A local Retrieval-Augmented Generation system that enables users to ingest markdown files into a FAISS-powered vector knowledge base for semantic search. It provides tools for document indexing and context retrieval to support informed LLM queries without external dependencies.
Semantic search and retrieval system for local documents using vector embeddings, enabling AI-powered search across your document collections with support for multiple embedding providers.
Enables AI assistants to search and query PDF documents through a local RAG system with vector embeddings. Provides semantic document search capabilities while keeping all data stored locally without external dependencies.
Enables intelligent search and question-answering over PDF documents using semantic similarity and keyword search. Supports OCR for scanned PDFs, persistent vector storage with ChromaDB, and maintains source tracking with page numbers.
A FastAPI-based application that enables document embedding and semantic retrieval using Qdrant vector database, allowing users to convert documents into embeddings and retrieve relevant content through natural language queries.
