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Progress on updating foundational examples to avoid using the newly-deprecated LLMMessagesFrame.

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Skipping over 07b-interruptible-langchain.py for now, as it requires deeper changes involving `LLMUserResponseAggregator` and `LLMAssistantResponseAggregator`.
This commit is contained in:
Paul Kompfner
2025-08-06 12:03:40 -04:00
parent 64e48e4660
commit f0391c3280
7 changed files with 47 additions and 60 deletions
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25
examples/foundational/22b-natural-conversation-proposal.py
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@@ -19,7 +19,6 @@ from pipecat.frames.frames import (
Frame,
FunctionCallInProgressFrame,
FunctionCallResultFrame,
LLMMessagesFrame,
StartFrame,
StartInterruptionFrame,
StopInterruptionFrame,
@@ -60,10 +59,6 @@ classifier_statement = "Determine if the user's statement ends with a complete t
class StatementJudgeContextFilter(FrameProcessor):
def __init__(self, notifier: BaseNotifier, **kwargs):
super().__init__(**kwargs)
self._notifier = notifier
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
# We must not block system frames.
@@ -71,13 +66,8 @@ class StatementJudgeContextFilter(FrameProcessor):
await self.push_frame(frame, direction)
return
# Just treat an LLMMessagesFrame as complete, no matter what.
if isinstance(frame, LLMMessagesFrame):
await self._notifier.notify()
return
# Otherwise, we only want to handle OpenAILLMContextFrames, and only want to push a simple
# messages frame that contains a system prompt and the most recent user messages,
# We only want to handle OpenAILLMContextFrames, and only want to push through a simplified
# context frame that contains a system prompt and the most recent user messages,
# concatenated.
if isinstance(frame, OpenAILLMContextFrame):
logger.debug(f"Context Frame: {frame}")
@@ -96,7 +86,7 @@ class StatementJudgeContextFilter(FrameProcessor):
for content in message["content"]:
if content["type"] == "text":
user_text_messages.insert(0, content["text"])
# If we have any user text content, push an LLMMessagesFrame
# If we have any user text content, push a context frame with the simplified context.
if user_text_messages:
logger.debug(f"User text messages: {user_text_messages}")
user_message = " ".join(reversed(user_text_messages))
@@ -110,7 +100,7 @@ class StatementJudgeContextFilter(FrameProcessor):
if last_assistant_message:
messages.append(last_assistant_message)
messages.append({"role": "user", "content": user_message})
await self.push_frame(LLMMessagesFrame(messages))
await self.push_frame(OpenAILLMContextFrame(OpenAILLMContext(messages)))
class CompletenessCheck(FrameProcessor):
@@ -296,7 +286,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
# This turns the LLM context into an inference request to classify the user's speech
# as complete or incomplete.
statement_judge_context_filter = StatementJudgeContextFilter(notifier=notifier)
statement_judge_context_filter = StatementJudgeContextFilter()
# This sends a UserStoppedSpeakingFrame and triggers the notifier event
completeness_check = CompletenessCheck(notifier=notifier)
@@ -316,7 +306,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def pass_only_llm_trigger_frames(frame):
return (
isinstance(frame, OpenAILLMContextFrame)
or isinstance(frame, LLMMessagesFrame)
or isinstance(frame, StartInterruptionFrame)
or isinstance(frame, StopInterruptionFrame)
or isinstance(frame, FunctionCallInProgressFrame)
@@ -331,14 +320,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
ParallelPipeline(
[
# Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
# LLMMessagesFrame to the statement classifier LLM. The only frame this
# simplified context frame to the statement classifier LLM. The only frame this
# sub-pipeline will output is a UserStoppedSpeakingFrame.
statement_judge_context_filter,
statement_llm,
completeness_check,
],
[
# Block everything except OpenAILLMContextFrame and LLMMessagesFrame
# Block everything except frames that trigger LLM inference.
FunctionFilter(filter=pass_only_llm_trigger_frames),
llm,
bot_output_gate, # Buffer all llm/tts output until notified.