mirror of
https://github.com/csunny/DB-GPT.git
synced 2025-07-23 04:12:13 +00:00
docs: readme update & contact (#1097)
This commit is contained in:
parent
4f833634df
commit
1484981b72
192
README.md
192
README.md
@ -33,42 +33,71 @@
|
||||
</p>
|
||||
|
||||
|
||||
[**简体中文**](README.zh.md) | [**Discord**](https://discord.gg/7uQnPuveTY) | [**Documents**](https://docs.dbgpt.site) | [**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) | [**Paper**](https://arxiv.org/pdf/2312.17449.pdf)
|
||||
[**简体中文**](README.zh.md) | [**Discord**](https://discord.gg/7uQnPuveTY) | [**Documents**](https://docs.dbgpt.site) | [**微信**](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) | [**Paper**](https://arxiv.org/pdf/2312.17449.pdf)
|
||||
|
||||
</div>
|
||||
|
||||
## What is DB-GPT?
|
||||
|
||||
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:
|
||||
DB-GPT is an open-source, data-domain large model framework. Its purpose is to build the infrastructure for the large model domain by developing a variety of technical capabilities, including multi-model management, Text2SQL performance optimization, RAG framework and optimization, and Multi-Agents framework collaboration. These capabilities aim to simplify and facilitate the construction of large model applications around databases.
|
||||
|
||||
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.
|
||||
In the Data 3.0 era, based on models and databases, enterprises and developers can build their own bespoke applications with less code.
|
||||
|
||||
### Data Agents
|
||||

|
||||
|
||||
## Contents
|
||||
- [Install](#install)
|
||||
- [Demo](#demo)
|
||||
- [Introduction](#introduction)
|
||||
- [Install](#install)
|
||||
- [Features](#features)
|
||||
- [Contribution](#contribution)
|
||||
- [Roadmap](#roadmap)
|
||||
- [Contact](#contact-information)
|
||||
|
||||
[DB-GPT Youtube Video](https://www.youtube.com/watch?v=f5_g0OObZBQ)
|
||||
## Introduction
|
||||
The architecture of DB-GPT is shown in the following figure:
|
||||
|
||||
## Demo
|
||||
##### Chat Data
|
||||

|
||||
<p align="center">
|
||||
<img src="./assets/dbgpt.png" width="800" />
|
||||
</p>
|
||||
|
||||
##### Chat Excel
|
||||

|
||||
The core capabilities include the following parts:
|
||||
|
||||
- **RAG (Retrieval Augmented Generation)**: RAG is currently the most practically implemented and urgently needed domain. DB-GPT has already implemented a framework based on RAG, allowing users to build knowledge-based applications using the RAG capabilities of DB-GPT.
|
||||
|
||||
- **GBI (Generative Business Intelligence)**: Generative BI is one of the core capabilities of the DB-GPT project, providing the foundational data intelligence technology to build enterprise report analysis and business insights.
|
||||
|
||||
- **Fine-tuning Framework**: Model fine-tuning is an indispensable capability for any enterprise to implement in vertical and niche domains. DB-GPT provides a complete fine-tuning framework that integrates seamlessly with the DB-GPT project. In recent fine-tuning efforts, an accuracy rate based on the Spider dataset has been achieved at 82.5%.
|
||||
|
||||
- **Data-Driven Multi-Agents Framework**: DB-GPT offers a data-driven self-evolving fine-tuning framework, aiming to continuously make decisions and execute based on data.
|
||||
|
||||
- **Data Factory**: The Data Factory is mainly about cleaning and processing trustworthy knowledge and data in the era of large models.
|
||||
|
||||
- **Data Sources**: Integrating various data sources to seamlessly connect production business data to the core capabilities of DB-GPT.
|
||||
|
||||
### SubModule
|
||||
- [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).
|
||||
|
||||
#### Text2SQL Finetune
|
||||
- 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 Accuracy
|
||||
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)
|
||||
|
||||
- [DB-GPT-Plugins](https://github.com/eosphoros-ai/DB-GPT-Plugins) DB-GPT Plugins that can run Auto-GPT plugin directly
|
||||
- [GPT-Vis](https://github.com/eosphoros-ai/GPT-Vis) Visualization protocol
|
||||
|
||||
## Install
|
||||

|
||||
@ -120,26 +149,7 @@ At present, we have introduced several key features to showcase our current capa
|
||||
- Support Datasources
|
||||
- [Datasources](http://docs.dbgpt.site/docs/modules/connections)
|
||||
|
||||
## Introduction
|
||||
The architecture of DB-GPT is shown in the following figure:
|
||||
|
||||
<p align="center">
|
||||
<img src="./assets/DB-GPT.png" width="800" />
|
||||
</p>
|
||||
|
||||
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 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)
|
||||
@ -151,106 +161,8 @@ The core capabilities primarily consist of the following components:
|
||||
## Contribution
|
||||
|
||||
- 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)
|
||||
- To check detailed guidelines for new contributions, please refer [how to contribute](https://github.com/eosphoros-ai/DB-GPT/blob/main/CONTRIBUTING.md)
|
||||
|
||||
## RoadMap
|
||||
|
||||
<p align="left">
|
||||
<img src="./assets/roadmap.jpg" width="800px" />
|
||||
</p>
|
||||
|
||||
### KBQA RAG optimization
|
||||
- [x] Multi Documents
|
||||
- [x] PDF
|
||||
- [x] Excel, CSV
|
||||
- [x] Word
|
||||
- [x] Text
|
||||
- [x] MarkDown
|
||||
- [ ] Code
|
||||
- [ ] Images
|
||||
|
||||
- [x] RAG
|
||||
- [ ] Graph Database
|
||||
- [ ] Neo4j Graph
|
||||
- [ ] Nebula Graph
|
||||
- [x] Multi-Vector Database
|
||||
- [x] Chroma
|
||||
- [x] Milvus
|
||||
- [x] Weaviate
|
||||
- [x] PGVector
|
||||
- [ ] Elasticsearch
|
||||
- [ ] ClickHouse
|
||||
- [ ] Faiss
|
||||
|
||||
- [ ] Testing and Evaluation Capability Building
|
||||
- [ ] Knowledge QA datasets
|
||||
- [ ] Question collection [easy, medium, hard]:
|
||||
- [ ] Scoring mechanism
|
||||
- [ ] Testing and evaluation using Excel + DB datasets
|
||||
|
||||
### Multi Datasource Support
|
||||
|
||||
- Multi Datasource Support
|
||||
- [x] MySQL
|
||||
- [x] PostgreSQL
|
||||
- [x] Spark
|
||||
- [x] DuckDB
|
||||
- [x] Sqlite
|
||||
- [x] MSSQL
|
||||
- [x] ClickHouse
|
||||
- [ ] Oracle
|
||||
- [ ] Redis
|
||||
- [ ] MongoDB
|
||||
- [ ] HBase
|
||||
- [x] Doris
|
||||
- [ ] DB2
|
||||
- [ ] Couchbase
|
||||
- [ ] Elasticsearch
|
||||
- [ ] OceanBase
|
||||
- [ ] TiDB
|
||||
- [ ] StarRocks
|
||||
|
||||
### Multi-Models And vLLM
|
||||
- [x] [Cluster Deployment](https://docs.dbgpt.site/docs/installation/model_service/cluster)
|
||||
- [x] [Fastchat Support](https://github.com/lm-sys/FastChat)
|
||||
- [x] [vLLM Support](https://docs.dbgpt.site/docs/installation/advanced_usage/vLLM_inference)
|
||||
- [ ] Cloud-native environment and support for Ray environment
|
||||
- [ ] Service Registry(eg:nacos)
|
||||
- [ ] Compatibility with OpenAI's interfaces
|
||||
- [ ] Expansion and optimization of embedding models
|
||||
|
||||
### Agents market and Plugins
|
||||
- [x] multi-agents framework
|
||||
- [x] custom plugin development
|
||||
- [x] plugin market
|
||||
- [ ] Integration with CoT
|
||||
- [ ] Enrich plugin sample library
|
||||
- [ ] Support for AutoGPT protocol
|
||||
- [ ] Integration of multi-agents and visualization capabilities, defining LLM+Vis new standards
|
||||
|
||||
### Cost and Observability
|
||||
- [x] [debugging](https://docs.dbgpt.site/docs/application_manual/advanced_tutorial/debugging)
|
||||
- [ ] Observability
|
||||
- [ ] cost & budgets
|
||||
|
||||
### Text2SQL Finetune
|
||||
- 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 Accuracy
|
||||
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)
|
||||
|
||||
## Licence
|
||||
The MIT License (MIT)
|
||||
@ -272,8 +184,4 @@ If you find `DB-GPT` useful for your research or development, please cite the fo
|
||||
We are working on building a community, if you have any ideas for building the community, feel free to contact us.
|
||||
[](https://discord.gg/7uQnPuveTY)
|
||||
|
||||
<p align="center">
|
||||
<img src="./assets/wechat.jpg" width="300px" />
|
||||
</p>
|
||||
|
||||
[](https://star-history.com/#csunny/DB-GPT)
|
||||
|
101
README.zh.md
101
README.zh.md
@ -8,19 +8,19 @@
|
||||
<div align="center">
|
||||
<p>
|
||||
<a href="https://github.com/eosphoros-ai/DB-GPT">
|
||||
<img alt="stars" src="https://img.shields.io/github/stars/csunny/db-gpt?style=social" />
|
||||
<img alt="stars" src="https://img.shields.io/github/stars/eosphoros-ai/db-gpt?style=social" />
|
||||
</a>
|
||||
<a href="https://github.com/eosphoros-ai/DB-GPT">
|
||||
<img alt="forks" src="https://img.shields.io/github/forks/csunny/db-gpt?style=social" />
|
||||
<img alt="forks" src="https://img.shields.io/github/forks/eosphoros-ai/db-gpt?style=social" />
|
||||
</a>
|
||||
<a href="https://opensource.org/licenses/MIT">
|
||||
<img alt="License: MIT" src="https://img.shields.io/badge/License-MIT-yellow.svg" />
|
||||
</a>
|
||||
<a href="https://github.com/eosphoros-ai/DB-GPT/releases">
|
||||
<img alt="Release Notes" src="https://img.shields.io/github/release/csunny/DB-GPT" />
|
||||
<img alt="Release Notes" src="https://img.shields.io/github/release/eosphoros-ai/DB-GPT" />
|
||||
</a>
|
||||
<a href="https://github.com/eosphoros-ai/DB-GPT/issues">
|
||||
<img alt="Open Issues" src="https://img.shields.io/github/issues-raw/csunny/DB-GPT" />
|
||||
<img alt="Open Issues" src="https://img.shields.io/github/issues-raw/eosphoros-ai/DB-GPT" />
|
||||
</a>
|
||||
<a href="https://discord.gg/7uQnPuveTY">
|
||||
<img alt="Discord" src="https://dcbadge.vercel.app/api/server/7uQnPuveTY?compact=true&style=flat" />
|
||||
@ -33,39 +33,56 @@
|
||||
</a>
|
||||
</p>
|
||||
|
||||
[**English**](README.md) | [**Discord**](https://discord.gg/7uQnPuveTY) | [**文档**](https://www.yuque.com/eosphoros/dbgpt-docs/bex30nsv60ru0fmx) | [**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) | [**Paper**](https://arxiv.org/pdf/2312.17449.pdf)
|
||||
[**English**](README.md) | [**Discord**](https://discord.gg/7uQnPuveTY) | [**文档**](https://www.yuque.com/eosphoros/dbgpt-docs/bex30nsv60ru0fmx) | [**微信**](https://github.com/eosphoros-ai/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) | [**Paper**](https://arxiv.org/pdf/2312.17449.pdf)
|
||||
</div>
|
||||
|
||||
## 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。
|
||||
|
||||
DB-GPT是一个开源的数据域大模型框架。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。
|
||||
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
|
||||
## 效果演示
|
||||
|
||||
- [安装](#安装)
|
||||
- [效果演示](#效果演示)
|
||||
### Data Agents
|
||||

|
||||
|
||||
|
||||
## 目录
|
||||
- [架构方案](#架构方案)
|
||||
- [安装](#安装)
|
||||
- [特性简介](#特性一览)
|
||||
- [贡献](#贡献)
|
||||
- [路线图](#路线图)
|
||||
- [联系我们](#联系我们)
|
||||
|
||||
[DB-GPT视频介绍](https://www.bilibili.com/video/BV1au41157bj/?spm_id_from=333.337.search-card.all.click&vd_source=7792e22c03b7da3c556a450eb42c8a0f)
|
||||
## 架构方案
|
||||
|
||||
## 效果演示
|
||||
|
||||
##### Chat Data
|
||||

|
||||
|
||||
##### Chat Excel
|
||||

|
||||
|
||||
#### 根据自然语言对话生成分析图表
|
||||
<p align="left">
|
||||
<img src="./assets/dashboard.png" width="800px" />
|
||||
<p align="center">
|
||||
<img src="./assets/dbgpt.png" width="800px" />
|
||||
</p>
|
||||
|
||||
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。
|
||||
|
||||
- **微调框架**: 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
|
||||
<p align="center">
|
||||
<img src="./assets/RAG-IN-ACTION.jpg" width="800px" />
|
||||
</p>
|
||||
|
||||
### 子模块
|
||||
- [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) 通过微调来持续提升Text2SQL效果
|
||||
- [DB-GPT-Plugins](https://github.com/eosphoros-ai/DB-GPT-Plugins) DB-GPT 插件仓库, 兼容Auto-GPT
|
||||
- [GPT-Vis](https://github.com/eosphoros-ai/DB-GPT-Web) 可视化协议
|
||||
|
||||
## 安装
|
||||
|
||||

|
||||
@ -84,7 +101,7 @@ 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
|
||||
- [**Excel对话**](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)
|
||||
- [**Agents**](https://www.yuque.com/eosphoros/dbgpt-docs/pom41m7oqtdd57hm)
|
||||
- [**模型服务部署**](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)
|
||||
@ -137,34 +154,6 @@ 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
|
||||
- [支持数据源](https://www.yuque.com/eosphoros/dbgpt-docs/rc4r27ybmdwg9472)
|
||||
|
||||
|
||||
## 架构方案
|
||||
整个DB-GPT的架构,如下图所示
|
||||
<p align="center">
|
||||
<img src="./assets/DB-GPT_zh.png" width="800px" />
|
||||
</p>
|
||||
|
||||
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。
|
||||
|
||||
- **微调框架**: 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
|
||||
<p align="center">
|
||||
<img src="./assets/RAG-IN-ACTION.jpg" width="800px" />
|
||||
</p>
|
||||
|
||||
### 子模块
|
||||
- [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
|
||||
|
||||
## Image
|
||||
|
||||
@ -180,7 +169,11 @@ 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
|
||||
|
||||
### 多模型使用
|
||||
|
||||
[使用指南](https://www.yuque.com/eosphoros/dbgpt-docs/huzgcf2abzvqy8uv)
|
||||
- [使用指南](https://www.yuque.com/eosphoros/dbgpt-docs/huzgcf2abzvqy8uv)
|
||||
|
||||
### 数据Agents使用
|
||||
|
||||
- [数据Agents](https://www.yuque.com/eosphoros/dbgpt-docs/gwz4rayfuwz78fbq)
|
||||
|
||||
# 贡献
|
||||
> 提交代码前请先执行 `black .`
|
||||
@ -193,10 +186,6 @@ The MIT License (MIT)
|
||||
|
||||
# 路线图
|
||||
|
||||
<p align="left">
|
||||
<img src="./assets/roadmap.jpg" width="800px" />
|
||||
</p>
|
||||
|
||||
### 知识库RAG检索优化
|
||||
|
||||
- [x] Multi Documents
|
||||
|
BIN
assets/dbgpt.png
Normal file
BIN
assets/dbgpt.png
Normal file
Binary file not shown.
After Width: | Height: | Size: 244 KiB |
Binary file not shown.
Before Width: | Height: | Size: 68 KiB |
Binary file not shown.
Before Width: | Height: | Size: 213 KiB After Width: | Height: | Size: 219 KiB |
@ -228,7 +228,7 @@ class ChartDrawOperator(MapOperator[Any, Any]):
|
||||
return str(df)
|
||||
|
||||
|
||||
with (DAG("simple_nl_schema_sql_chart_example") as dag):
|
||||
with DAG("simple_nl_schema_sql_chart_example") as dag:
|
||||
trigger = HttpTrigger(
|
||||
"/examples/rag/schema_linking", methods="POST", request_body=TriggerReqBody
|
||||
)
|
||||
|
Loading…
Reference in New Issue
Block a user