Merge 32930868de into 2f58d87c22

tests/integration/conftest.py

@@ -94,6 +94,19 @@ def pytest_configure(config):
             if not current:
                 setattr(config.option, dest, value)
 
+    # After processing CLI --env overrides, ensure global default embedding model is set for vector-store operations
+    embedding_model_opt = config.getoption("--embedding-model") or "sentence-transformers/all-MiniLM-L6-v2"
+    if embedding_model_opt and not os.getenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL"):
+        # Use first value in comma-separated list (if any)
+        default_model = embedding_model_opt.split(",")[0].strip()
+        os.environ["LLAMA_STACK_DEFAULT_EMBEDDING_MODEL"] = default_model
+        logger.info(f"Setting LLAMA_STACK_DEFAULT_EMBEDDING_MODEL={default_model}")
+
+    embedding_dim_opt = config.getoption("--embedding-dimension") or 384
+    if not os.getenv("LLAMA_STACK_DEFAULT_EMBEDDING_DIMENSION") and embedding_dim_opt:
+        os.environ["LLAMA_STACK_DEFAULT_EMBEDDING_DIMENSION"] = str(embedding_dim_opt)
+        logger.info(f"Setting LLAMA_STACK_DEFAULT_EMBEDDING_DIMENSION={embedding_dim_opt}")
+
 
 def pytest_addoption(parser):
     parser.addoption(

tests/unit/common/test_vector_store_config.py

@@ -0,0 +1,29 @@
+# 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 llama_stack.apis.common.vector_store_config import VectorStoreConfig
+
+
+def test_defaults(monkeypatch):
+    # ensure env is clean to avoid flaky defaults
+    monkeypatch.delenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL", raising=False)
+    monkeypatch.delenv("LLAMA_STACK_DEFAULT_EMBEDDING_DIMENSION", raising=False)
+    config = VectorStoreConfig()
+    assert config.default_embedding_model is None
+    assert config.default_embedding_dimension is None
+
+
+def test_env_loading(monkeypatch):
+    monkeypatch.setenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL", "test-model")
+    monkeypatch.setenv("LLAMA_STACK_DEFAULT_EMBEDDING_DIMENSION", "123")
+
+    config = VectorStoreConfig()
+    assert config.default_embedding_model == "test-model"
+    assert config.default_embedding_dimension == 123
+
+    # cleanup
+    monkeypatch.delenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL", raising=False)
+    monkeypatch.delenv("LLAMA_STACK_DEFAULT_EMBEDDING_DIMENSION", raising=False)

tests/unit/router/test_embedding_precedence.py

@@ -0,0 +1,112 @@
+# 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.
+
+
+import pytest
+
+from llama_stack.apis.models import ModelType
+from llama_stack.core.routers.vector_io import VectorIORouter
+
+pytestmark = pytest.mark.asyncio
+
+
+class _DummyModel:
+    def __init__(self, identifier: str, dim: int):
+        self.identifier = identifier
+        self.model_type = ModelType.embedding
+        self.metadata = {"embedding_dimension": dim}
+
+
+class _DummyRoutingTable:
+    """Just a fake routing table for testing."""
+
+    def __init__(self):
+        self._models = [
+            _DummyModel("first-model", 123),
+            _DummyModel("second-model", 512),
+        ]
+
+    async def get_all_with_type(self, _type: str):
+        # just return embedding models for tests
+        return self._models
+
+    # VectorIORouter needs these but we don't use them in tests
+    async def register_vector_db(self, *_args, **_kwargs):
+        raise NotImplementedError
+
+    async def get_provider_impl(self, *_args, **_kwargs):
+        raise NotImplementedError
+
+
+async def test_global_default_used(monkeypatch):
+    """Should use env var defaults when no explicit model given."""
+
+    monkeypatch.setenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL", "env-default-model")
+    monkeypatch.setenv("LLAMA_STACK_DEFAULT_EMBEDDING_DIMENSION", "256")
+
+    router = VectorIORouter(routing_table=_DummyRoutingTable())
+
+    model, dim = await router._resolve_embedding_model(None)
+    assert model == "env-default-model"
+    assert dim == 256
+
+    # cleanup
+    monkeypatch.delenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL", raising=False)
+    monkeypatch.delenv("LLAMA_STACK_DEFAULT_EMBEDDING_DIMENSION", raising=False)
+
+
+async def test_explicit_override(monkeypatch):
+    """Explicit model should win over env defaults."""
+
+    monkeypatch.setenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL", "env-default-model")
+
+    router = VectorIORouter(routing_table=_DummyRoutingTable())
+
+    model, dim = await router._resolve_embedding_model("first-model")
+    assert model == "first-model"
+    assert dim == 123
+
+    monkeypatch.delenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL", raising=False)
+
+
+async def test_fallback_to_default():
+    """Should fallback to first available embedding model when no defaults set."""
+
+    router = VectorIORouter(routing_table=_DummyRoutingTable())
+
+    model, dim = await router._resolve_embedding_model(None)
+    assert model == "first-model"
+    assert dim == 123
+
+
+async def test_missing_dimension_requirement(monkeypatch):
+    monkeypatch.setenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL", "some-model")
+
+    router = VectorIORouter(routing_table=_DummyRoutingTable())
+
+    with pytest.raises(ValueError, match="default_embedding_model.*is set but default_embedding_dimension is missing"):
+        await router._resolve_embedding_model(None)
+
+    monkeypatch.delenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL", raising=False)
+
+
+async def test_unregistered_model_error():
+    router = VectorIORouter(routing_table=_DummyRoutingTable())
+
+    with pytest.raises(ValueError, match="Embedding model 'unknown-model' not found in model registry"):
+        await router._resolve_embedding_model("unknown-model")
+
+
+class _EmptyRoutingTable:
+    async def get_all_with_type(self, _type: str):
+        return []
+
+
+async def test_no_models_available_error():
+    router = VectorIORouter(routing_table=_EmptyRoutingTable())
+
+    with pytest.raises(ValueError, match="No embedding model specified and no default configured"):
+        await router._resolve_embedding_model(None)