fix(faiss): handle case where distance is 0 by setting d to minimum positive… (#2387)

# What does this PR do?
Adds try-catch to faiss `query_vector` function for when the distance
between the query embedding and an embedding within the vector db is 0
(identical vectors). Catches `ZeroDivisionError` and then appends `(1.0
/ sys.float_info.min)` to `scores` to represent maximum similarity.

<!-- If resolving an issue, uncomment and update the line below -->
Closes [#2381]

## Test Plan
Checkout this PR

Execute this code and there will no longer be a `ZeroDivisionError`
exception
```
from llama_stack_client import LlamaStackClient

base_url = "http://localhost:8321"
client = LlamaStackClient(base_url=base_url)

models = client.models.list()
embedding_model = (
    em := next(m for m in models if m.model_type == "embedding")
).identifier
embedding_dimension = 384

_ = client.vector_dbs.register(
    vector_db_id="foo_db",
    embedding_model=embedding_model,
    embedding_dimension=embedding_dimension,
    provider_id="faiss",
)

chunk = {
    "content": "foo",
    "mime_type": "text/plain",
    "metadata": {
        "document_id": "foo-id"
    }
}

client.vector_io.insert(vector_db_id="foo_db", chunks=[chunk])
client.vector_io.query(vector_db_id="foo_db", query="foo")
```

### Running unit tests
`uv run pytest tests/unit/rag/test_rag_query.py -v`

---------

Signed-off-by: Ben Browning <bbrownin@redhat.com>
Co-authored-by: Ben Browning <bbrownin@redhat.com>

tests/unit/providers/vector_io/test_faiss.py

@@ -0,0 +1,120 @@
+# 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
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import numpy as np
+import pytest
+import pytest_asyncio
+
+from llama_stack.apis.inference import EmbeddingsResponse, Inference
+from llama_stack.apis.vector_dbs import VectorDB
+from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
+from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
+from llama_stack.providers.inline.vector_io.faiss.faiss import (
+    FaissIndex,
+    FaissVectorIOAdapter,
+)
+
+# This test is a unit test for the FaissVectorIOAdapter class. This should only contain
+# tests which are specific to this class. More general (API-level) tests should be placed in
+# tests/integration/vector_io/
+#
+# How to run this test:
+#
+# pytest tests/unit/providers/vector_io/test_faiss.py \
+# -v -s --tb=short --disable-warnings --asyncio-mode=auto
+
+FAISS_PROVIDER = "faiss"
+
+
+@pytest.fixture(scope="session")
+def loop():
+    return asyncio.new_event_loop()
+
+
+@pytest.fixture
+def embedding_dimension():
+    return 384
+
+
+@pytest.fixture
+def vector_db_id():
+    return "test_vector_db"
+
+
+@pytest.fixture
+def sample_chunks():
+    return [
+        Chunk(content="MOCK text content 1", mime_type="text/plain", metadata={"document_id": "mock-doc-1"}),
+        Chunk(content="MOCK text content 1", mime_type="text/plain", metadata={"document_id": "mock-doc-2"}),
+    ]
+
+
+@pytest.fixture
+def sample_embeddings(embedding_dimension):
+    return np.random.rand(2, embedding_dimension).astype(np.float32)
+
+
+@pytest.fixture
+def mock_vector_db(vector_db_id, embedding_dimension) -> MagicMock:
+    mock_vector_db = MagicMock(spec=VectorDB)
+    mock_vector_db.embedding_model = "mock_embedding_model"
+    mock_vector_db.identifier = vector_db_id
+    mock_vector_db.embedding_dimension = embedding_dimension
+    return mock_vector_db
+
+
+@pytest.fixture
+def mock_inference_api(sample_embeddings):
+    mock_api = MagicMock(spec=Inference)
+    mock_api.embeddings = AsyncMock(return_value=EmbeddingsResponse(embeddings=sample_embeddings))
+    return mock_api
+
+
+@pytest.fixture
+def faiss_config():
+    config = MagicMock(spec=FaissVectorIOConfig)
+    config.kvstore = None
+    return config
+
+
+@pytest_asyncio.fixture
+async def faiss_index(embedding_dimension):
+    index = await FaissIndex.create(dimension=embedding_dimension)
+    yield index
+
+
+@pytest_asyncio.fixture
+async def faiss_adapter(faiss_config, mock_inference_api) -> FaissVectorIOAdapter:
+    adapter = FaissVectorIOAdapter(config=faiss_config, inference_api=mock_inference_api)
+    await adapter.initialize()
+    yield adapter
+    await adapter.shutdown()
+
+
+@pytest.mark.asyncio
+async def test_faiss_query_vector_returns_infinity_when_query_and_embedding_are_identical(
+    faiss_index, sample_chunks, sample_embeddings, embedding_dimension
+):
+    await faiss_index.add_chunks(sample_chunks, sample_embeddings)
+    query_embedding = np.random.rand(embedding_dimension).astype(np.float32)
+
+    with patch.object(faiss_index.index, "search") as mock_search:
+        mock_search.return_value = (np.array([[0.0, 0.1]]), np.array([[0, 1]]))
+
+        response = await faiss_index.query_vector(embedding=query_embedding, k=2, score_threshold=0.0)
+
+        assert isinstance(response, QueryChunksResponse)
+        assert len(response.chunks) == 2
+        assert len(response.scores) == 2
+
+        assert response.scores[0] == float("inf")  # infinity (1.0 / 0.0)
+        assert response.scores[1] == 10.0  # (1.0 / 0.1 = 10.0)
+
+        assert response.chunks[0] == sample_chunks[0]
+        assert response.chunks[1] == sample_chunks[1]