community-automations/pinecone-rag

Research & Intel

PublicClaude subagent

Pinecone RAG

Most RAG demos look fine until the model answers from training memory instead of your documents. This baseline keeps it honest.

sonnet3 daysPineconeOpenAILangChainPython
ClaudeClaude
ROI for
README.md

Why this subagent

Most RAG demos look fine until the model answers from training memory instead of your documents. This baseline keeps it honest.

Documents are chunked and embedded into a Pinecone namespace; each question pulls the closest chunks, and the model answers only from those. Every answer cites the chunks it used, and when nothing relevant comes back, it says so instead of inventing one. A clean starting point you can extend.

How it runs

    • Read

      Used at step 01 to kick off the pipeline.

    • Write

      Used at step 01 to kick off the pipeline.

    • WebFetch

      Used at step 01 to kick off the pipeline.

    • WebSearch

      Used at step 01 to kick off the pipeline.

Sample output

json
// Sample output
// (generated when the pipeline finishes)

Answer questions from Pinecone-retrieved chunks only, citing the chunk IDs used and refusing when context is missing.

Unlock the rest

The full agent definition, install snippet, and starter task are gated for community members.

Members get the full `.md` agent file, the npm / pnpm install one-liners, a starter prompt that we've tuned against real runs, and the open-source repo when this automation ships there. One email, magic link, done.