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Connect AI to documents, embeddings, and semantic search through PostgREST APIs.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "RagLit MCP Server" yet — see the docs or source repo.
Use RagLit MCP Server to ingest this document folder, generate embeddings for all files, and build a semantic search index; then return the available data sources and retrieval recommendations.
A list of ingested documents, embedding status, searchable index details, and follow-up query suggestions.
Using RagLit MCP Server, search the ingested documents for content related to 'PostgREST API authentication and access control', return the top 5 results by relevance, and include summaries.
Top relevant document snippets with source information, relevance ranking, and brief summaries.
Use RagLit MCP Server to process this set of product manuals: ingest documents, chunk them, generate embeddings, and explain how a chatbot can call them for semantic retrieval.
RAG-ready processed data plus guidance for integrating semantic retrieval into a chatbot.
Turn unstructured documents into a searchable knowledge base for AI agents.
Let AI securely query private local documents with persistent memory.
Intelligent RAG tool that chooses between private knowledge and web search.
Index documents and retrieve relevant context for better LLM responses.
Retrieve relevant document chunks and generate suggested LLM prompts via REST and MCP.
Search local documents with vector similarity for RAG answers.