RAG for Local LLM, chat with PDF/doc/txt files, ChatPDF. 纯原生实现RAG功能，基于本地LLM、embedding模型、reranker模型实现，支持GraphRAG，无须安装任何第三方agent库。

An extensible Model Context Protocol (MCP-Local-MRL-RAG-AST) server that provides intelligent semantic code search for AI assistants. Built with local AI models, inspired by Cursor's semantic search.

Local-first RAG server for developers using MCP. Semantic + keyword search for code and technical docs. Fully private, zero setup.

Local RAG server for code editors. Scans your codebase, builds a local context index, and connects to any external LLM for context-aware completions and assistance.

Local FAISS vector store as an MCP server – Agent Memory, drop-in local semantic search for Claude / Copilot / Agents.

Local-first RAG desktop app & official MCP Server. Let any AI instantly search your private Markdown, PDF, and 1290+ document formats. (本地优先的 RAG 桌面端与官方 MCP 服务器。让任意 AI 瞬间检索你的私有 Markdown、PDF 及 1290+ 种文档格式。)

Local RAG system with a built-in governance agent that filters sensitive or restricted information with separated agent logging systems to keep privacy and security

Nous: A privacy-focused personal knowledge assistant using local LLMs to securely interact with your documents and enhance information retrieval.

A fully local, privacy-first personal AI assistant. Ask questions about your own data — emails, documents, chats — without anything leaving your machine.

All CPU efficient GPU-less Financial Analysis RAG Model with Qdrant, Langchain and GPT4All x Mistral-7B, run RAG without any GPU support!

⊛ Consciousness Architecture. Change the word. Change the world. ⊛

A RAG-based question-answering system that processes user queries using local documents. It extracts relevant information to answer questions, falling back to a large language model when local sources are insufficient, ensuring accurate and contextual responses.

Self-contained RAG for consumer laptops (16 GB RAM): local ingestion, hybrid retrieval (FAISS + BM25), streaming processing, GDPR-aligned privacy. Runs 10 GB corpora offline with Llama 3.2.

Local-first RAG framework for maximizing the value of private and on-device LLM systems.

Kash is a Go CLI that turns your raw documents (PDFs, Markdown, text files) into a self-contained AI agent packaged in a lightweight Docker container.

Chat to your documents on your local device using SentenceTransformers and Ollama for Privacy or configure the way you want using cloud LLMs for faster response. 100% private or 100% faster response time, choice is yours.

A completely local RAG.

Automates hermetic environments (macOS/HPC) to eliminate drift. Provisions offline RAG (Gemma 2), compiles LaTeX manuscripts, and indexes local knowledge. Unifies infrastructure, writing, and inference into a single, audit-ready artifact.

RAG your Language Model convos in local. OpenAI ChatGPT support

LocalRAG: Evaluating local RAG pipeline [University of St. Gallen Bachelor Thesis]