eosphorosai/dbgpt

By eosphorosai

•Updated about 1 month ago

dbgpt docker images repo

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eosphorosai/dbgpt repository overview

⁠DB-GPT: Revolutionizing Database Interactions with Private LLM Technology

⁠ ⁠ ⁠ ⁠ ⁠ ⁠ ⁠ ⁠

简体中文⁠ |Discord⁠ |Documents⁠|Wechat⁠|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 Youtube Video⁠

⁠Demo

Run on an RTX 4090 GPU.

⁠Chat Excel

excel

⁠Chat Plugin

auto_plugin_new

⁠LLM Management

llm_manage

⁠FastChat && vLLM

vllm

⁠Trace

trace_new

⁠Chat Knowledge

kbqa_new

⁠Install

Usage Tutorial⁠

⁠Features

Currently, we have released multiple key features, which are listed below to demonstrate our current capabilities:

Private KBQA & 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.
Multiple data sources & visualization

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

It 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
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.
Etc.
- Support API Proxy LLMs
Privacy and security

The privacy and security of data are ensured through various technologies, such as privatized large models and proxy desensitization.
Support Datasources

DataSource	support	Notes
MySQL⁠	Yes
PostgreSQL⁠	Yes
Spark⁠	Yes
DuckDB⁠	Yes
Sqlite⁠	Yes
MSSQL⁠	Yes
ClickHouse⁠	Yes
Oracle⁠	No	TODO
Redis⁠	No	TODO
MongoDB⁠	No	TODO
HBase⁠	No	TODO
Doris⁠	No	TODO
DB2⁠	No	TODO
Couchbase⁠	No	TODO
Elasticsearch⁠	No	TODO
OceanBase⁠	No	TODO
TiDB⁠	No	TODO
StarRocks⁠	No	TODO

⁠Introduction

The architecture of the entire DB-GPT is shown.

The core capabilities mainly consist of the following parts:

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

⁠RAG-IN-Action

⁠SubModule

DB-GPT-Hub⁠ Text-to-SQL performance by applying Supervised Fine-Tuning (SFT) on large language models.
DB-GPT-Plugins⁠ DB-GPT Plugins Can run autogpt plugin directly
DB-GPT-Web⁠ ChatUI for DB-GPT

⁠Image

🌐 AutoDL Image⁠

⁠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 contribute⁠

⁠RoadMap

roadmap

⁠KBQA RAG optimization

Multi Documents
- PDF
- Excel, CSV
- Word
- Text
- MarkDown
- Code
- Images
RAG
Graph Database
- Neo4j Graph
- Nebula Graph
Multi-Vector Database
- Chroma
- Milvus
- Weaviate
- 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
- MySQL
- PostgreSQL
- Spark
- DuckDB
- Sqlite
- MSSQL
- ClickHouse
- Oracle
- Redis
- MongoDB
- HBase
- Doris
- DB2
- Couchbase
- Elasticsearch
- OceanBase
- TiDB
- StarRocks

⁠Multi-Models And vLLM

Cluster Deployment⁠
Fastchat Support⁠
vLLM Support⁠
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

multi-agents framework
custom plugin development
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

debugging⁠
Observability
cost & budgets

⁠Text2SQL Finetune

support llms
- LLaMA
- LLaMA-2
- BLOOM
- BLOOMZ
- Falcon
- Baichuan
- Baichuan2
- InternLM
- Qwen
- XVERSE
- 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
ChatGPT	0.728	numbersstation-eval-res⁠
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
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

⁠Licence

The MIT License (MIT)

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