Enables AI assistants to search and retrieve information from your knowledge base using RAG (Retrieval-Augmented Generation) with hybrid search, document indexing, and ChromaDB vector storage.
Enables document Q&A and knowledge retrieval through hybrid semantic and keyword search, with tools for document ingestion, chunking, summarization, PII redaction, and RAGAS-based evaluation.
Enables AI assistants to perform semantic search, manage vectors, and interact with Pinecone vector databases through standardized MCP tools. Supports querying, upserting, deleting vectors and monitoring database statistics for knowledge base operations.
Enables RAG-powered documentation search using OpenAI embeddings and Pinecone vector database. Provides an extensible framework for adding additional tools with support for both local STDIO and production HTTP transports.
Enables searching and asking questions over your PDF documents using a Pinecone vector index with local embeddings and language model, no external LLM API key required.
A modular RAG framework exposing knowledge retrieval tools via MCP, enabling AI assistants to perform hybrid search, reranking, and multimodal document queries with full observability and evaluation.