A modular Retrieval-Augmented Generation (RAG) framework that provides hybrid search and knowledge retrieval capabilities via the Model Context Protocol. It enables users to integrate document-based knowledge into LLM workflows with support for dense/sparse retrieval, reranking, and observability.
Enables advanced RAG with knowledge graphs, supporting document ingestion, multimodal extraction, and multiple query modes (naive, local, global, hybrid) via the Model Context Protocol.
A pluggable RAG framework that exposes hybrid search, ingestion, and evaluation tools via the Model Context Protocol, enabling AI assistants like Copilot and Claude to query knowledge bases directly.
A pluggable and observable modular RAG framework that enables AI assistants to perform semantic search, document Q\&A, and knowledge base retrieval. It supports hybrid search, reranking, and multiple LLM backends through a standardized Model Context Protocol interface.
A modular RAG (Retrieval-Augmented Generation) service framework with pluggable architecture and full observability, enabling AI assistants to perform document Q\&A, semantic search, and knowledge base construction through the Model Context Protocol.
Exposes RAG-related tools (query_knowledge_hub, list_collections, get_document_summary) via the Model Context Protocol, enabling AI assistants to perform hybrid search, document ingestion, and evaluation on knowledge bases.
