor(e,t){this._host=e,this._models=Object.create(null),this._foreignModuleFactory=t,this._foreignModule=null}dispose(){this._models=Object.create(null)}_getModel(e){return this._models[e]}_getModels(){let e=[];return Object.keys(this._models).forEach(t=>e.push(this._models[t])),e}acceptNewModel(e){this._models[e.url]=new h1(rl.parse(e.url),e.lines,e.EOL,e.versionId)}acceptModelChanged(e,t){if(!this._models[e])return;let i=this._models[e];i.onEvents(t)}acceptRemovedModel(e){this._models[e]&&delete this._models[e]}computeUnicodeHighlights(e,t,i){return h0(this,void 0,void 0,function*(){let n=this._getModel(e);return n?l8.computeUnicodeHighlights(n,t,i):{ranges:[],hasMore:!1,ambiguousCharacterCount:0,invisibleCharacterCount:0,nonBasicAsciiCharacterCount:0}})}computeDiff(e,t,i,n){return h0(this,void 0,void 0,function*(){let r=this._getModel(e),o=this._getModel(t);return r&&o?h2.computeDiff(r,o,i,n):null})}static computeDiff(e,t,i,n){let r="advanced"===n?hW.getAdvanced():hW.getLegacy(),o=e.getLinesContent(),s=t.getLinesContent(),a=r.computeDiff(o,s,i),l=!(a.changes.length>0)&&this._modelsAreIdentical(e,t);function h(e){return e.map(e=>{var t;return[e.originalRange.startLineNumber,e.originalRange.endLineNumberExclusive,e.modifiedRange.startLineNumber,e.modifiedRange.endLineNumberExclusive,null===(t=e.innerChanges)||void 0===t?void 0:t.map(e=>[e.originalRange.startLineNumber,e.originalRange.startColumn,e.originalRange.endLineNumber,e.originalRange.endColumn,e.modifiedRange.startLineNumber,e.modifiedRange.startColumn,e.modifiedRange.endLineNumber,e.modifiedRange.endColumn])]})}return{identical:l,quitEarly:a.hitTimeout,changes:h(a.changes),moves:a.moves.map(e=>[e.lineRangeMapping.original.startLineNumber,e.lineRangeMapping.original.endLineNumberExclusive,e.lineRangeMapping.modified.startLineNumber,e.lineRangeMapping.modified.endLineNumberExclusive,h(e.changes)])}}static _modelsAreIdentical(e,t){let i=e.getLineCount(),n=t.getLineCount();if(i!==n)return!1;for(let n=1;n<=i;n++){let i=e.getLineContent(n),r=t.getLineContent(n);if(i!==r)return!1}return!0}computeMoreMinimalEdits(e,t,i){return h0(this,void 0,void 0,function*(){let n;let r=this._getModel(e);if(!r)return t;let o=[];for(let{range:e,text:a,eol:l}of t=t.slice(0).sort((e,t)=>{if(e.range&&t.range)return r_.compareRangesUsingStarts(e.range,t.range);let i=e.range?0:1,n=t.range?0:1;return i-n})){var s;if("number"==typeof l&&(n=l),r_.isEmpty(e)&&!a)continue;let t=r.getValueInRange(e);if(t===(a=a.replace(/\r\n|\n|\r/g,r.eol)))continue;if(Math.max(a.length,t.length)>h2._diffLimit){o.push({range:e,text:a});continue}let h=(s=a,new lH(new lP(t),new lP(s)).ComputeDiff(i).changes),u=r.offsetAt(r_.lift(e).getStartPosition());for(let e of h){let t=r.positionAt(u+e.originalStart),i=r.positionAt(u+e.originalStart+e.originalLength),n={text:a.substr(e.modifiedStart,e.modifiedLength),range:{startLineNumber:t.lineNumber,startColumn:t.column,endLineNumber:i.lineNumber,endColumn:i.column}};r.getValueInRange(n.range)!==n.text&&o.push(n)}}return"number"==typeof n&&o.push({eol:n,text:"",range:{startLineNumber:0,startColumn:0,endLineNumber:0,endColumn:0}}),o})}computeLinks(e){return h0(this,void 0,void 0,function*(){let t=this._getModel(e);return t?t&&"function"==typeof t.getLineCount&&"function"==typeof t.getLineContent?lQ.computeLinks(t):[]:null})}computeDefaultDocumentColors(e){return h0(this,void 0,void 0,function*(){let t=this._getModel(e);return t?t&&"function"==typeof t.getValue&&"function"==typeof t.positionAt?function(e){let t=[],i=hQ(e,/\b(rgb|rgba|hsl|hsla)(\([0-9\s,.\%]*\))|(#)([A-Fa-f0-9]{3})\b|(#)([A-Fa-f0-9]{4})\b|(#)([A-Fa-f0-9]{6})\b|(#)([A-Fa-f0-9]{8})\b/gm);if(i.length>0)for(let n of i){let i;let r=n.filter(e=>void 0!==e),o=r[1],s=r[2];if(s){if("rgb"===o){let t=/^\(\s*(25[0-5]|2[0-4][0-9]|1[0-9]{2}|[1-9][0-9]|[0-9])\s*,\s*(25[0-5]|2[0-4][0-9]|1[0-9]{2}|[1-9][0-9]|[0-9])\s*,\s*(25[0-5]|2[0-4][0-9]|1[0-9]{2}|[1-9][0-9]|[0-9])\s*\)$/gm;i=hZ(hq(e,n),hQ(s,t),!1)}else if("rgba"===o){let t=/^\(\s*(25[0-5]|2[0-4][0-9]|1[0-9]{2}|[1-9][0-9]|[0-9])\s*,\s*(25[0-5]|2[0-4][0-9]|1[0-9]{2}|[1-9][0-9]|[0-9])\s*,\s*(25[0-5]|2[0-4][0-9]|1[0-9]{2}|[1-9][0-9]|[0-9])\s*,\s*(0[.][0-9]+|[.][0-9]+|[01][.]|[01])\s*\)$/gm;i=hZ(hq(e,n),hQ(s,t),!0)}else if("hsl"===o){let t=/^\(\s*(36[0]|3[0-5][0-9]|[12][0-9][0-9]|[1-9]?[0-9])\s*,\s*(100|\d{1,2}[.]\d*|\d{1,2})%\s*,\s*(100|\d{1,2}[.]\d*|\d{1,2})%\s*\)$/gm;i=hJ(hq(e,n),hQ(s,t),!1)}else if("hsla"===o){let t=/^\(\s*(36[0]|3[0-5][0-9]|[12][0-9][0-9]|[1-9]?[0-9])\s*,\s*(100|\d{1,2}[.]\d*|\d{1,2})%\s*,\s*(100|\d{1,2}[.]\d*|\d{1,2})%\s*,\s*(0[.][0-9]+|[.][0-9]+|[01][.]|[01])\s*\)$/gm;i=hJ(hq(e,n),hQ(s,t),!0)}else"#"===o&&(i=function(e,t){if(!e)return;let i=h$.Format.CSS.parseHex(t);if(i)return{range:e,color:hj(i.rgba.r,i.rgba.g,i.rgba.b,i.rgba.a)}}(hq(e,n),o+s));i&&t.push(i)}}return t}(t):[]:null})}textualSuggest(e,t,i,n){return h0(this,void 0,void 0,function*(){let r=new nE,o=new RegExp(i,n),s=new Set;n:for(let i of e){let e=this._getModel(i);if(e){for(let i of e.words(o))if(i!==t&&isNaN(Number(i))&&(s.add(i),s.size>h2._suggestionsLimit))break n}}return{words:Array.from(s),duration:r.elapsed()}})}computeWordRanges(e,t,i,n){return h0(this,void 0,void 0,function*(){let r=this._getModel(e);if(!r)return Object.create(null);let o=new RegExp(i,n),s=Object.create(null);for(let e=t.startLineNumber;ethis._host.fhr(e,t));return this._foreignModuleFactory?(this._foreignModule=this._foreignModuleFactory({host:n,getMirrorModels:()=>this._getModels()},t),Promise.resolve((0,oj.$E)(this._foreignModule))):Promise.reject(Error("Unexpected usage"))}fmr(e,t){if(!this._foreignModule||"function"!=typeof this._foreignModule[e])return Promise.reject(Error("Missing requestHandler or method: "+e));try{return Promise.resolve(this._foreignModule[e].apply(this._foreignModule,t))}catch(e){return Promise.reject(e)}}}h2._diffLimit=1e5,h2._suggestionsLimit=1e4,"function"==typeof importScripts&&(globalThis.monaco=rk());let h4=oG("textResourceConfigurationService"),h5=oG("textResourcePropertiesService"),h6=oG("logService");(ey=t0||(t0={}))[ey.Off=0]="Off",ey[ey.Trace=1]="Trace",ey[ey.Debug=2]="Debug",ey[ey.Info=3]="Info",ey[ey.Warning=4]="Warning",ey[ey.Error=5]="Error";let h3=t0.Info;class h9 extends nc{constructor(){super(...arguments),this.level=h3,this._onDidChangeLogLevel=this._register(new nT),this.onDidChangeLogLevel=this._onDidChangeLogLevel.event}setLevel(e){this.level!==e&&(this.level=e,this._onDidChangeLogLevel.fire(this.level))}getLevel(){return this.level}checkLogLevel(e){return this.level!==t0.Off&&this.level<=e}}class h7 extends h9{constructor(e=h3,t=!0){super(),this.useColors=t,this.setLevel(e)}trace(e,...t){this.checkLogLevel(t0.Trace)&&(this.useColors?console.log("%cTRACE","color: #888",e,...t):console.log(e,...t))}debug(e,...t){this.checkLogLevel(t0.Debug)&&(this.useColors?console.log("%cDEBUG","background: #eee; color: #888",e,...t):console.log(e,...t))}info(e,...t){this.checkLogLevel(t0.Info)&&(this.useColors?console.log("%c INFO","color: #33f",e,...t):console.log(e,...t))}warn(e,...t){this.checkLogLevel(t0.Warning)&&(this.useColors?console.log("%c WARN","color: #993",e,...t):console.log(e,...t))}error(e,...t){this.checkLogLevel(t0.Error)&&(this.useColors?console.log("%c ERR","color: #f33",e,...t):console.error(e,...t))}dispose(){}}class h8 extends h9{constructor(e){super(),this.loggers=e,e.length&&this.setLevel(e[0].getLevel())}setLevel(e){for(let t of this.loggers)t.setLevel(e);super.setLevel(e)}trace(e,...t){for(let i of this.loggers)i.trace(e,...t)}debug(e,...t){for(let i of this.loggers)i.debug(e,...t)}info(e,...t){for(let i of this.loggers)i.info(e,...t)}warn(e,...t){for(let i of this.loggers)i.warn(e,...t)}error(e,...t){for(let i of this.loggers)i.error(e,...t)}dispose(){for(let e of this.loggers)e.dispose()}}new sR("logLevel",function(e){switch(e){case t0.Trace:return"trace";case t0.Debug:return"debug";case t0.Info:return"info";case t0.Warning:return"warn";case t0.Error:return"error";case t0.Off:return"off"}}(t0.Info));let ue=oG("ILanguageFeaturesService");var ut=function(e,t){return function(i,n){t(i,n,e)}},ui=function(e,t,i,n){return new(i||(i=Promise))(function(r,o){function s(e){try{l(n.next(e))}catch(e){o(e)}}function a(e){try{l(n.throw(e))}catch(e){o(e)}}function l(e){var t;e.done?r(e.value):((t=e.value)instanceof i?t:new i(function(e){e(t)})).then(s,a)}l((n=n.apply(e,t||[])).next())})};function un(e,t){let i=e.getModel(t);return!(!i||i.isTooLargeForSyncing())}let ur=class extends nc{constructor(e,t,i,n,r){super(),this._modelService=e,this._workerManager=this._register(new us(this._modelService,n)),this._logService=i,this._register(r.linkProvider.register({language:"*",hasAccessToAllModels:!0},{provideLinks:(e,t)=>un(this._modelService,e.uri)?this._workerManager.withWorker().then(t=>t.computeLinks(e.uri)).then(e=>e&&{links:e}):Promise.resolve({links:[]})})),this._register(r.completionProvider.register("*",new uo(this._workerManager,t,this._modelService,n)))}dispose(){super.dispose()}canComputeUnicodeHighlights(e){return un(this._modelService,e)}computedUnicodeHighlights(e,t,i){return this._workerManager.withWorker().then(n=>n.computedUnicodeHighlights(e,t,i))}computeDiff(e,t,i,n){return ui(this,void 0,void 0,function*(){let r=yield this._workerManager.withWorker().then(r=>r.computeDiff(e,t,i,n));if(!r)return null;let o={identical:r.identical,quitEarly:r.quitEarly,changes:s(r.changes),moves:r.moves.map(e=>new ha(new hs(new hi(e[0],e[1]),new hi(e[2],e[3])),s(e[4])))};return o;function s(e){return e.map(e=>{var t;return new hr(new hi(e[0],e[1]),new hi(e[2],e[3]),null===(t=e[4])||void 0===t?void 0:t.map(e=>new ho(new r_(e[0],e[1],e[2],e[3]),new r_(e[4],e[5],e[6],e[7]))))})}})}computeMoreMinimalEdits(e,t,i=!1){if(!(0,sq.Of)(t))return Promise.resolve(void 0);{if(!un(this._modelService,e))return Promise.resolve(t);let n=nE.create(),r=this._workerManager.withWorker().then(n=>n.computeMoreMinimalEdits(e,t,i));return r.finally(()=>this._logService.trace("FORMAT#computeMoreMinimalEdits",e.toString(!0),n.elapsed())),Promise.race([r,ls(1e3).then(()=>t)])}}canNavigateValueSet(e){return un(this._modelService,e)}navigateValueSet(e,t,i){return this._workerManager.withWorker().then(n=>n.navigateValueSet(e,t,i))}canComputeWordRanges(e){return un(this._modelService,e)}computeWordRanges(e,t){return this._workerManager.withWorker().then(i=>i.computeWordRanges(e,t))}};ur=function(e,t,i,n){var r,o=arguments.length,s=o<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,i):n;if("object"==typeof Reflect&&"function"==typeof Reflect.decorate)s=Reflect.decorate(e,t,i,n);else for(var a=e.length-1;a>=0;a--)(r=e[a])&&(s=(o<3?r(s):o>3?r(t,i,s):r(t,i))||s);return o>3&&s&&Object.defineProperty(t,i,s),s}([ut(0,a6),ut(1,h4),ut(2,h6),ut(3,az),ut(4,ue)],ur);class uo{constructor(e,t,i,n){this.languageConfigurationService=n,this._debugDisplayName="wordbasedCompletions",this._workerManager=e,this._configurationService=t,this._modelService=i}provideCompletionItems(e,t){return ui(this,void 0,void 0,function*(){let i=this._configurationService.getValue(e.uri,t,"editor");if(!i.wordBasedSuggestions)return;let n=[];if("currentDocument"===i.wordBasedSuggestionsMode)un(this._modelService,e.uri)&&n.push(e.uri);else for(let t of this._modelService.getModels())un(this._modelService,t.uri)&&(t===e?n.unshift(t.uri):("allDocuments"===i.wordBasedSuggestionsMode||t.getLanguageId()===e.getLanguageId())&&n.push(t.uri));if(0===n.length)return;let r=this.languageConfigurationService.getLanguageConfiguration(e.getLanguageId()).getWordDefinition(),o=e.getWordAtPosition(t),s=o?new r_(t.lineNumber,o.startColumn,t.lineNumber,o.endColumn):r_.fromPositions(t),a=s.setEndPosition(t.lineNumber,t.column),l=yield this._workerManager.withWorker(),h=yield l.textualSuggest(n,null==o?void 0:o.word,r);if(h)return{duration:h.duration,suggestions:h.words.map(e=>({kind:18,label:e,insertText:e,range:{insert:a,replace:s}}))}})}}class us extends nc{constructor(e,t){super(),this.languageConfigurationService=t,this._modelService=e,this._editorWorkerClient=null,this._lastWorkerUsedTime=new Date().getTime();let i=this._register(new lh);i.cancelAndSet(()=>this._checkStopIdleWorker(),Math.round(15e4)),this._register(this._modelService.onModelRemoved(e=>this._checkStopEmptyWorker()))}dispose(){this._editorWorkerClient&&(this._editorWorkerClient.dispose(),this._editorWorkerClient=null),super.dispose()}_checkStopEmptyWorker(){if(!this._editorWorkerClient)return;let e=this._modelService.getModels();0===e.length&&(this._editorWorkerClient.dispose(),this._editorWorkerClient=null)}_checkStopIdleWorker(){if(!this._editorWorkerClient)return;let e=new Date().getTime()-this._lastWorkerUsedTime;e>3e5&&(this._editorWorkerClient.dispose(),this._editorWorkerClient=null)}withWorker(){return this._lastWorkerUsedTime=new Date().getTime(),this._editorWorkerClient||(this._editorWorkerClient=new uu(this._modelService,!1,"editorWorkerService",this.languageConfigurationService)),Promise.resolve(this._editorWorkerClient)}}class ua extends nc{constructor(e,t,i){if(super(),this._syncedModels=Object.create(null),this._syncedModelsLastUsedTime=Object.create(null),this._proxy=e,this._modelService=t,!i){let e=new lh;e.cancelAndSet(()=>this._checkStopModelSync(),Math.round(3e4)),this._register(e)}}dispose(){for(let e in this._syncedModels)nl(this._syncedModels[e]);this._syncedModels=Object.create(null),this._syncedModelsLastUsedTime=Object.create(null),super.dispose()}ensureSyncedResources(e,t){for(let i of e){let e=i.toString();this._syncedModels[e]||this._beginModelSync(i,t),this._syncedModels[e]&&(this._syncedModelsLastUsedTime[e]=new Date().getTime())}}_checkStopModelSync(){let e=new Date().getTime(),t=[];for(let i in this._syncedModelsLastUsedTime){let n=e-this._syncedModelsLastUsedTime[i];n>6e4&&t.push(i)}for(let e of t)this._stopModelSync(e)}_beginModelSync(e,t){let i=this._modelService.getModel(e);if(!i||!t&&i.isTooLargeForSyncing())return;let n=e.toString();this._proxy.acceptNewModel({url:i.uri.toString(),lines:i.getLinesContent(),EOL:i.getEOL(),versionId:i.getVersionId()});let r=new nd;r.add(i.onDidChangeContent(e=>{this._proxy.acceptModelChanged(n.toString(),e)})),r.add(i.onWillDispose(()=>{this._stopModelSync(n)})),r.add(nu(()=>{this._proxy.acceptRemovedModel(n)})),this._syncedModels[n]=r}_stopModelSync(e){let t=this._syncedModels[e];delete this._syncedModels[e],delete this._syncedModelsLastUsedTime[e],nl(t)}}class ul{constructor(e){this._instance=e,this._proxyObj=Promise.resolve(this._instance)}dispose(){this._instance.dispose()}getProxyObject(){return this._proxyObj}}class uh{constructor(e){this._workerClient=e}fhr(e,t){return this._workerClient.fhr(e,t)}}class uu extends nc{constructor(e,t,i,n){super(),this.languageConfigurationService=n,this._disposed=!1,this._modelService=e,this._keepIdleModels=t,this._workerFactory=new lN(i),this._worker=null,this._modelManager=null}fhr(e,t){throw Error("Not implemented!")}_getOrCreateWorker(){if(!this._worker)try{this._worker=this._register(new ly(this._workerFactory,"vs/editor/common/services/editorSimpleWorker",new uh(this)))}catch(e){lf(e),this._worker=new ul(new h2(new uh(this),null))}return this._worker}_getProxy(){return this._getOrCreateWorker().getProxyObject().then(void 0,e=>(lf(e),this._worker=new ul(new h2(new uh(this),null)),this._getOrCreateWorker().getProxyObject()))}_getOrCreateModelManager(e){return this._modelManager||(this._modelManager=this._register(new ua(e,this._modelService,this._keepIdleModels))),this._modelManager}_withSyncedResources(e,t=!1){return ui(this,void 0,void 0,function*(){return this._disposed?Promise.reject(function(){let e=Error(i5);return e.name=e.message,e}()):this._getProxy().then(i=>(this._getOrCreateModelManager(i).ensureSyncedResources(e,t),i))})}computedUnicodeHighlights(e,t,i){return this._withSyncedResources([e]).then(n=>n.computeUnicodeHighlights(e.toString(),t,i))}computeDiff(e,t,i,n){return this._withSyncedResources([e,t],!0).then(r=>r.computeDiff(e.toString(),t.toString(),i,n))}computeMoreMinimalEdits(e,t,i){return this._withSyncedResources([e]).then(n=>n.computeMoreMinimalEdits(e.toString(),t,i))}computeLinks(e){return this._withSyncedResources([e]).then(t=>t.computeLinks(e.toString()))}computeDefaultDocumentColors(e){return this._withSyncedResources([e]).then(t=>t.computeDefaultDocumentColors(e.toString()))}textualSuggest(e,t,i){return ui(this,void 0,void 0,function*(){let n=yield this._withSyncedResources(e),r=i.source,o=rz(i);return n.textualSuggest(e.map(e=>e.toString()),t,r,o)})}computeWordRanges(e,t){return this._withSyncedResources([e]).then(i=>{let n=this._modelService.getModel(e);if(!n)return Promise.resolve(null);let r=this.languageConfigurationService.getLanguageConfiguration(n.getLanguageId()).getWordDefinition(),o=r.source,s=rz(r);return i.computeWordRanges(e.toString(),t,o,s)})}navigateValueSet(e,t,i){return this._withSyncedResources([e]).then(n=>{let r=this._modelService.getModel(e);if(!r)return null;let o=this.languageConfigurationService.getLanguageConfiguration(r.getLanguageId()).getWordDefinition(),s=o.source,a=rz(o);return n.navigateValueSet(e.toString(),t,i,s,a)})}dispose(){super.dispose(),this._disposed=!0}}class ud extends uu{constructor(e,t,i){super(e,i.keepIdleModels||!1,i.label,t),this._foreignModuleId=i.moduleId,this._foreignModuleCreateData=i.createData||null,this._foreignModuleHost=i.host||null,this._foreignProxy=null}fhr(e,t){if(!this._foreignModuleHost||"function"!=typeof this._foreignModuleHost[e])return Promise.reject(Error("Missing method "+e+" or missing main thread foreign host."));try{return Promise.resolve(this._foreignModuleHost[e].apply(this._foreignModuleHost,t))}catch(e){return Promise.reject(e)}}_getForeignProxy(){return this._foreignProxy||(this._foreignProxy=this._getProxy().then(e=>{let t=this._foreignModuleHost?(0,oj.$E)(this._foreignModuleHost):[];return e.loadForeignModule(this._foreignModuleId,this._foreignModuleCreateData,t).then(t=>{this._foreignModuleCreateData=null;let i=(t,i)=>e.fmr(t,i),n=(e,t)=>function(){let i=Array.prototype.slice.call(arguments,0);return t(e,i)},r={};for(let e of t)r[e]=n(e,i);return r})})),this._foreignProxy}getProxy(){return this._getForeignProxy()}withSyncedResources(e){return this._withSyncedResources(e).then(e=>this.getProxy())}}class uc{static getLanguageId(e){return(255&e)>>>0}static getTokenType(e){return(768&e)>>>8}static containsBalancedBrackets(e){return(1024&e)!=0}static getFontStyle(e){return(30720&e)>>>11}static getForeground(e){return(16744448&e)>>>15}static getBackground(e){return(4278190080&e)>>>24}static getClassNameFromMetadata(e){let t=this.getForeground(e),i="mtk"+t,n=this.getFontStyle(e);return 1&n&&(i+=" mtki"),2&n&&(i+=" mtkb"),4&n&&(i+=" mtku"),8&n&&(i+=" mtks"),i}static getInlineStyleFromMetadata(e,t){let i=this.getForeground(e),n=this.getFontStyle(e),r=`color: ${t[i]};`;1&n&&(r+="font-style: italic;"),2&n&&(r+="font-weight: bold;");let o="";return 4&n&&(o+=" underline"),8&n&&(o+=" line-through"),o&&(r+=`text-decoration:${o};`),r}static getPresentationFromMetadata(e){let t=this.getForeground(e),i=this.getFontStyle(e);return{foreground:t,italic:!!(1&i),bold:!!(2&i),underline:!!(4&i),strikethrough:!!(8&i)}}}class ug{static createEmpty(e,t){let i=ug.defaultTokenMetadata,n=new Uint32Array(2);return n[0]=e.length,n[1]=i,new ug(n,e,t)}constructor(e,t,i){this._lineTokensBrand=void 0,this._tokens=e,this._tokensCount=this._tokens.length>>>1,this._text=t,this._languageIdCodec=i}equals(e){return e instanceof ug&&this.slicedEquals(e,0,this._tokensCount)}slicedEquals(e,t,i){if(this._text!==e._text||this._tokensCount!==e._tokensCount)return!1;let n=t<<1,r=n+(i<<1);for(let t=n;t0?this._tokens[e-1<<1]:0}getMetadata(e){let t=this._tokens[(e<<1)+1];return t}getLanguageId(e){let t=this._tokens[(e<<1)+1],i=uc.getLanguageId(t);return this._languageIdCodec.decodeLanguageId(i)}getStandardTokenType(e){let t=this._tokens[(e<<1)+1];return uc.getTokenType(t)}getForeground(e){let t=this._tokens[(e<<1)+1];return uc.getForeground(t)}getClassName(e){let t=this._tokens[(e<<1)+1];return uc.getClassNameFromMetadata(t)}getInlineStyle(e,t){let i=this._tokens[(e<<1)+1];return uc.getInlineStyleFromMetadata(i,t)}getPresentation(e){let t=this._tokens[(e<<1)+1];return uc.getPresentationFromMetadata(t)}getEndOffset(e){return this._tokens[e<<1]}findTokenIndexAtOffset(e){return ug.findIndexInTokensArray(this._tokens,e)}inflate(){return this}sliceAndInflate(e,t,i){return new up(this,e,t,i)}static convertToEndOffset(e,t){let i=e.length>>>1,n=i-1;for(let t=0;t>>1)-1;for(;it&&(n=r)}return i}withInserted(e){if(0===e.length)return this;let t=0,i=0,n="",r=[],o=0;for(;;){let s=to){n+=this._text.substring(o,a.offset);let e=this._tokens[(t<<1)+1];r.push(n.length,e),o=a.offset}n+=a.text,r.push(n.length,a.tokenMetadata),i++}else break}return new ug(new Uint32Array(r),n,this._languageIdCodec)}}ug.defaultTokenMetadata=33587200;class up{constructor(e,t,i,n){this._source=e,this._startOffset=t,this._endOffset=i,this._deltaOffset=n,this._firstTokenIndex=e.findTokenIndexAtOffset(t),this._tokensCount=0;for(let t=this._firstTokenIndex,n=e.getCount();t=i)break;this._tokensCount++}}getMetadata(e){return this._source.getMetadata(this._firstTokenIndex+e)}getLanguageId(e){return this._source.getLanguageId(this._firstTokenIndex+e)}getLineContent(){return this._source.getLineContent().substring(this._startOffset,this._endOffset)}equals(e){return e instanceof up&&this._startOffset===e._startOffset&&this._endOffset===e._endOffset&&this._deltaOffset===e._deltaOffset&&this._source.slicedEquals(e._source,this._firstTokenIndex,this._tokensCount)}getCount(){return this._tokensCount}getForeground(e){return this._source.getForeground(this._firstTokenIndex+e)}getEndOffset(e){let t=this._source.getEndOffset(this._firstTokenIndex+e);return Math.min(this._endOffset,t)-this._startOffset+this._deltaOffset}getClassName(e){return this._source.getClassName(this._firstTokenIndex+e)}getInlineStyle(e,t){return this._source.getInlineStyle(this._firstTokenIndex+e,t)}getPresentation(e){return this._source.getPresentation(this._firstTokenIndex+e)}findTokenIndexAtOffset(e){return this._source.findTokenIndexAtOffset(e+this._startOffset-this._deltaOffset)-this._firstTokenIndex}}class uf{constructor(e,t,i,n){this.startColumn=e,this.endColumn=t,this.className=i,this.type=n,this._lineDecorationBrand=void 0}static _equals(e,t){return e.startColumn===t.startColumn&&e.endColumn===t.endColumn&&e.className===t.className&&e.type===t.type}static equalsArr(e,t){let i=e.length,n=t.length;if(i!==n)return!1;for(let n=0;n=r||(s[a++]=new uf(Math.max(1,t.startColumn-n+1),Math.min(o+1,t.endColumn-n+1),t.className,t.type));return s}static filter(e,t,i,n){if(0===e.length)return[];let r=[],o=0;for(let s=0,a=e.length;st||l.isEmpty()&&(0===a.type||3===a.type))continue;let h=l.startLineNumber===t?l.startColumn:i,u=l.endLineNumber===t?l.endColumn:n;r[o++]=new uf(h,u,a.inlineClassName,a.type)}return r}static _typeCompare(e,t){let i=[2,0,1,3];return i[e]-i[t]}static compare(e,t){if(e.startColumn!==t.startColumn)return e.startColumn-t.startColumn;if(e.endColumn!==t.endColumn)return e.endColumn-t.endColumn;let i=uf._typeCompare(e.type,t.type);return 0!==i?i:e.className!==t.className?e.className0&&this.stopOffsets[0]0&&t=e){this.stopOffsets.splice(n,0,e),this.classNames.splice(n,0,t),this.metadata.splice(n,0,i);break}this.count++}}class uE{static normalize(e,t){if(0===t.length)return[];let i=[],n=new uv,r=0;for(let o=0,s=t.length;o1){let t=e.charCodeAt(a-2);r3(t)&&a--}if(l>1){let t=e.charCodeAt(l-2);r3(t)&&l--}let d=a-1,c=l-2;r=n.consumeLowerThan(d,r,i),0===n.count&&(r=d),n.insert(c,h,u)}return n.consumeLowerThan(1073741824,r,i),i}}class u_{constructor(e,t,i,n){this.endIndex=e,this.type=t,this.metadata=i,this.containsRTL=n,this._linePartBrand=void 0}isWhitespace(){return!!(1&this.metadata)}isPseudoAfter(){return!!(4&this.metadata)}}class uC{constructor(e,t){this.startOffset=e,this.endOffset=t}equals(e){return this.startOffset===e.startOffset&&this.endOffset===e.endOffset}}class uS{constructor(e,t,i,n,r,o,s,a,l,h,u,d,c,g,p,f,m,v,E){this.useMonospaceOptimizations=e,this.canUseHalfwidthRightwardsArrow=t,this.lineContent=i,this.continuesWithWrappedLine=n,this.isBasicASCII=r,this.containsRTL=o,this.fauxIndentLength=s,this.lineTokens=a,this.lineDecorations=l.sort(uf.compare),this.tabSize=h,this.startVisibleColumn=u,this.spaceWidth=d,this.stopRenderingLineAfter=p,this.renderWhitespace="all"===f?4:"boundary"===f?1:"selection"===f?2:"trailing"===f?3:0,this.renderControlCharacters=m,this.fontLigatures=v,this.selectionsOnLine=E&&E.sort((e,t)=>e.startOffset>>16}static getCharIndex(e){return(65535&e)>>>0}constructor(e,t){this.length=e,this._data=new Uint32Array(this.length),this._horizontalOffset=new Uint32Array(this.length)}setColumnInfo(e,t,i,n){this._data[e-1]=(t<<16|i<<0)>>>0,this._horizontalOffset[e-1]=n}getHorizontalOffset(e){return 0===this._horizontalOffset.length?0:this._horizontalOffset[e-1]}charOffsetToPartData(e){return 0===this.length?0:e<0?this._data[0]:e>=this.length?this._data[this.length-1]:this._data[e]}getDomPosition(e){let t=this.charOffsetToPartData(e-1),i=uT.getPartIndex(t),n=uT.getCharIndex(t);return new uy(i,n)}getColumn(e,t){let i=this.partDataToCharOffset(e.partIndex,t,e.charIndex);return i+1}partDataToCharOffset(e,t,i){if(0===this.length)return 0;let n=(e<<16|i<<0)>>>0,r=0,o=this.length-1;for(;r+1>>1,t=this._data[e];if(t===n)return e;t>n?o=e:r=e}if(r===o)return r;let s=this._data[r],a=this._data[o];if(s===n)return r;if(a===n)return o;let l=uT.getPartIndex(s),h=uT.getCharIndex(s),u=uT.getPartIndex(a);return i-h<=(l!==u?t:uT.getCharIndex(a))-i?r:o}}class ub{constructor(e,t,i){this._renderLineOutputBrand=void 0,this.characterMapping=e,this.containsRTL=t,this.containsForeignElements=i}}function uA(e,t){if(0===e.lineContent.length){if(e.lineDecorations.length>0){t.appendString("");let i=0,n=0,r=0;for(let o of e.lineDecorations)(1===o.type||2===o.type)&&(t.appendString(''),1===o.type&&(r|=1,i++),2===o.type&&(r|=2,n++));t.appendString("");let o=new uT(1,i+n);return o.setColumnInfo(1,i,0,0),new ub(o,!1,r)}return t.appendString(""),new ub(new uT(0,0),!1,0)}return function(e,t){let i=e.fontIsMonospace,n=e.canUseHalfwidthRightwardsArrow,r=e.containsForeignElements,o=e.lineContent,s=e.len,a=e.isOverflowing,l=e.overflowingCharCount,h=e.parts,u=e.fauxIndentLength,d=e.tabSize,c=e.startVisibleColumn,g=e.containsRTL,p=e.spaceWidth,f=e.renderSpaceCharCode,m=e.renderWhitespace,v=e.renderControlCharacters,E=new uT(s+1,h.length),_=!1,C=0,S=c,y=0,T=0,b=0;g?t.appendString(''):t.appendString("");for(let e=0,a=h.length;e=u&&(t+=r)}}for(R&&(t.appendString(' style="width:'),t.appendString(String(p*i)),t.appendString('px"')),t.appendASCIICharCode(62);C1?t.appendCharCode(8594):t.appendCharCode(65515);for(let e=2;e<=r;e++)t.appendCharCode(160)}else i=2,r=1,t.appendCharCode(f),t.appendCharCode(8204);y+=i,T+=r,C>=u&&(S+=r)}}else for(t.appendASCIICharCode(62);C=u&&(S+=r)}L?b++:b=0,C>=s&&!_&&a.isPseudoAfter()&&(_=!0,E.setColumnInfo(C+1,e,y,T)),t.appendString("")}return _||E.setColumnInfo(s+1,h.length-1,y,T),a&&(t.appendString(''),t.appendString(rL.NC("showMore","Show more ({0})",l<1024?rL.NC("overflow.chars","{0} chars",l):l<1048576?`${(l/1024).toFixed(1)} KB`:`${(l/1024/1024).toFixed(1)} MB`)),t.appendString("")),t.appendString(""),new ub(E,g,r)}(function(e){let t,i,n;let r=e.lineContent;-1!==e.stopRenderingLineAfter&&e.stopRenderingLineAfter0&&(o[s++]=new u_(n,"",0,!1));let a=n;for(let l=0,h=i.getCount();l=r){let i=!!t&&or(e.substring(a,r));o[s++]=new u_(r,u,0,i);break}let d=!!t&&or(e.substring(a,h));o[s++]=new u_(h,u,0,d),a=h}return o}(r,e.containsRTL,e.lineTokens,e.fauxIndentLength,n);e.renderControlCharacters&&!e.isBasicASCII&&(o=function(e,t){let i=[],n=new u_(0,"",0,!1),r=0;for(let o of t){let t=o.endIndex;for(;rn.endIndex&&(n=new u_(r,o.type,o.metadata,o.containsRTL),i.push(n)),n=new u_(r+1,"mtkcontrol",o.metadata,!1),i.push(n))}r>n.endIndex&&(n=new u_(t,o.type,o.metadata,o.containsRTL),i.push(n))}return i}(r,o)),(4===e.renderWhitespace||1===e.renderWhitespace||2===e.renderWhitespace&&e.selectionsOnLine||3===e.renderWhitespace&&!e.continuesWithWrappedLine)&&(o=function(e,t,i,n){let r;let o=e.continuesWithWrappedLine,s=e.fauxIndentLength,a=e.tabSize,l=e.startVisibleColumn,h=e.useMonospaceOptimizations,u=e.selectionsOnLine,d=1===e.renderWhitespace,c=3===e.renderWhitespace,g=e.renderSpaceWidth!==e.spaceWidth,p=[],f=0,m=0,v=n[0].type,E=n[m].containsRTL,_=n[m].endIndex,C=n.length,S=!1,y=rK(t);-1===y?(S=!0,y=i,r=i):r=rX(t);let T=!1,b=0,A=u&&u[b],R=l%a;for(let e=s;e=A.endOffset&&(b++,A=u&&u[b]),er)o=!0;else if(9===l)o=!0;else if(32===l){if(d){if(T)o=!0;else{let n=e+1e),o&&c&&(o=S||e>r),o&&E&&e>=y&&e<=r&&(o=!1),T){if(!o||!h&&R>=a){if(g){let t=f>0?p[f-1].endIndex:s;for(let 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i=1;i=8234&&e<=8238||e>=8294&&e<=8297||e>=8206&&e<=8207||1564===e}class uw{constructor(e,t,i,n){this._viewportBrand=void 0,this.top=0|e,this.left=0|t,this.width=0|i,this.height=0|n}}class uO{constructor(e,t){this.tabSize=e,this.data=t}}class ux{constructor(e,t,i,n,r,o,s){this._viewLineDataBrand=void 0,this.content=e,this.continuesWithWrappedLine=t,this.minColumn=i,this.maxColumn=n,this.startVisibleColumn=r,this.tokens=o,this.inlineDecorations=s}}class uD{constructor(e,t,i,n,r,o,s,a,l,h){this.minColumn=e,this.maxColumn=t,this.content=i,this.continuesWithWrappedLine=n,this.isBasicASCII=uD.isBasicASCII(i,o),this.containsRTL=uD.containsRTL(i,this.isBasicASCII,r),this.tokens=s,this.inlineDecorations=a,this.tabSize=l,this.startVisibleColumn=h}static isBasicASCII(e,t){return!t||os(e)}static containsRTL(e,t,i){return!t&&!!i&&or(e)}}class uM{constructor(e,t,i){this.range=e,this.inlineClassName=t,this.type=i}}class 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