phoenix-oss/llama-stack
1
0
Fork 0
forked from phoenix-oss/llama-stack-mirror
Code Pull requests Releases Packages 2 Activity Actions

Fix precommit check after moving to ruff (#927)

Browse source 
Lint check in main branch is failing. This fixes the lint check after we
moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We
need to move to a `ruff.toml` file as well as fixing and ignoring some
additional checks.

Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
This commit is contained in:
Yuan Tang 2025-02-02 09:46:45 -05:00 • committed by GitHub
parent 4773092dd1
commit 34ab7a3b6c
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
217 changed files with 981 additions and 2681 deletions
Download patch file Download diff file Expand all files Collapse all files

4
llama_stack/providers/tests/vector_io/conftest.py
View file

@ -87,9 +87,7 @@ def pytest_generate_tests(metafunc):
"vector_io": VECTOR_IO_FIXTURES,
}
combinations = (
get_provider_fixture_overrides_from_test_config(
metafunc.config, "vector_io", DEFAULT_PROVIDER_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
)

63
llama_stack/providers/tests/vector_io/test_vector_io.py
View file

@ -48,11 +48,7 @@ def sample_chunks():
]
chunks = []
for doc in docs:
chunks.extend(
make_overlapped_chunks(
doc.document_id, doc.content, window_len=512, overlap_len=64
)
)
chunks.extend(make_overlapped_chunks(doc.document_id, doc.content, window_len=512, overlap_len=64))
return chunks
@ -71,31 +67,21 @@ class TestVectorIO:
_, vector_dbs_impl = vector_io_stack
# Register a test bank
registered_vector_db = await register_vector_db(
vector_dbs_impl, embedding_model
)
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
)
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
)
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
)
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):
@ -114,9 +100,7 @@ class TestVectorIO:
# 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
)
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(
@ -128,24 +112,13 @@ class TestVectorIO:
# 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
)
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
):
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):
@ -155,37 +128,27 @@ class TestVectorIO:
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
)
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
)
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
)
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
)
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
)
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)