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

21
llama_stack/providers/remote/vector_io/qdrant/qdrant.py
View file

@ -43,16 +43,14 @@ class QdrantIndex(EmbeddingIndex):
self.collection_name = collection_name
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)}"
)
if not await self.client.collection_exists(self.collection_name):
await self.client.create_collection(
self.collection_name,
vectors_config=models.VectorParams(
size=len(embeddings[0]), distance=models.Distance.COSINE
),
vectors_config=models.VectorParams(size=len(embeddings[0]), distance=models.Distance.COSINE),
)
points = []
@ -62,16 +60,13 @@ class QdrantIndex(EmbeddingIndex):
PointStruct(
id=convert_id(chunk_id),
vector=embedding,
payload={"chunk_content": chunk.model_dump()}
| {CHUNK_ID_KEY: chunk_id},
payload={"chunk_content": chunk.model_dump()} | {CHUNK_ID_KEY: chunk_id},
)
)
await self.client.upsert(collection_name=self.collection_name, points=points)
async def query(
self, embedding: NDArray, k: int, score_threshold: float
) -> QueryChunksResponse:
async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
results = (
await self.client.query_points(
collection_name=self.collection_name,
@ -124,9 +119,7 @@ class QdrantVectorDBAdapter(VectorIO, VectorDBsProtocolPrivate):
self.cache[vector_db.identifier] = index
async def _get_and_cache_vector_db_index(
self, vector_db_id: str
) -> Optional[VectorDBWithIndex]:
async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> Optional[VectorDBWithIndex]:
if vector_db_id in self.cache:
return self.cache[vector_db_id]