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.
Enables semantic search across documents and code repositories using RAG (Retrieval-Augmented Generation) with vector embeddings. Automatically indexes PDF documents and performs relevance-scored lookups through ChromaDB and sentence transformers.
Enables semantic search and retrieval-augmented generation (RAG) using Qdrant vector database. Supports indexing documents from URLs and local directories, with flexible embedding options using Ollama or OpenAI.
Provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, allowing users to add, search, list, and delete documentation with metadata support.