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chore: enable pyupgrade fixes (#1806)

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# What does this PR do?

The goal of this PR is code base modernization.

Schema reflection code needed a minor adjustment to handle UnionTypes
and collections.abc.AsyncIterator. (Both are preferred for latest Python
releases.)

Note to reviewers: almost all changes here are automatically generated
by pyupgrade. Some additional unused imports were cleaned up. The only
change worth of note can be found under `docs/openapi_generator` and
`llama_stack/strong_typing/schema.py` where reflection code was updated
to deal with "newer" types.

Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
This commit is contained in:
Ihar Hrachyshka 2025-05-01 17:23:50 -04:00 • committed by GitHub
parent ffe3d0b2cd
commit 9e6561a1ec
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GPG key ID: B5690EEEBB952194
319 changed files with 2843 additions and 3033 deletions
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16
llama_stack/providers/remote/vector_io/milvus/milvus.py
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@ -9,7 +9,7 @@ import hashlib
import logging
import os
import uuid
from typing import Any, Dict, List, Optional, Union
from typing import Any
from numpy.typing import NDArray
from pymilvus import MilvusClient
@ -39,7 +39,7 @@ class MilvusIndex(EmbeddingIndex):
if await asyncio.to_thread(self.client.has_collection, self.collection_name):
await asyncio.to_thread(self.client.drop_collection, collection_name=self.collection_name)
async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
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)}"
)
@ -89,7 +89,7 @@ class MilvusIndex(EmbeddingIndex):
class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
def __init__(
self, config: Union[RemoteMilvusVectorIOConfig, InlineMilvusVectorIOConfig], inference_api: Api.inference
self, config: RemoteMilvusVectorIOConfig | InlineMilvusVectorIOConfig, inference_api: Api.inference
) -> None:
self.config = config
self.cache = {}
@ -124,7 +124,7 @@ class MilvusVectorIOAdapter(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) -> VectorDBWithIndex | None:
if vector_db_id in self.cache:
return self.cache[vector_db_id]
@ -148,8 +148,8 @@ class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
async def insert_chunks(
self,
vector_db_id: str,
chunks: List[Chunk],
ttl_seconds: Optional[int] = None,
chunks: list[Chunk],
ttl_seconds: int | None = None,
) -> None:
index = await self._get_and_cache_vector_db_index(vector_db_id)
if not index:
@ -161,7 +161,7 @@ class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
self,
vector_db_id: str,
query: InterleavedContent,
params: Optional[Dict[str, Any]] = None,
params: dict[str, Any] | None = None,
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_db_index(vector_db_id)
if not index:
@ -172,7 +172,7 @@ class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
def generate_chunk_id(document_id: str, chunk_text: str) -> str:
"""Generate a unique chunk ID using a hash of document ID and chunk text."""
hash_input = f"{document_id}:{chunk_text}".encode("utf-8")
hash_input = f"{document_id}:{chunk_text}".encode()
return str(uuid.UUID(hashlib.md5(hash_input).hexdigest()))