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