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Merge pull request #2714 from pipecat-ai/pk/gated-openai-llm-context-aggregator-support-univeral-context

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Add support for universal `LLMContext` to `GatedOpenAILLMContextAggre…
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kompfner
2025-09-23 11:54:26 -04:00
committed by GitHub
parent 2cee6229ae 99731ca40a
commit 1f005e7075
3 changed files with 29 additions and 18 deletions
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17
examples/foundational/22-natural-conversation.py
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@@ -16,8 +16,9 @@ from pipecat.pipeline.parallel_pipeline import ParallelPipeline
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.gated_openai_llm_context import GatedOpenAILLMContextAggregator
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.aggregators.gated_llm_context import GatedLLMContextAggregator
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
from pipecat.processors.filters.null_filter import NullFilter
from pipecat.processors.filters.wake_notifier_filter import WakeNotifierFilter
from pipecat.processors.user_idle_processor import UserIdleProcessor
@@ -50,8 +51,8 @@ class TurnDetectionLLM(Pipeline):
},
]
statement_context = OpenAILLMContext(statement_messages)
statement_context_aggregator = statement_llm.create_context_aggregator(statement_context)
statement_context = LLMContext(statement_messages)
statement_context_aggregator = LLMContextAggregatorPair(statement_context)
# We have instructed the LLM to return 'YES' if it thinks the user
# completed a sentence. So, if it's 'YES' we will return true in this
@@ -72,9 +73,7 @@ class TurnDetectionLLM(Pipeline):
# This processor keeps the last context and will let it through once the
# notifier is woken up. We start with the gate open because we send an
# initial context frame to start the conversation.
gated_context_aggregator = GatedOpenAILLMContextAggregator(
notifier=notifier, start_open=True
)
gated_context_aggregator = GatedLLMContextAggregator(notifier=notifier, start_open=True)
# Notify if the user hasn't said anything.
async def user_idle_notifier(frame):
@@ -147,8 +146,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
},
]
context = OpenAILLMContext(messages)
context_aggregator = llm_main.create_context_aggregator(context)
context = LLMContext(messages)
context_aggregator = LLMContextAggregatorPair(context)
# LLM + turn detection (with an extra LLM as a judge)
llm = TurnDetectionLLM(llm_main, context_aggregator)