wx44wx/pipecat
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge pull request #3640 from lukepayyapilli/fix/openai-stream-close

Browse Source 
fix: close stream on cancellation to prevent socket leaks
This commit is contained in:
Mark Backman
2026-02-05 18:00:06 -05:00
committed by GitHub
parent 669f013970 55a3b10e70
commit fa85f7bbc7
3 changed files with 131 additions and 61 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
changelog/3589.fixed.md Normal file
View File

@@ -0,0 +1 @@
- Fixed OpenAI LLM stream not being closed on cancellation/exception, which could leak sockets.

125
src/pipecat/services/openai/base_llm.py
View File

@@ -362,74 +362,77 @@ class BaseOpenAILLMService(LLMService):
else self._stream_chat_completions_universal_context(context) else self._stream_chat_completions_universal_context(context)
) )
async for chunk in chunk_stream: # Use context manager to ensure stream is closed on cancellation/exception.
if chunk.usage: # Without this, CancelledError during iteration leaves the underlying socket open.
cached_tokens = ( async with chunk_stream:
chunk.usage.prompt_tokens_details.cached_tokens async for chunk in chunk_stream:
if chunk.usage.prompt_tokens_details if chunk.usage:
else None cached_tokens = (
) chunk.usage.prompt_tokens_details.cached_tokens
reasoning_tokens = ( if chunk.usage.prompt_tokens_details
chunk.usage.completion_tokens_details.reasoning_tokens else None
if chunk.usage.completion_tokens_details )
else None reasoning_tokens = (
) chunk.usage.completion_tokens_details.reasoning_tokens
tokens = LLMTokenUsage( if chunk.usage.completion_tokens_details
prompt_tokens=chunk.usage.prompt_tokens, else None
completion_tokens=chunk.usage.completion_tokens, )
total_tokens=chunk.usage.total_tokens, tokens = LLMTokenUsage(
cache_read_input_tokens=cached_tokens, prompt_tokens=chunk.usage.prompt_tokens,
reasoning_tokens=reasoning_tokens, completion_tokens=chunk.usage.completion_tokens,
) total_tokens=chunk.usage.total_tokens,
await self.start_llm_usage_metrics(tokens) cache_read_input_tokens=cached_tokens,
reasoning_tokens=reasoning_tokens,
)
await self.start_llm_usage_metrics(tokens)
if chunk.model and self.get_full_model_name() != chunk.model: if chunk.model and self.get_full_model_name() != chunk.model:
self.set_full_model_name(chunk.model) self.set_full_model_name(chunk.model)
if chunk.choices is None or len(chunk.choices) == 0: if chunk.choices is None or len(chunk.choices) == 0:
continue continue
await self.stop_ttfb_metrics() await self.stop_ttfb_metrics()
if not chunk.choices[0].delta: if not chunk.choices[0].delta:
continue continue
if chunk.choices[0].delta.tool_calls: if chunk.choices[0].delta.tool_calls:
# We're streaming the LLM response to enable the fastest response times. # We're streaming the LLM response to enable the fastest response times.
# For text, we just yield each chunk as we receive it and count on consumers # For text, we just yield each chunk as we receive it and count on consumers
# to do whatever coalescing they need (eg. to pass full sentences to TTS) # to do whatever coalescing they need (eg. to pass full sentences to TTS)
# #
# If the LLM is a function call, we'll do some coalescing here. # If the LLM is a function call, we'll do some coalescing here.
# If the response contains a function name, we'll yield a frame to tell consumers # If the response contains a function name, we'll yield a frame to tell consumers
# that they can start preparing to call the function with that name. # that they can start preparing to call the function with that name.
# We accumulate all the arguments for the rest of the streamed response, then when # We accumulate all the arguments for the rest of the streamed response, then when
# the response is done, we package up all the arguments and the function name and # the response is done, we package up all the arguments and the function name and
# yield a frame containing the function name and the arguments. # yield a frame containing the function name and the arguments.
tool_call = chunk.choices[0].delta.tool_calls[0] tool_call = chunk.choices[0].delta.tool_calls[0]
if tool_call.index != func_idx: if tool_call.index != func_idx:
functions_list.append(function_name) functions_list.append(function_name)
arguments_list.append(arguments) arguments_list.append(arguments)
tool_id_list.append(tool_call_id) tool_id_list.append(tool_call_id)
function_name = "" function_name = ""
arguments = "" arguments = ""
tool_call_id = "" tool_call_id = ""
func_idx += 1 func_idx += 1
if tool_call.function and tool_call.function.name: if tool_call.function and tool_call.function.name:
function_name += tool_call.function.name function_name += tool_call.function.name
tool_call_id = tool_call.id tool_call_id = tool_call.id
if tool_call.function and tool_call.function.arguments: if tool_call.function and tool_call.function.arguments:
# Keep iterating through the response to collect all the argument fragments # Keep iterating through the response to collect all the argument fragments
arguments += tool_call.function.arguments arguments += tool_call.function.arguments
elif chunk.choices[0].delta.content: elif chunk.choices[0].delta.content:
await self._push_llm_text(chunk.choices[0].delta.content) await self._push_llm_text(chunk.choices[0].delta.content)
# When gpt-4o-audio / gpt-4o-mini-audio is used for llm or stt+llm # When gpt-4o-audio / gpt-4o-mini-audio is used for llm or stt+llm
# we need to get LLMTextFrame for the transcript # we need to get LLMTextFrame for the transcript
elif hasattr(chunk.choices[0].delta, "audio") and chunk.choices[0].delta.audio.get( elif hasattr(chunk.choices[0].delta, "audio") and chunk.choices[0].delta.audio.get(
"transcript" "transcript"
): ):
await self.push_frame(LLMTextFrame(chunk.choices[0].delta.audio["transcript"])) await self.push_frame(LLMTextFrame(chunk.choices[0].delta.audio["transcript"]))
# if we got a function name and arguments, check to see if it's a function with # if we got a function name and arguments, check to see if it's a function with
# a registered handler. If so, run the registered callback, save the result to # a registered handler. If so, run the registered callback, save the result to

66
tests/test_openai_llm_timeout.py
View File

@@ -127,6 +127,72 @@ async def test_openai_llm_timeout_still_pushes_end_frame():
service.stop_processing_metrics.assert_called_once() service.stop_processing_metrics.assert_called_once()
@pytest.mark.asyncio
async def test_openai_llm_stream_closed_on_cancellation():
"""Test that the stream is closed when CancelledError occurs during iteration.
This prevents socket leaks when the pipeline is interrupted (e.g., user interruption).
See issue #3589.
"""
import asyncio
with patch.object(OpenAILLMService, "create_client"):
service = OpenAILLMService(model="gpt-4")
service._client = AsyncMock()
# Track if close was called
stream_closed = False
class MockAsyncStream:
"""Mock AsyncStream that tracks close() calls and raises CancelledError."""
def __init__(self):
self.iteration_count = 0
async def __aenter__(self):
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
nonlocal stream_closed
stream_closed = True
return False
def __aiter__(self):
return self
async def __anext__(self):
self.iteration_count += 1
if self.iteration_count > 1:
# Simulate cancellation during iteration
raise asyncio.CancelledError()
# Return a minimal chunk for first iteration
mock_chunk = AsyncMock()
mock_chunk.usage = None
mock_chunk.model = None
mock_chunk.choices = []
return mock_chunk
mock_stream = MockAsyncStream()
# Mock the stream creation methods
service._stream_chat_completions_specific_context = AsyncMock(return_value=mock_stream)
service._stream_chat_completions_universal_context = AsyncMock(return_value=mock_stream)
service.start_ttfb_metrics = AsyncMock()
service.stop_ttfb_metrics = AsyncMock()
service.start_llm_usage_metrics = AsyncMock()
context = LLMContext(
messages=[{"role": "user", "content": "Hello"}],
)
# Process context should raise CancelledError but stream should still be closed
with pytest.raises(asyncio.CancelledError):
await service._process_context(context)
# Verify stream was closed despite the cancellation
assert stream_closed, "Stream should be closed even when CancelledError occurs"
@pytest.mark.asyncio @pytest.mark.asyncio
async def test_openai_llm_emits_error_frame_on_exception(): async def test_openai_llm_emits_error_frame_on_exception():
"""Test that OpenAI LLM service emits ErrorFrame when a general exception occurs. """Test that OpenAI LLM service emits ErrorFrame when a general exception occurs.