Enables interaction with Pinecone vector databases for storing and searching embeddings. Supports similarity search, metadata filtering, and vector operations for semantic search and RAG applications.
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.
A modular tool that combines RAG-based retrieval with Pinecone vector storage to create intelligent assistants capable of answering domain-specific questions from your knowledge base.
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.
Enables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.
