Update GeminiLLMService to work with LLMContext and LLMContextAggregatorPair
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@@ -9,21 +9,21 @@ import os
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from datetime import datetime
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from dotenv import load_dotenv
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from google.genai.types import HttpOptions
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from loguru import logger
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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from pipecat.frames.frames import LLMRunFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
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from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
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from pipecat.services.google.gemini_live.llm_vertex import GeminiLiveVertexLLMService
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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@@ -139,10 +139,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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llm.register_function("get_current_weather", fetch_weather_from_api)
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llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
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context = OpenAILLMContext(
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[{"role": "user", "content": "Say hello."}],
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context = LLMContext([{"role": "user", "content": "Say hello."}])
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context_aggregator = LLMContextAggregatorPair(
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context,
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# `expect_stripped_words=False` needed when Gemini Live used with AUDIO
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# modality (the default)
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assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
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)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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