AI & RAG

Knowledge-Grounded Q&A Webhook API

Exposes an HTTP endpoint that takes a question, retrieves grounded context from the team's Confluence space and Dropbox folder.

CategoryAI & RAG
Enginesim
Difficultyadvanced
Triggerwebhook
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerHTTP webhook receives a question payloadHTTP webhook
  • ActionSearch Confluence team space for contextConfluenceConfluence
  • ActionSearch Dropbox folder for contextDropboxDropbox
  • LogicRank context; return empty result if no match
  • ActionGenerate grounded answer via OpenAIOpenAI
  • OutputRespond with cited JSON answer over HTTPHTTP webhook

What it does

Provides a reusable question-answering API backed by your team's documented knowledge. A caller POSTs a question to a webhook; the flow retrieves relevant passages from the configured Confluence space and Dropbox folder, generates an answer with OpenAI grounded only in that context, and responds with structured JSON including the answer text and the source pages and files it used. Embed it in an in-app help widget, a portal search box, or another workflow.

When to use it

Use it when you need programmatic, embeddable answers from one team's knowledge rather than a chat UI. Ideal for product help widgets, internal tools, or chaining into larger automations.

How it works

  1. 1An inbound HTTP webhook with a question payload triggers the flow.
  2. 2The flow searches the Confluence team space and Dropbox folder for relevant context.
  3. 3A logic step assembles ranked context and returns an empty-result response if nothing matches.
  4. 4OpenAI generates a grounded answer with source attribution.
  5. 5The webhook responds with a JSON answer and citation list.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HTTP webhookTrigger any URL on agent actions.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Connect DropboxFiles and folders.
  4. 4
    Connect OpenAIModels, embeddings, files.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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