An AI index of your database structure — updating only what changed

The scenario reads table structures from Databricks, turns each into a vector stored in Postgres PGVector Store, and uses dataTable to update only what actually changed.

  • AI gets an accurate map of your database structure
  • Only changes are updated — no wasted load
  • Each table is indexed as one complete context
  • Reprocessing is skipped via a schema fingerprint
Embeddings OpenAIDefault Data LoaderPinecone Vector Store
Connect Postgres PGVector Store and runScriptera

How it works

To keep Databricks and Postgres PGVector Store in sync, connect them with a ready-made scenario — changes flow automatically.

  1. Starts when: Sync DB Schema to Vector Store
  2. Then: Load Global Configuration
  3. Then: Fetch All Database Tables
  4. Check: Loop Over executable queries
  5. Then: Set Table Schema Context
  6. Then: Fetch Table Schema Definition
  7. Then: Generate Schema Hash
  8. Then: Check Existing Vector Metadata
  9. Check: Vector Exists?
  10. If yes: Insert Schema Vector to Pinecone
  11. Then: Upsert Vector Metadata Record
  12. If no: Schema Changed?
  13. If yes: Delete Existing Vector (Pinecone)
  14. Then: Delete Vector Metadata Record
  15. Then: Split Schema Text (No Chunking)
  16. Then: Generate Schema Embeddings
  17. Then: Prepare Schema Document

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

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