phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-06-28 02:53:30 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

chore: fixed formatting issues

Browse source
This commit is contained in:
Ibrahim Haroon 2025-06-06 10:32:39 -04:00
parent f60c3c4acf
commit 4b32cfa846
1 changed files with 11 additions and 25 deletions
Download patch file Download diff file Expand all files Collapse all files

36
tests/unit/providers/vector_io/test_faiss.py
View file

@ -14,12 +14,11 @@ 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,
)
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
# 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
@ -51,16 +50,8 @@ def vector_db_id():
@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"}
)
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"}),
]
@ -107,28 +98,23 @@ async def faiss_adapter(faiss_config, mock_inference_api) -> FaissVectorIOAdapte
@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):
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]])
)
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
)
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.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]