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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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319 changed files with 2843 additions and 3033 deletions
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16
llama_stack/apis/vector_io/vector_io.py
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@ -8,7 +8,7 @@
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
from typing import Any, Protocol, runtime_checkable
from pydantic import BaseModel, Field
@ -20,17 +20,17 @@ from llama_stack.schema_utils import json_schema_type, webmethod
class Chunk(BaseModel):
content: InterleavedContent
metadata: Dict[str, Any] = Field(default_factory=dict)
metadata: dict[str, Any] = Field(default_factory=dict)
@json_schema_type
class QueryChunksResponse(BaseModel):
chunks: List[Chunk]
scores: List[float]
chunks: list[Chunk]
scores: list[float]
class VectorDBStore(Protocol):
def get_vector_db(self, vector_db_id: str) -> Optional[VectorDB]: ...
def get_vector_db(self, vector_db_id: str) -> VectorDB | None: ...
@runtime_checkable
@ -44,8 +44,8 @@ class VectorIO(Protocol):
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: ...
@webmethod(route="/vector-io/query", method="POST")
@ -53,5 +53,5 @@ class VectorIO(Protocol):
self,
vector_db_id: str,
query: InterleavedContent,
params: Optional[Dict[str, Any]] = None,
params: dict[str, Any] | None = None,
) -> QueryChunksResponse: ...