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Use RAG tools for knowledge retrieval, document management, and search visualization.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-rag-assistant" yet — see the docs or source repo.
Using the connected document store, retrieve information on “the difference between hybrid search and reranking in a RAG system,” provide an answer, and show the main retrieval steps and supporting documents.
A document-grounded answer with retrieval-step details and relevant matched documents.
Please organize the current document list in the knowledge base, identify which materials are suitable to keep for question answering, and explain how they can be used in future retrieval.
Document management recommendations and an explanation of how the documents support RAG retrieval.
Run retrieval for a sample question and show visualization details for hybrid search, reranking, and the retrieval flow before final answer generation.
A visualized or structured view of the retrieval pipeline showing how the result was produced.
Developers building LLM-based Q&A systems or assistants can use it to add document retrieval and management capabilities. It fits scenarios that need hybrid search and reranking to improve recall and ranking quality.
Researchers or product managers can use retrieval-process visualization to inspect how knowledge-base answers are produced. This makes it easier to understand the relationship between retrieval, reranking, and final output.
Teams updating a knowledge base over time can use it for document management and to maintain a stable foundation for future retrieval. It is suitable for managing internal documents, reference materials, or documentation collections.
It provides RAG-based knowledge retrieval and document management, with support for hybrid search, reranking, and retrieval-process visualization. It is useful when you need to connect documents to an LLM retrieval pipeline.
Based on the provided information, it supports hybrid search, reranking, and retrieval-process visualization. These features help improve retrieval quality and make the result-generation process easier to inspect.
The provided material does not include installation steps, runtime requirements, or API key details. Please see the source repository for setup prerequisites and deployment instructions.
Search and add traceable RAG knowledge for each project workspace.
Build and query vector knowledge bases for semantic search and RAG workflows.
Turn unstructured documents into a searchable knowledge base for AI agents.
Use authenticated MCP tools for graph-augmented and hybrid RAG retrieval.
Search documents with hybrid retrieval, reranking, guardrails, and tracing.
Intelligent RAG tool that chooses between private knowledge and web search.