format

llama_stack/core/datatypes.py

@@ -23,8 +23,8 @@ from llama_stack.apis.scoring import Scoring
 from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnInput
 from llama_stack.apis.shields import Shield, ShieldInput
 from llama_stack.apis.tools import ToolGroup, ToolGroupInput, ToolRuntime
-from llama_stack.apis.vector_stores import VectorStore, VectorStoreInput
 from llama_stack.apis.vector_io import VectorIO
+from llama_stack.apis.vector_stores import VectorStore, VectorStoreInput
 from llama_stack.core.access_control.datatypes import AccessRule
 from llama_stack.core.storage.datatypes import (
     KVStoreReference,

llama_stack/core/resolver.py

@@ -29,8 +29,8 @@ from llama_stack.apis.scoring_functions import ScoringFunctions
 from llama_stack.apis.shields import Shields
 from llama_stack.apis.telemetry import Telemetry
 from llama_stack.apis.tools import ToolGroups, ToolRuntime
-from llama_stack.apis.vector_stores import VectorStores
 from llama_stack.apis.vector_io import VectorIO
+from llama_stack.apis.vector_stores import VectorStores
 from llama_stack.apis.version import LLAMA_STACK_API_V1ALPHA
 from llama_stack.core.client import get_client_impl
 from llama_stack.core.datatypes import (

llama_stack/providers/inline/vector_io/faiss/faiss.py

@@ -17,8 +17,8 @@ from numpy.typing import NDArray
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.files import Files
 from llama_stack.apis.inference import Inference, InterleavedContent
-from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import HealthResponse, HealthStatus, VectorStoresProtocolPrivate
 from llama_stack.providers.utils.kvstore import kvstore_impl

llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py

@@ -17,8 +17,8 @@ from numpy.typing import NDArray
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.files import Files
 from llama_stack.apis.inference import Inference
-from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import VectorStoresProtocolPrivate
 from llama_stack.providers.utils.kvstore import kvstore_impl
@@ -412,7 +412,9 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresPro
         return [v.vector_store for v in self.cache.values()]
 
     async def register_vector_store(self, vector_store: VectorStore) -> None:
-        index = await SQLiteVecIndex.create(vector_store.embedding_dimension, self.config.db_path, vector_store.identifier)
+        index = await SQLiteVecIndex.create(
+            vector_store.embedding_dimension, self.config.db_path, vector_store.identifier
+        )
         self.cache[vector_store.identifier] = VectorStoreWithIndex(vector_store, index, self.inference_api)
 
     async def _get_and_cache_vector_store_index(self, vector_store_id: str) -> VectorStoreWithIndex | None:

llama_stack/providers/remote/vector_io/chroma/chroma.py

@@ -13,8 +13,8 @@ from numpy.typing import NDArray
 
 from llama_stack.apis.files import Files
 from llama_stack.apis.inference import Inference, InterleavedContent
-from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import VectorStoresProtocolPrivate
 from llama_stack.providers.inline.vector_io.chroma import ChromaVectorIOConfig as InlineChromaVectorIOConfig

llama_stack/providers/remote/vector_io/milvus/milvus.py

@@ -14,8 +14,8 @@ from pymilvus import AnnSearchRequest, DataType, Function, FunctionType, MilvusC
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.files import Files
 from llama_stack.apis.inference import Inference, InterleavedContent
-from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import VectorStoresProtocolPrivate
 from llama_stack.providers.inline.vector_io.milvus import MilvusVectorIOConfig as InlineMilvusVectorIOConfig

llama_stack/providers/remote/vector_io/pgvector/pgvector.py

@@ -16,8 +16,8 @@ from pydantic import BaseModel, TypeAdapter
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.files import Files
 from llama_stack.apis.inference import Inference, InterleavedContent
-from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import VectorStoresProtocolPrivate
 from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str

llama_stack/providers/remote/vector_io/qdrant/qdrant.py

@@ -16,7 +16,6 @@ from qdrant_client.models import PointStruct
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.files import Files
 from llama_stack.apis.inference import Inference, InterleavedContent
-from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.apis.vector_io import (
     Chunk,
     QueryChunksResponse,
@@ -24,6 +23,7 @@ from llama_stack.apis.vector_io import (
     VectorStoreChunkingStrategy,
     VectorStoreFileObject,
 )
+from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import VectorStoresProtocolPrivate
 from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
@@ -171,7 +171,9 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
 
         for vector_store_data in stored_vector_stores:
             vector_store = VectorStore.model_validate_json(vector_store_data)
-            index = VectorStoreWithIndex(vector_store, QdrantIndex(self.client, vector_store.identifier), self.inference_api)
+            index = VectorStoreWithIndex(
+                vector_store, QdrantIndex(self.client, vector_store.identifier), self.inference_api
+            )
             self.cache[vector_store.identifier] = index
         self.openai_vector_stores = await self._load_openai_vector_stores()
 
@@ -186,7 +188,9 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
         await self.kvstore.set(key=key, value=vector_store.model_dump_json())
 
         index = VectorStoreWithIndex(
-            vector_store=vector_store, index=QdrantIndex(self.client, vector_store.identifier), inference_api=self.inference_api
+            vector_store=vector_store,
+            index=QdrantIndex(self.client, vector_store.identifier),
+            inference_api=self.inference_api,
         )
 
         self.cache[vector_store.identifier] = index

llama_stack/providers/remote/vector_io/weaviate/weaviate.py

@@ -16,8 +16,8 @@ from llama_stack.apis.common.content_types import InterleavedContent
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.files import Files
 from llama_stack.apis.inference import Inference
-from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.core.request_headers import NeedsRequestProviderData
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import VectorStoresProtocolPrivate

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -17,7 +17,6 @@ from pydantic import TypeAdapter
 
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.files import Files, OpenAIFileObject
-from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.apis.vector_io import (
     Chunk,
     OpenAICreateVectorStoreFileBatchRequestWithExtraBody,
@@ -43,6 +42,7 @@ from llama_stack.apis.vector_io import (
     VectorStoreSearchResponse,
     VectorStoreSearchResponsePage,
 )
+from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.core.id_generation import generate_object_id
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.kvstore.api import KVStore

llama_stack/providers/utils/memory/vector_store.py

@@ -23,8 +23,8 @@ from llama_stack.apis.common.content_types import (
 )
 from llama_stack.apis.inference import OpenAIEmbeddingsRequestWithExtraBody
 from llama_stack.apis.tools import RAGDocument
-from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse
+from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.log import get_logger
 from llama_stack.models.llama.llama3.tokenizer import Tokenizer
 from llama_stack.providers.datatypes import Api

tests/integration/vector_io/test_vector_io.py

@@ -49,7 +49,9 @@ def client_with_empty_registry(client_with_models):
 
 
 @vector_provider_wrapper
-def test_vector_store_retrieve(client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id):
+def test_vector_store_retrieve(
+    client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id
+):
     vector_store_name = "test_vector_store"
     create_response = client_with_empty_registry.vector_stores.create(
         name=vector_store_name,
@@ -69,7 +71,9 @@ def test_vector_store_retrieve(client_with_empty_registry, embedding_model_id, e
 
 
 @vector_provider_wrapper
-def test_vector_store_register(client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id):
+def test_vector_store_register(
+    client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id
+):
     vector_store_name = "test_vector_store"
     response = client_with_empty_registry.vector_stores.create(
         name=vector_store_name,

tests/unit/providers/vector_io/conftest.py

@@ -10,8 +10,8 @@ from unittest.mock import AsyncMock, MagicMock, patch
 import numpy as np
 import pytest
 
-from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse
+from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.core.storage.datatypes import KVStoreReference, SqliteKVStoreConfig
 from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
 from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter

tests/unit/providers/vector_io/test_faiss.py

@@ -11,8 +11,8 @@ import numpy as np
 import pytest
 
 from llama_stack.apis.files import Files
-from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
+from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.providers.datatypes import HealthStatus
 from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
 from llama_stack.providers.inline.vector_io.faiss.faiss import (

tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py

@@ -12,7 +12,6 @@ import numpy as np
 import pytest
 
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
-from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.apis.vector_io import (
     Chunk,
     OpenAICreateVectorStoreFileBatchRequestWithExtraBody,
@@ -21,6 +20,7 @@ from llama_stack.apis.vector_io import (
     VectorStoreChunkingStrategyAuto,
     VectorStoreFileObject,
 )
+from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import VECTOR_DBS_PREFIX
 
 # This test is a unit test for the inline VectorIO providers. This should only contain

tests/unit/registry/test_registry.py

@@ -134,7 +134,9 @@ async def test_duplicate_provider_registration(cached_disk_dist_registry):
         provider_resource_id="test_vector_store_2",
         provider_id="baz",  # Same provider_id
     )
-    with pytest.raises(ValueError, match="Object of type 'vector_store' and identifier 'test_vector_store_2' already exists"):
+    with pytest.raises(
+        ValueError, match="Object of type 'vector_store' and identifier 'test_vector_store_2' already exists"
+    ):
         await cached_disk_dist_registry.register(duplicate_vector_store)
 
     result = await cached_disk_dist_registry.get("vector_store", "test_vector_store_2")
@@ -289,7 +291,9 @@ async def test_double_registration_different_objects(disk_dist_registry):
     assert result1 is True
 
     # Second registration with different data should fail
-    with pytest.raises(ValueError, match="Object of type 'vector_store' and identifier 'test_vector_store' already exists"):
+    with pytest.raises(
+        ValueError, match="Object of type 'vector_store' and identifier 'test_vector_store' already exists"
+    ):
         await disk_dist_registry.register(vector_store2)
 
     # Verify original object is unchanged