mirror of
https://github.com/BerriAI/litellm.git
synced 2026-10-10 03:28:53 +00:00
fix: fix linting errors
This commit is contained in:
Krrish Dholakia
parent
7be9a32934
commit
c9f29dd0c5
8 changed files with 106 additions and 72 deletions
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@ -3,14 +3,15 @@ Transformation for Bedrock Invoke Agent
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https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent-runtime_InvokeAgent.html
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"""
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import base64
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import json
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from litellm._uuid import uuid
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
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import httpx
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from litellm._logging import verbose_logger
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from litellm._uuid import uuid
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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convert_content_list_to_str,
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)
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@ -22,6 +23,11 @@ from litellm.types.llms.bedrock_invoke_agents import (
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InvokeAgentEvent,
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InvokeAgentEventHeaders,
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InvokeAgentEventList,
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InvokeAgentMetadata,
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InvokeAgentModelInvocationInput,
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InvokeAgentModelInvocationOutput,
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InvokeAgentOrchestrationTrace,
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InvokeAgentPreProcessingTrace,
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InvokeAgentTrace,
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InvokeAgentTracePayload,
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InvokeAgentUsage,
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@ -389,15 +395,19 @@ class AmazonInvokeAgentConfig(BaseConfig, BaseAWSLLM):
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self, trace_data: InvokeAgentTrace, usage_info: InvokeAgentUsage
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) -> None:
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"""Extract usage information from preprocessing trace."""
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pre_processing = trace_data.get("preProcessingTrace", {})
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pre_processing: Optional[InvokeAgentPreProcessingTrace] = trace_data.get(
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"preProcessingTrace"
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)
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if not pre_processing:
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return
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model_output = pre_processing.get("modelInvocationOutput", {})
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model_output: Optional[InvokeAgentModelInvocationOutput] = pre_processing.get(
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"modelInvocationOutput", {}
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)
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if not model_output:
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return
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metadata = model_output.get("metadata", {})
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metadata: Optional[InvokeAgentMetadata] = model_output.get("metadata", {})
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if not metadata:
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return
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@ -412,11 +422,15 @@ class AmazonInvokeAgentConfig(BaseConfig, BaseAWSLLM):
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self, trace_data: InvokeAgentTrace
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) -> Optional[str]:
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"""Extract model information from orchestration trace."""
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orchestration_trace = trace_data.get("orchestrationTrace", {})
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orchestration_trace: Optional[InvokeAgentOrchestrationTrace] = trace_data.get(
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"orchestrationTrace"
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)
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if not orchestration_trace:
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return None
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model_invocation = orchestration_trace.get("modelInvocationInput", {})
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model_invocation: Optional[InvokeAgentModelInvocationInput] = (
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orchestration_trace.get("modelInvocationInput", {})
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)
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if not model_invocation:
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return None
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@ -1,6 +1,7 @@
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"""
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Transformation for Calling Google models in their native format.
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"""
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from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union, cast
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import httpx
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@ -25,27 +26,29 @@ else:
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GenerateContentContentListUnionDict = Any
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GenerateContentResponse = Any
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ToolConfigDict = Any
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from ..common_utils import get_api_key_from_env
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class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
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"""
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Configuration for calling Google models in their native format.
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"""
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##############################
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# Constants
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##############################
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XGOOGLE_API_KEY = "x-goog-api-key"
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##############################
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@property
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def custom_llm_provider(self) -> Literal["gemini", "vertex_ai"]:
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return "gemini"
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def __init__(self):
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super().__init__()
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VertexLLM.__init__(self)
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def get_supported_generate_content_optional_params(self, model: str) -> List[str]:
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"""
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Get the list of supported Google GenAI parameters for the model.
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@ -58,7 +61,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
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"""
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return [
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"http_options",
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"system_instruction",
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"system_instruction",
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"temperature",
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"top_p",
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"top_k",
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@ -84,10 +87,9 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
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"speech_config",
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"audio_timestamp",
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"automatic_function_calling",
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"thinking_config"
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"thinking_config",
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]
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def map_generate_content_optional_params(
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self,
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generate_content_config_dict: GenerateContentConfigDict,
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@ -103,26 +105,29 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
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Returns:
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Mapped parameters for the provider
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"""
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from litellm.types.google_genai.main import GenerateContentConfigDict
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_generate_content_config_dict = GenerateContentConfigDict()
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supported_google_genai_params = self.get_supported_generate_content_optional_params(model)
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_generate_content_config_dict: Dict[str, Any] = {}
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supported_google_genai_params = (
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self.get_supported_generate_content_optional_params(model)
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)
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for param, value in generate_content_config_dict.items():
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if param in supported_google_genai_params:
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_generate_content_config_dict[param] = value
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return dict(_generate_content_config_dict)
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return _generate_content_config_dict
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def validate_environment(
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self,
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self,
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api_key: Optional[str],
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headers: Optional[dict],
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model: str,
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litellm_params: Optional[Union[GenericLiteLLMParams, dict]]
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litellm_params: Optional[Union[GenericLiteLLMParams, dict]],
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) -> dict:
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default_headers = {
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"Content-Type": "application/json",
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}
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# Use the passed api_key first, then fall back to litellm_params and environment
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gemini_api_key = api_key or self._get_google_ai_studio_api_key(dict(litellm_params or {}))
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gemini_api_key = api_key or self._get_google_ai_studio_api_key(
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dict(litellm_params or {})
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)
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if gemini_api_key is not None:
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default_headers[self.XGOOGLE_API_KEY] = gemini_api_key
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if headers is not None:
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@ -137,14 +142,14 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
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or get_api_key_from_env()
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or litellm.api_key
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)
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def _get_common_auth_components(
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self,
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litellm_params: dict,
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) -> Tuple[Any, Optional[str], Optional[str]]:
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"""
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Get common authentication components used by both sync and async methods.
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Returns:
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Tuple of (vertex_credentials, vertex_project, vertex_location)
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"""
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@ -152,7 +157,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
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vertex_project = self.get_vertex_ai_project(litellm_params)
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vertex_location = self.get_vertex_ai_location(litellm_params)
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return vertex_credentials, vertex_project, vertex_location
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def _build_final_headers_and_url(
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self,
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model: str,
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@ -168,7 +173,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
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Build final headers and API URL from auth components.
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"""
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gemini_api_key = self._get_google_ai_studio_api_key(litellm_params)
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auth_header, api_base = self._get_token_and_url(
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model=model,
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gemini_api_key=gemini_api_key,
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@ -201,7 +206,9 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
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"""
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Sync version of get_auth_token_and_url.
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"""
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vertex_credentials, vertex_project, vertex_location = self._get_common_auth_components(litellm_params)
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vertex_credentials, vertex_project, vertex_location = (
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self._get_common_auth_components(litellm_params)
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)
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_auth_header, vertex_project = self._ensure_access_token(
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credentials=vertex_credentials,
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@ -238,7 +245,9 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
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Returns:
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Tuple of headers and API base
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"""
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vertex_credentials, vertex_project, vertex_location = self._get_common_auth_components(litellm_params)
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vertex_credentials, vertex_project, vertex_location = (
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self._get_common_auth_components(litellm_params)
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)
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_auth_header, vertex_project = await self._ensure_access_token_async(
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credentials=vertex_credentials,
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@ -256,7 +265,6 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
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api_base=api_base,
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litellm_params=litellm_params,
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)
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def transform_generate_content_request(
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self,
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@ -269,6 +277,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
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GenerateContentConfigDict,
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GenerateContentRequestDict,
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)
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typed_generate_content_request = GenerateContentRequestDict(
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model=model,
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contents=contents,
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@ -279,7 +288,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
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request_dict = cast(dict, typed_generate_content_request)
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return request_dict
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def transform_generate_content_response(
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self,
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model: str,
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@ -297,6 +306,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
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Transformed response data
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"""
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from litellm.types.google_genai.main import GenerateContentResponse
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try:
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response = raw_response.json()
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except Exception as e:
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@ -305,7 +315,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
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status_code=raw_response.status_code,
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headers=raw_response.headers,
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)
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logging_obj.model_call_details["httpx_response"] = raw_response
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return GenerateContentResponse(**response)
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return GenerateContentResponse(**response)
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@ -1,7 +1,8 @@
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"""
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Transformation for Calling Google models in their native format.
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"""
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from typing import Dict, Literal, Optional, Union
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from typing import Any, Dict, Literal, Optional, Union
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from litellm.llms.gemini.google_genai.transformation import GoogleGenAIConfig
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from litellm.types.router import GenericLiteLLMParams
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@ -58,22 +59,21 @@ class VertexAIGoogleGenAIConfig(GoogleGenAIConfig):
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Returns:
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Mapped parameters for the provider
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"""
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from litellm.types.google_genai.main import GenerateContentConfigDict
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_generate_content_config_dict = GenerateContentConfigDict()
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_generate_content_config_dict: Dict = {}
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for param, value in generate_content_config_dict.items():
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camel_case_key = self._camel_to_snake(param)
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_generate_content_config_dict[camel_case_key] = value
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return dict(_generate_content_config_dict)
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return _generate_content_config_dict
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def transform_generate_content_request(
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self,
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model: str,
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contents: any,
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tools: Optional[any],
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contents: Any,
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tools: Optional[Any],
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generate_content_config_dict: Dict,
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system_instruction: Optional[any] = None,
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system_instruction: Optional[Any] = None,
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) -> dict:
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"""
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Transform the generate content request for Vertex AI.
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@ -3,7 +3,6 @@ import asyncio
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import copy
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import json
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import traceback
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from litellm._uuid import uuid
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from base64 import b64encode
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from datetime import datetime
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from typing import Dict, List, Optional, Tuple, Union
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@ -25,6 +24,7 @@ from starlette.datastructures import UploadFile as StarletteUploadFile
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import litellm
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from litellm._logging import verbose_proxy_logger
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from litellm._uuid import uuid
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from litellm.constants import MAXIMUM_TRACEBACK_LINES_TO_LOG
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from litellm.integrations.custom_logger import CustomLogger
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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@ -424,10 +424,10 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils):
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for field_name, field_value in form_data.items():
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if isinstance(field_value, (StarletteUploadFile, UploadFile)):
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files[
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field_name
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] = await HttpPassThroughEndpointHelpers._build_request_files_from_upload_file(
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upload_file=field_value
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files[field_name] = (
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await HttpPassThroughEndpointHelpers._build_request_files_from_upload_file(
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upload_file=field_value
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)
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)
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else:
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form_data_dict[field_name] = field_value
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@ -476,7 +476,11 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils):
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user_api_key_request_route=user_api_key_dict.request_route,
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user_api_key_spend=user_api_key_dict.spend,
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user_api_key_max_budget=user_api_key_dict.max_budget,
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user_api_key_budget_reset_at=user_api_key_dict.budget_reset_at.isoformat() if user_api_key_dict.budget_reset_at else None,
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user_api_key_budget_reset_at=(
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user_api_key_dict.budget_reset_at.isoformat()
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if user_api_key_dict.budget_reset_at
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else None
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),
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)
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)
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@ -496,7 +500,7 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils):
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kwargs = {
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"litellm_params": {
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**litellm_params_in_body,
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**litellm_params_in_body, # type: ignore
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"metadata": _metadata,
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"proxy_server_request": {
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"url": str(request.url),
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@ -509,9 +513,9 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils):
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"passthrough_logging_payload": passthrough_logging_payload,
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}
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logging_obj.model_call_details[
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"passthrough_logging_payload"
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] = passthrough_logging_payload
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logging_obj.model_call_details["passthrough_logging_payload"] = (
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passthrough_logging_payload
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)
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return kwargs
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@ -923,7 +927,6 @@ def create_pass_through_route(
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):
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# check if target is an adapter.py or a url
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from litellm._uuid import uuid
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from litellm.proxy.types_utils.utils import get_instance_fn
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try:
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@ -1367,7 +1370,6 @@ async def create_pass_through_endpoints(
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Create new pass-through endpoint
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"""
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from litellm._uuid import uuid
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from litellm.proxy.proxy_server import (
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get_config_general_settings,
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update_config_general_settings,
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|
|
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|
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@ -10,7 +10,7 @@ from pydantic import BaseModel
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import litellm
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from litellm._logging import verbose_proxy_logger
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from litellm.constants import REDACTED_BY_LITELM_STRING, MAX_STRING_LENGTH_PROMPT_IN_DB
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from litellm.constants import MAX_STRING_LENGTH_PROMPT_IN_DB, REDACTED_BY_LITELM_STRING
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from litellm.litellm_core_utils.core_helpers import get_litellm_metadata_from_kwargs
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from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
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from litellm.proxy._types import SpendLogsMetadata, SpendLogsPayload
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|
@ -21,6 +21,7 @@ from litellm.types.utils import (
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StandardLoggingModelInformation,
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StandardLoggingPayload,
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StandardLoggingVectorStoreRequest,
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VectorStoreSearchResponse,
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)
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from litellm.utils import get_end_user_id_for_cost_tracking
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|
@ -297,7 +298,9 @@ def get_logging_payload( # noqa: PLR0915
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id = f"{id}_cache_hit{time.time()}" # SpendLogs does not allow duplicate request_id
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mcp_namespaced_tool_name = None
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mcp_tool_call_metadata = clean_metadata.get("mcp_tool_call_metadata", {})
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mcp_tool_call_metadata: Optional[StandardLoggingMCPToolCall] = clean_metadata.get(
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"mcp_tool_call_metadata"
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)
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if mcp_tool_call_metadata is not None:
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mcp_namespaced_tool_name = mcp_tool_call_metadata.get(
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"namespaced_tool_name", None
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|
|
@ -505,23 +508,23 @@ def _sanitize_request_body_for_spend_logs_payload(
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# This split ensures we keep more context from the end of conversations
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start_ratio = 0.35
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end_ratio = 0.65
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# Calculate character distribution
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start_chars = int(MAX_STRING_LENGTH_PROMPT_IN_DB * start_ratio)
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end_chars = int(MAX_STRING_LENGTH_PROMPT_IN_DB * end_ratio)
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# Ensure we don't exceed the total limit
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total_keep = start_chars + end_chars
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if total_keep > MAX_STRING_LENGTH_PROMPT_IN_DB:
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end_chars = MAX_STRING_LENGTH_PROMPT_IN_DB - start_chars
|
||||
|
||||
|
||||
# If the string length is less than what we want to keep, just truncate normally
|
||||
if len(value) <= MAX_STRING_LENGTH_PROMPT_IN_DB:
|
||||
return value
|
||||
|
||||
|
||||
# Calculate how many characters are being skipped
|
||||
skipped_chars = len(value) - total_keep
|
||||
|
||||
|
||||
# Build the truncated string: beginning + truncation marker + end
|
||||
truncated_value = (
|
||||
f"{value[:start_chars]}"
|
||||
|
|
@ -567,8 +570,9 @@ def _get_vector_store_request_for_spend_logs_payload(
|
|||
if vector_store_request_metadata is None:
|
||||
return None
|
||||
for vector_store_request in vector_store_request_metadata:
|
||||
vector_store_search_response = (
|
||||
vector_store_request.get("vector_store_search_response", {}) or {}
|
||||
vector_store_search_response: VectorStoreSearchResponse = (
|
||||
vector_store_request.get("vector_store_search_response")
|
||||
or VectorStoreSearchResponse()
|
||||
)
|
||||
response_data = vector_store_search_response.get("data", []) or []
|
||||
for response_item in response_data:
|
||||
|
|
|
|||
|
|
@ -3442,7 +3442,7 @@ class Router:
|
|||
*[try_retrieve_batch(model) for model in filtered_model_list]
|
||||
)
|
||||
|
||||
final_results = {
|
||||
final_results: Dict = {
|
||||
"object": "list",
|
||||
"data": [],
|
||||
"first_id": None,
|
||||
|
|
|
|||
|
|
@ -1308,7 +1308,7 @@ class MCPListToolsFailedEvent(BaseLiteLLMOpenAIResponseObject):
|
|||
item_id: str
|
||||
|
||||
|
||||
# MCP Call Events
|
||||
# MCP Call Events
|
||||
class MCPCallInProgressEvent(BaseLiteLLMOpenAIResponseObject):
|
||||
type: Literal[ResponsesAPIStreamEvents.MCP_CALL_IN_PROGRESS]
|
||||
sequence_number: int
|
||||
|
|
|
|||
|
|
@ -1,10 +1,14 @@
|
|||
main.py:1503: error: Argument "api_key" to "completion" of "AzureChatCompletion" has incompatible type "str | None"; expected "str" [arg-type]
|
||||
main.py:1508: error: Argument "azure_ad_token" to "completion" of "AzureChatCompletion" has incompatible type "Any | str | None"; expected "str" [arg-type]
|
||||
main.py:1509: error: Argument "azure_ad_token_provider" to "completion" of "AzureChatCompletion" has incompatible type "Any | None"; expected "Callable[..., Any]" [arg-type]
|
||||
main.py:1579: error: Argument "api_key" to "completion" of "AzureTextCompletion" has incompatible type "str | None"; expected "str" [arg-type]
|
||||
main.py:1580: error: Argument "api_base" to "completion" of "AzureTextCompletion" has incompatible type "str | None"; expected "str" [arg-type]
|
||||
main.py:1583: error: Argument "azure_ad_token" to "completion" of "AzureTextCompletion" has incompatible type "Any | str | None"; expected "str" [arg-type]
|
||||
proxy/hooks/parallel_request_limiter_v3.py:383: error: Item "None" of "RateLimitDescriptorRateLimitObject | None" has no attribute "get" [union-attr]
|
||||
proxy/hooks/parallel_request_limiter_v3.py:384: error: Item "None" of "RateLimitDescriptorRateLimitObject | None" has no attribute "get" [union-attr]
|
||||
proxy/hooks/parallel_request_limiter_v3.py:385: error: Item "None" of "RateLimitDescriptorRateLimitObject | None" has no attribute "get" [union-attr]
|
||||
proxy/hooks/parallel_request_limiter_v3.py:386: error: Item "None" of "RateLimitDescriptorRateLimitObject | None" has no attribute "get" [union-attr]
|
||||
types/llms/openai.py:46: error: Module "openai.types.responses.response_create_params" has no attribute "Text" [attr-defined]
|
||||
router.py:3445: error: Need type annotation for "final_results" [var-annotated]
|
||||
main.py:1581: error: Argument "api_base" to "completion" of "AzureTextCompletion" has incompatible type "str | None"; expected "str" [arg-type]
|
||||
llms/gemini/google_genai/transformation.py:111: error: TypedDict key must be a string literal; expected one of ("http_options", "system_instruction", "temperature", "top_p", "top_k", ...) [literal-required]
|
||||
llms/bedrock/chat/invoke_agent/transformation.py:392: error: Need type annotation for "pre_processing" [var-annotated]
|
||||
llms/bedrock/chat/invoke_agent/transformation.py:396: error: Need type annotation for "model_output" [var-annotated]
|
||||
llms/bedrock/chat/invoke_agent/transformation.py:400: error: Need type annotation for "metadata" [var-annotated]
|
||||
llms/bedrock/chat/invoke_agent/transformation.py:415: error: Need type annotation for "orchestration_trace" [var-annotated]
|
||||
llms/bedrock/chat/invoke_agent/transformation.py:419: error: Need type annotation for "model_invocation" [var-annotated]
|
||||
llms/vertex_ai/google_genai/transformation.py:67: error: TypedDict key must be a string literal; expected one of ("http_options", "system_instruction", "temperature", "top_p", "top_k", ...) [literal-required]
|
||||
proxy/spend_tracking/spend_tracking_utils.py:300: error: Need type annotation for "mcp_tool_call_metadata" [var-annotated]
|
||||
proxy/spend_tracking/spend_tracking_utils.py:571: error: Need type annotation for "vector_store_search_response" [var-annotated]
|
||||
proxy/pass_through_endpoints/pass_through_endpoints.py:499: error: Unsupported type "dict[str, Any]" for ** expansion in TypedDict [typeddict-item]
|
||||
Found 13 errors in 8 files (checked 1114 source files)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue