199 lines
7.8 KiB
Markdown
199 lines
7.8 KiB
Markdown
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🌍 [中文文档](README.md)
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🌍 [日本語で読む](README_ja.md)
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📃 **LangChain-Chatchat** (formerly Langchain-ChatGLM):
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A LLM application aims to implement knowledge and search engine based QA based on Langchain and open-source or remote
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LLM API.
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⚠️`0.2.10` will be the last version of the `0.2.x` series. The `0.2.x` series will stop updating and technical support,
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and strive to develop `Langchain-Chachat 0.3.x with stronger applicability. `.
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---
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## Table of Contents
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- [Introduction](README.md#Introduction)
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- [Pain Points Addressed](README.md#Pain-Points-Addressed)
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- [Quick Start](README.md#Quick-Start)
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- [1. Environment Setup](README.md#1-Environment-Setup)
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- [2. Model Download](README.md#2-Model-Download)
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- [3. Initialize Knowledge Base and Configuration Files](README.md#3-Initialize-Knowledge-Base-and-Configuration-Files)
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- [4. One-Click Startup](README.md#4-One-Click-Startup)
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- [5. Startup Interface Examples](README.md#5-Startup-Interface-Examples)
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- [Contact Us](README.md#Contact-Us)
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## Introduction
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🤖️ A Q&A application based on local knowledge base implemented using the idea
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of [langchain](https://github.com/langchain-ai/langchain). The goal is to build a KBQA(Knowledge based Q&A) solution
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that
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is friendly to Chinese scenarios and open source models and can run both offline and online.
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💡 Inspired by [document.ai](https://github.com/GanymedeNil/document.ai)
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and [ChatGLM-6B Pull Request](https://github.com/THUDM/ChatGLM-6B/pull/216) , we build a local knowledge base question
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answering application that can be implemented using an open source model or remote LLM api throughout the process. In
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the latest version of this project, [FastChat](https://github.com/lm-sys/FastChat) is used to access Vicuna, Alpaca,
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LLaMA, Koala, RWKV and many other models. Relying on [langchain](https://github.com/langchain-ai/langchain) , this
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project supports calling services through the API provided based on [FastAPI](https://github.com/tiangolo/fastapi), or
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using the WebUI based on [Streamlit](https://github.com/streamlit/streamlit).
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✅ Relying on the open source LLM and Embedding models, this project can realize full-process **offline private
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deployment**. At the same time, this project also supports the call of OpenAI GPT API- and Zhipu API, and will continue
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to expand the access to various models and remote APIs in the future.
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⛓️ The implementation principle of this project is shown in the graph below. The main process includes: loading files ->
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reading text -> text segmentation -> text vectorization -> question vectorization -> matching the `top-k` most similar
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to the question vector in the text vector -> The matched text is added to `prompt `as context and question -> submitte
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to `LLM` to generate an answer.
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📺[video introduction](https://www.bilibili.com/video/BV13M4y1e7cN/?share_source=copy_web&vd_source=e6c5aafe684f30fbe41925d61ca6d514)
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The main process analysis from the aspect of document process:
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🚩 The training or fine-tuning are not involved in the project, but still, one always can improve performance by do
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these.
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🌐 [AutoDL image](https://www.codewithgpu.com/i/chatchat-space/Langchain-Chatchat/Langchain-Chatchat) is supported, and in v13 the codes are update to v0.2.9.
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🐳 [Docker image](registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.7) is supported to 0.2.7
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## Pain Points Addressed
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This project is a solution for enhancing knowledge bases with fully localized inference, specifically addressing the
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pain points of data security and private deployments for businesses.
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This open-source solution is under the Apache License and can be used for commercial purposes for free, with no fees
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required.
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We support mainstream local large prophecy models and Embedding models available in the market, as well as open-source
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local vector databases. For a detailed list of supported models and databases, please refer to
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our [Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/)
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## Quick Start
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### Environment Setup
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First, make sure your machine has Python 3.10 installed.
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```
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$ python --version
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Python 3.10.12
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```
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Then, create a virtual environment and install the project's dependencies within the virtual environment.
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```shell
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# 拉取仓库
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$ git clone https://github.com/chatchat-space/Langchain-Chatchat.git
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# 进入目录
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$ cd Langchain-Chatchat
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# 安装全部依赖
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$ pip install -r requirements.txt
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$ pip install -r requirements_api.txt
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$ pip install -r requirements_webui.txt
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# 默认依赖包括基本运行环境（FAISS向量库）。如果要使用 milvus/pg_vector 等向量库，请将 requirements.txt 中相应依赖取消注释再安装。
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```
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Please note that the LangChain-Chachat `0.2.x` series is for the Langchain `0.0.x` series version. If you are using the
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Langchain `0.1.x` series version, you need to downgrade.
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### Model Download
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If you need to run this project locally or in an offline environment, you must first download the required models for
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the project. Typically, open-source LLM and Embedding models can be downloaded from HuggingFace.
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Taking the default LLM model used in this project, [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b), and
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the Embedding model [moka-ai/m3e-base](https://huggingface.co/moka-ai/m3e-base) as examples:
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To download the models, you need to first
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install [Git LFS](https://docs.github.com/zh/repositories/working-with-files/managing-large-files/installing-git-large-file-storage)
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and then run:
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```Shell
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$ git lfs install
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$ git clone https://huggingface.co/THUDM/chatglm2-6b
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$ git clone https://huggingface.co/moka-ai/m3e-base
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```
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### Initializing the Knowledge Base and Config File
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Follow the steps below to initialize your own knowledge base and config file:
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```shell
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$ python copy_config_example.py
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$ python init_database.py --recreate-vs
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```
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### One-Click Launch
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To start the project, run the following command:
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```shell
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$ python startup.py -a
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```
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### Example of Launch Interface
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1. FastAPI docs interface
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2. webui page
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- Web UI dialog page:
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- Web UI knowledge base management page:
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### Note
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The above instructions are provided for a quick start. If you need more features or want to customize the launch method,
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please refer to the [Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/).
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---
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## Project Milestones
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+ `April 2023`: `Langchain-ChatGLM 0.1.0` released, supporting local knowledge base question and answer based on the
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ChatGLM-6B model.
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+ `August 2023`: `Langchain-ChatGLM` was renamed to `Langchain-Chatchat`, `0.2.0` was released, using `fastchat` as the
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model loading solution, supporting more models and databases.
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+ `October 2023`: `Langchain-Chachat 0.2.5` was released, Agent content was launched, and the open source project won
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the third prize in the hackathon held by `Founder Park & Zhipu AI & Zilliz`.
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+ `December 2023`: `Langchain-Chachat` open source project received more than **20K** stars.
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+ `January 2024`: `LangChain 0.1.x` is launched, `Langchain-Chachat 0.2.x` is released. After the stable
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version `0.2.10` is released, updates and technical support will be stopped, and all efforts will be made to
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develop `Langchain with stronger applicability -Chat 0.3.x`.
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+ 🔥 Let’s look forward to the future Chatchat stories together···
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---
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## Contact Us
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### Telegram
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[](https://t.me/+RjliQ3jnJ1YyN2E9)
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### WeChat Group
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<img src="img/qr_code_87.jpg" alt="二维码" width="300" height="300" />
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### WeChat Official Account
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<img src="img/official_wechat_mp_account.png" alt="图片" width="900" height="300" />
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