324 lines
14 KiB
Python
324 lines
14 KiB
Python
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"""
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Background Streaming Task for Polling Via Cache Feature
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Handles streaming responses from LLM providers and updates Redis cache
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with partial results for polling.
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Follows OpenAI Response Streaming format:
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https://platform.openai.com/docs/api-reference/responses-streaming
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"""
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import asyncio
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import json
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from typing import Any
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from fastapi import Request, Response
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from litellm._logging import verbose_proxy_logger
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from litellm.proxy.auth.user_api_key_auth import UserAPIKeyAuth
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from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
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from litellm.proxy.response_polling.polling_handler import ResponsePollingHandler
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async def background_streaming_task( # noqa: PLR0915
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polling_id: str,
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data: dict,
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polling_handler: ResponsePollingHandler,
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request: Request,
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fastapi_response: Response,
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user_api_key_dict: UserAPIKeyAuth,
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general_settings: dict,
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llm_router,
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proxy_config,
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proxy_logging_obj,
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select_data_generator,
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user_model,
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user_temperature,
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user_request_timeout,
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user_max_tokens,
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user_api_base,
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version,
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):
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"""
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Background task to stream response and update cache
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Follows OpenAI Response Streaming format:
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https://platform.openai.com/docs/api-reference/responses-streaming
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Processes streaming events and builds Response object:
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https://platform.openai.com/docs/api-reference/responses/object
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"""
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try:
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verbose_proxy_logger.info(f"Starting background streaming for {polling_id}")
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# Update status to in_progress (OpenAI format)
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await polling_handler.update_state(
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polling_id=polling_id,
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status="in_progress",
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)
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# Force streaming mode and remove background flag
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data["stream"] = True
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data.pop("background", None)
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# Create processor
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processor = ProxyBaseLLMRequestProcessing(data=data)
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# Make streaming request
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response = await processor.base_process_llm_request(
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request=request,
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fastapi_response=fastapi_response,
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user_api_key_dict=user_api_key_dict,
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route_type="aresponses",
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proxy_logging_obj=proxy_logging_obj,
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llm_router=llm_router,
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general_settings=general_settings,
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proxy_config=proxy_config,
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select_data_generator=select_data_generator,
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model=None,
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user_model=user_model,
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user_temperature=user_temperature,
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user_request_timeout=user_request_timeout,
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user_max_tokens=user_max_tokens,
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user_api_base=user_api_base,
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version=version,
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)
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# Process streaming response following OpenAI events format
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# https://platform.openai.com/docs/api-reference/responses-streaming
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output_items: dict[str, dict[str, Any]] = {} # Track output items by ID
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accumulated_text = (
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{}
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) # Track accumulated text deltas by (item_id, content_index)
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# ResponsesAPIResponse fields to extract from response.completed
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usage_data = None
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reasoning_data = None
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tool_choice_data = None
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tools_data = None
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model_data = None
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instructions_data = None
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temperature_data = None
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top_p_data = None
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max_output_tokens_data = None
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previous_response_id_data = None
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text_data = None
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truncation_data = None
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parallel_tool_calls_data = None
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user_data = None
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store_data = None
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incomplete_details_data = None
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state_dirty = False # Track if state needs to be synced
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last_update_time = asyncio.get_event_loop().time()
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UPDATE_INTERVAL = 0.150 # 150ms batching interval
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async def flush_state_if_needed(force: bool = False) -> None:
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"""Flush accumulated state to Redis if interval elapsed or forced"""
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nonlocal state_dirty, last_update_time
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current_time = asyncio.get_event_loop().time()
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if state_dirty and (
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force or (current_time - last_update_time) >= UPDATE_INTERVAL
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):
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# Convert output_items dict to list for update
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output_list = list(output_items.values())
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await polling_handler.update_state(
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polling_id=polling_id,
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output=output_list,
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)
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state_dirty = False
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last_update_time = current_time
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# Handle StreamingResponse
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if hasattr(response, "body_iterator"):
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async for chunk in response.body_iterator:
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# Parse chunk
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if isinstance(chunk, bytes):
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chunk = chunk.decode("utf-8")
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if isinstance(chunk, str) and chunk.startswith("data: "):
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chunk_data = chunk[6:].strip()
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if chunk_data == "[DONE]":
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break
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try:
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event = json.loads(chunk_data)
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event_type = event.get("type", "")
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# Process different event types based on OpenAI streaming spec
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if event_type == "response.output_item.added":
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# New output item added
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item = event.get("item", {})
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item_id = item.get("id")
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if item_id:
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output_items[item_id] = item
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state_dirty = True
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elif event_type == "response.content_part.added":
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# Content part added to an output item
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item_id = event.get("item_id")
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content_part = event.get("part", {})
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if item_id and item_id in output_items:
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# Update the output item with new content
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if "content" not in output_items[item_id]:
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output_items[item_id]["content"] = []
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output_items[item_id]["content"].append(content_part)
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state_dirty = True
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elif event_type == "response.output_text.delta":
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# Text delta - accumulate text content
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# https://platform.openai.com/docs/api-reference/responses-streaming/response-text-delta
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item_id = event.get("item_id")
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content_index = event.get("content_index", 0)
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delta = event.get("delta", "")
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if item_id and item_id in output_items:
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# Accumulate text delta
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key = (item_id, content_index)
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if key not in accumulated_text:
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accumulated_text[key] = ""
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accumulated_text[key] += delta
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# Update the content in output_items
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if "content" in output_items[item_id]:
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content_list = output_items[item_id]["content"]
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if content_index < len(content_list):
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# Update existing content part with accumulated text
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if isinstance(
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content_list[content_index], dict
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):
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content_list[content_index][
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"text"
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] = accumulated_text[key]
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state_dirty = True
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elif event_type == "response.content_part.done":
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# Content part completed
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item_id = event.get("item_id")
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content_part = event.get("part", {})
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content_index = event.get("content_index", 0)
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if item_id and item_id in output_items:
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# Update with final content from event
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if "content" in output_items[item_id]:
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content_list = output_items[item_id]["content"]
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if content_index < len(content_list):
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content_list[content_index] = content_part
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state_dirty = True
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elif event_type == "response.output_item.done":
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# Output item completed - use final item data
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item = event.get("item", {})
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item_id = item.get("id")
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if item_id:
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output_items[item_id] = item
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state_dirty = True
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elif event_type == "response.in_progress":
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# Response is now in progress
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# https://platform.openai.com/docs/api-reference/responses-streaming/response-in-progress
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await polling_handler.update_state(
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polling_id=polling_id,
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status="in_progress",
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)
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elif event_type == "response.completed":
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# Response completed - extract all ResponsesAPIResponse fields
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# https://platform.openai.com/docs/api-reference/responses-streaming/response-completed
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response_data = event.get("response", {})
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# Core response fields
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usage_data = response_data.get("usage")
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reasoning_data = response_data.get("reasoning")
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tool_choice_data = response_data.get("tool_choice")
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tools_data = response_data.get("tools")
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# Additional ResponsesAPIResponse fields
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model_data = response_data.get("model")
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instructions_data = response_data.get("instructions")
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temperature_data = response_data.get("temperature")
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top_p_data = response_data.get("top_p")
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max_output_tokens_data = response_data.get(
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"max_output_tokens"
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)
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previous_response_id_data = response_data.get(
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"previous_response_id"
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)
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text_data = response_data.get("text")
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truncation_data = response_data.get("truncation")
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parallel_tool_calls_data = response_data.get(
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"parallel_tool_calls"
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)
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user_data = response_data.get("user")
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store_data = response_data.get("store")
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incomplete_details_data = response_data.get(
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"incomplete_details"
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)
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# Also update output from final response if available
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if "output" in response_data:
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final_output = response_data.get("output", [])
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for item in final_output:
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item_id = item.get("id")
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if item_id:
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output_items[item_id] = item
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state_dirty = True
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# Flush state to Redis if interval elapsed
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await flush_state_if_needed()
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except json.JSONDecodeError as e:
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verbose_proxy_logger.warning(
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f"Failed to parse streaming chunk: {e}"
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)
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pass
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# Final flush to ensure all accumulated state is saved
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await flush_state_if_needed(force=True)
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# Mark as completed with all ResponsesAPIResponse fields
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await polling_handler.update_state(
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polling_id=polling_id,
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status="completed",
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usage=usage_data,
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reasoning=reasoning_data,
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tool_choice=tool_choice_data,
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tools=tools_data,
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model=model_data,
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instructions=instructions_data,
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temperature=temperature_data,
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top_p=top_p_data,
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max_output_tokens=max_output_tokens_data,
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previous_response_id=previous_response_id_data,
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text=text_data,
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truncation=truncation_data,
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parallel_tool_calls=parallel_tool_calls_data,
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user=user_data,
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store=store_data,
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incomplete_details=incomplete_details_data,
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)
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verbose_proxy_logger.info(
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f"Completed background streaming for {polling_id}, output_items={len(output_items)}"
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)
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except Exception as e:
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verbose_proxy_logger.error(
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f"Error in background streaming task for {polling_id}: {str(e)}"
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)
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import traceback
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verbose_proxy_logger.error(traceback.format_exc())
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await polling_handler.update_state(
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polling_id=polling_id,
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status="failed",
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error={
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"type": "internal_error",
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"message": str(e),
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"code": "background_streaming_error",
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},
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)
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