veyliss/ai-localbase

一个本地优先的AI知识库系统（RAG），用于把本地文档接入辅导搜索与大模型对话流程。目前支持md、txt、pdf（文本）、xlsx、cvs类型。支持mcp服务

What it solves

AI LocalBase is a local-first Retrieval-Augmented Generation (RAG) system designed to let individuals and small teams quickly build a private knowledge base. It solves the problem of integrating local documents into a vector search and LLM conversation flow without relying on external cloud services.

How it works

The system uses a Go-based backend and a React frontend. It integrates with Qdrant as the vector database to store and retrieve document embeddings. Users can upload files (TXT, Markdown, PDF, xlsx, csv) which are automatically split and embedded. The system supports connecting to LLMs via Ollama or OpenAI-compatible APIs for the actual conversation and embedding generation.

Who it’s for

  • Individuals seeking a private, local personal knowledge base.
  • Small teams needing internal document Q&A systems.
  • Developers wanting to prototype and experiment with different RAG retrieval strategies.
  • Users of AI agents (like Cursor or Claude Desktop) who want to connect their local knowledge base via the built-in MCP server.

Highlights

  • Local-first Architecture: Fully self-hosted via Docker Compose, using SQLite for chat history and JSON for configuration.
  • Advanced Retrieval: Includes features like MMR (Maximal Marginal Relevance) for redundancy removal, hybrid search, semantic reranking, and query rewriting.
  • MCP Server Integration: Built-in Model Context Protocol (MCP) support allows external AI agents to access the knowledge base and session capabilities.
  • Curation and Evaluation: Ability to generate RAG evaluation datasets from existing documents to verify retrieval quality.
  • Flexible Model Access: Compatible with both local (Ollama) and cloud (OpenAI) providers.

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