Langchain-Chatchat and Langchain-ChatBI
These are ecosystem siblings—Langchain-Chatchat is a general-purpose RAG chatbot framework, while Langchain-ChatBI is a specialized application layer built on the same Langchain foundation but tailored specifically for conversational data analysis and business intelligence use cases.
About Langchain-Chatchat
chatchat-space/Langchain-Chatchat
Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain
Supports flexible model deployment via Xinference, Ollama, and LocalAI frameworks with OpenAI API compatibility, enabling unified LLM and embedding model integration. Implements end-to-end RAG with hybrid retrieval methods (BM25+KNN), multi-modal image understanding, and agent-based tool orchestration for tasks like database queries, web search, and ArXiv paper analysis. Built on FastAPI backend and Streamlit frontend, supporting both local offline deployment and API-based access across diverse open-source models.
About Langchain-ChatBI
dynamiclu/Langchain-ChatBI
一种利用Langchain框架和本地向量库实现的对话式BI，它的目标是帮助用户寻找、理解数据知识，并能够分析数据、洞察结果，通过自然语言对话，降低数据分析的门槛。
Implements metric matching algorithms to ensure data accuracy in conversational queries, addressing a core challenge in dialogue-based BI systems. Supports flexible model deployment with local LLMs (ChatGLM-6B) or HTTP-based calls to proprietary models (Baichuan, Qwen), with vector embeddings (BGE, Text2Vec) for semantic understanding. Built on Langchain with local vector databases and Gradio for the web interface, enabling offline-first analytics without cloud dependencies.
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