541 lines
27 KiB
Markdown
541 lines
27 KiB
Markdown
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[](https://shields.io/)
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[](https://pypi.org/project/pypiserver/)
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🌍 [READ THIS IN CHINESE](README.md)
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📃 **LangChain-Chatchat** (formerly Langchain-ChatGLM)
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An open-source, offline-deployable RAG and Agent application project based on large language models like ChatGLM and
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application frameworks like Langchain.
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---
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## Contents
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* [Overview](README_en.md#Overview)
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* [Features](README_en.md#What-Does-Langchain-Chatchat-Offer)
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* [Quick Start](README_en.md#Quick-Start)
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* [Installation](README_en.md#Installation)
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* [Contact Us](README_en.md#Contact-Us)
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## Overview
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🤖️ A question-answering application based on local knowledge bases using
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the [langchain](https://github.com/langchain-ai/langchain) concept. The goal is to create a friendly and
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offline-operable knowledge base Q&A solution that supports Chinese scenarios and open-source models.
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💡 Inspired by [GanymedeNil](https://github.com/GanymedeNil)'s
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project [document.ai](https://github.com/GanymedeNil/document.ai) and [AlexZhangji](https://github.com/AlexZhangji)'
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s [ChatGLM-6B Pull Request](https://github.com/THUDM/ChatGLM-6B/pull/216), this project aims to establish a local
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knowledge base Q&A application fully utilizing open-source models. The latest version of the project
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uses [FastChat](https://github.com/lm-sys/FastChat) to integrate models like Vicuna, Alpaca, LLaMA, Koala, and RWKV,
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leveraging the [langchain](https://github.com/langchain-ai/langchain) framework to support API calls provided
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by [FastAPI](https://github.com/tiangolo/fastapi) or operations using a WebUI based
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on [Streamlit](https://github.com/streamlit/streamlit).
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✅ This project supports mainstream open-source LLMs, embedding models, and vector databases, allowing full **open-source
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** model **offline private deployment**. Additionally, the project supports OpenAI GPT API
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calls and will continue to expand access to various models and model APIs.
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⛓️ The implementation principle of this project is as shown below, including loading files -> reading text -> text
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segmentation -> text vectorization -> question vectorization -> matching the `top k` most similar text vectors with the
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question vector -> adding the matched text as context along with the question to the `prompt` -> submitting to the `LLM`
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for generating answers.
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📺 [Introduction Video](https://www.bilibili.com/video/BV13M4y1e7cN/?share_source=copy_web&vd_source=e6c5aafe684f30fbe41925d61ca6d514)
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From the document processing perspective, the implementation process is as follows:
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🚩 This project does not involve fine-tuning or training processes but can utilize fine-tuning or training to optimize
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the project's performance.
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🌐 The `0.3.0` version code used in
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the [AutoDL Mirror](https://www.codewithgpu.com/i/chatchat-space/Langchain-Chatchat/Langchain-Chatchat) has been updated
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to version `v0.3.0` of this project.
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🐳 Docker images will be updated soon.
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🧑💻 If you want to contribute to this project, please refer to the [Developer Guide](docs/contributing/README_dev.md)
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for more information on development and deployment.
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## What Does Langchain-Chatchat Offer
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### Features of Version 0.3.x
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| Features | 0.2.x | 0.3.x |
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|---------------------------------|-------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|
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| Model Integration | Local: fastchat<br>Online: XXXModelWorker | Local: model_provider, supports most mainstream model loading frameworks<br>Online: oneapi<br>All model integrations are compatible with the openai sdk |
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| Agent | ❌ Unstable | ✅ Optimized for ChatGLM3 and QWen, significantly enhanced Agent capabilities ||
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| LLM Conversations | ✅ | ✅ ||
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| Knowledge Base Conversations | ✅ | ✅ ||
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| Search Engine Conversations | ✅ | ✅ ||
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| File Conversations | ✅ Only vector search | ✅ Unified as File RAG feature, supports BM25+KNN and other retrieval methods ||
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| Database Conversations | ❌ | ✅ ||
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| ARXIV Document Conversations | ❌ | ✅ ||
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| Wolfram Conversations | ❌ | ✅ ||
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| Text-to-Image | ❌ | ✅ ||
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| Local Knowledge Base Management | ✅ | ✅ ||
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| WEBUI | ✅ | ✅ Better multi-session support, custom system prompts... |
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The core functionality of 0.3.x is implemented by Agent, but users can also manually perform tool calls:
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|Operation Method|Function Implemented|Applicable Scenario|
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|----------------|--------------------|-------------------|
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|Select "Enable Agent", choose multiple tools|Automatic tool calls by LLM|Using models with Agent capabilities like
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ChatGLM3/Qwen or online APIs|
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|Select "Enable Agent", choose a single tool|LLM only parses tool parameters|Using models with general Agent
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capabilities, unable to choose tools well<br>Want to manually select functions|
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|Do not select "Enable Agent", choose a single tool|Manually fill in parameters for tool calls without using Agent
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function|Using models without Agent capabilities|
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More features and updates can be experienced in the actual deployment.
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### Supported Model Deployment Frameworks and Models
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This project already supports mainstream models on the market, such as [GLM-4-Chat](https://github.com/THUDM/GLM-4)
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and [Qwen2-Instruct](https://github.com/QwenLM/Qwen2), among the latest open-source large language models and embedding
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models. Users need to start the model deployment framework and load the required models by modifying the configuration
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||
information. The supported local model deployment frameworks in this project are as follows:
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| Model Deployment Framework | Xinference | LocalAI | Ollama | FastChat |
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|------------------------------------|-----------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------|-------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
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| Aligned with OpenAI API | ✅ | ✅ | ✅ | ✅ |
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| Accelerated Inference Engine | GPTQ, GGML, vLLM, TensorRT | GPTQ, GGML, vLLM, TensorRT | GGUF, GGML | vLLM |
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| Model Types Supported | LLM, Embedding, Rerank, Text-to-Image, Vision, Audio | LLM, Embedding, Rerank, Text-to-Image, Vision, Audio | LLM, Text-to-Image, Vision | LLM, Vision |
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| Function Call | ✅ | ✅ | ✅ | / |
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| More Platform Support (CPU, Metal) | ✅ | ✅ | ✅ | ✅ |
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| Heterogeneous | ✅ | ✅ | / | / |
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| Cluster | ✅ | ✅ | / | / |
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| Documentation Link | [Xinference Documentation](https://inference.readthedocs.io/zh-cn/latest/models/builtin/index.html) | [LocalAI Documentation](https://localai.io/model-compatibility/) | [Ollama Documentation](https://github.com/ollama/ollama?tab=readme-ov-file#model-library) | [FastChat Documentation](https://github.com/lm-sys/FastChat#install) |
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| Available Models | [Xinference Supported Models](https://inference.readthedocs.io/en/latest/models/builtin/index.html) | [LocalAI Supported Models](https://localai.io/model-compatibility/#/) | [Ollama Supported Models](https://ollama.com/library#) | [FastChat Supported Models](https://github.com/lm-sys/FastChat/blob/main/docs/model_support.md) |
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In addition to the above local model loading frameworks, the project also supports
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the [One API](https://github.com/songquanpeng/one-api) framework for integrating online APIs, supporting commonly used
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online APIs such
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as [OpenAI ChatGPT](https://platform.openai.com/docs/guides/gpt/chat-completions-api), [Azure OpenAI API](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference), [Anthropic Claude](https://anthropic.com/), [Zhipu Qingyan](https://bigmodel.cn/),
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and [Baichuan](https://platform.baichuan-ai.com/).
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||
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> [!Note]
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> Regarding Xinference loading local models:
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> Xinference built-in models will automatically download. To load locally downloaded models, you can
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> execute `streamlit run xinference_manager.py` in the tools/model_loaders directory of the project after starting the
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> Xinference service and set the local path for the specified model as prompted on the page.
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## Quick Start
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### Installation
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||
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#### 0. Software and Hardware Requirements
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💡 On the software side, this project supports Python 3.8-3.11 environments and has been tested on Windows, macOS, and
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Linux operating systems.
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💻 On the hardware side, as version 0.3.0 has been modified to support integration with different model deployment
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frameworks, it can be used under various hardware conditions such as CPU, GPU, NPU, and MPS.
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#### 1. Install Langchain-Chatchat
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Starting from version 0.3.0, Langchain-Chatchat provides installation in the form of a Python library. Execute the
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following command for installation:
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```shell
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pip install langchain-chatchat -U
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```
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[!Note]
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> Since the model deployment framework Xinference requires additional Python dependencies when integrated with
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||
> Langchain-Chatchat, it is recommended to use the following installation method if you want to use it with the
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> Xinference
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> framework:
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> ```shell
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> pip install "langchain-chatchat[xinference]" -U
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> ```
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2. Model Inference Framework and Load Models
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2. Model Inference Framework and Load Models
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Starting from version 0.3.0, Langchain-Chatchat no longer directly loads models based on the local model path entered by
|
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users. The involved model types include LLM, Embedding, Reranker, and the multi-modal models to be supported in the
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future. Instead, it supports integration with mainstream model inference frameworks such
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as [Xinference](https://github.com/xorbitsai/inference), [Ollama](https://github.com/ollama/ollama), [LocalAI](https://github.com/mudler/LocalAI), [FastChat](https://github.com/lm-sys/FastChat)
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and [One API](https://github.com/songquanpeng/one-api).
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||
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Therefore, please ensure to run the model inference framework and load the required models before starting
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||
Langchain-Chatchat.
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Here is an example of Xinference. Please refer to
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the [Xinference Document](https://inference.readthedocs.io/zh-cn/latest/getting_started/installation.html) for framework
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deployment and model loading.
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||
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> [!WARNING]
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||
> To avoid dependency conflicts, place Langchain-Chatchat and model deployment frameworks like Xinference in different
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> Python virtual environments, such as conda, venv, virtualenv, etc.
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||
|
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#### 3. View and Modify Langchain-Chatchat Configuration
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||
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||
Starting from version 0.3.0, Langchain-Chatchat no longer modifies the configuration through local files but uses
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command-line methods and will add configuration item modification pages in future versions.
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The following introduces how to view and modify the configuration.
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||
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##### 3.1 View chatchat-config Command Help
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Enter the following command to view the optional configuration types:
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||
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||
```shell
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chatchat-config --help
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||
```
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||
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||
You will get the following response:
|
||
|
||
```text
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Usage: chatchat-config [OPTIONS] COMMAND [ARGS]...
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指令` chatchat-config` 工作空间配置
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||
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||
Options:
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||
--help Show this message and exit.
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||
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||
Commands:
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basic 基础配置
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||
kb 知识库配置
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model 模型配置
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||
server 服务配置
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||
```
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You can choose the required configuration type based on the above commands. For example, to view or
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modify `basic configuration`, you can enter the following command to get help information:
|
||
|
||
```shell
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chatchat-config basic --help
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||
```
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||
|
||
You will get the following response:
|
||
|
||
```text
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||
Usage: chatchat-config basic [OPTIONS]
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||
|
||
基础配置
|
||
|
||
Options:
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||
--verbose [true|false] 是否开启详细日志
|
||
--data TEXT 初始化数据存放路径，注意：目录会清空重建
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||
--format TEXT 日志格式
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||
--clear 清除配置
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||
--show 显示配置
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||
--help Show this message and exit.
|
||
```
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||
|
||
##### 3.2 Use chatchat-config to Modify Corresponding Configuration Parameters
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||
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||
To modify the `default llm` model in `model configuration`, you can execute the following command to view the
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||
configuration item names:
|
||
|
||
```shell
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||
chatchat-config basic --show
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||
```
|
||
|
||
If no configuration item modification is made, the default configuration is as follows:
|
||
|
||
```text
|
||
{
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||
"log_verbose": false,
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"CHATCHAT_ROOT": "/root/anaconda3/envs/chatchat/lib/python3.11/site-packages/chatchat",
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||
"DATA_PATH": "/root/anaconda3/envs/chatchat/lib/python3.11/site-packages/chatchat/data",
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||
"IMG_DIR": "/root/anaconda3/envs/chatchat/lib/python3.11/site-packages/chatchat/img",
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||
"NLTK_DATA_PATH": "/root/anaconda3/envs/chatchat/lib/python3.11/site-packages/chatchat/data/nltk_data",
|
||
"LOG_FORMAT": "%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s",
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"LOG_PATH": "/root/anaconda3/envs/chatchat/lib/python3.11/site-packages/chatchat/data/logs",
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"MEDIA_PATH": "/root/anaconda3/envs/chatchat/lib/python3.11/site-packages/chatchat/data/media",
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"BASE_TEMP_DIR": "/root/anaconda3/envs/chatchat/lib/python3.11/site-packages/chatchat/data/temp",
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"class_name": "ConfigBasic"
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||
}
|
||
```
|
||
|
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##### 3.3 Use chatchat-config to Modify Corresponding Configuration Parameters
|
||
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||
To modify the default `llm model` in `model configuration`, you can execute the following command to view the
|
||
configuration item names:
|
||
|
||
```shell
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chatchat-config model --help
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||
```
|
||
|
||
You will get:
|
||
|
||
```text
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Usage: chatchat-config model [OPTIONS]
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|
||
模型配置
|
||
|
||
Options:
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||
--default_llm_model TEXT 默认llm模型
|
||
--default_embedding_model TEXT 默认embedding模型
|
||
--agent_model TEXT agent模型
|
||
--history_len INTEGER 历史长度
|
||
--max_tokens INTEGER 最大tokens
|
||
--temperature FLOAT 温度
|
||
--support_agent_models TEXT 支持的agent模型
|
||
--set_model_platforms TEXT 模型平台配置 as a JSON string.
|
||
--set_tool_config TEXT 工具配置项 as a JSON string.
|
||
--clear 清除配置
|
||
--show 显示配置
|
||
--help Show this message and exit.
|
||
```
|
||
|
||
First, view the current `model configuration` parameters:
|
||
|
||
```shell
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chatchat-config model --show
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||
```
|
||
|
||
You will get:
|
||
|
||
```text
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{
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||
"DEFAULT_LLM_MODEL": "glm4-chat",
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"DEFAULT_EMBEDDING_MODEL": "bge-large-zh-v1.5",
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||
"Agent_MODEL": null,
|
||
"HISTORY_LEN": 3,
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||
"MAX_TOKENS": null,
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||
"TEMPERATURE": 0.7,
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||
...
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||
"class_name": "ConfigModel"
|
||
}
|
||
```
|
||
|
||
To modify the `default llm` model to `qwen2-instruct`, execute:
|
||
|
||
```shell
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chatchat-config model --default_llm_model qwen2-instruct
|
||
```
|
||
|
||
For more configuration modification help, refer to [README.md](libs/chatchat-server/README.md)
|
||
|
||
4. Custom Model Integration Configuration
|
||
|
||
After completing the above project configuration item viewing and modification, proceed to step 2. Model Inference
|
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Framework and Load Models and select the model inference framework and loaded models. Model inference frameworks include
|
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[Xinference](https://github.com/xorbitsai/inference),[Ollama](https://github.com/ollama/ollama),[LocalAI](https://github.com/mudler/LocalAI),[FastChat](https://github.com/lm-sys/FastChat)
|
||
and [One API](https://github.com/songquanpeng/one-api), supporting new Chinese open-source models
|
||
like [GLM-4-Chat](https://github.com/THUDM/GLM-4) and [Qwen2-Instruct](https://github.com/QwenLM/Qwen2)
|
||
|
||
If you already have an address with the capability of an OpenAI endpoint, you can directly configure it in
|
||
MODEL_PLATFORMS as follows:
|
||
|
||
```text
|
||
chatchat-config model --set_model_platforms TEXT Configure model platforms as a JSON string.
|
||
```
|
||
|
||
- `platform_name` can be arbitrarily filled, just ensure it is unique.
|
||
- `platform_type` might be used in the future for functional distinctions based on platform types, so it should match
|
||
the platform_name.
|
||
- List the models deployed on the framework in the corresponding list. Different frameworks can load models with the
|
||
same name, and the project will automatically balance the load.
|
||
- Set up the model
|
||
|
||
```shell
|
||
$ chatchat-config model --set_model_platforms "[{
|
||
\"platform_name\": \"xinference\",
|
||
\"platform_type\": \"xinference\",
|
||
\"api_base_url\": \"http://127.0.0.1:9997/v1\",
|
||
\"api_key\": \"EMPT\",
|
||
\"api_concurrencies\": 5,
|
||
\"llm_models\": [
|
||
\"autodl-tmp-glm-4-9b-chat\"
|
||
],
|
||
\"embed_models\": [
|
||
\"bge-large-zh-v1.5\"
|
||
],
|
||
\"image_models\": [],
|
||
\"reranking_models\": [],
|
||
\"speech2text_models\": [],
|
||
\"tts_models\": []
|
||
}]"
|
||
```
|
||
|
||
#### 5. Initialize Knowledge Base
|
||
|
||
> [!WARNING]
|
||
> Before initializing the knowledge base, ensure that the model inference framework and corresponding embedding model
|
||
> are
|
||
> running, and complete the model integration configuration as described in steps 3 and 4.
|
||
|
||
```shell
|
||
cd # Return to the original directory
|
||
chatchat-kb -r
|
||
```
|
||
|
||
Specify text-embedding model for initialization (if needed):
|
||
|
||
```
|
||
cd # Return to the original directory
|
||
chatchat-kb -r --embed-model=text-embedding-3-smal
|
||
```
|
||
|
||
Successful output will be:
|
||
|
||
```text
|
||
|
||
----------------------------------------------------------------------------------------------------
|
||
知识库名称 ：samples
|
||
知识库类型 ：faiss
|
||
向量模型： ：bge-large-zh-v1.5
|
||
知识库路径 ：/root/anaconda3/envs/chatchat/lib/python3.11/site-packages/chatchat/data/knowledge_base/samples
|
||
文件总数量 ：47
|
||
入库文件数 ：42
|
||
知识条目数 ：740
|
||
用时 ：0:02:29.701002
|
||
----------------------------------------------------------------------------------------------------
|
||
|
||
总计用时 ：0:02:33.414425
|
||
|
||
```
|
||
|
||
The knowledge base path is in the knowledge_base directory under the path pointed by the *DATA_PATH* variable in
|
||
step `3.2`:
|
||
|
||
```shell
|
||
(chatchat) [root@VM-centos ~]# ls /root/anaconda3/envs/chatchat/lib/python3.11/site-packages/chatchat/data/knowledge_base/samples/vector_store
|
||
bge-large-zh-v1.5 text-embedding-3-small
|
||
```
|
||
|
||
##### Frequently asked questions
|
||
|
||
##### 1. Stuck when rebuilding the knowledge base or adding knowledge files under Windows
|
||
|
||
This issue often occurs in newly created virtual environments and can be confirmed through the following methods:
|
||
|
||
`from unstructured.partition.auto import partition`
|
||
|
||
If the statement gets stuck and cannot be executed, the following command can be executed:
|
||
|
||
```shell
|
||
pip uninstall python-magic-bin
|
||
# check the version of the uninstalled package
|
||
pip install 'python-magic-bin=={version}'
|
||
```
|
||
|
||
Then follow the instructions in this section to recreate the knowledge base.
|
||
|
||
#### 6. Start the Project
|
||
|
||
```shell
|
||
chatchat -a
|
||
```
|
||
|
||
Successful startup output:
|
||
|
||
|
||
|
||
> [!WARNING]
|
||
> As the `DEFAULT_BIND_HOST` of the chatchat-config server configuration is set to `127.0.0.1` by default, it cannot be
|
||
> accessed through other IPs.
|
||
>
|
||
> To modify, refer to the following method:
|
||
> <details>
|
||
> <summary>Instructions</summary>
|
||
>
|
||
> ```shell
|
||
> chatchat-config server --show
|
||
> ```
|
||
> You will get:
|
||
> ```text
|
||
> {
|
||
> "HTTPX_DEFAULT_TIMEOUT": 300.0,
|
||
> "OPEN_CROSS_DOMAIN": true,
|
||
> "DEFAULT_BIND_HOST": "127.0.0.1",
|
||
> "WEBUI_SERVER_PORT": 8501,
|
||
> "API_SERVER_PORT": 7861,
|
||
> "WEBUI_SERVER": {
|
||
> "host": "127.0.0.1",
|
||
> "port": 8501
|
||
> },
|
||
> "API_SERVER": {
|
||
> "host": "127.0.0.1",
|
||
> "port": 7861
|
||
> },
|
||
> "class_name": "ConfigServer"
|
||
> }
|
||
> ```
|
||
> To access via the machine's IP (such as in a Linux system), change the listening address to `0.0.0.0`.
|
||
> ```shell
|
||
> chatchat-config server --default_bind_host=0.0.0.0
|
||
> ```
|
||
> You will get:
|
||
> ```text
|
||
> {
|
||
> "HTTPX_DEFAULT_TIMEOUT": 300.0,
|
||
> "OPEN_CROSS_DOMAIN": true,
|
||
> "DEFAULT_BIND_HOST": "0.0.0.0",
|
||
> "WEBUI_SERVER_PORT": 8501,
|
||
> "API_SERVER_PORT": 7861,
|
||
> "WEBUI_SERVER": {
|
||
> "host": "0.0.0.0",
|
||
> "port": 8501
|
||
> },
|
||
> "API_SERVER": {
|
||
> "host": "0.0.0.0",
|
||
> "port": 7861
|
||
> },
|
||
> "class_name": "ConfigServer"
|
||
> }
|
||
> ```
|
||
> </details>
|
||
|
||
### Migration from Older Versions
|
||
|
||
* The structure of 0.3.x has changed significantly, it is strongly recommended to redeploy according to the
|
||
documentation.
|
||
The following guide does not guarantee 100% compatibility and success. Remember to backup important data in advance!
|
||
|
||
- First configure the operating environment according to the steps in `Installation`.
|
||
- Configure `DATA` and other options.
|
||
- Copy the knowledge_base directory of the 0.2.x project to the configured `DATA` directory.
|
||
|
||
---
|
||
|
||
## License
|
||
|
||
The code of this project follows the [Apache-2.0](LICENSE) agreement.
|
||
|
||
## Project Milestones
|
||
|
||
+ `April 2023`: `Langchain-ChatGLM 0.1.0` released, supporting local knowledge base question and answer based on
|
||
ChatGLM-6B model.
|
||
+ `August 2023`: `Langchain-ChatGLM` renamed to `Langchain-Chatchat`, released `0.2.0` version, using `fastchat` as
|
||
model loading solution, supporting more models and databases.
|
||
+ `October 2023`: `Langchain-Chatchat 0.2.5` released, launching Agent content, open source project won the third prize
|
||
in the hackathon held by `Founder Park & Zhipu AI & Zilliz`.
|
||
+ `December 2023`: `Langchain-Chatchat` open source project received more than **20K** stars.
|
||
+ `June 2024`: `Langchain-Chatchat 0.3.0` released, bringing a new project architecture.
|
||
|
||
+ 🔥 Let us look forward to the future story of Chatchat ···
|
||
|
||
## Contact Us
|
||
|
||
### Telegram
|
||
|
||
[](https://t.me/+RjliQ3jnJ1YyN2E9)
|
||
|
||
### Project Exchange Group
|
||
|
||
<img src="docs/img/qr_code_109.jpg" alt="二维码" width="300" />
|
||
|
||
🎉 Langchain-Chatchat project WeChat exchange group, if you are also interested in this project, welcome to join the
|
||
group chat to participate in the discussion.
|
||
|
||
### Official Account
|
||
|
||
<img src="docs/img/official_wechat_mp_account.png" alt="二维码" width="300" />
|
||
|
||
🎉 Langchain-Chatchat project official public account, welcome to scan the code to follow.
|