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docs: update roadmap & text2sql
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README.md
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README.md
@ -84,102 +84,15 @@ Currently, we have released multiple key features, which are listed below to dem
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- ChatDashboard
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- Multi-Agents&Plugins
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- Unified vector storage/indexing of knowledge base
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- Support for unstructured data such as PDF, TXT, Markdown, CSV, DOC, PPT, and WebURL
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- Support for unstructured data
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- PDF
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- TXT
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- Markdown
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- CSV
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- DOC
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- PPT
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- WebURL
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- Multi LLMs Support, Supports multiple large language models, currently supporting
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- 🔥 InternLM(7b,20b)
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- 🔥 Baichuan2(7b,13b)
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- 🔥 Vicuna-v1.5(7b,13b)
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- 🔥 llama-2(7b,13b,70b)
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- WizardLM-v1.2(13b)
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- Vicuna (7b,13b)
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- ChatGLM-6b (int4,int8)
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- ChatGLM2-6b (int4,int8)
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- guanaco(7b,13b,33b)
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- Gorilla(7b,13b)
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- baichuan(7b,13b)
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- Support API Proxy LLMs
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- [x] ChatGPT
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- [x] Tongyi
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- [x] Wenxin
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- [x] Spark
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- [x] MiniMax
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- [x] ChatGLM
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## Introduction
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DB-GPT creates a vast model operating system using [FastChat](https://github.com/lm-sys/FastChat) and offers a large language model powered by vicuna. In addition, we provide private domain knowledge base question-answering capability. Furthermore, we also provide support for additional plugins, and our design natively supports the Auto-GPT plugin.Our vision is to make it easier and more convenient to build applications around databases and llm.
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Is the architecture of the entire DB-GPT shown in the following figure:
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<p align="center">
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<img src="./assets/DB-GPT.png" width="800" />
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</p>
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The core capabilities mainly consist of the following parts:
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1. Knowledge base capability: Supports private domain knowledge base question-answering capability.
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2. Large-scale model management capability: Provides a large model operating environment based on FastChat.
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3. Unified data vector storage and indexing: Provides a uniform way to store and index various data types.
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4. Connection module: Used to connect different modules and data sources to achieve data flow and interaction.
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5. Agent and plugins: Provides Agent and plugin mechanisms, allowing users to customize and enhance the system's behavior.
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6. Prompt generation and optimization: Automatically generates high-quality prompts and optimizes them to improve system response efficiency.
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7. Multi-platform product interface: Supports various client products, such as web, mobile applications, and desktop applications.
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### SubModule
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- [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) Text-to-SQL parsing with LLMs
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- [DB-GPT-Plugins](https://github.com/eosphoros-ai/DB-GPT-Plugins) DB-GPT Plugins, Can run autogpt plugin directly
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- [DB-GPT-Web](https://github.com/eosphoros-ai/DB-GPT-Web) ChatUI for DB-GPT
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## Image
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🌐 [AutoDL Image](https://www.codewithgpu.com/i/eosphoros-ai/DB-GPT/dbgpt)
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## Install
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[**Quickstart**](https://db-gpt.readthedocs.io/en/latest/getting_started/install/deploy/deploy.html)
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### Language Switching
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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).
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## Contribution
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- Please run `black .` before submitting the code. contributing guidelines, [how to contribution](https://github.com/csunny/DB-GPT/blob/main/CONTRIBUTING.md)
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## RoadMap
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<p align="left">
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<img src="./assets/roadmap.jpg" width="800px" />
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</p>
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### KBQA RAG optimization
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- [] KnownledgeGraph
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### Multi Datasource Support
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| DataSource | support | Notes |
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| ------------------------------------------------------------------------------ | ----------- | ------------------------------------------- |
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| [MySQL](https://www.mysql.com/) | Yes | |
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| [PostgresSQL](https://www.postgresql.org/) | Yes | |
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| [Spark](https://github.com/apache/spark) | Yes | |
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| [DuckDB](https://github.com/duckdb/duckdb) | Yes | |
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| [Sqlite](https://github.com/sqlite/sqlite) | Yes | |
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| [MSSQL](https://github.com/microsoft/mssql-jdbc) | Yes | |
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| [ClickHouse](https://github.com/ClickHouse/ClickHouse) | Yes | |
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| [Oracle](https://github.com/oracle) | No | TODO |
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| [Redis](https://github.com/redis/redis) | No | TODO |
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| [MongoDB](https://github.com/mongodb/mongo) | No | TODO |
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| [HBase](https://github.com/apache/hbase) | No | TODO |
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| [Doris](https://github.com/apache/doris) | No | TODO |
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| [DB2](https://github.com/IBM/Db2) | No | TODO |
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| [Couchbase](https://github.com/couchbase) | No | TODO |
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| [Elasticsearch](https://github.com/elastic/elasticsearch) | No | TODO |
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| [OceanBase](https://github.com/OceanBase) | No | TODO |
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| [TiDB](https://github.com/pingcap/tidb) | No | TODO |
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| [StarRocks](https://github.com/StarRocks/starrocks) | No | TODO |
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### Multi-Models And vLLM
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- [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
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- [baichuan2-7b/baichuan2-13b](https://huggingface.co/baichuan-inc)
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- [internlm/internlm-chat-7b](https://huggingface.co/internlm/internlm-chat-7b)
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@ -216,61 +129,156 @@ The core capabilities mainly consist of the following parts:
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- [Spicyboros](https://huggingface.co/jondurbin/spicyboros-7b-2.2?not-for-all-audiences=true) + [airoboros 2.2](https://huggingface.co/jondurbin/airoboros-l2-13b-2.2)
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- [VMware's OpenLLaMa OpenInstruct](https://huggingface.co/VMware/open-llama-7b-open-instruct)
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- Support API Proxy LLMs
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- [x] ChatGPT
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- [x] Tongyi
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- [x] Wenxin
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- [x] ChatGLM
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- Support Datasources
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| DataSource | support | Notes |
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| ------------------------------------------------------------------------------ | ----------- | ------------------------------------------- |
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| [MySQL](https://www.mysql.com/) | Yes | |
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| [PostgresSQL](https://www.postgresql.org/) | Yes | |
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| [Spark](https://github.com/apache/spark) | Yes | |
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| [DuckDB](https://github.com/duckdb/duckdb) | Yes | |
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| [Sqlite](https://github.com/sqlite/sqlite) | Yes | |
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| [MSSQL](https://github.com/microsoft/mssql-jdbc) | Yes | |
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| [ClickHouse](https://github.com/ClickHouse/ClickHouse) | Yes | |
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| [Oracle](https://github.com/oracle) | No | TODO |
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| [Redis](https://github.com/redis/redis) | No | TODO |
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| [MongoDB](https://github.com/mongodb/mongo) | No | TODO |
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| [HBase](https://github.com/apache/hbase) | No | TODO |
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| [Doris](https://github.com/apache/doris) | No | TODO |
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| [DB2](https://github.com/IBM/Db2) | No | TODO |
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| [Couchbase](https://github.com/couchbase) | No | TODO |
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| [Elasticsearch](https://github.com/elastic/elasticsearch) | No | TODO |
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| [OceanBase](https://github.com/OceanBase) | No | TODO |
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| [TiDB](https://github.com/pingcap/tidb) | No | TODO |
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| [StarRocks](https://github.com/StarRocks/starrocks) | No | TODO |
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## Introduction
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Is the architecture of the entire DB-GPT shown in the following figure:
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<p align="center">
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<img src="./assets/DB-GPT.png" width="800" />
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</p>
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The core capabilities mainly consist of the following parts:
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1. Multi-Models: Support multi-LLMs, such as LLaMA/LLaMA2、CodeLLaMA、ChatGLM, QWen、Vicuna and proxy model ChatGPT、Baichuan、tongyi、wenxin etc
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2. Knowledge Based QA: You can perform high-quality intelligent Q&A based on local documents such as pdf, word, excel and other data.
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3. Embedding: Unified data vector storage and indexing, Embed data as vectors and store them in vector databases, providing content similarity search.
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4. Multi-Datasources: Used to connect different modules and data sources to achieve data flow and interaction.
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5. Multi-Agents: Provides Agent and plugin mechanisms, allowing users to customize and enhance the system's behavior.
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6. Privacy & Secure: You can be assured that there is no risk of data leakage, and your data is 100% private and secure.
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7. Text2SQL: We enhance the Text-to-SQL performance by applying Supervised Fine-Tuning (SFT) on large language models
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### SubModule
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- [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.
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- [DB-GPT-Plugins](https://github.com/eosphoros-ai/DB-GPT-Plugins) DB-GPT Plugins, Can run autogpt plugin directly
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- [DB-GPT-Web](https://github.com/eosphoros-ai/DB-GPT-Web) ChatUI for DB-GPT
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## Image
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🌐 [AutoDL Image](https://www.codewithgpu.com/i/eosphoros-ai/DB-GPT/dbgpt)
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## Install
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[**Quickstart**](https://db-gpt.readthedocs.io/en/latest/getting_started/install/deploy/deploy.html)
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### Language Switching
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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).
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## Contribution
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- Please run `black .` before submitting the code. contributing guidelines, [how to contribution](https://github.com/csunny/DB-GPT/blob/main/CONTRIBUTING.md)
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## RoadMap
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<p align="left">
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<img src="./assets/roadmap.jpg" width="800px" />
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</p>
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### KBQA RAG optimization
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- [x] Multi Documents
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- [x] PDF
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- [x] Excel, csv
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- [x] Word
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- [x] Text
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- [x] MarkDown
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- [ ] Code
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- [ ] Images
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- [x]RAG
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- [ ]KnownledgeGraph
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### Multi Datasource Support
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- Multi Datasource Support
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- [x]MySQL
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- [x]PostgresSQL
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- [x]Spark
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- [x]DuckDB
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- [x]Sqlite
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- [x]MSSQL
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- [x]ClickHouse
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- [ ]Oracle
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- [ ]Redis
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- [ ]MongoDB
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- [ ]HBase
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- [ ]Doris
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- [ ]DB2
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- [ ]Couchbase
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- [ ]Elasticsearch
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- [ ]OceanBase
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- [ ]TiDB
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- [ ]StarRocks
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### Multi-Models And vLLM
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- [x] [cluster deployment](https://db-gpt.readthedocs.io/en/latest/getting_started/install/cluster/vms/index.html)
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- [x] [fastchat support](https://github.com/lm-sys/FastChat)
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- [x] [vLLM support](https://db-gpt.readthedocs.io/en/latest/getting_started/install/llm/vllm/vllm.html)
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### Agents market and Plugins
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- multi-agents framework
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- custom plugin development
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- plugin market
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- [x] multi-agents framework
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- [x] custom plugin development
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- [ ] plugin market
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### Cost and Observability
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- [x] [debugging](https://db-gpt.readthedocs.io/en/latest/getting_started/observability.html)
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- [ ] Observability
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### Text2SQL Finetune
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- support llms
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- [x]LLaMA
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- [x]LLaMA-2
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- [x]BLOOM
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- [x]BLOOMZ
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- [x]Falcon
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- [x]Baichuan
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- [x]Baichuan2
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- [x]InternLM
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- [x]Qwen
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- [x]XVERSE
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- [x]ChatGLM2
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| LLMs | Size | Module | Template |
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| -------------------------------------------------------- | --------------------------- | ----------------- |----------|
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| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - |
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| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 |
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| [BLOOM](https://huggingface.co/bigscience/bloom) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
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| [BLOOMZ](https://huggingface.co/bigscience/bloomz) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
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| [Falcon](https://huggingface.co/tiiuae/falcon-7b) | 7B/40B | query_key_value | - |
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| [Baichuan](https://github.com/baichuan-inc/baichuan-13B) | 7B/13B | W_pack | baichuan |
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| [Baichuan2](https://github.com/baichuan-inc/Baichuan2) | 7B/13B | W_pack | baichuan2 |
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| [InternLM](https://github.com/InternLM/InternLM) | 7B | q_proj,v_proj | intern |
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| [Qwen](https://github.com/QwenLM/Qwen-7B) | 7B | c_attn | chatml |
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| [XVERSE](https://github.com/xverse-ai/XVERSE-13B) | 13B | q_proj,v_proj | xverse |
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| [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B) | 6B | query_key_value | chatglm2 |
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- SFT Accuracy
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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!
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Datasets
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| Datasets | License | Link |
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| ---------------------- | ------------ | -------------------------------------------------------------------------------------------------------------------- |
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| academic | Not Found | [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) |
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| advising | CC-BY-4.0 | [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) |
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| atis | Not Found | [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) |
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| restaurants | Not Found | [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) |
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| scholar | Not Found | [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) |
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| imdb | Not Found | [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) |
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| yelp | Not Found | [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) |
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| criteria2sql | Apache-2.0 | [https://github.com/xiaojingyu92/Criteria2SQL](https://github.com/xiaojingyu92/Criteria2SQL) |
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| css | CC-BY-4.0 | [https://huggingface.co/datasets/zhanghanchong/css](https://huggingface.co/datasets/zhanghanchong/css) |
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| eICU | CC-BY-4.0 | [https://github.com/glee4810/EHRSQL](https://github.com/glee4810/EHRSQL) |
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| mimic_iii | CC-BY-4.0 | [https://github.com/glee4810/EHRSQL](https://github.com/glee4810/EHRSQL) |
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| geonucleardata | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
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| greatermanchestercrime | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
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| studentmathscore | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
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| thehistoryofbaseball | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
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| uswildfires | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
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| whatcdhiphop | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
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| worldsoccerdatabase | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
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| pesticide | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
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| mimicsql_data | MIT | [https://github.com/wangpinggl/TREQS](https://github.com/wangpinggl/TREQS) |
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| nvbench | MIT | [https://github.com/TsinghuaDatabaseGroup/nvBench](https://github.com/TsinghuaDatabaseGroup/nvBench) |
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| sede | Apache-2.0 | [https://github.com/hirupert/sede](https://github.com/hirupert/sede) |
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| spider | CC-BY-SA-4.0 | [https://huggingface.co/datasets/spider](https://huggingface.co/datasets/spider) |
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| sql_create_context | CC-BY-4.0 | [https://huggingface.co/datasets/b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context) |
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| squall | CC-BY-SA-4.0 | [https://github.com/tzshi/squall](https://github.com/tzshi/squall) |
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| wikisql | BSD 3-Clause | [https://github.com/salesforce/WikiSQL](https://github.com/salesforce/WikiSQL) |
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| BIRD | Not Found | https://bird-bench.github.io/ |
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| CHASE | MIT LICENSE | https://xjtu-intsoft.github.io/chase/ |
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| cosql | Not Found | https://yale-lily.github.io/cosql/ |
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| name | Execution Accuracy | reference |
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| ----------------------------------| ------------------ | ------------------------------------------------------------------------------------------------------------------------------ |
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| **GPT-4** | **0.762** | [numbersstation-eval-res](https://www.numbersstation.ai/post/nsql-llama-2-7b) |
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| ChatGPT | 0.728 | [numbersstation-eval-res](https://www.numbersstation.ai/post/nsql-llama-2-7b) |
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| **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 |
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| 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 |
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| wizardcoder | 0.610 | [text-to-sql-wizardcoder](https://github.com/cuplv/text-to-sql-wizardcoder/tree/main) |
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| CodeLlama-13b-Instruct-hf | 0.556 | eval in this project default param |
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| 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 |
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[More Information about Text2SQL finetune](https://github.com/eosphoros-ai/DB-GPT-Hub)
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|
||||
173
README.zh.md
173
README.zh.md
@ -117,22 +117,75 @@ 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
|
||||
- 插件模型
|
||||
- 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
|
||||
- 多模型支持
|
||||
- 多模型支持与管理
|
||||
- 支持多种大语言模型, 当前已支持如下模型:
|
||||
- 🔥 InternLM(7b,20b)
|
||||
- 🔥 Baichuan2(7b,13b)
|
||||
- 🔥 Vicuna-v1.5(7b,13b)
|
||||
- 🔥 llama-2(7b,13b,70b)
|
||||
- WizardLM-v1.2(13b)
|
||||
- Vicuna (7b,13b)
|
||||
- ChatGLM-6b (int4,int8)
|
||||
- ChatGLM2-6b (int4,int8)
|
||||
- guanaco(7b,13b,33b)
|
||||
- Gorilla(7b,13b)
|
||||
- baichuan(7b,13b)
|
||||
- [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
|
||||
- [baichuan2-7b/baichuan2-13b](https://huggingface.co/baichuan-inc)
|
||||
- [internlm/internlm-chat-7b](https://huggingface.co/internlm/internlm-chat-7b)
|
||||
- [Qwen/Qwen-7B-Chat/Qwen-14B-Chat](https://huggingface.co/Qwen/)
|
||||
- [Vicuna](https://huggingface.co/Tribbiani/vicuna-13b)
|
||||
- [BlinkDL/RWKV-4-Raven](https://huggingface.co/BlinkDL/rwkv-4-raven)
|
||||
- [camel-ai/CAMEL-13B-Combined-Data](https://huggingface.co/camel-ai/CAMEL-13B-Combined-Data)
|
||||
- [databricks/dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b)
|
||||
- [FreedomIntelligence/phoenix-inst-chat-7b](https://huggingface.co/FreedomIntelligence/phoenix-inst-chat-7b)
|
||||
- [h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b)
|
||||
- [lcw99/polyglot-ko-12.8b-chang-instruct-chat](https://huggingface.co/lcw99/polyglot-ko-12.8b-chang-instruct-chat)
|
||||
- [lmsys/fastchat-t5-3b-v1.0](https://huggingface.co/lmsys/fastchat-t5)
|
||||
- [mosaicml/mpt-7b-chat](https://huggingface.co/mosaicml/mpt-7b-chat)
|
||||
- [Neutralzz/BiLLa-7B-SFT](https://huggingface.co/Neutralzz/BiLLa-7B-SFT)
|
||||
- [nomic-ai/gpt4all-13b-snoozy](https://huggingface.co/nomic-ai/gpt4all-13b-snoozy)
|
||||
- [NousResearch/Nous-Hermes-13b](https://huggingface.co/NousResearch/Nous-Hermes-13b)
|
||||
- [openaccess-ai-collective/manticore-13b-chat-pyg](https://huggingface.co/openaccess-ai-collective/manticore-13b-chat-pyg)
|
||||
- [OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5](https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5)
|
||||
- [project-baize/baize-v2-7b](https://huggingface.co/project-baize/baize-v2-7b)
|
||||
- [Salesforce/codet5p-6b](https://huggingface.co/Salesforce/codet5p-6b)
|
||||
- [StabilityAI/stablelm-tuned-alpha-7b](https://huggingface.co/stabilityai/stablelm-tuned-alpha-7b)
|
||||
- [THUDM/chatglm-6b](https://huggingface.co/THUDM/chatglm-6b)
|
||||
- [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b)
|
||||
- [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b)
|
||||
- [timdettmers/guanaco-33b-merged](https://huggingface.co/timdettmers/guanaco-33b-merged)
|
||||
- [togethercomputer/RedPajama-INCITE-7B-Chat](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Chat)
|
||||
- [WizardLM/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)
|
||||
- [baichuan-inc/baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B)
|
||||
- [HuggingFaceH4/starchat-beta](https://huggingface.co/HuggingFaceH4/starchat-beta)
|
||||
- [FlagAlpha/Llama2-Chinese-13b-Chat](https://huggingface.co/FlagAlpha/Llama2-Chinese-13b-Chat)
|
||||
- [BAAI/AquilaChat-7B](https://huggingface.co/BAAI/AquilaChat-7B)
|
||||
- [all models of OpenOrca](https://huggingface.co/Open-Orca)
|
||||
- [Spicyboros](https://huggingface.co/jondurbin/spicyboros-7b-2.2?not-for-all-audiences=true) + [airoboros 2.2](https://huggingface.co/jondurbin/airoboros-l2-13b-2.2)
|
||||
- [VMware's OpenLLaMa OpenInstruct](https://huggingface.co/VMware/open-llama-7b-open-instruct)
|
||||
|
||||
- 支持在线代理模型
|
||||
- [x] ChatGPT
|
||||
- [x] Tongyi
|
||||
- [x] Wenxin
|
||||
- [x] ChatGLM
|
||||
|
||||
|
||||
- 支持数据源
|
||||
| DataSource | support | Notes |
|
||||
| ------------------------------------------------------------------------------ | ----------- | ------------------------------------------- |
|
||||
| [MySQL](https://www.mysql.com/) | Yes | |
|
||||
| [PostgresSQL](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 | |
|
||||
| [MSSQL](https://github.com/microsoft/mssql-jdbc) | Yes | |
|
||||
| [ClickHouse](https://github.com/ClickHouse/ClickHouse) | Yes | |
|
||||
| [Oracle](https://github.com/oracle) | No | TODO |
|
||||
| [Redis](https://github.com/redis/redis) | No | TODO |
|
||||
| [MongoDB](https://github.com/mongodb/mongo) | No | TODO |
|
||||
| [HBase](https://github.com/apache/hbase) | No | TODO |
|
||||
| [Doris](https://github.com/apache/doris) | No | TODO |
|
||||
| [DB2](https://github.com/IBM/Db2) | No | TODO |
|
||||
| [Couchbase](https://github.com/couchbase) | No | TODO |
|
||||
| [Elasticsearch](https://github.com/elastic/elasticsearch) | No | TODO |
|
||||
| [OceanBase](https://github.com/OceanBase) | No | TODO |
|
||||
| [TiDB](https://github.com/pingcap/tidb) | No | TODO |
|
||||
| [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，并提供 vicuna 作为基础的大语言模型。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。
|
||||
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
|
||||
|
||||
@ -141,16 +194,16 @@ DB-GPT基于 [FastChat](https://github.com/lm-sys/FastChat) 构建大模型运
|
||||
</p>
|
||||
|
||||
核心能力主要有以下几个部分。
|
||||
1. 知识库能力：支持私域知识库问答能力
|
||||
2. 大模型管理能力：基于FastChat提供一个大模型的运营环境。
|
||||
3. 统一的数据向量化存储与索引：提供一种统一的方式来存储和索引各种数据类型。
|
||||
4. 连接模块：用于连接不同的模块和数据源，实现数据的流转和交互。
|
||||
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. Prompt自动生成与优化：自动化生成高质量的Prompt，并进行优化，提高系统的响应效率。
|
||||
7. 多端产品界面：支持多种不同的客户端产品，例如Web、移动应用和桌面应用等。
|
||||
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
|
||||
|
||||
### 子模块
|
||||
- [DB-GPT-Hub](https://github.com/csunny/DB-GPT-Hub) 使用 LLM 进行文本到 SQL 解析
|
||||
- [DB-GPT-Hub](https://github.com/csunny/DB-GPT-Hub) 通过微调来持续提升Text2SQL效果
|
||||
- [DB-GPT-Plugins](https://github.com/csunny/DB-GPT-Plugins) DB-GPT 插件仓库, 兼容Auto-GPT
|
||||
- [DB-GPT-Web](https://github.com/csunny/DB-GPT-Web) 多端交互前端界面
|
||||
|
||||
@ -194,6 +247,84 @@ The MIT License (MIT)
|
||||
<img src="./assets/roadmap.jpg" width="800px" />
|
||||
</p>
|
||||
|
||||
### 知识库RAG检索优化
|
||||
- [x] Multi Documents
|
||||
- [x] PDF
|
||||
- [x] Excel, csv
|
||||
- [x] Word
|
||||
- [x] Text
|
||||
- [x] MarkDown
|
||||
- [ ] Code
|
||||
- [ ] Images
|
||||
- [x]RAG
|
||||
- [ ]KnownledgeGraph
|
||||
|
||||
### 多数据源支持
|
||||
|
||||
- 支持数据源
|
||||
- [x]MySQL
|
||||
- [x]PostgresSQL
|
||||
- [x]Spark
|
||||
- [x]DuckDB
|
||||
- [x]Sqlite
|
||||
- [x]MSSQL
|
||||
- [x]ClickHouse
|
||||
- [ ]Oracle
|
||||
- [ ]Redis
|
||||
- [ ]MongoDB
|
||||
- [ ]HBase
|
||||
- [ ]Doris
|
||||
- [ ]DB2
|
||||
- [ ]Couchbase
|
||||
- [ ]Elasticsearch
|
||||
- [ ]OceanBase
|
||||
- [ ]TiDB
|
||||
- [ ]StarRocks
|
||||
|
||||
### 多模型管理与推理优化
|
||||
- [x] [集群部署](https://db-gpt.readthedocs.io/en/latest/getting_started/install/cluster/vms/index.html)
|
||||
- [x] [fastchat支持](https://github.com/lm-sys/FastChat)
|
||||
- [x] [vLLM 支持](https://db-gpt.readthedocs.io/en/latest/getting_started/install/llm/vllm/vllm.html)
|
||||
|
||||
### Agents与插件市场
|
||||
- [x] 多Agents框架
|
||||
- [x] 自定义Agents
|
||||
- [ ] 插件市场
|
||||
|
||||
### 成本与可观测性
|
||||
- [x] [debugging](https://db-gpt.readthedocs.io/en/latest/getting_started/observability.html)
|
||||
- [ ] 可观测性
|
||||
- [ ] 推理预算
|
||||
|
||||
### Text2SQL微调
|
||||
- support llms
|
||||
- [x]LLaMA
|
||||
- [x]LLaMA-2
|
||||
- [x]BLOOM
|
||||
- [x]BLOOMZ
|
||||
- [x]Falcon
|
||||
- [x]Baichuan
|
||||
- [x]Baichuan2
|
||||
- [x]InternLM
|
||||
- [x]Qwen
|
||||
- [x]XVERSE
|
||||
- [x]ChatGLM2
|
||||
|
||||
- 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
|
||||
|
||||
<p align="center">
|
||||
|
||||
@ -34,7 +34,7 @@ def tongyi_generate_stream(
|
||||
elif message.role == ModelMessageRoleType.SYSTEM:
|
||||
history.append({"role": "system", "content": message.content})
|
||||
elif message.role == ModelMessageRoleType.AI:
|
||||
history.append({"role": "assistant", "content": message.content})
|
||||
history.append({"role": "system", "content": message.content})
|
||||
else:
|
||||
pass
|
||||
|
||||
@ -49,7 +49,7 @@ def tongyi_generate_stream(
|
||||
history.remove(last_user_input)
|
||||
history.append(last_user_input)
|
||||
|
||||
print("history", history)
|
||||
print(history)
|
||||
gen = Generation()
|
||||
res = gen.call(
|
||||
proxyllm_backend,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user