Implement auto-refresh vector store from OCI Object Storage bucket (#… #267
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) Add comprehensive functionality to automatically refresh vector stores when documents are added or modified in OCI Object Storage buckets while preserving original embedding parameters. Key features: - Change detection using object metadata (etag, time_modified) - Parameter preservation from existing vector stores - Incremental processing of only new/modified files - New REST API endpoint for refresh operations - Comprehensive status reporting Files modified: - common/schema.py: Add VectorStoreRefreshRequest and VectorStoreRefreshStatus schemas - server/api/utils/oci.py: Add get_bucket_objects_with_metadata() and detect_changed_objects() - server/api/utils/embed.py: Add refresh functionality with get_vector_store_by_alias(), get_processed_objects_metadata(), and refresh_vector_store_from_bucket() - server/api/v1/embed.py: Add POST /v1/embed/refresh endpoint
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to use last minimal SpringBoot version and the sys prompt defined for vector search.
Signed-off-by: Christopher Jones <christopher.jones@oracle.com>
* Added Unit Tests * Updated Documents * Updated Images
* Fix Release Action
* Shift pyproject.toml
* Expose FastMCP endpoints
Add reference to the new auto-refresh vector store functionality from OCI Object Storage buckets feature in the AI Optimizer Features section.
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Add comprehensive functionality to automatically refresh vector stores when documents are added or modified in OCI Object Storage buckets while preserving original embedding parameters.
Key features:
Files modified: