refactor(test): unify vector_io tests and make them configurable (#1398)

## Test Plan


`LLAMA_STACK_CONFIG=inference=sentence-transformers,vector_io=sqlite-vec
pytest -s -v test_vector_io.py --embedding-model all-miniLM-L6-V2
--inference-model='' --vision-inference-model=''`

```
test_vector_io.py::test_vector_db_retrieve[txt=:vis=:emb=all-miniLM-L6-V2] PASSED
test_vector_io.py::test_vector_db_register[txt=:vis=:emb=all-miniLM-L6-V2] PASSED
test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case0] PASSED
test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case1] PASSED
test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case2] PASSED
test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case3] PASSED
test_vector_io.py::test_insert_chunks[txt=:vis=:emb=all-miniLM-L6-V2-test_case4] PASSED
```

Same thing with:
- LLAMA_STACK_CONFIG=inference=sentence-transformers,vector_io=faiss
- LLAMA_STACK_CONFIG=fireworks

(Note that ergonomics will soon be improved re: cmd-line options and env
variables)

llama_stack/providers/inline/telemetry/meta_reference/config.py

@@ -44,9 +44,9 @@ class TelemetryConfig(BaseModel):
         return v
 
     @classmethod
-    def sample_run_config(cls, __distro_dir__: str = "runtime", db_name: str = "trace_store.db") -> Dict[str, Any]:
+    def sample_run_config(cls, __distro_dir__: str, db_name: str = "trace_store.db") -> Dict[str, Any]:
         return {
             "service_name": "${env.OTEL_SERVICE_NAME:llama-stack}",
             "sinks": "${env.TELEMETRY_SINKS:console,sqlite}",
-            "sqlite_db_path": "${env.SQLITE_DB_PATH:~/.llama/" + __distro_dir__ + "/" + db_name + "}",
+            "sqlite_db_path": "${env.SQLITE_DB_PATH:" + __distro_dir__ + "/" + db_name + "}",
         }

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

@@ -15,5 +15,5 @@ class SQLiteVectorIOConfig(BaseModel):
     @classmethod
     def sample_run_config(cls, __distro_dir__: str) -> Dict[str, Any]:
         return {
-            "db_path": "${env.SQLITE_STORE_DIR:~/.llama/" + __distro_dir__ + "}/" + "sqlite_vec.db",
+            "db_path": "${env.SQLITE_STORE_DIR:" + __distro_dir__ + "}/" + "sqlite_vec.db",
         }

llama_stack/providers/tests/vector_io/__init__.py

@@ -1,5 +0,0 @@
-# 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.

llama_stack/providers/tests/vector_io/conftest.py

@@ -1,108 +0,0 @@
-# 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 ..conftest import (
-    get_provider_fixture_overrides,
-    get_provider_fixture_overrides_from_test_config,
-    get_test_config_for_api,
-)
-from ..inference.fixtures import INFERENCE_FIXTURES
-from .fixtures import VECTOR_IO_FIXTURES
-
-DEFAULT_PROVIDER_COMBINATIONS = [
-    pytest.param(
-        {
-            "inference": "sentence_transformers",
-            "vector_io": "faiss",
-        },
-        id="sentence_transformers",
-        marks=pytest.mark.sentence_transformers,
-    ),
-    pytest.param(
-        {
-            "inference": "ollama",
-            "vector_io": "pgvector",
-        },
-        id="pgvector",
-        marks=pytest.mark.pgvector,
-    ),
-    pytest.param(
-        {
-            "inference": "ollama",
-            "vector_io": "faiss",
-        },
-        id="ollama",
-        marks=pytest.mark.ollama,
-    ),
-    pytest.param(
-        {
-            "inference": "ollama",
-            "vector_io": "sqlite_vec",
-        },
-        id="sqlite_vec",
-        marks=pytest.mark.ollama,
-    ),
-    pytest.param(
-        {
-            "inference": "sentence_transformers",
-            "vector_io": "chroma",
-        },
-        id="chroma",
-        marks=pytest.mark.chroma,
-    ),
-    pytest.param(
-        {
-            "inference": "ollama",
-            "vector_io": "qdrant",
-        },
-        id="qdrant",
-        marks=pytest.mark.qdrant,
-    ),
-    pytest.param(
-        {
-            "inference": "fireworks",
-            "vector_io": "weaviate",
-        },
-        id="weaviate",
-        marks=pytest.mark.weaviate,
-    ),
-]
-
-
-def pytest_configure(config):
-    for fixture_name in VECTOR_IO_FIXTURES:
-        config.addinivalue_line(
-            "markers",
-            f"{fixture_name}: marks tests as {fixture_name} specific",
-        )
-
-
-def pytest_generate_tests(metafunc):
-    test_config = get_test_config_for_api(metafunc.config, "vector_io")
-    if "embedding_model" in metafunc.fixturenames:
-        model = getattr(test_config, "embedding_model", None)
-        # Fall back to the default if not specified by the config file
-        model = model or metafunc.config.getoption("--embedding-model")
-        if model:
-            params = [pytest.param(model, id="")]
-        else:
-            params = [pytest.param("all-minilm:l6-v2", id="")]
-
-        metafunc.parametrize("embedding_model", params, indirect=True)
-
-    if "vector_io_stack" in metafunc.fixturenames:
-        available_fixtures = {
-            "inference": INFERENCE_FIXTURES,
-            "vector_io": VECTOR_IO_FIXTURES,
-        }
-        combinations = (
-            get_provider_fixture_overrides_from_test_config(metafunc.config, "vector_io", DEFAULT_PROVIDER_COMBINATIONS)
-            or get_provider_fixture_overrides(metafunc.config, available_fixtures)
-            or DEFAULT_PROVIDER_COMBINATIONS
-        )
-        metafunc.parametrize("vector_io_stack", combinations, indirect=True)

llama_stack/providers/tests/vector_io/fixtures.py

@@ -1,180 +0,0 @@
-# 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 os
-import tempfile
-
-import pytest
-import pytest_asyncio
-
-from llama_stack.apis.models import ModelInput, ModelType
-from llama_stack.distribution.datatypes import Api, Provider
-from llama_stack.providers.inline.vector_io.chroma import ChromaVectorIOConfig as InlineChromaVectorIOConfig
-from llama_stack.providers.inline.vector_io.faiss import FaissVectorIOConfig
-from llama_stack.providers.inline.vector_io.sqlite_vec import SQLiteVectorIOConfig
-from llama_stack.providers.remote.vector_io.chroma import ChromaVectorIOConfig
-from llama_stack.providers.remote.vector_io.pgvector import PGVectorVectorIOConfig
-from llama_stack.providers.remote.vector_io.qdrant import QdrantVectorIOConfig
-from llama_stack.providers.remote.vector_io.weaviate import WeaviateVectorIOConfig
-from llama_stack.providers.tests.resolver import construct_stack_for_test
-from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
-
-from ..conftest import ProviderFixture, remote_stack_fixture
-from ..env import get_env_or_fail
-
-
-@pytest.fixture(scope="session")
-def embedding_model(request):
-    if hasattr(request, "param"):
-        return request.param
-    return request.config.getoption("--embedding-model", None)
-
-
-@pytest.fixture(scope="session")
-def vector_io_remote() -> ProviderFixture:
-    return remote_stack_fixture()
-
-
-@pytest.fixture(scope="session")
-def vector_io_faiss() -> ProviderFixture:
-    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".db")
-    return ProviderFixture(
-        providers=[
-            Provider(
-                provider_id="faiss",
-                provider_type="inline::faiss",
-                config=FaissVectorIOConfig(
-                    kvstore=SqliteKVStoreConfig(db_path=temp_file.name).model_dump(),
-                ).model_dump(),
-            )
-        ],
-    )
-
-
-@pytest.fixture(scope="session")
-def vector_io_sqlite_vec() -> ProviderFixture:
-    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".db")
-    return ProviderFixture(
-        providers=[
-            Provider(
-                provider_id="sqlite_vec",
-                provider_type="inline::sqlite-vec",
-                config=SQLiteVectorIOConfig(
-                    kvstore=SqliteKVStoreConfig(db_path=temp_file.name).model_dump(),
-                ).model_dump(),
-            )
-        ],
-    )
-
-
-@pytest.fixture(scope="session")
-def vector_io_pgvector() -> ProviderFixture:
-    return ProviderFixture(
-        providers=[
-            Provider(
-                provider_id="pgvector",
-                provider_type="remote::pgvector",
-                config=PGVectorVectorIOConfig(
-                    host=os.getenv("PGVECTOR_HOST", "localhost"),
-                    port=os.getenv("PGVECTOR_PORT", 5432),
-                    db=get_env_or_fail("PGVECTOR_DB"),
-                    user=get_env_or_fail("PGVECTOR_USER"),
-                    password=get_env_or_fail("PGVECTOR_PASSWORD"),
-                ).model_dump(),
-            )
-        ],
-    )
-
-
-@pytest.fixture(scope="session")
-def vector_io_weaviate() -> ProviderFixture:
-    return ProviderFixture(
-        providers=[
-            Provider(
-                provider_id="weaviate",
-                provider_type="remote::weaviate",
-                config=WeaviateVectorIOConfig().model_dump(),
-            )
-        ],
-        provider_data=dict(
-            weaviate_api_key=get_env_or_fail("WEAVIATE_API_KEY"),
-            weaviate_cluster_url=get_env_or_fail("WEAVIATE_CLUSTER_URL"),
-        ),
-    )
-
-
-@pytest.fixture(scope="session")
-def vector_io_chroma() -> ProviderFixture:
-    url = os.getenv("CHROMA_URL")
-    if url:
-        config = ChromaVectorIOConfig(url=url)
-        provider_type = "remote::chromadb"
-    else:
-        if not os.getenv("CHROMA_DB_PATH"):
-            raise ValueError("CHROMA_DB_PATH or CHROMA_URL must be set")
-        config = InlineChromaVectorIOConfig(db_path=os.getenv("CHROMA_DB_PATH"))
-        provider_type = "inline::chromadb"
-    return ProviderFixture(
-        providers=[
-            Provider(
-                provider_id="chroma",
-                provider_type=provider_type,
-                config=config.model_dump(),
-            )
-        ]
-    )
-
-
-@pytest.fixture(scope="session")
-def vector_io_qdrant() -> ProviderFixture:
-    url = os.getenv("QDRANT_URL")
-    if url:
-        config = QdrantVectorIOConfig(url=url)
-        provider_type = "remote::qdrant"
-    else:
-        raise ValueError("QDRANT_URL must be set")
-    return ProviderFixture(
-        providers=[
-            Provider(
-                provider_id="qdrant",
-                provider_type=provider_type,
-                config=config.model_dump(),
-            )
-        ]
-    )
-
-
-VECTOR_IO_FIXTURES = ["faiss", "pgvector", "weaviate", "chroma", "qdrant", "sqlite_vec"]
-
-
-@pytest_asyncio.fixture(scope="session")
-async def vector_io_stack(embedding_model, request):
-    fixture_dict = request.param
-
-    providers = {}
-    provider_data = {}
-    for key in ["inference", "vector_io"]:
-        fixture = request.getfixturevalue(f"{key}_{fixture_dict[key]}")
-        providers[key] = fixture.providers
-        if fixture.provider_data:
-            provider_data.update(fixture.provider_data)
-
-    test_stack = await construct_stack_for_test(
-        [Api.vector_io, Api.inference],
-        providers,
-        provider_data,
-        models=[
-            ModelInput(
-                model_id=embedding_model,
-                model_type=ModelType.embedding,
-                metadata={
-                    "embedding_dimension": get_env_or_fail("EMBEDDING_DIMENSION"),
-                },
-            )
-        ],
-    )
-
-    return test_stack.impls[Api.vector_io], test_stack.impls[Api.vector_dbs]

llama_stack/providers/tests/vector_io/test_sqlite_vec.py

@@ -1,160 +0,0 @@
-# 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 asyncio
-import sqlite3
-
-import numpy as np
-import pytest
-import sqlite_vec
-
-from llama_stack.apis.vector_dbs import VectorDB
-from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
-from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import (
-    SQLiteVecIndex,
-    SQLiteVecVectorIOAdapter,
-    generate_chunk_id,
-)
-
-# How to run this test:
-#
-# pytest llama_stack/providers/tests/vector_io/test_sqlite_vec.py \
-# -v -s --tb=short --disable-warnings --asyncio-mode=auto
-
-SQLITE_VEC_PROVIDER = "sqlite_vec"
-EMBEDDING_DIMENSION = 384
-EMBEDDING_MODEL = "all-MiniLM-L6-v2"
-
-
-@pytest.fixture(scope="session")
-def loop():
-    return asyncio.new_event_loop()
-
-
-@pytest.fixture(scope="session", autouse=True)
-def sqlite_connection(loop):
-    conn = sqlite3.connect(":memory:")
-    try:
-        conn.enable_load_extension(True)
-        sqlite_vec.load(conn)
-        yield conn
-    finally:
-        conn.close()
-
-
-@pytest.fixture(scope="session", autouse=True)
-async def sqlite_vec_index(sqlite_connection):
-    return await SQLiteVecIndex.create(dimension=EMBEDDING_DIMENSION, connection=sqlite_connection, bank_id="test_bank")
-
-
-@pytest.fixture(scope="session")
-def sample_chunks():
-    """Generates chunks that force multiple batches for a single document to expose ID conflicts."""
-    n, k = 10, 3
-    sample = [
-        Chunk(content=f"Sentence {i} from document {j}", metadata={"document_id": f"document-{j}"})
-        for j in range(k)
-        for i in range(n)
-    ]
-    return sample
-
-
-@pytest.fixture(scope="session")
-def sample_embeddings(sample_chunks):
-    np.random.seed(42)
-    return np.array([np.random.rand(EMBEDDING_DIMENSION).astype(np.float32) for _ in sample_chunks])
-
-
-@pytest.mark.asyncio
-async def test_add_chunks(sqlite_vec_index, sample_chunks, sample_embeddings):
-    await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings, batch_size=2)
-    cur = sqlite_vec_index.connection.cursor()
-    cur.execute(f"SELECT COUNT(*) FROM {sqlite_vec_index.metadata_table}")
-    count = cur.fetchone()[0]
-    assert count == len(sample_chunks)
-
-
-@pytest.mark.asyncio
-async def test_query_chunks(sqlite_vec_index, sample_chunks, sample_embeddings):
-    await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings)
-    query_embedding = np.random.rand(EMBEDDING_DIMENSION).astype(np.float32)
-    response = await sqlite_vec_index.query(query_embedding, k=2, score_threshold=0.0)
-    assert isinstance(response, QueryChunksResponse)
-    assert len(response.chunks) == 2
-
-
-@pytest.mark.asyncio
-async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks):
-    """Test that chunk IDs do not conflict across batches when inserting chunks."""
-    # Reduce batch size to force multiple batches for same document
-    # since there are 10 chunks per document and batch size is 2
-    batch_size = 2
-    sample_embeddings = np.random.rand(len(sample_chunks), EMBEDDING_DIMENSION).astype(np.float32)
-
-    await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings, batch_size=batch_size)
-
-    cur = sqlite_vec_index.connection.cursor()
-
-    # Retrieve all chunk IDs to check for duplicates
-    cur.execute(f"SELECT id FROM {sqlite_vec_index.metadata_table}")
-    chunk_ids = [row[0] for row in cur.fetchall()]
-    cur.close()
-
-    # Ensure all chunk IDs are unique
-    assert len(chunk_ids) == len(set(chunk_ids)), "Duplicate chunk IDs detected across batches!"
-
-
-@pytest.fixture(scope="session")
-async def sqlite_vec_adapter(sqlite_connection):
-    config = type("Config", (object,), {"db_path": ":memory:"})  # Mock config with in-memory database
-    adapter = SQLiteVecVectorIOAdapter(config=config, inference_api=None)
-    await adapter.initialize()
-    yield adapter
-    await adapter.shutdown()
-
-
-@pytest.mark.asyncio
-async def test_register_vector_db(sqlite_vec_adapter):
-    vector_db = VectorDB(
-        identifier="test_db",
-        embedding_model=EMBEDDING_MODEL,
-        embedding_dimension=EMBEDDING_DIMENSION,
-        metadata={},
-        provider_id=SQLITE_VEC_PROVIDER,
-    )
-    await sqlite_vec_adapter.register_vector_db(vector_db)
-    vector_dbs = await sqlite_vec_adapter.list_vector_dbs()
-    assert any(db.identifier == "test_db" for db in vector_dbs)
-
-
-@pytest.mark.asyncio
-async def test_unregister_vector_db(sqlite_vec_adapter):
-    vector_db = VectorDB(
-        identifier="test_db",
-        embedding_model=EMBEDDING_MODEL,
-        embedding_dimension=EMBEDDING_DIMENSION,
-        metadata={},
-        provider_id=SQLITE_VEC_PROVIDER,
-    )
-    await sqlite_vec_adapter.register_vector_db(vector_db)
-    await sqlite_vec_adapter.unregister_vector_db("test_db")
-    vector_dbs = await sqlite_vec_adapter.list_vector_dbs()
-    assert not any(db.identifier == "test_db" for db in vector_dbs)
-
-
-def test_generate_chunk_id():
-    chunks = [
-        Chunk(content="test", metadata={"document_id": "doc-1"}),
-        Chunk(content="test ", metadata={"document_id": "doc-1"}),
-        Chunk(content="test 3", metadata={"document_id": "doc-1"}),
-    ]
-
-    chunk_ids = sorted([generate_chunk_id(chunk.metadata["document_id"], chunk.content) for chunk in chunks])
-    assert chunk_ids == [
-        "177a1368-f6a8-0c50-6e92-18677f2c3de3",
-        "bc744db3-1b25-0a9c-cdff-b6ba3df73c36",
-        "f68df25d-d9aa-ab4d-5684-64a233add20d",
-    ]

llama_stack/providers/tests/vector_io/test_vector_io.py

@@ -1,160 +0,0 @@
-# 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 uuid
-
-import pytest
-
-from llama_stack.apis.tools import RAGDocument
-from llama_stack.apis.vector_dbs import ListVectorDBsResponse, VectorDB
-from llama_stack.apis.vector_io import QueryChunksResponse
-from llama_stack.providers.utils.memory.vector_store import make_overlapped_chunks
-
-# How to run this test:
-#
-# pytest llama_stack/providers/tests/vector_io/test_vector_io.py \
-#   -m "pgvector" --env EMBEDDING_DIMENSION=384 PGVECTOR_PORT=7432 \
-#   -v -s --tb=short --disable-warnings
-
-
-@pytest.fixture(scope="session")
-def sample_chunks():
-    docs = [
-        RAGDocument(
-            document_id="doc1",
-            content="Python is a high-level programming language.",
-            metadata={"category": "programming", "difficulty": "beginner"},
-        ),
-        RAGDocument(
-            document_id="doc2",
-            content="Machine learning is a subset of artificial intelligence.",
-            metadata={"category": "AI", "difficulty": "advanced"},
-        ),
-        RAGDocument(
-            document_id="doc3",
-            content="Data structures are fundamental to computer science.",
-            metadata={"category": "computer science", "difficulty": "intermediate"},
-        ),
-        RAGDocument(
-            document_id="doc4",
-            content="Neural networks are inspired by biological neural networks.",
-            metadata={"category": "AI", "difficulty": "advanced"},
-        ),
-    ]
-    chunks = []
-    for doc in docs:
-        chunks.extend(make_overlapped_chunks(doc.document_id, doc.content, window_len=512, overlap_len=64))
-    return chunks
-
-
-async def register_vector_db(vector_dbs_impl: VectorDB, embedding_model: str):
-    vector_db_id = f"test_vector_db_{uuid.uuid4().hex}"
-    return await vector_dbs_impl.register_vector_db(
-        vector_db_id=vector_db_id,
-        embedding_model=embedding_model,
-        embedding_dimension=384,
-    )
-
-
-class TestVectorIO:
-    @pytest.mark.asyncio
-    async def test_banks_list(self, vector_io_stack, embedding_model):
-        _, vector_dbs_impl = vector_io_stack
-
-        # Register a test bank
-        registered_vector_db = await register_vector_db(vector_dbs_impl, embedding_model)
-
-        try:
-            # Verify our bank shows up in list
-            response = await vector_dbs_impl.list_vector_dbs()
-            assert isinstance(response, ListVectorDBsResponse)
-            assert any(vector_db.vector_db_id == registered_vector_db.vector_db_id for vector_db in response.data)
-        finally:
-            # Clean up
-            await vector_dbs_impl.unregister_vector_db(registered_vector_db.vector_db_id)
-
-        # Verify our bank was removed
-        response = await vector_dbs_impl.list_vector_dbs()
-        assert isinstance(response, ListVectorDBsResponse)
-        assert all(vector_db.vector_db_id != registered_vector_db.vector_db_id for vector_db in response.data)
-
-    @pytest.mark.asyncio
-    async def test_banks_register(self, vector_io_stack, embedding_model):
-        _, vector_dbs_impl = vector_io_stack
-
-        vector_db_id = f"test_vector_db_{uuid.uuid4().hex}"
-
-        try:
-            # Register initial bank
-            await vector_dbs_impl.register_vector_db(
-                vector_db_id=vector_db_id,
-                embedding_model=embedding_model,
-                embedding_dimension=384,
-            )
-
-            # Verify our bank exists
-            response = await vector_dbs_impl.list_vector_dbs()
-            assert isinstance(response, ListVectorDBsResponse)
-            assert any(vector_db.vector_db_id == vector_db_id for vector_db in response.data)
-
-            # Try registering same bank again
-            await vector_dbs_impl.register_vector_db(
-                vector_db_id=vector_db_id,
-                embedding_model=embedding_model,
-                embedding_dimension=384,
-            )
-
-            # Verify still only one instance of our bank
-            response = await vector_dbs_impl.list_vector_dbs()
-            assert isinstance(response, ListVectorDBsResponse)
-            assert len([vector_db for vector_db in response.data if vector_db.vector_db_id == vector_db_id]) == 1
-        finally:
-            # Clean up
-            await vector_dbs_impl.unregister_vector_db(vector_db_id)
-
-    @pytest.mark.asyncio
-    async def test_query_documents(self, vector_io_stack, embedding_model, sample_chunks):
-        vector_io_impl, vector_dbs_impl = vector_io_stack
-
-        with pytest.raises(ValueError):
-            await vector_io_impl.insert_chunks("test_vector_db", sample_chunks)
-
-        registered_db = await register_vector_db(vector_dbs_impl, embedding_model)
-        await vector_io_impl.insert_chunks(registered_db.vector_db_id, sample_chunks)
-
-        query1 = "programming language"
-        response1 = await vector_io_impl.query_chunks(registered_db.vector_db_id, query1)
-        assert_valid_response(response1)
-        assert any("Python" in chunk.content for chunk in response1.chunks)
-
-        # Test case 3: Query with semantic similarity
-        query3 = "AI and brain-inspired computing"
-        response3 = await vector_io_impl.query_chunks(registered_db.vector_db_id, query3)
-        assert_valid_response(response3)
-        assert any("neural networks" in chunk.content.lower() for chunk in response3.chunks)
-
-        # Test case 4: Query with limit on number of results
-        query4 = "computer"
-        params4 = {"max_chunks": 2}
-        response4 = await vector_io_impl.query_chunks(registered_db.vector_db_id, query4, params4)
-        assert_valid_response(response4)
-        assert len(response4.chunks) <= 2
-
-        # Test case 5: Query with threshold on similarity score
-        query5 = "quantum computing"  # Not directly related to any document
-        params5 = {"score_threshold": 0.01}
-        response5 = await vector_io_impl.query_chunks(registered_db.vector_db_id, query5, params5)
-        assert_valid_response(response5)
-        print("The scores are:", response5.scores)
-        assert all(score >= 0.01 for score in response5.scores)
-
-
-def assert_valid_response(response: QueryChunksResponse):
-    assert len(response.chunks) > 0
-    assert len(response.scores) > 0
-    assert len(response.chunks) == len(response.scores)
-    for chunk in response.chunks:
-        assert isinstance(chunk.content, str)

llama_stack/providers/utils/kvstore/config.py

@@ -55,11 +55,11 @@ class SqliteKVStoreConfig(CommonConfig):
     )
 
     @classmethod
-    def sample_run_config(cls, __distro_dir__: str = "runtime", db_name: str = "kvstore.db"):
+    def sample_run_config(cls, __distro_dir__: str, db_name: str = "kvstore.db"):
         return {
             "type": "sqlite",
             "namespace": None,
-            "db_path": "${env.SQLITE_STORE_DIR:~/.llama/" + __distro_dir__ + "}/" + db_name,
+            "db_path": "${env.SQLITE_STORE_DIR:" + __distro_dir__ + "}/" + db_name,
         }