This project provides a web UI for building local knowledge-based question-answering systems using LangChain and various large language models (LLMs) like ChatGLM-6B. It targets developers and researchers looking to deploy LLM applications with custom data, offering support for multiple document formats and a range of LLMs and embedding models.

How It Works

The system leverages the LangChain framework to orchestrate LLM interactions. It processes uploaded documents (txt, docx, md, pdf) by chunking and embedding them using models like text2vec-large-chinese. These embeddings are stored in a vector database, enabling efficient retrieval of relevant context for user queries. The retrieved context is then passed to an LLM, such as ChatGLM-6B, to generate an answer based on the local knowledge.

Quick Start & Requirements

• Install: Requires Python >= 3.8.1. Specific installation commands depend on whether the ModelScope or HuggingFace version is used.
• Prerequisites: Access to LLM and embedding model files (downloadable from provided links if network issues arise).
• Resources: No specific hardware requirements are listed, but running LLMs locally typically requires significant GPU memory.
• Documentation: 部署文档, 视频教程

Highlighted Details

• Supports a wide array of LLMs including ChatGLM-6B series, Vicuna, and BELLE.
• Integrates multiple embedding models like text2vec-large-chinese and Ernie-3.0 series.
• Offers online demos via HuggingFace, OpenXLab, ModelScope, and PaddleAI Studio.
• Allows uploading of various document formats: txt, docx, md, pdf.

Maintenance & Community

The project is actively seeking community contributions for further development and optimization. Links to community communication channels are available.

Licensing & Compatibility

The project's licensing is not explicitly stated in the README, but it relies on and cites other projects with their own licenses. Compatibility for commercial use or closed-source linking would require careful review of the licenses of all dependencies.

Limitations & Caveats

The project is described as being in its early stages, indicating potential for instability, missing features, and ongoing changes. The lack of explicit licensing information is a significant caveat for adoption.