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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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217 changed files with 981 additions and 2681 deletions
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22
llama_stack/providers/remote/vector_io/chroma/chroma.py
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@ -42,9 +42,9 @@ class ChromaIndex(EmbeddingIndex):
self.collection = collection
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)}"
)
ids = [f"{c.metadata['document_id']}:chunk-{i}" for i, c in enumerate(chunks)]
await maybe_await(
@ -55,9 +55,7 @@ class ChromaIndex(EmbeddingIndex):
)
)
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 maybe_await(
self.collection.query(
query_embeddings=[embedding.tolist()],
@ -109,9 +107,7 @@ class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
if parsed.path and parsed.path != "/":
raise ValueError("URL should not contain a path")
self.client = await chromadb.AsyncHttpClient(
host=parsed.hostname, port=parsed.port
)
self.client = await chromadb.AsyncHttpClient(host=parsed.hostname, port=parsed.port)
else:
log.info(f"Connecting to Chroma local db at: {self.config.db_path}")
self.client = chromadb.PersistentClient(path=self.config.db_path)
@ -157,9 +153,7 @@ class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
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]
@ -169,8 +163,6 @@ class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
collection = await maybe_await(self.client.get_collection(vector_db_id))
if not collection:
raise ValueError(f"Vector DB {vector_db_id} not found in Chroma")
index = VectorDBWithIndex(
vector_db, ChromaIndex(self.client, collection), self.inference_api
)
index = VectorDBWithIndex(vector_db, ChromaIndex(self.client, collection), self.inference_api)
self.cache[vector_db_id] = index
return index