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feat: add OpenAI's async batch API support for vectorizer#564
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Adds support for OpenAI's async batch API to process large amounts of embeddings at a lower cost, and higher rate limits. Key features: - New AsyncBatchEmbedder interface for handling async batch operations - Support for OpenAI's batch API implementation - New database tables for tracking batch status and chunks - Configurable polling interval for batch status checks - Automatic retry mechanism for failed batches Database changes: - New async_batch_queue_table for tracking batch status - New async_batch_chunks_table for storing chunks pending processing - Added async_batch_polling_interval column to vectorizer table - New SQL functions for managing async batch operations API changes: - New async_batch_enabled parameter in ai.embedding_openai() - New ai.vectorizer_enable_async_batches() and ai.vectorizer_disable_async_batches() functions - Extended vectorizer configuration to support async batch operations The async batch workflow: 1. Chunks are collected and submitted as a batch to OpenAI 2. Batch status is monitored through polling 3. When ready, embeddings are retrieved and stored 4. Batch resources are cleaned up after successful processing
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@kolaente this is a first draft of the PR we have some pending changes to our interfaces and we need to see how it'll affect this work. |
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Adds support for OpenAI's async batch API to process large amounts of embeddings at a lower cost, and higher rate limits.
Key features:
Database changes:
API changes:
The async batch workflow:
https://www.loom.com/share/09ff363c52204bf6851a797d4c4c4d50?sid=79e95d06-017d-4de5-a740-aa5af916b971