Update examples
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@@ -22,10 +22,10 @@ from pipecat.processors.aggregators.llm_response_universal import (
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)
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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.openai.base_llm import BaseOpenAILLMService
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from pipecat.services.openai.base_llm import OpenAILLMSettings
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
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from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
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from pipecat.services.speechmatics.stt import SpeechmaticsSTTService, SpeechmaticsSTTSettings
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from pipecat.services.speechmatics.tts import SpeechmaticsTTSService, SpeechmaticsTTSSettings
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from pipecat.transcriptions.language import Language
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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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@@ -76,7 +76,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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async with aiohttp.ClientSession() as session:
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stt = SpeechmaticsSTTService(
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api_key=os.getenv("SPEECHMATICS_API_KEY"),
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params=SpeechmaticsSTTService.InputParams(
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settings=SpeechmaticsSTTSettings(
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language=Language.EN,
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speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
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),
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@@ -84,31 +84,21 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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tts = SpeechmaticsTTSService(
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api_key=os.getenv("SPEECHMATICS_API_KEY"),
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voice_id="sarah",
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settings=SpeechmaticsTTSSettings(
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voice="sarah",
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),
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aiohttp_session=session,
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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params=BaseOpenAILLMService.InputParams(temperature=0.75),
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settings=OpenAILLMSettings(
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temperature=0.75,
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),
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system_instruction="You are a helpful British assistant called Sarah. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Always include punctuation in your responses. Give very short replies - do not give longer replies unless strictly necessary. Respond to what the user said in a concise, funny, creative and helpful way. Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to.",
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)
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messages = [
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{
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"role": "system",
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"content": (
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"You are a helpful British assistant called Sarah. "
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"Your goal is to demonstrate your capabilities in a succinct way. "
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"Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. "
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"Always include punctuation in your responses. "
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"Give very short replies - do not give longer replies unless strictly necessary. "
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"Respond to what the user said in a concise, funny, creative and helpful way. "
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"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
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),
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},
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]
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context = LLMContext(messages)
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context = LLMContext()
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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@@ -139,7 +129,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation.
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messages.append({"role": "system", "content": "Say a short hello to the user."})
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context.add_message({"role": "system", "content": "Say a short hello to the user."})
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await task.queue_frames([LLMRunFrame()])
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@transport.event_handler("on_client_disconnected")
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