Questions to your own documents — answered from the text, not from memory

Upload your documents — the scenario processes them into a searchable memory and answers questions strictly from their content, returning repeat questions instantly.

  • The answer is built from your documents, not invented
  • The search works by meaning, not by exact word match
  • A repeat question comes back instantly, from cache
  • Uploads and questions go through one entry point
WebhookDefault Data LoaderEmbeddings OpenAIPostgres PGVector Store
Connect Qdrant Vector Store and run itScriptera

How it works

To connect Qdrant Vector Store and Databricks, you don't need a developer: a ready-made scenario links them in minutes.

  1. Starts when: Webhook Trigger
  2. Then: Workflow Configuration
  3. Check: Route by Action
  4. If yes: Extract Text from Document
  5. Then: Store Embeddings in PGVector
  6. Then: Log Upload to Cache
  7. Starts when: Respond Upload Success
  8. If no: Check Query Cache
  9. Check: Cache Hit or Miss
  10. If yes: Format Cached Response
  11. Starts when: Respond with Cached Answer
  12. If no: Answer Query with Context
  13. Then: Save to Query Cache
  14. Starts when: Respond with Answer
  15. Then: Text Splitter
  16. Then: Document Loader
  17. Then: OpenAI Embeddings
  18. Then: Retrieve Relevant Chunks
  19. Then: OpenAI Chat Model
  20. Then: Answer questions with a vector store

You can launch this Qdrant Vector Store + Databricks integration in Scriptera: describe the task in plain words — the scenario is built, launched and monitored for you.

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