Bring Rag Framework
to LangChain

Connect your Cognita (TrueFoundry) instance to any AI agent and take full control of your modular RAG workflows through natural conversation.

What you can do

• Knowledge Collections — List and audit RAG collections to inspect embedding configurations, token lengths, and parser details
• Data Ingestion — Force sync remote files from SQL, Cloud Storage, or APIs into your vector space to update your knowledge base
• RAG Queries — Dispatch automated AI questions that query your vector store and synthesize accurate answers from stored context
• Chunk Auditing — Perform lexical or semantic searches to pull raw document chunks and verify precise text segments
• Model Registry — Enumerate available LLMs and embedding models registered inside your modular Cognita installation
• DataSource Management — List all connected data sources to verify which external data is mapped into your AI workflows

How it works

1. Subscribe to this server
2. Enter your Cognita Base URL and API Key (if required by your TrueFoundry or self-hosted setup)
3. Start managing your RAG pipelines from Claude, Cursor, or any MCP-compatible client

Who is this for?

• AI Engineers — test and debug RAG queries and chunk retrieval logic without writing Python scripts
• Data Scientists — monitor ingestion pipelines and verify document chunking consistency across collections
• Product Teams — quickly audit what knowledge is being fed to AI agents during the prototyping phase
• DevOps Teams — monitor Cognita model registries and ensure that all LLM endpoints are active and reachable