AI & RAG
Incremental Dropbox Spec Corpus Indexer
On a schedule, detects added or changed engineering specs in a Dropbox folder, chunks and embeds them.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled run (hourly or nightly)
- ActionList Dropbox spec files with modified timestampsDropbox
- LogicKeep only new, changed, or deleted files
- ActionChunk and embed changed file contentsOpenAI
- OutputUpsert vectors and purge deleted rows in PostgresPostgres
What it does
Keeps the retrieval index fresh. It scans a Dropbox engineering-spec folder, finds files that are new or modified since the last run, re-chunks and re-embeds only those, and upserts the vectors into Postgres. Deleted files are purged so stale answers can't surface.
When to use it
Run this as the backbone for any spec-grounded assistant. Without it, your retrieval corpus drifts from reality the moment someone edits a design doc. Schedule it hourly or nightly depending on how fast your specs change.
How it works
- 1A scheduled trigger fires on your chosen cadence.
- 2Dropbox is listed and each file's modified timestamp is compared against the last indexed state in Postgres.
- 3A filter keeps only files that are new, changed, or deleted.
- 4Changed file contents are chunked and embedded with OpenAI.
- 5Vectors are upserted into Postgres and removed rows are deleted, leaving a current, queryable index.
Set it up
What you configure once, before turning it on.
- 1Connect DropboxFiles and folders.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect PostgresAny Postgres URL — query, write, migrate.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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