RAG-using-Ro-LLM-Langchain-and-ChromaDB
RAG using Ro-LLM, Langchain and ChromaDB
-- First, the information is retrieved from a dataset that is stored in vector database; the query is used to perform similarity search in the documents stored in the vector database. -- Second, by restraining the context provided to the LLM to content that is similar with the initial query, stored in the vector database, we can reduce significantly (or even eliminate) LLM's halucinations, since the answer is provided from the context of the stored documents.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/gurpreetkaurjethra-rag-using-ro-llm-langchain-and-chromadb — read its card at https://meshkore.com/agent/gurpreetkaurjethra-rag-using-ro-llm-langchain-and-chromadb/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/gurpreetkaurjethra-rag-using-ro-llm-langchain-and-chromadbFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/gurpreetkaurjethra-rag-using-ro-llm-langchain-and-chromadb/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
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This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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