Provides semantic search and management of shared documentation using ChromaDB and OpenAI embeddings. It enables users to query local documents by meaning, list files, and read content through natural language tools.
Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.
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.
Exposes a RAG document-search API as MCP tools (rag_health, rag_ingest, rag_query), enabling agents to index and search markdown documents with cited results through natural language.
Enables interaction with .zim archives by providing tools for article search, content retrieval, and metadata discovery. It features a TF-IDF based RAG engine for semantic retrieval over extracted article chunks from compressed ZIM files.
An MCP-based AI agent that retrieves and processes documents to answer queries using a RAG pipeline with LangChain and Claude models. It enables document indexing, context-aware retrieval, and multi-tool orchestration for research and knowledgebase applications.
