feat(vector-io): configurable embedding models for all providers (v2)\n\nAdds embedding_model and embedding_dimension fields to all VectorIOConfig classes.\nRouter respects provider defaults with fallback.\nIntroduces embedding_utils helper.\nComprehensive docs & samples.\nResolves #2729

llama_stack/providers/inline/vector_io/chroma/config.py

@@ -6,12 +6,25 @@
 
 from typing import Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 
 class ChromaVectorIOConfig(BaseModel):
     db_path: str
+    embedding_model: str | None = Field(
+        default=None,
+        description="Optional default embedding model for this provider. If not specified, will use system default.",
+    )
+    embedding_dimension: int | None = Field(
+        default=None,
+        description="Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry.",
+    )
 
     @classmethod
     def sample_run_config(cls, db_path: str = "${env.CHROMADB_PATH}", **kwargs: Any) -> dict[str, Any]:
-        return {"db_path": db_path}
+        return {
+            "db_path": db_path,
+            # Optional: Configure default embedding model for this provider
+            # "embedding_model": "all-MiniLM-L6-v2",
+            # "embedding_dimension": 384,  # Only needed for variable-dimension models
+        }