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