Merge 32930868de into 2f58d87c22

llama_stack/apis/common/vector_store_config.py

@@ -0,0 +1,33 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from __future__ import annotations
+
+"""Vector store global config stuff.
+
+Basically just holds default embedding model settings so we don't have to
+pass them around everywhere. Router picks these up when client doesn't specify.
+"""
+
+import os
+
+from pydantic import BaseModel, ConfigDict, Field
+
+__all__ = ["VectorStoreConfig"]
+
+
+class VectorStoreConfig(BaseModel):
+    """Default embedding model config that gets picked up from env vars."""
+
+    default_embedding_model: str | None = Field(
+        default_factory=lambda: os.getenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL")
+    )
+    # dimension from env - fallback to None if not set or invalid
+    default_embedding_dimension: int | None = Field(
+        default_factory=lambda: int(os.getenv("LLAMA_STACK_DEFAULT_EMBEDDING_DIMENSION", 0)) or None, ge=1
+    )
+
+    model_config = ConfigDict(frozen=True)

llama_stack/core/datatypes.py

@@ -12,6 +12,7 @@ from urllib.parse import urlparse
 from pydantic import BaseModel, Field, field_validator, model_validator
 
 from llama_stack.apis.benchmarks import Benchmark, BenchmarkInput
+from llama_stack.apis.common.vector_store_config import VectorStoreConfig
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Dataset, DatasetInput
 from llama_stack.apis.eval import Eval
@@ -474,6 +475,12 @@ InferenceStoreConfig (with queue tuning parameters) or a SqlStoreConfig (depreca
 If not specified, a default SQLite store will be used.""",
     )
 
+    # Global vector-store defaults (embedding model etc.)
+    vector_store_config: VectorStoreConfig = Field(
+        default_factory=VectorStoreConfig,
+        description="Global defaults for vector-store creation (embedding model, dimension, …)",
+    )
+
     # registry of "resources" in the distribution
     models: list[ModelInput] = Field(default_factory=list)
     shields: list[ShieldInput] = Field(default_factory=list)

llama_stack/core/routers/vector_io.py

@@ -11,6 +11,7 @@ from typing import Any
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
 )
+from llama_stack.apis.common.vector_store_config import VectorStoreConfig
 from llama_stack.apis.models import ModelType
 from llama_stack.apis.vector_io import (
     Chunk,
@@ -76,6 +77,41 @@ class VectorIORouter(VectorIO):
             logger.error(f"Error getting embedding models: {e}")
             return None
 
+    async def _resolve_embedding_model(self, explicit_model: str | None = None) -> tuple[str, int]:
+        """Figure out which embedding model to use and what dimension it has."""
+
+        # if they passed a model explicitly, use that
+        if explicit_model is not None:
+            # try to look up dimension from our routing table
+            models = await self.routing_table.get_all_with_type("model")
+            for model in models:
+                if getattr(model, "identifier", None) == explicit_model:
+                    dim = model.metadata.get("embedding_dimension")
+                    if dim is None:
+                        raise ValueError(f"Model {explicit_model} found but no embedding dimension in metadata")
+                    return explicit_model, dim
+            # model not found in registry - this is an error
+            raise ValueError(f"Embedding model '{explicit_model}' not found in model registry")
+
+        # check if we have global defaults set via env vars
+        config = VectorStoreConfig()
+        if config.default_embedding_model is not None:
+            if config.default_embedding_dimension is None:
+                raise ValueError(
+                    f"default_embedding_model '{config.default_embedding_model}' is set but default_embedding_dimension is missing"
+                )
+            return config.default_embedding_model, config.default_embedding_dimension
+
+        # fallback to first available embedding model for compatibility
+        fallback = await self._get_first_embedding_model()
+        if fallback is not None:
+            return fallback
+
+        # if no models available, raise error
+        raise ValueError(
+            "No embedding model specified and no default configured. Either provide an embedding_model parameter or set vector_store_config.default_embedding_model"
+        )
+
     async def register_vector_db(
         self,
         vector_db_id: str,
@@ -102,7 +138,7 @@ class VectorIORouter(VectorIO):
         ttl_seconds: int | None = None,
     ) -> None:
         logger.debug(
-            f"VectorIORouter.insert_chunks: {vector_db_id}, {len(chunks)} chunks, ttl_seconds={ttl_seconds}, chunk_ids={[chunk.metadata['document_id'] for chunk in chunks[:3]]}{' and more...' if len(chunks) > 3 else ''}",
+            f"VectorIORouter.insert_chunks: {vector_db_id}, {len(chunks)} chunks, ttl_seconds={ttl_seconds}, chunk_ids={[chunk.chunk_id for chunk in chunks[:3]]}{' and more...' if len(chunks) > 3 else ''}",
         )
         provider = await self.routing_table.get_provider_impl(vector_db_id)
         return await provider.insert_chunks(vector_db_id, chunks, ttl_seconds)
@@ -131,13 +167,8 @@ class VectorIORouter(VectorIO):
     ) -> VectorStoreObject:
         logger.debug(f"VectorIORouter.openai_create_vector_store: name={name}, provider_id={provider_id}")
 
-        # If no embedding model is provided, use the first available one
-        if embedding_model is None:
-            embedding_model_info = await self._get_first_embedding_model()
-            if embedding_model_info is None:
-                raise ValueError("No embedding model provided and no embedding models available in the system")
-            embedding_model, embedding_dimension = embedding_model_info
-            logger.info(f"No embedding model specified, using first available: {embedding_model}")
+        # Determine which embedding model to use based on new precedence
+        embedding_model, embedding_dimension = await self._resolve_embedding_model(embedding_model)
 
         vector_db_id = f"vs_{uuid.uuid4()}"
         registered_vector_db = await self.routing_table.register_vector_db(

llama_stack/distributions/watsonx/build.yaml

@@ -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]