Many agent projects struggle with the complex, multi-step process of integrating document knowledge, requiring PDF parsing, text chunking, embedding generation, and vector database operations. This server streamlines the ingestion half of a production RAG system, collapsing the entire pipeline into a single agent-callable tool. It autonomously extracts content from PDF directories, chunks the text, generates local embeddings, and stores them in pgvector, making private knowledge bases readily available for semantic search and AI applications.
Key Features
01Idempotent indexing for efficient updates of new or changed files
02Automatically creates HNSW indexes for fast similarity search
03Simplified single command setup for deployment
049 GitHub stars
05Supports background PDF ingestion with progress monitoring
06Generates local embeddings without external API dependencies
