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@@ -34,6 +34,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
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from pipecat.processors.frame_processor import FrameProcessor
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.google.llm import GoogleLLMContext, GoogleLLMService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_transport import TransportParams
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from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
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from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
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@@ -286,16 +287,18 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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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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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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# our system message in the messages list.
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system_instruction=conversation_system_message,
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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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# 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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# # our system message in the messages list.
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# system_instruction=conversation_system_message,
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# )
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conversation_llm = OpenAILLMService(name="Conversation", api_key=os.getenv("OPENAI_API_KEY"))
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input_transcription_llm = GoogleLLMService(
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name="Transcription",
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