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- Backend Environment Setup
First, ensure your machine has Python 3.8 - 3.10 installed.
$ python --version
Python 3.10.12
Next, create a virtual environment and install the project dependencies within it.
# Clone the repository
$ git clone https://github.com/zhouxh19/ChatBase.git
# Enter the directory
$ cd ChatBase
# Install all dependencies
$ pip3 install -r requirements.txt
# To run only the API service
$ pip3 install -r requirements_api.txt
# The default dependencies include the basic runtime environment (Chroma-DB vector library). If you want to use other vector libraries, uncomment the corresponding dependencies in requirements.txt before installing.- Frontend Service Setup
- First, ensure your machine has Node (>= 18.15.0) installed.
$ node -v
v18.15.0
Next, install the project dependencies.
cd webui
# pnpm address https://pnpm.io/zh/motivation
# Install dependencies (Recommend using pnpm)
# You can use "npm -g i pnpm" to install pnpm
pnpm install- Download Embedding Model from HuggingFace.
To download the model, you need to install Git LFS first, then run:
$ git lfs install
$ git clone https://huggingface.co/moka-ai/m3e-baseAdjust the model settings to the download path, for example:
EMBEDDING_MODEL = "m3e-base"
LLM_MODELS = ["Qwen-1_8B-Chat"]
MODEL_PATH = {
"embed_model": {
"m3e-base": "m3e-base", # Download path of embedding model.
},
"llm_model": {
"Qwen-1_8B-Chat": "Qwen-1_8B-Chat", # Download path of LLM.
},
}- Modify Configuration Files
Copy the configuration files and check each file's comments to modify them according to your needs.
$ python copy_config_example.py
# The generated configuration files are in the configs/ directory
# basic_config.py is the basic configuration file and doesn't need to be modified.
# kb_config.py is the knowledge base configuration file, you can modify DEFAULT_VS_TYPE to specify the storage vector library of the knowledge base, and you can also modify the relevant paths.
# model_config.py is the model configuration file, you can modify LLM_MODELS to specify the models used. The current model configuration is mainly for knowledge base search, diagnostic-related models have some hard coding in the code, and will be unified here later.
# prompt_config.py is the prompt configuration file, mainly for LLM dialogue and knowledge base prompts.
# server_config.py is the service configuration file, mainly for the service port number, etc.!!! Note: Please modify the following configurations before initializing the knowledge base, otherwise it may cause database initialization failure.
- model_config.py
# EMBEDDING_MODEL Vectorization model, if you choose a local model, download it to the root directory as needed.
# LLM_MODELS LLM, if you choose a local model, download it to the root directory as needed.
# ONLINE_LLM_MODEL If you use an online model, modify the configuration.- server_config.py
# WEBUI_SERVER.api_base_url Pay attention to this parameter. If deploying the project on a server, modify the configuration.- Initialize the Knowledge Base
Initialize your knowledge base and simply copy the configuration files as follows:
$ python init_database.py --recreate-vsStart the project with the following command:
$ python startup.py -aExample of the startup interface
If it starts successfully, you will see the following interface:
- RAG Dialogue Page
- Database Dialogue Page:
- Database Dialogue Start Page:
- Database Dialogue History Page:
- Multi-file Linked Dialogue Page:
- Knowledge Base Page
- Knowledge Base Management Page:
- Knowledge Base Details Page:
-
Data-Driven workflow orchestration
-
ES Service
Feel free to cite us if you like this project.
@misc{zhao2024llmdbdemo,
title={Chat2Data: An Interactive Data Analysis System with RAG, Vector Databases and LLMs},
author={Xinyang Zhao, Xuanhe Zhou, Guoliang Li},
year={2024},
journal={Proc. {VLDB} Endow.},
}