AI & RAG

Index new Dropbox call recordings into a searchable Postgres knowledge base

Watches a Dropbox folder for new sales call recordings, transcribes and chunks them, embeds the chunks.

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew recording added to Dropbox folderDropboxDropbox
  • ActionDownload and transcribe audio with OpenAIOpenAI
  • LogicChunk transcript and skip already-ingested files
  • ActionEmbed passages with OpenAIOpenAI
  • OutputUpsert vectors and metadata into PostgresPostgreSQLPostgres

What it does

Keeps your RAG knowledge base fresh by ingesting every new sales call recording dropped into Dropbox. It transcribes the audio, splits it into passages with speaker and timestamp metadata, embeds each passage, and stores everything in Postgres so later question-answering flows can cite specific moments from real customer conversations.

When to use it

Run this as the always-on ingestion backbone behind any call-recording RAG. Use it when your reps and recorders save calls to Dropbox and you want those calls searchable within minutes, not stuck in a folder nobody reopens.

How it works

  1. 1A new audio file landing in the watched Dropbox folder triggers the flow.
  2. 2The file is downloaded and sent to OpenAI for transcription.
  3. 3The transcript is chunked into overlapping passages tagged with timestamps and the source file path.
  4. 4Each chunk is embedded via OpenAI.
  5. 5A dedupe check skips files already ingested by content hash.
  6. 6The embeddings and metadata are upserted into the pgvector table, ready for retrieval.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DropboxFiles and folders.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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