Local Rag AI Agent Skills

The whole local AI stack in one executable: it runs and manages the models, and searches everything you own with them. Project site  ·  Tutorial reels  ·  PyPI  ·  Obsidian plugin  ·  REST API  ·  Chat (lilbee) lilbee runs and manages your models: chat, embedding, vision, and rerank, placed across every GPU you have. It puts them to work as a searc

A Model Context Protocol (MCP) server that provides local vector database functionality using FAISS for Retrieval-Augmented Generation (RAG) applications. - Local Vector Storage: Uses FAISS for efficient similarity search without external dependencies - Document Ingestion: Automatically chunks and embeds documents for storage - Semantic Search: Query documents using natural language with sentence

Provides score interpretation (< 0.3 good, > 0.5 skip), query optimization, and source naming for query_documents, ingest_file, ingest_data tools. Use this skill when working with RAG, searching documents, ingesting files, saving web content, or handling PDF, HTML, DOCX, TXT, Markdown.