[memory refactor][2/n] Update faiss and make it pass tests (#830)

See https://github.com/meta-llama/llama-stack/issues/827 for the broader
design.

Second part:

- updates routing table / router code 
- updates the faiss implementation


## Test Plan

```
pytest -s -v -k sentence test_vector_io.py --env EMBEDDING_DIMENSION=384
```

llama_stack/providers/tests/vector_io/__init__.py

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

@@ -0,0 +1,96 @@
+# 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": "faiss",
+        },
+        id="ollama",
+        marks=pytest.mark.ollama,
+    ),
+    pytest.param(
+        {
+            "inference": "sentence_transformers",
+            "vector_io": "chroma",
+        },
+        id="chroma",
+        marks=pytest.mark.chroma,
+    ),
+    pytest.param(
+        {
+            "inference": "bedrock",
+            "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

@@ -0,0 +1,144 @@
+# 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 ChromaInlineImplConfig
+from llama_stack.providers.inline.vector_io.faiss import FaissImplConfig
+from llama_stack.providers.remote.vector_io.chroma import ChromaRemoteImplConfig
+from llama_stack.providers.remote.vector_io.pgvector import PGVectorConfig
+from llama_stack.providers.remote.vector_io.weaviate import WeaviateConfig
+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=FaissImplConfig(
+                    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=PGVectorConfig(
+                    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=WeaviateConfig().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 = ChromaRemoteImplConfig(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 = ChromaInlineImplConfig(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(),
+            )
+        ]
+    )
+
+
+VECTOR_IO_FIXTURES = ["faiss", "pgvector", "weaviate", "chroma"]
+
+
+@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_vector_io.py

@@ -0,0 +1,200 @@
+# 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.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,
+    MemoryBankDocument,
+)
+
+# How to run this test:
+#
+# pytest llama_stack/providers/tests/memory/test_memory.py
+#   -m "sentence_transformers" --env EMBEDDING_DIMENSION=384
+#   -v -s --tb=short --disable-warnings
+
+
+@pytest.fixture(scope="session")
+def sample_chunks():
+    docs = [
+        MemoryBankDocument(
+            document_id="doc1",
+            content="Python is a high-level programming language.",
+            metadata={"category": "programming", "difficulty": "beginner"},
+        ),
+        MemoryBankDocument(
+            document_id="doc2",
+            content="Machine learning is a subset of artificial intelligence.",
+            metadata={"category": "AI", "difficulty": "advanced"},
+        ),
+        MemoryBankDocument(
+            document_id="doc3",
+            content="Data structures are fundamental to computer science.",
+            metadata={"category": "computer science", "difficulty": "intermediate"},
+        ),
+        MemoryBankDocument(
+            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/tests/vector_io/test_vector_store.py

@@ -0,0 +1,79 @@
+# 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 base64
+import mimetypes
+import os
+from pathlib import Path
+
+import pytest
+
+from llama_stack.providers.utils.memory.vector_store import (
+    content_from_doc,
+    MemoryBankDocument,
+    URL,
+)
+
+DUMMY_PDF_PATH = Path(os.path.abspath(__file__)).parent / "fixtures" / "dummy.pdf"
+
+
+def read_file(file_path: str) -> bytes:
+    with open(file_path, "rb") as file:
+        return file.read()
+
+
+def data_url_from_file(file_path: str) -> str:
+    with open(file_path, "rb") as file:
+        file_content = file.read()
+
+    base64_content = base64.b64encode(file_content).decode("utf-8")
+    mime_type, _ = mimetypes.guess_type(file_path)
+
+    data_url = f"data:{mime_type};base64,{base64_content}"
+
+    return data_url
+
+
+class TestVectorStore:
+    @pytest.mark.asyncio
+    async def test_returns_content_from_pdf_data_uri(self):
+        data_uri = data_url_from_file(DUMMY_PDF_PATH)
+        doc = MemoryBankDocument(
+            document_id="dummy",
+            content=data_uri,
+            mime_type="application/pdf",
+            metadata={},
+        )
+        content = await content_from_doc(doc)
+        assert content == "Dummy PDF file"
+
+    @pytest.mark.asyncio
+    async def test_downloads_pdf_and_returns_content(self):
+        # Using GitHub to host the PDF file
+        url = "https://raw.githubusercontent.com/meta-llama/llama-stack/da035d69cfca915318eaf485770a467ca3c2a238/llama_stack/providers/tests/memory/fixtures/dummy.pdf"
+        doc = MemoryBankDocument(
+            document_id="dummy",
+            content=url,
+            mime_type="application/pdf",
+            metadata={},
+        )
+        content = await content_from_doc(doc)
+        assert content == "Dummy PDF file"
+
+    @pytest.mark.asyncio
+    async def test_downloads_pdf_and_returns_content_with_url_object(self):
+        # Using GitHub to host the PDF file
+        url = "https://raw.githubusercontent.com/meta-llama/llama-stack/da035d69cfca915318eaf485770a467ca3c2a238/llama_stack/providers/tests/memory/fixtures/dummy.pdf"
+        doc = MemoryBankDocument(
+            document_id="dummy",
+            content=URL(
+                uri=url,
+            ),
+            mime_type="application/pdf",
+            metadata={},
+        )
+        content = await content_from_doc(doc)
+        assert content == "Dummy PDF file"