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Merge pull request #970 from pipecat-ai/mb/user-controlled-run-llm

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Add an override_run_llm option to optionally defer function call completion
This commit is contained in:
Mark Backman
2025-01-13 18:48:00 -05:00
committed by GitHub
parent 92313d6ce7 8c0ecb89de
commit 98e80b7d4a
7 changed files with 78 additions and 13 deletions
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9
CHANGELOG.md
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@@ -9,6 +9,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Added
- Added `FunctionCallResultProperties` dataclass to provide a structured way to
control function call behavior, including:
- `run_llm`: Controls whether to trigger LLM completion
- `on_context_updated`: Optional callback triggered after context update
- Added a new foundational example `07e-interruptible-playht-http.py` for easy
testing of `PlayHTHttpTTSService`.
@@ -30,6 +36,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Changed
- Modified `OpenAIAssistantContextAggregator` to support controlled completions
and to emit context update callbacks via `FunctionCallResultProperties`.
- Added `aws_session_token` to the `PollyTTSService`.
- Changed the default model for `PlayHTHttpTTSService` to `Play3.0-mini-http`.

12
src/pipecat/frames/frames.py
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@@ -5,7 +5,7 @@
#
from dataclasses import dataclass, field
from typing import Any, List, Literal, Mapping, Optional, Tuple
from typing import Any, Awaitable, Callable, List, Literal, Mapping, Optional, Tuple
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.clocks.base_clock import BaseClock
@@ -321,6 +321,14 @@ class LLMEnablePromptCachingFrame(DataFrame):
enable: bool
@dataclass
class FunctionCallResultProperties:
"""Properties for a function call result frame."""
run_llm: Optional[bool] = None
on_context_updated: Optional[Callable[[], Awaitable[None]]] = None
@dataclass
class FunctionCallResultFrame(DataFrame):
"""A frame containing the result of an LLM function (tool) call."""
@@ -329,7 +337,7 @@ class FunctionCallResultFrame(DataFrame):
tool_call_id: str
arguments: str
result: Any
run_llm: bool = True
properties: Optional[FunctionCallResultProperties] = None
@dataclass

9
src/pipecat/processors/aggregators/openai_llm_context.py
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@@ -9,7 +9,7 @@ import copy
import io
import json
from dataclasses import dataclass
from typing import Any, Awaitable, Callable, List
from typing import Any, Awaitable, Callable, List, Optional
from loguru import logger
from PIL import Image
@@ -218,23 +218,22 @@ class OpenAILLMContext:
await llm.push_frame(progress_frame_upstream, FrameDirection.UPSTREAM)
# Define a callback function that pushes a FunctionCallResultFrame upstream & downstream.
async def function_call_result_callback(result):
async def function_call_result_callback(result, *, properties=None):
result_frame_downstream = FunctionCallResultFrame(
function_name=function_name,
tool_call_id=tool_call_id,
arguments=arguments,
result=result,
run_llm=run_llm,
properties=properties,
)
result_frame_upstream = FunctionCallResultFrame(
function_name=function_name,
tool_call_id=tool_call_id,
arguments=arguments,
result=result,
run_llm=run_llm,
properties=properties,
)
# Push frame both downstream and upstream
await llm.push_frame(result_frame_downstream, FrameDirection.DOWNSTREAM)
await llm.push_frame(result_frame_upstream, FrameDirection.UPSTREAM)

14
src/pipecat/services/anthropic.py
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@@ -21,6 +21,7 @@ from pipecat.frames.frames import (
Frame,
FunctionCallInProgressFrame,
FunctionCallResultFrame,
FunctionCallResultProperties,
LLMEnablePromptCachingFrame,
LLMFullResponseEndFrame,
LLMFullResponseStartFrame,
@@ -742,6 +743,7 @@ class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
return
run_llm = False
properties: Optional[FunctionCallResultProperties] = None
aggregation = self._aggregation
self._reset()
@@ -749,6 +751,7 @@ class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
try:
if self._function_call_result:
frame = self._function_call_result
properties = frame.properties
self._function_call_result = None
if frame.result:
assistant_message = {"role": "assistant", "content": []}
@@ -775,7 +778,12 @@ class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
],
}
)
run_llm = True
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
run_llm = True
elif aggregation:
self._context.add_message({"role": "assistant", "content": aggregation})
@@ -793,6 +801,10 @@ class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
if run_llm:
await self._user_context_aggregator.push_context_frame()
# 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()
# Push context frame
frame = OpenAILLMContextFrame(self._context)
await self.push_frame(frame)

14
src/pipecat/services/google.py
View File

@@ -19,6 +19,7 @@ from pipecat.frames.frames import (
AudioRawFrame,
ErrorFrame,
Frame,
FunctionCallResultProperties,
LLMFullResponseEndFrame,
LLMFullResponseStartFrame,
LLMMessagesFrame,
@@ -245,6 +246,7 @@ class GoogleAssistantContextAggregator(OpenAIAssistantContextAggregator):
return
run_llm = False
properties: Optional[FunctionCallResultProperties] = None
aggregation = self._aggregation
self._reset()
@@ -252,6 +254,7 @@ class GoogleAssistantContextAggregator(OpenAIAssistantContextAggregator):
try:
if self._function_call_result:
frame = self._function_call_result
properties = frame.properties
self._function_call_result = None
if frame.result:
logger.debug(f"FunctionCallResultFrame result: {frame.arguments}")
@@ -282,7 +285,12 @@ class GoogleAssistantContextAggregator(OpenAIAssistantContextAggregator):
],
)
)
run_llm = not bool(self._function_calls_in_progress)
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)
else:
if aggregation.strip():
self._context.add_message(
@@ -303,6 +311,10 @@ class GoogleAssistantContextAggregator(OpenAIAssistantContextAggregator):
if run_llm:
await self._user_context_aggregator.push_context_frame()
# 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()
# Push context frame
frame = OpenAILLMContextFrame(self._context)
await self.push_frame(frame)

17
src/pipecat/services/grok.py
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@@ -7,9 +7,11 @@
import json
from dataclasses import dataclass
from typing import Optional
from loguru import logger
from pipecat.frames.frames import FunctionCallResultProperties
from pipecat.metrics.metrics import LLMTokenUsage
from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
@@ -32,6 +34,7 @@ class GrokAssistantContextAggregator(OpenAIAssistantContextAggregator):
return
run_llm = False
properties: Optional[FunctionCallResultProperties] = None
aggregation = self._aggregation
self._reset()
@@ -39,6 +42,7 @@ class GrokAssistantContextAggregator(OpenAIAssistantContextAggregator):
try:
if self._function_call_result:
frame = self._function_call_result
properties = frame.properties
self._function_call_result = None
if frame.result:
# Grok requires an empty content field for function calls
@@ -65,8 +69,13 @@ class GrokAssistantContextAggregator(OpenAIAssistantContextAggregator):
"tool_call_id": frame.tool_call_id,
}
)
# Only run the LLM if there are no more function calls in progress.
run_llm = not bool(self._function_calls_in_progress)
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)
else:
self._context.add_message({"role": "assistant", "content": aggregation})
@@ -84,6 +93,10 @@ class GrokAssistantContextAggregator(OpenAIAssistantContextAggregator):
if run_llm:
await self._user_context_aggregator.push_context_frame()
# 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()
frame = OpenAILLMContextFrame(self._context)
await self.push_frame(frame)

16
src/pipecat/services/openai.py
View File

@@ -21,6 +21,7 @@ from pipecat.frames.frames import (
Frame,
FunctionCallInProgressFrame,
FunctionCallResultFrame,
FunctionCallResultProperties,
LLMFullResponseEndFrame,
LLMFullResponseStartFrame,
LLMMessagesFrame,
@@ -549,6 +550,7 @@ class OpenAIAssistantContextAggregator(LLMAssistantContextAggregator):
return
run_llm = False
properties: Optional[FunctionCallResultProperties] = None
aggregation = self._aggregation
self._reset()
@@ -556,6 +558,7 @@ class OpenAIAssistantContextAggregator(LLMAssistantContextAggregator):
try:
if self._function_call_result:
frame = self._function_call_result
properties = frame.properties
self._function_call_result = None
if frame.result:
self._context.add_message(
@@ -580,8 +583,13 @@ class OpenAIAssistantContextAggregator(LLMAssistantContextAggregator):
"tool_call_id": frame.tool_call_id,
}
)
# Only run the LLM if there are no more function calls in progress.
run_llm = not bool(self._function_calls_in_progress)
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)
else:
self._context.add_message({"role": "assistant", "content": aggregation})
@@ -599,6 +607,10 @@ class OpenAIAssistantContextAggregator(LLMAssistantContextAggregator):
if run_llm:
await self._user_context_aggregator.push_context_frame()
# 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()
# Push context frame
frame = OpenAILLMContextFrame(self._context)
await self.push_frame(frame)