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openai_realtime: fix and update function calling

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This commit is contained in:
Aleix Conchillo Flaqué
2025-03-20 11:14:59 -07:00
parent 44380bc8c0
commit efa5f133d7
2 changed files with 11 additions and 65 deletions
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72
src/pipecat/services/openai_realtime_beta/context.py
View File

@@ -12,6 +12,7 @@ from loguru import logger
from pipecat.frames.frames import (
Frame,
FunctionCallResultFrame,
FunctionCallResultProperties,
LLMMessagesUpdateFrame,
LLMSetToolsFrame,
@@ -174,67 +175,12 @@ class OpenAIRealtimeUserContextAggregator(OpenAIUserContextAggregator):
class OpenAIRealtimeAssistantContextAggregator(OpenAIAssistantContextAggregator):
async def push_aggregation(self):
# the only thing we implement here is function calling. in all other cases, messages
# are added to the context when we receive openai realtime api events
if not self._function_call_result:
return
async def handle_function_call_result(self, frame: FunctionCallResultFrame):
await super().handle_function_call_result(frame)
properties: Optional[FunctionCallResultProperties] = None
self.reset()
try:
run_llm = True
frame = self._function_call_result
properties = frame.properties
self._function_call_result = None
if frame.result:
# The "tool_call" message from the LLM that triggered the function call
self._context.add_message(
{
"role": "assistant",
"tool_calls": [
{
"id": frame.tool_call_id,
"function": {
"name": frame.function_name,
"arguments": json.dumps(frame.arguments),
},
"type": "function",
}
],
}
)
# The result of the function call. Need to add this both to our context here and to
# the openai realtime api context.
result_message = {
"role": "tool",
"content": json.dumps(frame.result),
"tool_call_id": frame.tool_call_id,
}
self._context.add_message(result_message)
# The standard function callback code path pushes the FunctionCallResultFrame from the llm itself,
# so we didn't have a chance to add the result to the openai realtime api context. Let's push a
# special frame to do that.
await self.push_frame(
RealtimeFunctionCallResultFrame(result_frame=frame), FrameDirection.UPSTREAM
)
if properties and properties.run_llm is not None:
# If the tool call result has a run_llm property, use it
run_llm = properties.run_llm
else:
# Default behavior is to run the LLM if there are no function calls in progress
run_llm = not bool(self._function_calls_in_progress)
if run_llm:
await self.push_context_frame(FrameDirection.UPSTREAM)
# Emit the on_context_updated callback once the function call result is added to the context
if properties and properties.on_context_updated is not None:
await properties.on_context_updated()
await self.push_context_frame()
except Exception as e:
logger.error(f"Error processing frame: {e}")
# The standard function callback code path pushes the FunctionCallResultFrame from the llm itself,
# so we didn't have a chance to add the result to the openai realtime api context. Let's push a
# special frame to do that.
await self.push_frame(
RealtimeFunctionCallResultFrame(result_frame=frame), FrameDirection.UPSTREAM
)

4
src/pipecat/services/openai_realtime_beta/openai.py
View File

@@ -579,7 +579,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
arguments = json.loads(item.arguments)
if self.has_function(function_name):
run_llm = index == total_items - 1
if function_name in self._callbacks.keys():
if function_name in self._functions.keys():
await self.call_function(
context=self._context,
tool_call_id=tool_id,
@@ -587,7 +587,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
arguments=arguments,
run_llm=run_llm,
)
elif None in self._callbacks.keys():
elif None in self._functions.keys():
await self.call_function(
context=self._context,
tool_call_id=tool_id,