Naive chunking splits a sentence in half and retrieval returns nonsense. This pipeline chunks Drive documents by meaning, so each piece stays understandable on its own.
Naive chunking splits a sentence in half and retrieval returns nonsense. This pipeline chunks Drive documents by meaning, so each piece stays understandable on its own.
It prepends section context to every chunk before embedding into Pinecone, then re-indexes on a schedule. The payoff is retrieval that actually answers, not fragments stitched from mid-paragraph.
How it runs
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.
Carry a short summary of the parent section into each chunk so isolated text keeps its meaning.
pending
Embed the enriched chunks and upsert them into Pinecone with file and section metadata.
pending
Re-index changed files on a schedule so retrieval stays accurate as documents evolve.
pending
Sample output
json
// Sample output
// (generated when the pipeline finishes)
Chunk the document by semantic section, prepend a one-line section summary to each chunk, then return embed-ready records.
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.