chore: initial snapshot for gitea/github upload
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"""
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Response Polling Handler for Background Responses with Cache
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"""
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import json
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from datetime import datetime, timezone
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from typing import Any, Dict, List, Optional
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from litellm._logging import verbose_proxy_logger
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from litellm._uuid import uuid4
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from litellm.caching.redis_cache import RedisCache
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from litellm.types.llms.openai import ResponsesAPIResponse, ResponsesAPIStatus
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class ResponsePollingHandler:
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"""Handles polling-based responses with Redis cache"""
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CACHE_KEY_PREFIX = "litellm:polling:response:"
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POLLING_ID_PREFIX = "litellm_poll_" # Clear prefix to identify polling IDs
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def __init__(self, redis_cache: Optional[RedisCache] = None, ttl: int = 3600):
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self.redis_cache = redis_cache
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self.ttl = ttl # Time-to-live for cache entries (default: 1 hour)
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@classmethod
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def generate_polling_id(cls) -> str:
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"""Generate a unique UUID for polling with clear prefix"""
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return f"{cls.POLLING_ID_PREFIX}{uuid4()}"
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@classmethod
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def is_polling_id(cls, response_id: str) -> bool:
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"""Check if a response_id is a polling ID"""
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return response_id.startswith(cls.POLLING_ID_PREFIX)
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@classmethod
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def get_cache_key(cls, polling_id: str) -> str:
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"""Get Redis cache key for a polling ID"""
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return f"{cls.CACHE_KEY_PREFIX}{polling_id}"
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async def create_initial_state(
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self,
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polling_id: str,
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request_data: Dict[str, Any],
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) -> ResponsesAPIResponse:
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"""
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Create initial state in Redis for a polling request
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Uses OpenAI ResponsesAPIResponse object:
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https://platform.openai.com/docs/api-reference/responses/object
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Args:
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polling_id: Unique identifier for this polling request
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request_data: Original request data
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Returns:
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ResponsesAPIResponse object following OpenAI spec
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"""
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created_timestamp = int(datetime.now(timezone.utc).timestamp())
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# Create OpenAI-compliant response object
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response = ResponsesAPIResponse(
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id=polling_id,
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object="response",
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status="queued", # OpenAI native status
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created_at=created_timestamp,
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output=[],
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metadata=request_data.get("metadata", {}),
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usage=None,
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)
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cache_key = self.get_cache_key(polling_id)
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if self.redis_cache:
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# Store ResponsesAPIResponse directly in Redis
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await self.redis_cache.async_set_cache(
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key=cache_key,
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value=response.model_dump_json(), # Pydantic v2 method
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ttl=self.ttl,
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)
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verbose_proxy_logger.debug(
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f"Created initial polling state for {polling_id} with TTL={self.ttl}s"
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)
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return response
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async def update_state(
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self,
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polling_id: str,
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status: Optional[ResponsesAPIStatus] = None,
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usage: Optional[Dict] = None,
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error: Optional[Dict] = None,
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incomplete_details: Optional[Dict] = None,
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reasoning: Optional[Dict] = None,
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tool_choice: Optional[Any] = None,
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tools: Optional[list] = None,
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output: Optional[list] = None,
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# Additional ResponsesAPIResponse fields
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model: Optional[str] = None,
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instructions: Optional[str] = None,
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temperature: Optional[float] = None,
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top_p: Optional[float] = None,
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max_output_tokens: Optional[int] = None,
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previous_response_id: Optional[str] = None,
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text: Optional[Dict] = None,
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truncation: Optional[str] = None,
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parallel_tool_calls: Optional[bool] = None,
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user: Optional[str] = None,
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store: Optional[bool] = None,
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) -> None:
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"""
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Update the polling state in Redis
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Uses OpenAI Response object format with native status types:
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https://platform.openai.com/docs/api-reference/responses/object
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Args:
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polling_id: Unique identifier for this polling request
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status: OpenAI ResponsesAPIStatus value
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usage: Usage information
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error: Error dict (automatically sets status to "failed")
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incomplete_details: Details for incomplete responses
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reasoning: Reasoning configuration from response.completed
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tool_choice: Tool choice configuration from response.completed
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tools: Tools list from response.completed
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output: Full output list to replace current output
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model: Model identifier
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instructions: System instructions
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temperature: Sampling temperature
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top_p: Nucleus sampling parameter
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max_output_tokens: Maximum output tokens
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previous_response_id: ID of previous response in conversation
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text: Text configuration
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truncation: Truncation setting
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parallel_tool_calls: Whether parallel tool calls are enabled
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user: User identifier
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store: Whether to store the response
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"""
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if not self.redis_cache:
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return
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cache_key = self.get_cache_key(polling_id)
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# Get current state
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cached_state = await self.redis_cache.async_get_cache(cache_key)
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if not cached_state:
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verbose_proxy_logger.warning(
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f"No cached state found for polling_id: {polling_id}"
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)
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return
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# Parse existing ResponsesAPIResponse from cache
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state = json.loads(cached_state)
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# Update status (using OpenAI native status values)
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if status:
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state["status"] = status
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# Replace full output list if provided
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if output is not None:
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state["output"] = output
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# Update usage
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if usage:
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state["usage"] = usage
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# Handle error (sets status to OpenAI's "failed")
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if error:
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state["status"] = "failed"
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state["error"] = error # Use OpenAI's 'error' field
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# Handle incomplete details
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if incomplete_details:
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state["incomplete_details"] = incomplete_details
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# Update reasoning, tool_choice, tools from response.completed
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if reasoning is not None:
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state["reasoning"] = reasoning
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if tool_choice is not None:
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state["tool_choice"] = tool_choice
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if tools is not None:
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state["tools"] = tools
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# Update additional ResponsesAPIResponse fields
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if model is not None:
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state["model"] = model
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if instructions is not None:
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state["instructions"] = instructions
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if temperature is not None:
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state["temperature"] = temperature
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if top_p is not None:
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state["top_p"] = top_p
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if max_output_tokens is not None:
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state["max_output_tokens"] = max_output_tokens
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if previous_response_id is not None:
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state["previous_response_id"] = previous_response_id
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if text is not None:
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state["text"] = text
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if truncation is not None:
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state["truncation"] = truncation
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if parallel_tool_calls is not None:
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state["parallel_tool_calls"] = parallel_tool_calls
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if user is not None:
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state["user"] = user
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if store is not None:
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state["store"] = store
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# Update cache with configured TTL
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await self.redis_cache.async_set_cache(
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key=cache_key,
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value=json.dumps(state),
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ttl=self.ttl,
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)
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output_count = len(state.get("output", []))
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verbose_proxy_logger.debug(
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f"Updated polling state for {polling_id}: status={state['status']}, output_items={output_count}"
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)
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async def get_state(self, polling_id: str) -> Optional[Dict[str, Any]]:
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"""Get current polling state from Redis"""
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if not self.redis_cache:
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return None
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cache_key = self.get_cache_key(polling_id)
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cached_state = await self.redis_cache.async_get_cache(cache_key)
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if cached_state:
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return json.loads(cached_state)
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return None
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async def cancel_polling(self, polling_id: str) -> bool:
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"""
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Cancel a polling request
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Following OpenAI Response object format for cancelled status
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"""
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await self.update_state(
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polling_id=polling_id,
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status="cancelled",
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)
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return True
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async def delete_polling(self, polling_id: str) -> bool:
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"""Delete a polling request from cache"""
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if not self.redis_cache:
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return False
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cache_key = self.get_cache_key(polling_id)
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# Use RedisCache's async_delete_cache method which handles Redis/RedisCluster
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await self.redis_cache.async_delete_cache(cache_key)
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return True
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def should_use_polling_for_request(
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background_mode: bool,
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polling_via_cache_enabled, # Can be False, "all", or List[str]
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redis_cache, # RedisCache or None
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model: str,
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llm_router, # Router instance or None
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native_background_mode: Optional[
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List[str]
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] = None, # List of models that should use native background mode
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) -> bool:
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"""
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Determine if polling via cache should be used for a request.
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Args:
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background_mode: Whether background=true was set in the request
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polling_via_cache_enabled: Config value - False, "all", or list of providers
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redis_cache: Redis cache instance (required for polling)
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model: Model name from the request (e.g., "gpt-5" or "openai/gpt-4o")
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llm_router: LiteLLM router instance for looking up model deployments
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native_background_mode: List of model names that should use native provider
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background mode instead of polling via cache
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Returns:
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True if polling should be used, False otherwise
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"""
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# All conditions must be met
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if not (background_mode and polling_via_cache_enabled and redis_cache):
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return False
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# Check if model is in native_background_mode list - these use native provider background mode
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if native_background_mode and model in native_background_mode:
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verbose_proxy_logger.debug(
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f"Model {model} is in native_background_mode list, skipping polling via cache"
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)
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return False
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# "all" enables polling for all providers
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if polling_via_cache_enabled == "all":
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return True
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# Check if provider is in the enabled list
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if isinstance(polling_via_cache_enabled, list):
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# First, try to get provider from model string format "provider/model"
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if "/" in model:
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provider = model.split("/")[0]
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if provider in polling_via_cache_enabled:
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return True
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# Otherwise, check ALL deployments for this model_name in router
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elif llm_router is not None:
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try:
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# Get all deployment indices for this model name
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indices = llm_router.model_name_to_deployment_indices.get(model, [])
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for idx in indices:
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deployment_dict = llm_router.model_list[idx]
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litellm_params = deployment_dict.get("litellm_params", {})
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# Check custom_llm_provider first
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dep_provider = litellm_params.get("custom_llm_provider")
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# Then try to extract from model (e.g., "openai/gpt-5")
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if not dep_provider:
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dep_model = litellm_params.get("model", "")
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if "/" in dep_model:
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dep_provider = dep_model.split("/")[0]
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# If ANY deployment's provider matches, enable polling
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if dep_provider and dep_provider in polling_via_cache_enabled:
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verbose_proxy_logger.debug(
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f"Polling enabled for model={model}, provider={dep_provider}"
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)
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return True
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except Exception as e:
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verbose_proxy_logger.debug(
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f"Could not resolve provider for model {model}: {e}"
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)
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return False
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