Update examples
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@@ -32,8 +32,8 @@ from pipecat.processors.aggregators.llm_response_universal import LLMContextAggr
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from pipecat.processors.frame_processor import FrameProcessor
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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.cartesia.tts import CartesiaTTSService
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from pipecat.services.google.llm import GoogleLLMService
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from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
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from pipecat.services.google.llm import GoogleLLMService, GoogleLLMSettings
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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@@ -290,13 +290,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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settings=CartesiaTTSSettings(
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voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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),
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)
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conversation_llm = GoogleLLMService(
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name="Conversation",
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model="gemini-2.0-flash-001",
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# model="gemini-exp-1121",
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settings=GoogleLLMSettings(
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model="gemini-2.5-flash",
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),
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api_key=os.getenv("GOOGLE_API_KEY"),
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# we can give the GoogleLLMService a system instruction to use directly
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# in the GenerativeModel constructor. Let's do that rather than put
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@@ -306,8 +309,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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input_transcription_llm = GoogleLLMService(
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name="Transcription",
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model="gemini-2.0-flash-001",
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# model="gemini-exp-1121",
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settings=GoogleLLMSettings(
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model="gemini-2.5-flash",
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),
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api_key=os.getenv("GOOGLE_API_KEY"),
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system_instruction=transcriber_system_message,
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)
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