MCP server for document ingestion and semantic search on Qdrant. Enables ingesting local documents, generating embeddings with OpenAI, and performing vector search with metadata filters.
A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.
MCP server for building knowledge graphs from documents. It ingests PDF/PPTX/DOCX files, transcribes to Markdown, and uses LLM to bootstrap entity patterns and build a JSONL knowledge graph with RAG chunks.