Verified project record
chatchat-space/Langchain-Chatchat
Langchain-Chatchat is a locally deployable RAG and Agent application designed for Chinese-language scenarios and open-source models. It requires a separate model inference framework to provide offline knowledge base Q&A, agent tool calling, and multi-modal dialogue through a web UI or API.
Project overview
Langchain-Chatchat is a locally deployable RAG and Agent application designed for Chinese-language scenarios and open-source models. It requires a separate model inference framework to provide offline knowledge base Q&A, agent tool calling, and multi-modal dialogue through a web UI or API.
- Project type
- RAG · Model Runtime
- Use cases
- Chat Assistants · Knowledge Q&A
- Deployment
- Refer to project documentation
- License
- Apache-2.0
Best for
- Developers and AI engineers needing an offline-deployable knowledge base Q&A solution for Chinese scenarios.
- Teams that want to use local files for knowledge base ingestion and mainstream open-source LLMs without relying on external online APIs.
Key capabilities
- Retrieval-Augmented Generation for local knowledge bases using file loading, text splitting, vector embedding, and similarity matching.
- Enables LLM-based automatic tool selection and parameter parsing. The source notes optimization for ChatGLM3 and Qwen and indicates this capability may be unstable.
- Unified file dialogue capability supporting BM25+KNN and other retrieval methods.
- Conversational interface to query databases using natural language.
- Conversational interface that integrates with search engines.
- Streamlit-based Web UI with multi-session support and customizable system prompts.
- API service for programmatic access to the application's features.
- Tools and commands to initialize and manage local FAISS knowledge bases.
Limitations and risks
- No longer directly loads local model paths; it relies on external inference frameworks for model execution.
- Default configuration binds to 127.0.0.1, preventing direct access from other IPs without changing settings.
- Agent capability depends on the chosen model's inherent capabilities.
- High risk of dependency conflicts between Langchain-Chatchat and model inference frameworks if environments are not isolated.
Getting started
- Install langchain-chatchat via pip, start an external model inference framework, run chatchat init, initialize the knowledge base with chatchat kb -r, and start all services with chatchat start -a.
- Requires modifying YAML configuration files and setting up a separate model inference framework. A GPU is optional as it runs on CPU, GPU, NPU, or MPS hardware.
Evidence and sources
- GitHub project description: local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain
- README: 目标期望建立一套对中文场景与开源模型支持友好、可离线运行的知识库问答解决方案。
- README: 本项目支持市面上主流的开源 LLM、 Embedding 模型与向量数据库，可实现全部使用**开源**模型**离线私有部署**。与此同时，本项目也支持 OpenAI GPT API 的调用，并将在后续持续扩充对各类模型及模型 API 的接入。
- README: 过程包括加载文件 -> 读取文本 -> 文本分割 -> 文本向量化 -> 问句向量化 -> 在文本向量中匹配出与问句向量最相似的 `top k`个 -> 匹配出的文本作为上下文和问题一起添加到 `prompt`中 -> 提交给 `LLM`生成回答。
- README: 除上述本地模型加载框架外，项目中也为可接入在线 API 的 [One API](https://github.com/songquanpeng/one-api) 框架接入提供了支持，支持包括 [OpenAI ChatGPT](https://platform.openai.com/docs/guides/gpt/chat-completions-api)、…
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