skamenan7 17fbd21c0d feat(vector-io): implement global default embedding model configuration (Issue #2729) ... - Add VectorStoreConfig with global default_embedding_model and default_embedding_dimension - Support environment variables LLAMA_STACK_DEFAULT_EMBEDDING_MODEL and LLAMA_STACK_DEFAULT_EMBEDDING_DIMENSION - Implement precedence: explicit model > global default > clear error (no fallback) - Update VectorIORouter with _resolve_embedding_model() precedence logic - Remove non-deterministic 'first model in run.yaml' fallback behavior - Add vector_store_config to StackRunConfig and all distribution templates - Include comprehensive unit tests for config loading and router precedence - Update documentation with configuration examples and usage patterns - Fix error messages to include 'Failed to' prefix per coding standards Resolves deterministic vector store creation by eliminating unpredictable fallbacks and providing clear configuration options at the stack level.		2025-09-18 10:11:44 -04:00
..
__init__.py	API Updates (#73)	2024-09-17 19:51:35 -07:00
content_types.py	docs: Add detailed docstrings to API models and update OpenAPI spec (#2889)	2025-07-30 16:32:59 -07:00
errors.py	feat: Add Kubernetes auth provider to use SelfSubjectReview and kubernetes api server (#2559)	2025-09-08 11:25:10 +02:00
job_types.py	docs: Add detailed docstrings to API models and update OpenAPI spec (#2889)	2025-07-30 16:32:59 -07:00
responses.py	docs: Add detailed docstrings to API models and update OpenAPI spec (#2889)	2025-07-30 16:32:59 -07:00
training_types.py	docs: Add detailed docstrings to API models and update OpenAPI spec (#2889)	2025-07-30 16:32:59 -07:00
type_system.py	docs: Add detailed docstrings to API models and update OpenAPI spec (#2889)	2025-07-30 16:32:59 -07:00
vector_store_config.py	feat(vector-io): implement global default embedding model configuration (Issue #2729)	2025-09-18 10:11:44 -04:00