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Fix precommit check after moving to ruff (#927)

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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
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GPG key ID: B5690EEEBB952194
217 changed files with 981 additions and 2681 deletions
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22
llama_stack/providers/remote/vector_io/pgvector/pgvector.py
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@ -71,9 +71,9 @@ class PGVectorIndex(EmbeddingIndex):
)
async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
assert len(chunks) == len(
embeddings
), f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}"
assert len(chunks) == len(embeddings), (
f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}"
)
values = []
for i, chunk in enumerate(chunks):
@ -94,9 +94,7 @@ class PGVectorIndex(EmbeddingIndex):
)
execute_values(self.cursor, query, values, template="(%s, %s, %s::vector)")
async def query(
self, embedding: NDArray, k: int, score_threshold: float
) -> QueryChunksResponse:
async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
self.cursor.execute(
f"""
SELECT document, embedding <-> %s::vector AS distance
@ -166,9 +164,7 @@ class PGVectorVectorDBAdapter(VectorIO, VectorDBsProtocolPrivate):
upsert_models(self.cursor, [(vector_db.identifier, vector_db)])
index = PGVectorIndex(vector_db, vector_db.embedding_dimension, self.cursor)
self.cache[vector_db.identifier] = VectorDBWithIndex(
vector_db, index, self.inference_api
)
self.cache[vector_db.identifier] = VectorDBWithIndex(vector_db, index, self.inference_api)
async def unregister_vector_db(self, vector_db_id: str) -> None:
await self.cache[vector_db_id].index.delete()
@ -192,15 +188,11 @@ class PGVectorVectorDBAdapter(VectorIO, VectorDBsProtocolPrivate):
index = await self._get_and_cache_vector_db_index(vector_db_id)
return await index.query_chunks(query, params)
async def _get_and_cache_vector_db_index(
self, vector_db_id: str
) -> VectorDBWithIndex:
async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> VectorDBWithIndex:
if vector_db_id in self.cache:
return self.cache[vector_db_id]
vector_db = await self.vector_db_store.get_vector_db(vector_db_id)
index = PGVectorIndex(vector_db, vector_db.embedding_dimension, self.cursor)
self.cache[vector_db_id] = VectorDBWithIndex(
vector_db, index, self.inference_api
)
self.cache[vector_db_id] = VectorDBWithIndex(vector_db, index, self.inference_api)
return self.cache[vector_db_id]