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initialize assistant aggregators with context and push upstream instead

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This commit is contained in:
Aleix Conchillo Flaqué
2025-02-13 11:48:04 -08:00
parent 84510fd521
commit 463078e375
7 changed files with 44 additions and 42 deletions
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4
src/pipecat/processors/aggregators/llm_response.py
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@@ -173,9 +173,9 @@ class LLMContextResponseAggregator(BaseLLMResponseAggregator):
def get_context_frame(self) -> OpenAILLMContextFrame:
return OpenAILLMContextFrame(context=self._context)
async def push_context_frame(self):
async def push_context_frame(self, direction: FrameDirection = FrameDirection.DOWNSTREAM):
frame = self.get_context_frame()
await self.push_frame(frame)
await self.push_frame(frame, direction)
def add_messages(self, messages):
self._context.add_messages(messages)

14
src/pipecat/services/anthropic.py
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@@ -126,9 +126,11 @@ class AnthropicLLMService(LLMService):
def create_context_aggregator(
context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True
) -> AnthropicContextAggregatorPair:
if isinstance(context, OpenAILLMContext):
context = AnthropicLLMContext.from_openai_context(context)
user = AnthropicUserContextAggregator(context)
assistant = AnthropicAssistantContextAggregator(
user, expect_stripped_words=assistant_expect_stripped_words
context, expect_stripped_words=assistant_expect_stripped_words
)
return AnthropicContextAggregatorPair(_user=user, _assistant=assistant)
@@ -654,9 +656,6 @@ class AnthropicUserContextAggregator(LLMUserContextAggregator):
def __init__(self, context: OpenAILLMContext | AnthropicLLMContext, **kwargs):
super().__init__(context=context, **kwargs)
if isinstance(context, OpenAILLMContext):
self._context = AnthropicLLMContext.from_openai_context(context)
async def process_frame(self, frame, direction):
await super().process_frame(frame, direction)
# Our parent method has already called push_frame(). So we can't interrupt the
@@ -703,9 +702,8 @@ class AnthropicUserContextAggregator(LLMUserContextAggregator):
class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
def __init__(self, user_context_aggregator: AnthropicUserContextAggregator, **kwargs):
super().__init__(context=user_context_aggregator._context, **kwargs)
self._user_context_aggregator = user_context_aggregator
def __init__(self, context: OpenAILLMContext | AnthropicLLMContext, **kwargs):
super().__init__(context=context, **kwargs)
self._function_call_in_progress = None
self._function_call_result = None
self._pending_image_frame_message = None
@@ -799,7 +797,7 @@ class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
run_llm = True
if run_llm:
await self._user_context_aggregator.push_context_frame()
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:

2
src/pipecat/services/google/google.py
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@@ -626,7 +626,7 @@ class GoogleAssistantContextAggregator(OpenAIAssistantContextAggregator):
run_llm = True
if run_llm:
await self._user_context_aggregator.push_context_frame()
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:

3
src/pipecat/services/grok.py
View File

@@ -17,6 +17,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
OpenAILLMContextFrame,
)
from pipecat.processors.frame_processor import FrameDirection
from pipecat.services.openai import (
OpenAIAssistantContextAggregator,
OpenAILLMService,
@@ -91,7 +92,7 @@ class GrokAssistantContextAggregator(OpenAIAssistantContextAggregator):
run_llm = True
if run_llm:
await self._user_context_aggregator.push_context_frame()
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:

9
src/pipecat/services/openai.py
View File

@@ -355,7 +355,7 @@ class OpenAILLMService(BaseOpenAILLMService):
) -> OpenAIContextAggregatorPair:
user = OpenAIUserContextAggregator(context)
assistant = OpenAIAssistantContextAggregator(
user, expect_stripped_words=assistant_expect_stripped_words
context, expect_stripped_words=assistant_expect_stripped_words
)
return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)
@@ -592,9 +592,8 @@ class OpenAIUserContextAggregator(LLMUserContextAggregator):
class OpenAIAssistantContextAggregator(LLMAssistantContextAggregator):
def __init__(self, user_context_aggregator: OpenAIUserContextAggregator, **kwargs):
super().__init__(context=user_context_aggregator._context, **kwargs)
self._user_context_aggregator = user_context_aggregator
def __init__(self, context: OpenAILLMContext, **kwargs):
super().__init__(context=context, **kwargs)
self._function_calls_in_progress = {}
self._function_call_result = None
self._pending_image_frame_message = None
@@ -686,7 +685,7 @@ class OpenAIAssistantContextAggregator(LLMAssistantContextAggregator):
run_llm = True
if run_llm:
await self._user_context_aggregator.push_context_frame()
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:

6
src/pipecat/services/openai_realtime_beta/context.py
View File

@@ -217,8 +217,8 @@ class OpenAIRealtimeAssistantContextAggregator(OpenAIAssistantContextAggregator)
# 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._user_context_aggregator.push_frame(
RealtimeFunctionCallResultFrame(result_frame=frame)
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
@@ -228,7 +228,7 @@ class OpenAIRealtimeAssistantContextAggregator(OpenAIAssistantContextAggregator)
run_llm = not bool(self._function_calls_in_progress)
if run_llm:
await self._user_context_aggregator.push_context_frame()
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: