Merge pull request #4231 from pipecat-ai/pk/llm-messages-transform-frame
Add a `LLMMessagesTransformFrame` to facilitate programmatically edit…
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@@ -38,7 +38,7 @@ from pipecat.utils.time import nanoseconds_to_str
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from pipecat.utils.utils import obj_count, obj_id
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if TYPE_CHECKING:
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from pipecat.processors.aggregators.llm_context import LLMContext, NotGiven
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from pipecat.processors.aggregators.llm_context import LLMContext, LLMContextMessage, NotGiven
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from pipecat.processors.frame_processor import FrameProcessor
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from pipecat.services.settings import ServiceSettings
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from pipecat.utils.context.llm_context_summarization import LLMContextSummaryConfig
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@@ -587,6 +587,23 @@ class LLMMessagesUpdateFrame(DataFrame):
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run_llm: Optional[bool] = None
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@dataclass
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class LLMMessagesTransformFrame(DataFrame):
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"""Frame containing a transform function to modify the current context's LLM messages.
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A frame containing a transform function that takes the context's current list
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of LLM messages and returns a modified list.
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Parameters:
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transform: A function that takes a list of messages and returns a
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modified list.
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run_llm: Whether the context update should be sent to the LLM.
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"""
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transform: Callable[[List["LLMContextMessage"]], List["LLMContextMessage"]]
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run_llm: Optional[bool] = None
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@dataclass
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class LLMSetToolsFrame(DataFrame):
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"""Frame containing tools for LLM function calling.
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@@ -19,7 +19,7 @@ import base64
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import io
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import wave
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from dataclasses import dataclass
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from typing import Any, List, Optional, TypeAlias, Union
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from typing import Any, Callable, List, Optional, TypeAlias, Union
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from loguru import logger
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from openai._types import NOT_GIVEN as OPEN_AI_NOT_GIVEN
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@@ -266,6 +266,17 @@ class LLMContext:
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"""
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self._messages[:] = messages
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def transform_messages(
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self, transform: Callable[[List[LLMContextMessage]], List[LLMContextMessage]]
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):
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"""Transform the current messages using the provided function.
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Args:
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transform: A function that takes the current list of messages and returns
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a modified list of messages to set in the context.
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"""
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self.set_messages(transform(self._messages))
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def set_tools(self, tools: ToolsSchema | NotGiven = NOT_GIVEN):
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"""Set the available tools for the LLM.
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@@ -16,7 +16,7 @@ import json
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import warnings
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from abc import abstractmethod
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Literal, Optional, Set, Type
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from typing import Any, Callable, Dict, List, Literal, Optional, Set, Type
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from loguru import logger
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@@ -42,6 +42,7 @@ from pipecat.frames.frames import (
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LLMFullResponseEndFrame,
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LLMFullResponseStartFrame,
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LLMMessagesAppendFrame,
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LLMMessagesTransformFrame,
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LLMMessagesUpdateFrame,
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LLMRunFrame,
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LLMSetToolChoiceFrame,
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@@ -315,6 +316,17 @@ class LLMContextAggregator(FrameProcessor):
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"""
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self._context.set_messages(messages)
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def transform_messages(
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self, transform: Callable[[List[LLMContextMessage]], List[LLMContextMessage]]
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):
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"""Transform the context messages using a provided function.
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Args:
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transform: A function that takes the current list of messages and returns
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a modified list of messages to set in the context.
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"""
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self._context.transform_messages(transform)
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def set_tools(self, tools: ToolsSchema | NotGiven):
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"""Set tools in the context.
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@@ -512,6 +524,8 @@ class LLMUserAggregator(LLMContextAggregator):
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await self._handle_llm_messages_append(frame)
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elif isinstance(frame, LLMMessagesUpdateFrame):
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await self._handle_llm_messages_update(frame)
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elif isinstance(frame, LLMMessagesTransformFrame):
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await self._handle_llm_messages_transform(frame)
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elif isinstance(frame, LLMSetToolsFrame):
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self.set_tools(frame.tools)
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# Push the LLMSetToolsFrame as well, since speech-to-speech LLM
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@@ -635,6 +649,11 @@ class LLMUserAggregator(LLMContextAggregator):
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if frame.run_llm:
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await self.push_context_frame()
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async def _handle_llm_messages_transform(self, frame: LLMMessagesTransformFrame):
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self.transform_messages(frame.transform)
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if frame.run_llm:
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await self.push_context_frame()
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async def _handle_transcription(self, frame: TranscriptionFrame):
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text = frame.text
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@@ -923,6 +942,8 @@ class LLMAssistantAggregator(LLMContextAggregator):
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await self._handle_llm_messages_append(frame)
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elif isinstance(frame, LLMMessagesUpdateFrame):
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await self._handle_llm_messages_update(frame)
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elif isinstance(frame, LLMMessagesTransformFrame):
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await self._handle_llm_messages_transform(frame)
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elif isinstance(frame, LLMSetToolsFrame):
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self.set_tools(frame.tools)
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elif isinstance(frame, LLMSetToolChoiceFrame):
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@@ -982,6 +1003,11 @@ class LLMAssistantAggregator(LLMContextAggregator):
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if frame.run_llm:
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await self.push_context_frame(FrameDirection.UPSTREAM)
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async def _handle_llm_messages_transform(self, frame: LLMMessagesTransformFrame):
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self.transform_messages(frame.transform)
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if frame.run_llm:
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await self.push_context_frame(FrameDirection.UPSTREAM)
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async def _handle_interruptions(self, frame: InterruptionFrame):
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await self._trigger_assistant_turn_stopped()
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await self.reset()
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@@ -127,6 +127,7 @@ async def run_test(
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expected_down_frames: Optional[Sequence[type]] = None,
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expected_up_frames: Optional[Sequence[type]] = None,
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frames_to_send: Sequence[Frame],
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frames_to_send_direction: FrameDirection = FrameDirection.DOWNSTREAM,
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ignore_start: bool = True,
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observers: Optional[List[BaseObserver]] = None,
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pipeline_params: Optional[PipelineParams] = None,
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@@ -144,6 +145,9 @@ async def run_test(
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expected_down_frames: Expected frame types flowing downstream (optional).
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expected_up_frames: Expected frame types flowing upstream (optional).
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frames_to_send: Sequence of frames to send through the processor.
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frames_to_send_direction: Direction to send frames_to_send. Downstream
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frames are pushed from the beginning of the pipeline, upstream frames
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from the end. Defaults to DOWNSTREAM.
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ignore_start: Whether to ignore StartFrames in frame validation.
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observers: Optional list of observers to attach to the pipeline.
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pipeline_params: Optional pipeline parameters.
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@@ -188,7 +192,7 @@ async def run_test(
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if isinstance(frame, SleepFrame):
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await asyncio.sleep(frame.sleep)
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else:
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await task.queue_frame(frame)
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await task.queue_frame(frame, frames_to_send_direction)
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if send_end_frame:
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await task.queue_frame(EndFrame())
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