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chore: enable pyupgrade fixes (#1806)
# 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
GitHub
parent
ffe3d0b2cd
commit
9e6561a1ec
319 changed files with 2843 additions and 3033 deletions
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@ -6,12 +6,12 @@
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import asyncio
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import time
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from typing import Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Union
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from collections.abc import AsyncGenerator, AsyncIterator
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from typing import Annotated, Any
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from openai.types.chat import ChatCompletionToolChoiceOptionParam as OpenAIChatCompletionToolChoiceOptionParam
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from openai.types.chat import ChatCompletionToolParam as OpenAIChatCompletionToolParam
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from pydantic import Field, TypeAdapter
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from typing_extensions import Annotated
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from llama_stack.apis.common.content_types import (
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URL,
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@ -100,9 +100,9 @@ class VectorIORouter(VectorIO):
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self,
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vector_db_id: str,
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embedding_model: str,
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embedding_dimension: Optional[int] = 384,
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provider_id: Optional[str] = None,
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provider_vector_db_id: Optional[str] = None,
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embedding_dimension: int | None = 384,
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provider_id: str | None = None,
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provider_vector_db_id: str | None = None,
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) -> None:
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logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}")
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await self.routing_table.register_vector_db(
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@ -116,8 +116,8 @@ class VectorIORouter(VectorIO):
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async def insert_chunks(
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self,
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vector_db_id: str,
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chunks: List[Chunk],
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ttl_seconds: Optional[int] = None,
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chunks: list[Chunk],
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ttl_seconds: int | None = None,
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) -> None:
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logger.debug(
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f"VectorIORouter.insert_chunks: {vector_db_id}, {len(chunks)} chunks, ttl_seconds={ttl_seconds}, chunk_ids={[chunk.metadata['document_id'] for chunk in chunks[:3]]}{' and more...' if len(chunks) > 3 else ''}",
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@ -128,7 +128,7 @@ class VectorIORouter(VectorIO):
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self,
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vector_db_id: str,
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query: InterleavedContent,
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params: Optional[Dict[str, Any]] = None,
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params: dict[str, Any] | None = None,
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) -> QueryChunksResponse:
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logger.debug(f"VectorIORouter.query_chunks: {vector_db_id}")
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return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(vector_db_id, query, params)
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@ -140,7 +140,7 @@ class InferenceRouter(Inference):
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def __init__(
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self,
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routing_table: RoutingTable,
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telemetry: Optional[Telemetry] = None,
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telemetry: Telemetry | None = None,
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) -> None:
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logger.debug("Initializing InferenceRouter")
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self.routing_table = routing_table
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@ -160,10 +160,10 @@ class InferenceRouter(Inference):
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async def register_model(
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self,
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model_id: str,
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provider_model_id: Optional[str] = None,
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provider_id: Optional[str] = None,
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metadata: Optional[Dict[str, Any]] = None,
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model_type: Optional[ModelType] = None,
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provider_model_id: str | None = None,
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provider_id: str | None = None,
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metadata: dict[str, Any] | None = None,
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model_type: ModelType | None = None,
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) -> None:
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logger.debug(
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f"InferenceRouter.register_model: {model_id=} {provider_model_id=} {provider_id=} {metadata=} {model_type=}",
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@ -176,7 +176,7 @@ class InferenceRouter(Inference):
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completion_tokens: int,
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total_tokens: int,
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model: Model,
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) -> List[MetricEvent]:
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) -> list[MetricEvent]:
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"""Constructs a list of MetricEvent objects containing token usage metrics.
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Args:
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@ -221,7 +221,7 @@ class InferenceRouter(Inference):
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completion_tokens: int,
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total_tokens: int,
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model: Model,
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) -> List[MetricInResponse]:
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) -> list[MetricInResponse]:
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metrics = self._construct_metrics(prompt_tokens, completion_tokens, total_tokens, model)
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if self.telemetry:
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for metric in metrics:
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@ -230,9 +230,9 @@ class InferenceRouter(Inference):
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async def _count_tokens(
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self,
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messages: List[Message] | InterleavedContent,
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tool_prompt_format: Optional[ToolPromptFormat] = None,
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) -> Optional[int]:
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messages: list[Message] | InterleavedContent,
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tool_prompt_format: ToolPromptFormat | None = None,
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) -> int | None:
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if isinstance(messages, list):
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encoded = self.formatter.encode_dialog_prompt(messages, tool_prompt_format)
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else:
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@ -242,16 +242,16 @@ class InferenceRouter(Inference):
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async def chat_completion(
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self,
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model_id: str,
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messages: List[Message],
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sampling_params: Optional[SamplingParams] = None,
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response_format: Optional[ResponseFormat] = None,
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tools: Optional[List[ToolDefinition]] = None,
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tool_choice: Optional[ToolChoice] = None,
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tool_prompt_format: Optional[ToolPromptFormat] = None,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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tool_config: Optional[ToolConfig] = None,
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) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
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messages: list[Message],
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sampling_params: SamplingParams | None = None,
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response_format: ResponseFormat | None = None,
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tools: list[ToolDefinition] | None = None,
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tool_choice: ToolChoice | None = None,
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tool_prompt_format: ToolPromptFormat | None = None,
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stream: bool | None = False,
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logprobs: LogProbConfig | None = None,
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tool_config: ToolConfig | None = None,
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) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]:
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logger.debug(
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f"InferenceRouter.chat_completion: {model_id=}, {stream=}, {messages=}, {tools=}, {tool_config=}, {response_format=}",
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)
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@ -351,12 +351,12 @@ class InferenceRouter(Inference):
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async def batch_chat_completion(
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self,
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model_id: str,
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messages_batch: List[List[Message]],
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tools: Optional[List[ToolDefinition]] = None,
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tool_config: Optional[ToolConfig] = None,
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sampling_params: Optional[SamplingParams] = None,
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response_format: Optional[ResponseFormat] = None,
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logprobs: Optional[LogProbConfig] = None,
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messages_batch: list[list[Message]],
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tools: list[ToolDefinition] | None = None,
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tool_config: ToolConfig | None = None,
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sampling_params: SamplingParams | None = None,
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response_format: ResponseFormat | None = None,
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logprobs: LogProbConfig | None = None,
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) -> BatchChatCompletionResponse:
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logger.debug(
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f"InferenceRouter.batch_chat_completion: {model_id=}, {len(messages_batch)=}, {sampling_params=}, {response_format=}, {logprobs=}",
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@ -376,10 +376,10 @@ class InferenceRouter(Inference):
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self,
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model_id: str,
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content: InterleavedContent,
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sampling_params: Optional[SamplingParams] = None,
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response_format: Optional[ResponseFormat] = None,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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sampling_params: SamplingParams | None = None,
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response_format: ResponseFormat | None = None,
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stream: bool | None = False,
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logprobs: LogProbConfig | None = None,
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) -> AsyncGenerator:
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if sampling_params is None:
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sampling_params = SamplingParams()
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@ -439,10 +439,10 @@ class InferenceRouter(Inference):
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async def batch_completion(
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self,
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model_id: str,
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content_batch: List[InterleavedContent],
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sampling_params: Optional[SamplingParams] = None,
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response_format: Optional[ResponseFormat] = None,
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logprobs: Optional[LogProbConfig] = None,
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content_batch: list[InterleavedContent],
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sampling_params: SamplingParams | None = None,
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response_format: ResponseFormat | None = None,
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logprobs: LogProbConfig | None = None,
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) -> BatchCompletionResponse:
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logger.debug(
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f"InferenceRouter.batch_completion: {model_id=}, {len(content_batch)=}, {sampling_params=}, {response_format=}, {logprobs=}",
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@ -453,10 +453,10 @@ class InferenceRouter(Inference):
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async def embeddings(
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self,
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model_id: str,
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contents: List[str] | List[InterleavedContentItem],
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text_truncation: Optional[TextTruncation] = TextTruncation.none,
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output_dimension: Optional[int] = None,
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task_type: Optional[EmbeddingTaskType] = None,
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contents: list[str] | list[InterleavedContentItem],
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text_truncation: TextTruncation | None = TextTruncation.none,
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output_dimension: int | None = None,
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task_type: EmbeddingTaskType | None = None,
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) -> EmbeddingsResponse:
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logger.debug(f"InferenceRouter.embeddings: {model_id}")
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model = await self.routing_table.get_model(model_id)
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@ -475,24 +475,24 @@ class InferenceRouter(Inference):
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async def openai_completion(
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self,
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model: str,
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prompt: Union[str, List[str], List[int], List[List[int]]],
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best_of: Optional[int] = None,
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echo: Optional[bool] = None,
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frequency_penalty: Optional[float] = None,
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logit_bias: Optional[Dict[str, float]] = None,
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logprobs: Optional[bool] = None,
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max_tokens: Optional[int] = None,
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n: Optional[int] = None,
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presence_penalty: Optional[float] = None,
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seed: Optional[int] = None,
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stop: Optional[Union[str, List[str]]] = None,
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stream: Optional[bool] = None,
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stream_options: Optional[Dict[str, Any]] = None,
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temperature: Optional[float] = None,
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top_p: Optional[float] = None,
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user: Optional[str] = None,
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guided_choice: Optional[List[str]] = None,
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prompt_logprobs: Optional[int] = None,
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prompt: str | list[str] | list[int] | list[list[int]],
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best_of: int | None = None,
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echo: bool | None = None,
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frequency_penalty: float | None = None,
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logit_bias: dict[str, float] | None = None,
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logprobs: bool | None = None,
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max_tokens: int | None = None,
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n: int | None = None,
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presence_penalty: float | None = None,
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seed: int | None = None,
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stop: str | list[str] | None = None,
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stream: bool | None = None,
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stream_options: dict[str, Any] | None = None,
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temperature: float | None = None,
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top_p: float | None = None,
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user: str | None = None,
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guided_choice: list[str] | None = None,
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prompt_logprobs: int | None = None,
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) -> OpenAICompletion:
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logger.debug(
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f"InferenceRouter.openai_completion: {model=}, {stream=}, {prompt=}",
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@ -531,29 +531,29 @@ class InferenceRouter(Inference):
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async def openai_chat_completion(
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self,
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model: str,
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messages: Annotated[List[OpenAIMessageParam], Field(..., min_length=1)],
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frequency_penalty: Optional[float] = None,
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function_call: Optional[Union[str, Dict[str, Any]]] = None,
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functions: Optional[List[Dict[str, Any]]] = None,
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logit_bias: Optional[Dict[str, float]] = None,
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logprobs: Optional[bool] = None,
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max_completion_tokens: Optional[int] = None,
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max_tokens: Optional[int] = None,
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n: Optional[int] = None,
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parallel_tool_calls: Optional[bool] = None,
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presence_penalty: Optional[float] = None,
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response_format: Optional[OpenAIResponseFormatParam] = None,
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seed: Optional[int] = None,
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stop: Optional[Union[str, List[str]]] = None,
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stream: Optional[bool] = None,
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stream_options: Optional[Dict[str, Any]] = None,
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temperature: Optional[float] = None,
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tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
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tools: Optional[List[Dict[str, Any]]] = None,
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top_logprobs: Optional[int] = None,
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top_p: Optional[float] = None,
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user: Optional[str] = None,
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) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]:
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messages: Annotated[list[OpenAIMessageParam], Field(..., min_length=1)],
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frequency_penalty: float | None = None,
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function_call: str | dict[str, Any] | None = None,
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functions: list[dict[str, Any]] | None = None,
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logit_bias: dict[str, float] | None = None,
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logprobs: bool | None = None,
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max_completion_tokens: int | None = None,
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max_tokens: int | None = None,
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n: int | None = None,
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parallel_tool_calls: bool | None = None,
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presence_penalty: float | None = None,
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response_format: OpenAIResponseFormatParam | None = None,
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seed: int | None = None,
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stop: str | list[str] | None = None,
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stream: bool | None = None,
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stream_options: dict[str, Any] | None = None,
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temperature: float | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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tools: list[dict[str, Any]] | None = None,
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top_logprobs: int | None = None,
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top_p: float | None = None,
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user: str | None = None,
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) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
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logger.debug(
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f"InferenceRouter.openai_chat_completion: {model=}, {stream=}, {messages=}",
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)
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@ -602,7 +602,7 @@ class InferenceRouter(Inference):
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provider = self.routing_table.get_provider_impl(model_obj.identifier)
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return await provider.openai_chat_completion(**params)
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async def health(self) -> Dict[str, HealthResponse]:
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async def health(self) -> dict[str, HealthResponse]:
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health_statuses = {}
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timeout = 0.5
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for provider_id, impl in self.routing_table.impls_by_provider_id.items():
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@ -645,9 +645,9 @@ class SafetyRouter(Safety):
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async def register_shield(
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self,
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shield_id: str,
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provider_shield_id: Optional[str] = None,
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provider_id: Optional[str] = None,
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params: Optional[Dict[str, Any]] = None,
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provider_shield_id: str | None = None,
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provider_id: str | None = None,
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params: dict[str, Any] | None = None,
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) -> Shield:
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logger.debug(f"SafetyRouter.register_shield: {shield_id}")
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return await self.routing_table.register_shield(shield_id, provider_shield_id, provider_id, params)
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@ -655,8 +655,8 @@ class SafetyRouter(Safety):
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async def run_shield(
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self,
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shield_id: str,
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messages: List[Message],
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params: Dict[str, Any] = None,
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messages: list[Message],
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params: dict[str, Any] = None,
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) -> RunShieldResponse:
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logger.debug(f"SafetyRouter.run_shield: {shield_id}")
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return await self.routing_table.get_provider_impl(shield_id).run_shield(
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@ -686,8 +686,8 @@ class DatasetIORouter(DatasetIO):
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self,
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purpose: DatasetPurpose,
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source: DataSource,
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metadata: Optional[Dict[str, Any]] = None,
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dataset_id: Optional[str] = None,
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metadata: dict[str, Any] | None = None,
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dataset_id: str | None = None,
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) -> None:
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logger.debug(
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f"DatasetIORouter.register_dataset: {purpose=} {source=} {metadata=} {dataset_id=}",
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@ -702,8 +702,8 @@ class DatasetIORouter(DatasetIO):
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async def iterrows(
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self,
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dataset_id: str,
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start_index: Optional[int] = None,
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limit: Optional[int] = None,
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start_index: int | None = None,
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limit: int | None = None,
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) -> PaginatedResponse:
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logger.debug(
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f"DatasetIORouter.iterrows: {dataset_id}, {start_index=} {limit=}",
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@ -714,7 +714,7 @@ class DatasetIORouter(DatasetIO):
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limit=limit,
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)
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async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None:
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async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None:
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logger.debug(f"DatasetIORouter.append_rows: {dataset_id}, {len(rows)} rows")
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return await self.routing_table.get_provider_impl(dataset_id).append_rows(
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dataset_id=dataset_id,
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@ -741,7 +741,7 @@ class ScoringRouter(Scoring):
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async def score_batch(
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self,
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dataset_id: str,
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scoring_functions: Dict[str, Optional[ScoringFnParams]] = None,
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scoring_functions: dict[str, ScoringFnParams | None] = None,
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save_results_dataset: bool = False,
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) -> ScoreBatchResponse:
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logger.debug(f"ScoringRouter.score_batch: {dataset_id}")
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@ -762,8 +762,8 @@ class ScoringRouter(Scoring):
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async def score(
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self,
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input_rows: List[Dict[str, Any]],
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scoring_functions: Dict[str, Optional[ScoringFnParams]] = None,
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input_rows: list[dict[str, Any]],
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scoring_functions: dict[str, ScoringFnParams | None] = None,
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) -> ScoreResponse:
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logger.debug(f"ScoringRouter.score: {len(input_rows)} rows, {len(scoring_functions)} functions")
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res = {}
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@ -808,8 +808,8 @@ class EvalRouter(Eval):
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async def evaluate_rows(
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self,
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benchmark_id: str,
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input_rows: List[Dict[str, Any]],
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scoring_functions: List[str],
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input_rows: list[dict[str, Any]],
|
||||
scoring_functions: list[str],
|
||||
benchmark_config: BenchmarkConfig,
|
||||
) -> EvaluateResponse:
|
||||
logger.debug(f"EvalRouter.evaluate_rows: {benchmark_id}, {len(input_rows)} rows")
|
||||
|
|
@ -863,8 +863,8 @@ class ToolRuntimeRouter(ToolRuntime):
|
|||
async def query(
|
||||
self,
|
||||
content: InterleavedContent,
|
||||
vector_db_ids: List[str],
|
||||
query_config: Optional[RAGQueryConfig] = None,
|
||||
vector_db_ids: list[str],
|
||||
query_config: RAGQueryConfig | None = None,
|
||||
) -> RAGQueryResult:
|
||||
logger.debug(f"ToolRuntimeRouter.RagToolImpl.query: {vector_db_ids}")
|
||||
return await self.routing_table.get_provider_impl("knowledge_search").query(
|
||||
|
|
@ -873,7 +873,7 @@ class ToolRuntimeRouter(ToolRuntime):
|
|||
|
||||
async def insert(
|
||||
self,
|
||||
documents: List[RAGDocument],
|
||||
documents: list[RAGDocument],
|
||||
vector_db_id: str,
|
||||
chunk_size_in_tokens: int = 512,
|
||||
) -> None:
|
||||
|
|
@ -904,7 +904,7 @@ class ToolRuntimeRouter(ToolRuntime):
|
|||
logger.debug("ToolRuntimeRouter.shutdown")
|
||||
pass
|
||||
|
||||
async def invoke_tool(self, tool_name: str, kwargs: Dict[str, Any]) -> Any:
|
||||
async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> Any:
|
||||
logger.debug(f"ToolRuntimeRouter.invoke_tool: {tool_name}")
|
||||
return await self.routing_table.get_provider_impl(tool_name).invoke_tool(
|
||||
tool_name=tool_name,
|
||||
|
|
@ -912,7 +912,7 @@ class ToolRuntimeRouter(ToolRuntime):
|
|||
)
|
||||
|
||||
async def list_runtime_tools(
|
||||
self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None
|
||||
self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None
|
||||
) -> ListToolDefsResponse:
|
||||
logger.debug(f"ToolRuntimeRouter.list_runtime_tools: {tool_group_id}")
|
||||
return await self.routing_table.get_provider_impl(tool_group_id).list_tools(tool_group_id, mcp_endpoint)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue