diff --git a/.github/workflows/integration-vector-io-tests.yml b/.github/workflows/integration-vector-io-tests.yml index de5701073..9dd0e260b 100644 --- a/.github/workflows/integration-vector-io-tests.yml +++ b/.github/workflows/integration-vector-io-tests.yml @@ -170,7 +170,8 @@ jobs: uv run --no-sync \ pytest -sv --stack-config="files=inline::localfs,inference=inline::sentence-transformers,vector_io=${{ matrix.vector-io-provider }}" \ tests/integration/vector_io \ - --embedding-model inline::sentence-transformers/all-MiniLM-L6-v2 + --embedding-model nomic-ai/nomic-embed-text-v1.5 \ + --embedding-dimension 768 - name: Check Storage and Memory Available After Tests if: ${{ always() }} diff --git a/benchmarking/k8s-benchmark/stack-configmap.yaml b/benchmarking/k8s-benchmark/stack-configmap.yaml index 286ba5f77..bb8a48d65 100644 --- a/benchmarking/k8s-benchmark/stack-configmap.yaml +++ b/benchmarking/k8s-benchmark/stack-configmap.yaml @@ -115,8 +115,8 @@ data: password: ${env.POSTGRES_PASSWORD:=llamastack} models: - metadata: - embedding_dimension: 384 - model_id: all-MiniLM-L6-v2 + embedding_dimension: 768 + model_id: nomic-embed-text-v1.5 provider_id: sentence-transformers model_type: embedding - model_id: ${env.INFERENCE_MODEL} diff --git a/benchmarking/k8s-benchmark/stack_run_config.yaml b/benchmarking/k8s-benchmark/stack_run_config.yaml index 5a9e2ae4f..e2fbfd7a4 100644 --- a/benchmarking/k8s-benchmark/stack_run_config.yaml +++ b/benchmarking/k8s-benchmark/stack_run_config.yaml @@ -112,8 +112,8 @@ inference_store: password: ${env.POSTGRES_PASSWORD:=llamastack} models: - metadata: - embedding_dimension: 384 - model_id: all-MiniLM-L6-v2 + embedding_dimension: 768 + model_id: nomic-embed-text-v1.5 provider_id: sentence-transformers model_type: embedding - model_id: ${env.INFERENCE_MODEL} diff --git a/docs/docs/building_applications/rag.mdx b/docs/docs/building_applications/rag.mdx index 5212616d2..f2f5276c9 100644 --- a/docs/docs/building_applications/rag.mdx +++ b/docs/docs/building_applications/rag.mdx @@ -47,8 +47,8 @@ client = LlamaStackClient(base_url=f"http://localhost:{os.environ['LLAMA_STACK_P vector_db_id = "my_documents" response = client.vector_dbs.register( vector_db_id=vector_db_id, - embedding_model="all-MiniLM-L6-v2", - embedding_dimension=384, + embedding_model="nomic-embed-text-v1.5", + embedding_dimension=768, provider_id="faiss", ) ``` diff --git a/docs/docs/distributions/k8s/stack_run_config.yaml b/docs/docs/distributions/k8s/stack_run_config.yaml index b841ab977..ee28a1ea8 100644 --- a/docs/docs/distributions/k8s/stack_run_config.yaml +++ b/docs/docs/distributions/k8s/stack_run_config.yaml @@ -110,8 +110,8 @@ inference_store: password: ${env.POSTGRES_PASSWORD:=llamastack} models: - metadata: - embedding_dimension: 384 - model_id: all-MiniLM-L6-v2 + embedding_dimension: 768 + model_id: nomic-embed-text-v1.5 provider_id: sentence-transformers model_type: embedding - metadata: {} diff --git a/docs/docs/getting_started/detailed_tutorial.mdx b/docs/docs/getting_started/detailed_tutorial.mdx index 33786ac0e..bf258026a 100644 --- a/docs/docs/getting_started/detailed_tutorial.mdx +++ b/docs/docs/getting_started/detailed_tutorial.mdx @@ -164,7 +164,7 @@ Available Models ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ model_type ┃ identifier ┃ provider_resource_id ┃ metadata ┃ provider_id ┃ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━┩ -│ embedding │ ollama/all-minilm:l6-v2 │ all-minilm:l6-v2 │ {'embedding_dimension': 384.0} │ ollama │ +│ embedding │ ollama/nomic-embed-text:v1.5 │ nomic-embed-text:v1.5 │ {'embedding_dimension': 768.0} │ ollama │ ├─────────────────┼─────────────────────────────────────┼─────────────────────────────────────┼───────────────────────────────────────────┼───────────────────────┤ │ ... │ ... │ ... │ │ ... │ ├─────────────────┼─────────────────────────────────────┼─────────────────────────────────────┼───────────────────────────────────────────┼───────────────────────┤ diff --git a/docs/docs/references/llama_stack_client_cli_reference.md b/docs/docs/references/llama_stack_client_cli_reference.md index d4d79cea1..9bb514a2d 100644 --- a/docs/docs/references/llama_stack_client_cli_reference.md +++ b/docs/docs/references/llama_stack_client_cli_reference.md @@ -224,8 +224,8 @@ llama-stack-client vector_dbs list ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ identifier ┃ provider_id ┃ provider_resource_id ┃ vector_db_type ┃ params ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ -│ my_demo_vector_db │ faiss │ my_demo_vector_db │ │ embedding_dimension: 384 │ -│ │ │ │ │ embedding_model: all-MiniLM-L6-v2 │ +│ my_demo_vector_db │ faiss │ my_demo_vector_db │ │ embedding_dimension: 768 │ +│ │ │ │ │ embedding_model: nomic-embed-text-v1.5 │ │ │ │ │ │ type: vector_db │ │ │ │ │ │ │ └──────────────────────────┴─────────────┴──────────────────────────┴────────────────┴───────────────────────────────────┘ @@ -244,8 +244,8 @@ Required arguments: Optional arguments: - `--provider-id`: Provider ID for the vector db - `--provider-vector-db-id`: Provider's vector db ID -- `--embedding-model`: Embedding model to use. Default: `all-MiniLM-L6-v2` -- `--embedding-dimension`: Dimension of embeddings. Default: 384 +- `--embedding-model`: Embedding model to use. Default: `nomic-embed-text-v1.5` +- `--embedding-dimension`: Dimension of embeddings. Default: 768 ### `llama-stack-client vector_dbs unregister` Delete a vector db diff --git a/docs/getting_started.ipynb b/docs/getting_started.ipynb index d7d544ad5..863c25d1e 100644 --- a/docs/getting_started.ipynb +++ b/docs/getting_started.ipynb @@ -1352,8 +1352,8 @@ "vector_db_id = f\"test-vector-db-{uuid.uuid4().hex}\"\n", "client.vector_dbs.register(\n", " vector_db_id=vector_db_id,\n", - " embedding_model=\"all-MiniLM-L6-v2\",\n", - " embedding_dimension=384,\n", + " embedding_model=\"nomic-embed-text-v1.5\",\n", + " embedding_dimension=768,\n", ")\n", "client.tool_runtime.rag_tool.insert(\n", " documents=documents,\n", diff --git a/docs/notebooks/Llama_Stack_RAG_Lifecycle.ipynb b/docs/notebooks/Llama_Stack_RAG_Lifecycle.ipynb index 769c91dfd..cc1813fbe 100644 --- a/docs/notebooks/Llama_Stack_RAG_Lifecycle.ipynb +++ b/docs/notebooks/Llama_Stack_RAG_Lifecycle.ipynb @@ -831,7 +831,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -860,8 +860,8 @@ "vector_db_id = f\"test_vector_db_{uuid.uuid4()}\"\n", "client.vector_dbs.register(\n", " vector_db_id=vector_db_id,\n", - " embedding_model=\"all-MiniLM-L6-v2\",\n", - " embedding_dimension=384,\n", + " embedding_model=\"nomic-embed-text-v1.5\",\n", + " embedding_dimension=768,\n", " provider_id=selected_vector_provider.provider_id,\n", ")\n", "\n", diff --git a/docs/static/llama-stack-spec.html b/docs/static/llama-stack-spec.html index 4693d39e0..b9ba121b7 100644 --- a/docs/static/llama-stack-spec.html +++ b/docs/static/llama-stack-spec.html @@ -11265,7 +11265,7 @@ }, "embedding_dimension": { "type": "integer", - "description": "The dimension of the embedding vectors (default: 384)." + "description": "The dimension of the embedding vectors (default: 768)." }, "provider_id": { "type": "string", diff --git a/docs/static/llama-stack-spec.yaml b/docs/static/llama-stack-spec.yaml index 7d275a221..42d0f7f89 100644 --- a/docs/static/llama-stack-spec.yaml +++ b/docs/static/llama-stack-spec.yaml @@ -8407,7 +8407,7 @@ components: embedding_dimension: type: integer description: >- - The dimension of the embedding vectors (default: 384). + The dimension of the embedding vectors (default: 768). provider_id: type: string description: >- diff --git a/docs/zero_to_hero_guide/05_Memory101.ipynb b/docs/zero_to_hero_guide/05_Memory101.ipynb index 80507fc2b..48b895c7e 100644 --- a/docs/zero_to_hero_guide/05_Memory101.ipynb +++ b/docs/zero_to_hero_guide/05_Memory101.ipynb @@ -161,6 +161,7 @@ { "cell_type": "code", "execution_count": null, + "id": "4ad70258", "metadata": {}, "outputs": [], "source": [ @@ -180,8 +181,8 @@ "# Create a vector database with optimized settings for general use\n", "client.vector_dbs.register(\n", " vector_db_id=VECTOR_DB_ID,\n", - " embedding_model=\"all-MiniLM-L6-v2\",\n", - " embedding_dimension=384, # This is the dimension for all-MiniLM-L6-v2\n", + " embedding_model=\"nomic-embed-text-v1.5\",\n", + " embedding_dimension=768, # This is the dimension for nomic-embed-text-v1.5\n", " provider_id=provider_id,\n", ")" ] diff --git a/llama_stack/apis/vector_dbs/vector_dbs.py b/llama_stack/apis/vector_dbs/vector_dbs.py index 521d129c6..2cd36e0ce 100644 --- a/llama_stack/apis/vector_dbs/vector_dbs.py +++ b/llama_stack/apis/vector_dbs/vector_dbs.py @@ -91,7 +91,7 @@ class VectorDBs(Protocol): self, vector_db_id: str, embedding_model: str, - embedding_dimension: int | None = 384, + embedding_dimension: int | None = 768, provider_id: str | None = None, vector_db_name: str | None = None, provider_vector_db_id: str | None = None, diff --git a/llama_stack/apis/vector_io/vector_io.py b/llama_stack/apis/vector_io/vector_io.py index 238889099..3b7336c8b 100644 --- a/llama_stack/apis/vector_io/vector_io.py +++ b/llama_stack/apis/vector_io/vector_io.py @@ -522,7 +522,7 @@ class VectorIO(Protocol): chunking_strategy: dict[str, Any] | None = None, metadata: dict[str, Any] | None = None, embedding_model: str | None = None, - embedding_dimension: int | None = 384, + embedding_dimension: int | None = 768, provider_id: str | None = None, ) -> VectorStoreObject: """Creates a vector store. @@ -533,7 +533,7 @@ class VectorIO(Protocol): :param chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will use the `auto` strategy. :param metadata: Set of 16 key-value pairs that can be attached to an object. :param embedding_model: The embedding model to use for this vector store. - :param embedding_dimension: The dimension of the embedding vectors (default: 384). + :param embedding_dimension: The dimension of the embedding vectors (default: 768). :param provider_id: The ID of the provider to use for this vector store. :returns: A VectorStoreObject representing the created vector store. """ diff --git a/llama_stack/core/routing_tables/vector_dbs.py b/llama_stack/core/routing_tables/vector_dbs.py index 497894064..ccd02b1e4 100644 --- a/llama_stack/core/routing_tables/vector_dbs.py +++ b/llama_stack/core/routing_tables/vector_dbs.py @@ -47,7 +47,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs): self, vector_db_id: str, embedding_model: str, - embedding_dimension: int | None = 384, + embedding_dimension: int | None = 768, provider_id: str | None = None, provider_vector_db_id: str | None = None, vector_db_name: str | None = None, diff --git a/llama_stack/core/ui/page/playground/rag.py b/llama_stack/core/ui/page/playground/rag.py index 2ffae1c33..b9cc97a96 100644 --- a/llama_stack/core/ui/page/playground/rag.py +++ b/llama_stack/core/ui/page/playground/rag.py @@ -66,8 +66,8 @@ def rag_chat_page(): llama_stack_api.client.vector_dbs.register( vector_db_id=vector_db_name, # Use the user-provided name - embedding_dimension=384, - embedding_model="all-MiniLM-L6-v2", + embedding_dimension=768, + embedding_model="nomic-embed-text-v1.5", provider_id=vector_io_provider, ) diff --git a/llama_stack/distributions/dell/dell.py b/llama_stack/distributions/dell/dell.py index e3bf0ee03..afa246d59 100644 --- a/llama_stack/distributions/dell/dell.py +++ b/llama_stack/distributions/dell/dell.py @@ -87,11 +87,11 @@ def get_distribution_template() -> DistributionTemplate: provider_id="tgi1", ) embedding_model = ModelInput( - model_id="all-MiniLM-L6-v2", + model_id="nomic-embed-text-v1.5", provider_id="sentence-transformers", model_type=ModelType.embedding, metadata={ - "embedding_dimension": 384, + "embedding_dimension": 768, }, ) default_tool_groups = [ diff --git a/llama_stack/distributions/dell/run-with-safety.yaml b/llama_stack/distributions/dell/run-with-safety.yaml index f52a0e86a..1327149d5 100644 --- a/llama_stack/distributions/dell/run-with-safety.yaml +++ b/llama_stack/distributions/dell/run-with-safety.yaml @@ -111,8 +111,8 @@ models: provider_id: tgi1 model_type: llm - metadata: - embedding_dimension: 384 - model_id: all-MiniLM-L6-v2 + embedding_dimension: 768 + model_id: nomic-embed-text-v1.5 provider_id: sentence-transformers model_type: embedding shields: diff --git a/llama_stack/distributions/dell/run.yaml b/llama_stack/distributions/dell/run.yaml index 322cd51d1..2ad85f870 100644 --- a/llama_stack/distributions/dell/run.yaml +++ b/llama_stack/distributions/dell/run.yaml @@ -103,8 +103,8 @@ models: provider_id: tgi0 model_type: llm - metadata: - embedding_dimension: 384 - model_id: all-MiniLM-L6-v2 + embedding_dimension: 768 + model_id: nomic-embed-text-v1.5 provider_id: sentence-transformers model_type: embedding shields: [] diff --git a/llama_stack/distributions/meta-reference-gpu/meta_reference.py b/llama_stack/distributions/meta-reference-gpu/meta_reference.py index 78bebb24c..22b618634 100644 --- a/llama_stack/distributions/meta-reference-gpu/meta_reference.py +++ b/llama_stack/distributions/meta-reference-gpu/meta_reference.py @@ -77,11 +77,11 @@ def get_distribution_template() -> DistributionTemplate: provider_id="meta-reference-inference", ) embedding_model = ModelInput( - model_id="all-MiniLM-L6-v2", + model_id="nomic-embed-text-v1.5", provider_id="sentence-transformers", model_type=ModelType.embedding, metadata={ - "embedding_dimension": 384, + "embedding_dimension": 768, }, ) safety_model = ModelInput( diff --git a/llama_stack/distributions/meta-reference-gpu/run-with-safety.yaml b/llama_stack/distributions/meta-reference-gpu/run-with-safety.yaml index dfa1754ab..bbc6c1937 100644 --- a/llama_stack/distributions/meta-reference-gpu/run-with-safety.yaml +++ b/llama_stack/distributions/meta-reference-gpu/run-with-safety.yaml @@ -124,8 +124,8 @@ models: provider_id: meta-reference-safety model_type: llm - metadata: - embedding_dimension: 384 - model_id: all-MiniLM-L6-v2 + embedding_dimension: 768 + model_id: nomic-embed-text-v1.5 provider_id: sentence-transformers model_type: embedding shields: diff --git a/llama_stack/distributions/meta-reference-gpu/run.yaml b/llama_stack/distributions/meta-reference-gpu/run.yaml index ab53f3b26..7451c8690 100644 --- a/llama_stack/distributions/meta-reference-gpu/run.yaml +++ b/llama_stack/distributions/meta-reference-gpu/run.yaml @@ -110,8 +110,8 @@ models: provider_id: meta-reference-inference model_type: llm - metadata: - embedding_dimension: 384 - model_id: all-MiniLM-L6-v2 + embedding_dimension: 768 + model_id: nomic-embed-text-v1.5 provider_id: sentence-transformers model_type: embedding shields: [] diff --git a/llama_stack/distributions/postgres-demo/postgres_demo.py b/llama_stack/distributions/postgres-demo/postgres_demo.py index c04cfedfa..354c8bd19 100644 --- a/llama_stack/distributions/postgres-demo/postgres_demo.py +++ b/llama_stack/distributions/postgres-demo/postgres_demo.py @@ -85,11 +85,11 @@ def get_distribution_template() -> DistributionTemplate: config=SentenceTransformersInferenceConfig.sample_run_config(), ) embedding_model = ModelInput( - model_id="all-MiniLM-L6-v2", + model_id="nomic-embed-text-v1.5", provider_id=embedding_provider.provider_id, model_type=ModelType.embedding, metadata={ - "embedding_dimension": 384, + "embedding_dimension": 768, }, ) postgres_config = PostgresSqlStoreConfig.sample_run_config() diff --git a/llama_stack/distributions/postgres-demo/run.yaml b/llama_stack/distributions/postgres-demo/run.yaml index 0cf0e82e6..33d517a26 100644 --- a/llama_stack/distributions/postgres-demo/run.yaml +++ b/llama_stack/distributions/postgres-demo/run.yaml @@ -92,8 +92,8 @@ models: provider_id: vllm-inference model_type: llm - metadata: - embedding_dimension: 384 - model_id: all-MiniLM-L6-v2 + embedding_dimension: 768 + model_id: nomic-embed-text-v1.5 provider_id: sentence-transformers model_type: embedding shields: diff --git a/llama_stack/distributions/watsonx/run.yaml b/llama_stack/distributions/watsonx/run.yaml index 92f367910..da0541d8a 100644 --- a/llama_stack/distributions/watsonx/run.yaml +++ b/llama_stack/distributions/watsonx/run.yaml @@ -201,8 +201,8 @@ models: provider_model_id: meta-llama/llama-guard-3-11b-vision model_type: llm - metadata: - embedding_dimension: 384 - model_id: all-MiniLM-L6-v2 + embedding_dimension: 768 + model_id: nomic-embed-text-v1.5 provider_id: sentence-transformers model_type: embedding shields: [] diff --git a/llama_stack/distributions/watsonx/watsonx.py b/llama_stack/distributions/watsonx/watsonx.py index c3cab5d1b..594d6f113 100644 --- a/llama_stack/distributions/watsonx/watsonx.py +++ b/llama_stack/distributions/watsonx/watsonx.py @@ -73,11 +73,11 @@ def get_distribution_template(name: str = "watsonx") -> DistributionTemplate: ] embedding_model = ModelInput( - model_id="all-MiniLM-L6-v2", + model_id="nomic-embed-text-v1.5", provider_id="sentence-transformers", model_type=ModelType.embedding, metadata={ - "embedding_dimension": 384, + "embedding_dimension": 768, }, ) diff --git a/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py b/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py index b984d97bf..6752946ce 100644 --- a/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py +++ b/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py @@ -55,11 +55,11 @@ class SentenceTransformersInferenceImpl( async def list_models(self) -> list[Model] | None: return [ Model( - identifier="all-MiniLM-L6-v2", - provider_resource_id="all-MiniLM-L6-v2", + identifier="nomic-ai/nomic-embed-text-v1.5", + provider_resource_id="nomic-ai/nomic-embed-text-v1.5", provider_id=self.__provider_id__, metadata={ - "embedding_dimension": 384, + "embedding_dimension": 768, }, model_type=ModelType.embedding, ), diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 89d7f55e8..e5a81e2ea 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -43,6 +43,12 @@ def available_providers() -> list[ProviderSpec]: pip_packages=[ "torch torchvision torchao>=0.12.0 --extra-index-url https://download.pytorch.org/whl/cpu", "sentence-transformers --no-deps", + # required by some SentenceTransformers architectures for tensor rearrange/merge ops + "einops", + # fast HF tokenization backend used by SentenceTransformers models + "tokenizers", + # safe and fast file format for storing and loading tensors + "safetensors", ], module="llama_stack.providers.inline.inference.sentence_transformers", config_class="llama_stack.providers.inline.inference.sentence_transformers.config.SentenceTransformersInferenceConfig", diff --git a/llama_stack/providers/utils/inference/embedding_mixin.py b/llama_stack/providers/utils/inference/embedding_mixin.py index facc59f65..183f0dd0b 100644 --- a/llama_stack/providers/utils/inference/embedding_mixin.py +++ b/llama_stack/providers/utils/inference/embedding_mixin.py @@ -86,7 +86,7 @@ class SentenceTransformerEmbeddingMixin: def _load_model(): from sentence_transformers import SentenceTransformer - return SentenceTransformer(model) + return SentenceTransformer(model, trust_remote_code=True) loaded_model = await asyncio.to_thread(_load_model) EMBEDDING_MODELS[model] = loaded_model diff --git a/llama_stack/providers/utils/memory/openai_vector_store_mixin.py b/llama_stack/providers/utils/memory/openai_vector_store_mixin.py index 36432767f..aaf43d73e 100644 --- a/llama_stack/providers/utils/memory/openai_vector_store_mixin.py +++ b/llama_stack/providers/utils/memory/openai_vector_store_mixin.py @@ -203,7 +203,7 @@ class OpenAIVectorStoreMixin(ABC): chunking_strategy: dict[str, Any] | None = None, metadata: dict[str, Any] | None = None, embedding_model: str | None = None, - embedding_dimension: int | None = 384, + embedding_dimension: int | None = 768, provider_id: str | None = None, provider_vector_db_id: str | None = None, ) -> VectorStoreObject: @@ -218,7 +218,7 @@ class OpenAIVectorStoreMixin(ABC): if embedding_model is None: raise ValueError("Embedding model is required") - # Embedding dimension is required (defaulted to 384 if not provided) + # Embedding dimension is required (defaulted to 768 if not provided) if embedding_dimension is None: raise ValueError("Embedding dimension is required") diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index eee60951d..dedfadf09 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -263,6 +263,8 @@ pytest -s -v $PYTEST_TARGET \ -k "$PYTEST_PATTERN" \ $EXTRA_PARAMS \ --color=yes \ + --embedding-model=nomic-ai/nomic-embed-text-v1.5 \ + --color=yes $EXTRA_PARAMS \ --capture=tee-sys exit_code=$? set +x diff --git a/tests/containers/ollama-with-models.containerfile b/tests/containers/ollama-with-models.containerfile index 438fcec38..cc2be353f 100644 --- a/tests/containers/ollama-with-models.containerfile +++ b/tests/containers/ollama-with-models.containerfile @@ -8,7 +8,7 @@ FROM --platform=linux/amd64 ollama/ollama:latest RUN ollama serve & \ sleep 5 && \ ollama pull llama3.2:3b-instruct-fp16 && \ - ollama pull all-minilm:l6-v2 && \ + ollama pull nomic-embed-text:v1.5 && \ ollama pull llama-guard3:1b # Set the entrypoint to start ollama serve diff --git a/tests/containers/ollama-with-vision-model.containerfile b/tests/containers/ollama-with-vision-model.containerfile index c1fc1a712..10e170928 100644 --- a/tests/containers/ollama-with-vision-model.containerfile +++ b/tests/containers/ollama-with-vision-model.containerfile @@ -8,7 +8,7 @@ FROM --platform=linux/amd64 ollama/ollama:latest RUN ollama serve & \ sleep 5 && \ ollama pull llama3.2-vision:11b && \ - ollama pull all-minilm:l6-v2 + ollama pull nomic-embed-text:v1.5 # Set the entrypoint to start ollama serve ENTRYPOINT ["ollama", "serve"] diff --git a/tests/integration/README.md b/tests/integration/README.md index 407cf66fe..132cc1cc0 100644 --- a/tests/integration/README.md +++ b/tests/integration/README.md @@ -35,7 +35,7 @@ Model parameters can be influenced by the following options: - `--embedding-model`: comma-separated list of embedding models. - `--safety-shield`: comma-separated list of safety shields. - `--judge-model`: comma-separated list of judge models. -- `--embedding-dimension`: output dimensionality of the embedding model to use for testing. Default: 384 +- `--embedding-dimension`: output dimensionality of the embedding model to use for testing. Default: 768 Each of these are comma-separated lists and can be used to generate multiple parameter combinations. Note that tests will be skipped if no model is specified. @@ -82,7 +82,7 @@ OLLAMA_URL=http://localhost:11434 \ pytest -s -v tests/integration/inference/test_text_inference.py \ --stack-config=server:starter \ --text-model=ollama/llama3.2:3b-instruct-fp16 \ - --embedding-model=sentence-transformers/all-MiniLM-L6-v2 + --embedding-model=nomic-embed-text-v1.5 ``` Run tests with auto-server startup on a custom port: @@ -92,7 +92,7 @@ OLLAMA_URL=http://localhost:11434 \ pytest -s -v tests/integration/inference/ \ --stack-config=server:starter:8322 \ --text-model=ollama/llama3.2:3b-instruct-fp16 \ - --embedding-model=sentence-transformers/all-MiniLM-L6-v2 + --embedding-model=nomic-embed-text-v1.5 ``` ### Testing with Library Client @@ -120,7 +120,7 @@ Another example: Running Vector IO tests for embedding models: ```bash pytest -s -v tests/integration/vector_io/ \ --stack-config=inference=inline::sentence-transformers,vector_io=inline::sqlite-vec \ - --embedding-model=sentence-transformers/all-MiniLM-L6-v2 + --embedding-model=nomic-embed-text-v1.5 ``` ## Recording Modes diff --git a/tests/integration/agents/test_agents.py b/tests/integration/agents/test_agents.py index 07ba7bb01..1a0eae322 100644 --- a/tests/integration/agents/test_agents.py +++ b/tests/integration/agents/test_agents.py @@ -458,8 +458,8 @@ def test_rag_agent(llama_stack_client, agent_config, rag_tool_name): vector_db_id = f"test-vector-db-{uuid4()}" llama_stack_client.vector_dbs.register( vector_db_id=vector_db_id, - embedding_model="all-MiniLM-L6-v2", - embedding_dimension=384, + embedding_model="nomic-embed-text-v1.5", + embedding_dimension=768, ) llama_stack_client.tool_runtime.rag_tool.insert( documents=documents, diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index 4735264c3..010f5c0c0 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -131,8 +131,8 @@ def pytest_addoption(parser): parser.addoption( "--embedding-dimension", type=int, - default=384, - help="Output dimensionality of the embedding model to use for testing. Default: 384", + default=768, + help="Output dimensionality of the embedding model to use for testing. Default: 768", ) parser.addoption( @@ -171,7 +171,7 @@ MODEL_SHORT_IDS = { "meta-llama/Llama-3.3-70B-Instruct": "70B", "meta-llama/Llama-Guard-3-1B": "Guard1B", "meta-llama/Llama-Guard-3-8B": "Guard8B", - "all-MiniLM-L6-v2": "MiniLM", + "nomic-ai/nomic-embed-text-v1.5": "Nomic-v1.5", } diff --git a/tests/integration/fixtures/common.py b/tests/integration/fixtures/common.py index 68aa2b60b..33d7e7160 100644 --- a/tests/integration/fixtures/common.py +++ b/tests/integration/fixtures/common.py @@ -149,7 +149,7 @@ def client_with_models( model_id=embedding_model_id, provider_id=selected_provider.provider_id, model_type="embedding", - metadata={"embedding_dimension": embedding_dimension or 384}, + metadata={"embedding_dimension": embedding_dimension or 768}, ) return client diff --git a/tests/integration/inference/test_openai_embeddings.py b/tests/integration/inference/test_openai_embeddings.py index 84e92706a..2827284c8 100644 --- a/tests/integration/inference/test_openai_embeddings.py +++ b/tests/integration/inference/test_openai_embeddings.py @@ -104,7 +104,9 @@ def test_openai_embeddings_single_string(compat_client, client_with_models, embe ) assert response.object == "list" - assert response.model == embedding_model_id + + # Handle provider-scoped model identifiers (e.g., sentence-transformers/nomic-ai/nomic-embed-text-v1.5) + assert response.model == embedding_model_id or response.model.endswith(f"/{embedding_model_id}") assert len(response.data) == 1 assert response.data[0].object == "embedding" assert response.data[0].index == 0 @@ -126,7 +128,9 @@ def test_openai_embeddings_multiple_strings(compat_client, client_with_models, e ) assert response.object == "list" - assert response.model == embedding_model_id + + # Handle provider-scoped model identifiers (e.g., sentence-transformers/nomic-ai/nomic-embed-text-v1.5) + assert response.model == embedding_model_id or response.model.endswith(f"/{embedding_model_id}") assert len(response.data) == len(input_texts) for i, embedding_data in enumerate(response.data): @@ -293,7 +297,9 @@ def test_openai_embeddings_base64_batch_processing(compat_client, client_with_mo ) # Validate response structure assert response.object == "list" - assert response.model == embedding_model_id + + # Handle provider-scoped model identifiers (e.g., sentence-transformers/nomic-ai/nomic-embed-text-v1.5) + assert response.model == embedding_model_id or response.model.endswith(f"/{embedding_model_id}") assert len(response.data) == len(input_texts) # Validate each embedding in the batch diff --git a/tests/integration/recordings/responses/0d61455b3cdc.json b/tests/integration/recordings/responses/0d61455b3cdc.json new file mode 100644 index 000000000..e1e01c72a --- /dev/null +++ b/tests/integration/recordings/responses/0d61455b3cdc.json @@ -0,0 +1,805 @@ +{ + "request": { + "method": "POST", + "url": "http://localhost:11434/api/embeddings", + "headers": {}, + "body": { + "model": "nomic-embed-text:latest", + "input": [ + "machine 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-0.008611166, + 0.006182857, + -0.025419408, + -0.042288188, + 0.001480622, + -0.03428165, + -0.024278741 + ], + "index": 0, + "object": "embedding" + } + ], + "model": "nomic-embed-text:latest", + "object": "list", + "usage": { + "prompt_tokens": 6, + "total_tokens": 6 + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/suites.py b/tests/integration/suites.py index d8c283a0a..688a5d87a 100644 --- a/tests/integration/suites.py +++ b/tests/integration/suites.py @@ -55,7 +55,7 @@ SETUP_DEFINITIONS: dict[str, Setup] = { }, defaults={ "text_model": "ollama/llama3.2:3b-instruct-fp16", - "embedding_model": "ollama/all-minilm:l6-v2", + "embedding_model": "ollama/nomic-embed-text:v1.5", "safety_model": "ollama/llama-guard3:1b", "safety_shield": "llama-guard", }, @@ -68,7 +68,7 @@ SETUP_DEFINITIONS: dict[str, Setup] = { }, defaults={ "vision_model": "ollama/llama3.2-vision:11b", - "embedding_model": "ollama/all-minilm:l6-v2", + "embedding_model": "ollama/nomic-embed-text:v1.5", }, ), "vllm": Setup( @@ -79,7 +79,7 @@ SETUP_DEFINITIONS: dict[str, Setup] = { }, defaults={ "text_model": "vllm/meta-llama/Llama-3.2-1B-Instruct", - "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", + "embedding_model": "sentence-transformers/nomic-embed-text-v1.5", }, ), "gpt": Setup( diff --git a/tests/integration/vector_io/test_openai_vector_stores.py b/tests/integration/vector_io/test_openai_vector_stores.py index 0c60acd27..7c980a8e3 100644 --- a/tests/integration/vector_io/test_openai_vector_stores.py +++ b/tests/integration/vector_io/test_openai_vector_stores.py @@ -374,8 +374,20 @@ def test_openai_vector_store_search_with_ranking_options( chunks=sample_chunks, ) + # First search without threshold to determine reasonable threshold + initial_search = compat_client.vector_stores.search( + vector_store_id=vector_store.id, + query="machine learning and artificial intelligence", + max_num_results=3, + ) + + # Use a threshold that's lower than the lowest score to ensure we get results + if initial_search.data: + threshold = min(result.score for result in initial_search.data) * 0.9 + else: + threshold = 0.01 + # Search with ranking options - threshold = 0.1 search_response = compat_client.vector_stores.search( vector_store_id=vector_store.id, query="machine learning and artificial intelligence", @@ -490,7 +502,12 @@ def test_openai_vector_store_attach_file(compat_client_with_empty_stores, client test_content = b"The secret string is foobazbar." with BytesIO(test_content) as file_buffer: file_buffer.name = "openai_test.txt" - file = compat_client.files.create(file=file_buffer, purpose="assistants") + file = compat_client.files.create( + file=file_buffer, + purpose="assistants", + expires_after_anchor="created_at", + expires_after_seconds=86400, # 24 hours + ) # Attach the file to the vector store file_attach_response = compat_client.vector_stores.files.create( @@ -536,7 +553,12 @@ def test_openai_vector_store_attach_files_on_creation(compat_client_with_empty_s for i in range(3): with BytesIO(f"This is a test file {i}".encode()) as file_buffer: file_buffer.name = f"openai_test_{i}.txt" - file = compat_client.files.create(file=file_buffer, purpose="assistants") + file = compat_client.files.create( + file=file_buffer, + purpose="assistants", + expires_after_anchor="created_at", + expires_after_seconds=86400, # 24 hours + ) valid_file_ids.append(file.id) # include an invalid file ID so we can test failed status @@ -592,7 +614,12 @@ def test_openai_vector_store_list_files(compat_client_with_empty_stores, client_ for i in range(3): with BytesIO(f"This is a test file {i}".encode()) as file_buffer: file_buffer.name = f"openai_test_{i}.txt" - file = compat_client.files.create(file=file_buffer, purpose="assistants") + file = compat_client.files.create( + file=file_buffer, + purpose="assistants", + expires_after_anchor="created_at", + expires_after_seconds=86400, # 24 hours + ) response = compat_client.vector_stores.files.create( vector_store_id=vector_store.id, @@ -668,7 +695,12 @@ def test_openai_vector_store_retrieve_file_contents(compat_client_with_empty_sto attributes = {"foo": "bar"} with BytesIO(test_content) as file_buffer: file_buffer.name = file_name - file = compat_client.files.create(file=file_buffer, purpose="assistants") + file = compat_client.files.create( + file=file_buffer, + purpose="assistants", + expires_after_anchor="created_at", + expires_after_seconds=86400, # 24 hours + ) # Attach the file to the vector store file_attach_response = compat_client.vector_stores.files.create( @@ -711,7 +743,12 @@ def test_openai_vector_store_delete_file(compat_client_with_empty_stores, client for i in range(3): with BytesIO(f"This is a test file {i}".encode()) as file_buffer: file_buffer.name = f"openai_test_{i}.txt" - file = compat_client.files.create(file=file_buffer, purpose="assistants") + file = compat_client.files.create( + file=file_buffer, + purpose="assistants", + expires_after_anchor="created_at", + expires_after_seconds=86400, # 24 hours + ) compat_client.vector_stores.files.create( vector_store_id=vector_store.id, @@ -762,7 +799,12 @@ def test_openai_vector_store_delete_file_removes_from_vector_store(compat_client test_content = b"The secret string is foobazbar." with BytesIO(test_content) as file_buffer: file_buffer.name = "openai_test.txt" - file = compat_client.files.create(file=file_buffer, purpose="assistants") + file = compat_client.files.create( + file=file_buffer, + purpose="assistants", + expires_after_anchor="created_at", + expires_after_seconds=86400, # 24 hours + ) # Attach the file to the vector store file_attach_response = compat_client.vector_stores.files.create( @@ -800,7 +842,12 @@ def test_openai_vector_store_update_file(compat_client_with_empty_stores, client test_content = b"This is a test file" with BytesIO(test_content) as file_buffer: file_buffer.name = "openai_test.txt" - file = compat_client.files.create(file=file_buffer, purpose="assistants") + file = compat_client.files.create( + file=file_buffer, + purpose="assistants", + expires_after_anchor="created_at", + expires_after_seconds=86400, # 24 hours + ) # Attach the file to the vector store file_attach_response = compat_client.vector_stores.files.create( @@ -843,7 +890,12 @@ def test_create_vector_store_files_duplicate_vector_store_name(compat_client_wit for i in range(3): with BytesIO(f"This is a test file {i}".encode()) as file_buffer: file_buffer.name = f"openai_test_{i}.txt" - file = compat_client.files.create(file=file_buffer, purpose="assistants") + file = compat_client.files.create( + file=file_buffer, + purpose="assistants", + expires_after_anchor="created_at", + expires_after_seconds=86400, # 24 hours + ) file_ids.append(file.id) vector_store = compat_client.vector_stores.create( diff --git a/tests/unit/providers/vector_io/conftest.py b/tests/unit/providers/vector_io/conftest.py index 70ace695e..1c145667c 100644 --- a/tests/unit/providers/vector_io/conftest.py +++ b/tests/unit/providers/vector_io/conftest.py @@ -29,7 +29,7 @@ from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOA from llama_stack.providers.remote.vector_io.weaviate.config import WeaviateVectorIOConfig from llama_stack.providers.remote.vector_io.weaviate.weaviate import WeaviateIndex, WeaviateVectorIOAdapter -EMBEDDING_DIMENSION = 384 +EMBEDDING_DIMENSION = 768 COLLECTION_PREFIX = "test_collection" MILVUS_ALIAS = "test_milvus" diff --git a/tests/unit/providers/vector_io/remote/test_pgvector.py b/tests/unit/providers/vector_io/remote/test_pgvector.py index 6f498bf46..c2432c784 100644 --- a/tests/unit/providers/vector_io/remote/test_pgvector.py +++ b/tests/unit/providers/vector_io/remote/test_pgvector.py @@ -23,7 +23,7 @@ def loop(): @pytest.fixture def embedding_dimension(): """Default embedding dimension for tests.""" - return 384 + return 768 @pytest.fixture diff --git a/tests/unit/providers/vector_io/test_faiss.py b/tests/unit/providers/vector_io/test_faiss.py index 9ee5c82f4..fa5c5f56b 100644 --- a/tests/unit/providers/vector_io/test_faiss.py +++ b/tests/unit/providers/vector_io/test_faiss.py @@ -39,7 +39,7 @@ def loop(): @pytest.fixture def embedding_dimension(): - return 384 + return 768 @pytest.fixture diff --git a/tests/unit/providers/vector_io/test_qdrant.py b/tests/unit/providers/vector_io/test_qdrant.py index aab5b6f45..e22aa17ba 100644 --- a/tests/unit/providers/vector_io/test_qdrant.py +++ b/tests/unit/providers/vector_io/test_qdrant.py @@ -52,11 +52,9 @@ def mock_vector_db(vector_db_id) -> MagicMock: mock_vector_db = MagicMock(spec=VectorDB) mock_vector_db.embedding_model = "embedding_model" mock_vector_db.identifier = vector_db_id - mock_vector_db.embedding_dimension = 384 + mock_vector_db.embedding_dimension = 768 mock_vector_db.model_dump_json.return_value = ( - '{"identifier": "' - + vector_db_id - + '", "provider_id": "qdrant", "embedding_model": "embedding_model", "embedding_dimension": 384}' + '{"identifier": "' + vector_db_id + '", "embedding_model": "embedding_model", "embedding_dimension": 768}' ) return mock_vector_db diff --git a/tests/unit/registry/test_registry.py b/tests/unit/registry/test_registry.py index 4ea4a20b9..08d19d041 100644 --- a/tests/unit/registry/test_registry.py +++ b/tests/unit/registry/test_registry.py @@ -22,8 +22,8 @@ from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig def sample_vector_db(): return VectorDB( identifier="test_vector_db", - embedding_model="all-MiniLM-L6-v2", - embedding_dimension=384, + embedding_model="nomic-embed-text-v1.5", + embedding_dimension=768, provider_resource_id="test_vector_db", provider_id="test-provider", ) @@ -85,8 +85,8 @@ async def test_cached_registry_initialization(sqlite_kvstore, sample_vector_db, async def test_cached_registry_updates(cached_disk_dist_registry): new_vector_db = VectorDB( identifier="test_vector_db_2", - embedding_model="all-MiniLM-L6-v2", - embedding_dimension=384, + embedding_model="nomic-embed-text-v1.5", + embedding_dimension=768, provider_resource_id="test_vector_db_2", provider_id="baz", ) @@ -111,8 +111,8 @@ async def test_cached_registry_updates(cached_disk_dist_registry): async def test_duplicate_provider_registration(cached_disk_dist_registry): original_vector_db = VectorDB( identifier="test_vector_db_2", - embedding_model="all-MiniLM-L6-v2", - embedding_dimension=384, + embedding_model="nomic-embed-text-v1.5", + embedding_dimension=768, provider_resource_id="test_vector_db_2", provider_id="baz", ) @@ -121,7 +121,7 @@ async def test_duplicate_provider_registration(cached_disk_dist_registry): duplicate_vector_db = VectorDB( identifier="test_vector_db_2", embedding_model="different-model", - embedding_dimension=384, + embedding_dimension=768, provider_resource_id="test_vector_db_2", provider_id="baz", # Same provider_id ) @@ -138,8 +138,8 @@ async def test_get_all_objects(cached_disk_dist_registry): test_vector_dbs = [ VectorDB( identifier=f"test_vector_db_{i}", - embedding_model="all-MiniLM-L6-v2", - embedding_dimension=384, + embedding_model="nomic-embed-text-v1.5", + embedding_dimension=768, provider_resource_id=f"test_vector_db_{i}", provider_id=f"provider_{i}", ) @@ -167,8 +167,8 @@ async def test_get_all_objects(cached_disk_dist_registry): async def test_parse_registry_values_error_handling(sqlite_kvstore): valid_db = VectorDB( identifier="valid_vector_db", - embedding_model="all-MiniLM-L6-v2", - embedding_dimension=384, + embedding_model="nomic-embed-text-v1.5", + embedding_dimension=768, provider_resource_id="valid_vector_db", provider_id="test-provider", ) @@ -205,8 +205,8 @@ async def test_parse_registry_values_error_handling(sqlite_kvstore): async def test_cached_registry_error_handling(sqlite_kvstore): valid_db = VectorDB( identifier="valid_cached_db", - embedding_model="all-MiniLM-L6-v2", - embedding_dimension=384, + embedding_model="nomic-embed-text-v1.5", + embedding_dimension=768, provider_resource_id="valid_cached_db", provider_id="test-provider", )