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Public Preview: AI pipelines in Azure HorizonDB
AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database.
Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed step.
This removes the boilerplate ingestion service that many GenAI applications rebuild today. Pipelines run on change, so embeddings stay in sync as source rows are inserted or updated, and only new or changed rows need to be re-embedded.
When the embedding model, dimensions, or chunk size change, backfill reprocesses every row as a single durable instance that resumes from the last checkpoint instead of restarting from zero. AI pipelines are part of the azure_ai extension, built on pg_durable, and work with AI model management, pgvector, DiskANN vector search, and full-text search to provide an end-to-end retrieval path inside one database.
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