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Fix: Properly close OpenAI SSE stream on interruption #2902

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Fix: Properly close OpenAI SSE stream on interruption #2902

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176 changes: 92 additions & 84 deletions src/pipecat/services/openai/base_llm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -339,92 +339,100 @@ async def _process_context(self, context: OpenAILLMContext | LLMContext):
else self._stream_chat_completions_universal_context(context)
)

async for chunk in chunk_stream:
if chunk.usage:
cached_tokens = (
chunk.usage.prompt_tokens_details.cached_tokens
if chunk.usage.prompt_tokens_details
else None
)
tokens = LLMTokenUsage(
prompt_tokens=chunk.usage.prompt_tokens,
completion_tokens=chunk.usage.completion_tokens,
total_tokens=chunk.usage.total_tokens,
cache_read_input_tokens=cached_tokens,
)
await self.start_llm_usage_metrics(tokens)

if chunk.choices is None or len(chunk.choices) == 0:
continue

await self.stop_ttfb_metrics()

if not chunk.choices[0].delta:
continue

if chunk.choices[0].delta.tool_calls:
# 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
# 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 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.
# 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
# yield a frame containing the function name and the arguments.

tool_call = chunk.choices[0].delta.tool_calls[0]
if tool_call.index != func_idx:
functions_list.append(function_name)
arguments_list.append(arguments)
tool_id_list.append(tool_call_id)
function_name = ""
arguments = ""
tool_call_id = ""
func_idx += 1
if tool_call.function and tool_call.function.name:
function_name += tool_call.function.name
tool_call_id = tool_call.id
if tool_call.function and tool_call.function.arguments:
# Keep iterating through the response to collect all the argument fragments
arguments += tool_call.function.arguments
elif chunk.choices[0].delta.content:
await self.push_frame(LLMTextFrame(chunk.choices[0].delta.content))

# When gpt-4o-audio / gpt-4o-mini-audio is used for llm or stt+llm
# we need to get LLMTextFrame for the transcript
elif hasattr(chunk.choices[0].delta, "audio") and chunk.choices[0].delta.audio.get(
"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
# a registered handler. If so, run the registered callback, save the result to
# the context, and re-prompt to get a chat answer. If we don't have a registered
# handler, raise an exception.
if function_name and arguments:
# added to the list as last function name and arguments not added to the list
functions_list.append(function_name)
arguments_list.append(arguments)
tool_id_list.append(tool_call_id)

function_calls = []

for function_name, arguments, tool_id in zip(
functions_list, arguments_list, tool_id_list
):
arguments = json.loads(arguments)
function_calls.append(
FunctionCallFromLLM(
context=context,
tool_call_id=tool_id,
function_name=function_name,
arguments=arguments,
try:
async for chunk in chunk_stream:
if chunk.usage:
cached_tokens = (
chunk.usage.prompt_tokens_details.cached_tokens
if chunk.usage.prompt_tokens_details
else None
)
tokens = LLMTokenUsage(
prompt_tokens=chunk.usage.prompt_tokens,
completion_tokens=chunk.usage.completion_tokens,
total_tokens=chunk.usage.total_tokens,
cache_read_input_tokens=cached_tokens,
)
await self.start_llm_usage_metrics(tokens)

if chunk.choices is None or len(chunk.choices) == 0:
continue

await self.stop_ttfb_metrics()

if not chunk.choices[0].delta:
continue

if chunk.choices[0].delta.tool_calls:
# 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
# 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 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.
# 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
# yield a frame containing the function name and the arguments.

tool_call = chunk.choices[0].delta.tool_calls[0]
if tool_call.index != func_idx:
functions_list.append(function_name)
arguments_list.append(arguments)
tool_id_list.append(tool_call_id)
function_name = ""
arguments = ""
tool_call_id = ""
func_idx += 1
if tool_call.function and tool_call.function.name:
function_name += tool_call.function.name
tool_call_id = tool_call.id
if tool_call.function and tool_call.function.arguments:
# Keep iterating through the response to collect all the argument fragments
arguments += tool_call.function.arguments
elif chunk.choices[0].delta.content:
await self.push_frame(LLMTextFrame(chunk.choices[0].delta.content))

# When gpt-4o-audio / gpt-4o-mini-audio is used for llm or stt+llm
# we need to get LLMTextFrame for the transcript
elif hasattr(chunk.choices[0].delta, "audio") and chunk.choices[0].delta.audio.get(
"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
# a registered handler. If so, run the registered callback, save the result to
# the context, and re-prompt to get a chat answer. If we don't have a registered
# handler, raise an exception.
if function_name and arguments:
# added to the list as last function name and arguments not added to the list
functions_list.append(function_name)
arguments_list.append(arguments)
tool_id_list.append(tool_call_id)

function_calls = []

for function_name, arguments, tool_id in zip(
functions_list, arguments_list, tool_id_list
):
arguments = json.loads(arguments)
function_calls.append(
FunctionCallFromLLM(
context=context,
tool_call_id=tool_id,
function_name=function_name,
arguments=arguments,
)
)
)

await self.run_function_calls(function_calls)
await self.run_function_calls(function_calls)
except asyncio.CancelledError:
# Handle cancellation gracefully (e.g., from InterruptionFrame)
logger.debug(f"{self}: Stream processing cancelled due to interruption")
raise
finally:
# Ensure the SSE stream is properly closed to avoid connection leaks
await chunk_stream.close()
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Contributor Author

@jamsea jamsea Oct 22, 2025

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@aconchillo are we intentionally not calling this function presently? The main change I'm interested in is line 431, adding a debug log


async def process_frame(self, frame: Frame, direction: FrameDirection):
"""Process frames for LLM completion requests.
Expand Down