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feat(vector-io): implement global default embedding model configuration (Issue #2729)

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- 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.
This commit is contained in:
skamenan7 2025-07-25 17:06:43 -04:00
parent 8422bd102a
commit 17fbd21c0d
7 changed files with 243 additions and 8 deletions
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3
llama_stack/distributions/watsonx/build.yaml
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@ -39,6 +39,9 @@ distribution_spec:
- provider_type: remote::tavily-search
- provider_type: inline::rag-runtime
- provider_type: remote::model-context-protocol
vector_store_config:
default_embedding_model: ${env.LLAMA_STACK_DEFAULT_EMBEDDING_MODEL:=all-MiniLM-L6-v2}
default_embedding_dimension: ${env.LLAMA_STACK_DEFAULT_EMBEDDING_DIMENSION:=384}
image_type: venv
additional_pip_packages:
- sqlalchemy[asyncio]