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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.
Ingest documents and query them with natural-language semantic search.
Build and query vector knowledge bases for semantic search and RAG workflows.
Retrieve and process docs with vector search to enrich AI responses.
Turn unstructured documents into a searchable knowledge base for AI agents.
Crawl websites, build a vector knowledge base, and run semantic search.
Intelligent RAG tool that chooses between private knowledge and web search.