"Exploring RAG through defined workspaces" matching MCP servers:

Enables indexing a folder of documents or uploaded files into a Qdrant vector store and retrieving relevant chunks with cosine similarity, metadata filters, and heuristic reranking. Exposes rag_stats, rag_search, and rag_index tools over HTTP and Streamable HTTP MCP so clients can fetch ready-to-use context blocks without any built-in chat model.

MCP server for Google's Gemini File Search (RAG). Manage file search stores, upload documents, and query with RAG through 12 tools.

Enables document-based Q&A with multi-modal RAG, hybrid retrieval, knowledge graph reasoning, and multi-agent orchestration via MCP tools.

Enables AI assistants to perform retrieval-augmented generation with hybrid search, reranking, multi-modal image processing, and RAG evaluation through standardized MCP tools.

Enables MCP clients to query a modular RAG knowledge hub with hybrid dense/sparse retrieval, reranking, multimodal image support, and traceable observability, all through natural language tools.

Enables AI clients to retrieve study-abroad admissions knowledge through a local, pluggable RAG pipeline with hybrid search, reranking, citations, and observability. It provides MCP tools, document ingestion, and a Streamlit dashboard for managing and evaluating retrieval.

Enables creating Manim animations via natural language commands using DeepSeek API, with a RAG system for Manim documentation retrieval.

A lightweight RAG server deployed on Azure Container Apps that ingests PDFs, creates embeddings, and exposes a search_documents tool over MCP for retrieving relevant document chunks.

Enables querying internal documents via a FastAPI REST API and MCP server, using retrieval-augmented generation and an agentic loop that can invoke tools like document search and calculations.

Generates RAG pipeline configurations from natural language descriptions, providing recommendations for chunking, embeddings, and vector stores.

Enables asking natural language questions about local videos, using Whisper transcription, ChromaDB vector storage, and Groq for timestamped answers via MCP.

A local Retrieval-Augmented Generation system that enables AI agents to query and retrieve information from document collections using ChromaDB vector search and Ollama LLMs through a FastAPI interface.

Enables deterministic auditing of RAG retrieval faithfulness and hallucination scores, providing structured JSON output for AI agents and MCP-compliant clients.

Enables querying local documents via retrieval-augmented generation, along with current UTC datetime and safe arithmetic evaluation through MCP tools over HTTP.

The MCP server provides tools for entity extraction, query refinement, and relevance checking to enhance RAG applications by integrating with OpenAI and Gemini.

An MCP server for AI-driven hybrid search over Xilinx/AMD FPGA documentation, enabling assistants to retrieve datasheets, user guides, and application notes through tools like search_docs.

Enables LLM clients to chat with locally indexed markdown engineering documentation through semantic search, keeping all indexing and retrieval on-device for privacy.

A hosted multimodal RAG MCP server that enables natural language queries over text and visual content (PDFs, images, etc.) with verifiable citations, easily run via npx with a Calypso API key.

A local-first AI backend that combines Model Context Protocol, Retrieval-Augmented Generation, and an agent runtime to answer questions from a private knowledge base.

Exposes the RAG pipeline as a callable rag_query tool so external agents and clients can retrieve governed, context-grounded answers backed by FAISS retrieval and a Groq LLM.