Remove deprecated OpenAILLMContext as well as everything (code paths or whole types) dependent on it (all of which were also deprecated)
This commit is contained in:
Paul Kompfner
@@ -1,162 +0,0 @@
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#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import os
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from dotenv import load_dotenv
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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 ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
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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.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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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.deepgram.stt import DeepgramSTTService
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from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
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from pipecat.services.google.openai.llm import GoogleLLMOpenAIBetaService
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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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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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async def fetch_weather_from_api(params: FunctionCallParams):
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await params.result_callback({"conditions": "nice", "temperature": "75"})
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot")
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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tts = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY", ""),
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settings=ElevenLabsTTSService.Settings(
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voice=os.getenv("ELEVENLABS_VOICE_ID", ""),
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),
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)
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llm = GoogleLLMOpenAIBetaService(
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api_key=os.getenv("GOOGLE_API_KEY"),
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settings=GoogleLLMOpenAIBetaService.Settings(
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system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
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),
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)
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# You can aslo register a function_name of None to get all functions
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# sent to the same callback with an additional function_name parameter.
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llm.register_function("get_current_weather", fetch_weather_from_api)
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@llm.event_handler("on_function_calls_started")
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async def on_function_calls_started(service, function_calls):
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await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
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weather_function = FunctionSchema(
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name="get_current_weather",
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description="Get the current weather",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the user's location.",
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},
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},
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required=["location", "format"],
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)
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tools = ToolsSchema(standard_tools=[weather_function])
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messages = [
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{
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"role": "developer",
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"content": "Start a conversation with 'Hey there' to get the current weather.",
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},
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]
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context = OpenAILLMContext(messages, tools)
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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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)
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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user_aggregator,
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llm,
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tts,
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transport.output(),
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assistant_aggregator,
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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@transport.event_handler("on_client_connected")
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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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await task.queue_frames([LLMRunFrame()])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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@@ -1,219 +0,0 @@
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#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import os
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from datetime import datetime
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from dotenv import load_dotenv
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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 ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
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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.transcript_processor import TranscriptProcessor
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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.llm_service import FunctionCallParams
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from pipecat.services.openai_realtime_beta import (
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InputAudioNoiseReduction,
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InputAudioTranscription,
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OpenAIRealtimeBetaLLMService,
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SemanticTurnDetection,
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SessionProperties,
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)
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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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load_dotenv(override=True)
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async def fetch_weather_from_api(params: FunctionCallParams):
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temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
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await params.result_callback(
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{
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"conditions": "nice",
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"temperature": temperature,
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"format": params.arguments["format"],
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"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
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}
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)
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async def fetch_restaurant_recommendation(params: FunctionCallParams):
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await params.result_callback({"name": "The Golden Dragon"})
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weather_function = FunctionSchema(
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name="get_current_weather",
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description="Get the current weather",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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},
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required=["location", "format"],
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)
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restaurant_function = FunctionSchema(
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name="get_restaurant_recommendation",
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description="Get a restaurant recommendation",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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},
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required=["location"],
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)
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# Create tools schema
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tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot")
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session_properties = SessionProperties(
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input_audio_transcription=InputAudioTranscription(),
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# Set openai TurnDetection parameters. Not setting this at all will turn it
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# on by default
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turn_detection=SemanticTurnDetection(),
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# Or set to False to disable openai turn detection and use transport VAD
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# turn_detection=False,
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input_audio_noise_reduction=InputAudioNoiseReduction(type="near_field"),
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# tools=tools,
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instructions="""You are a helpful and friendly AI.
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Act like a human, but remember that you aren't a human and that you can't do human
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things in the real world. Your voice and personality should be warm and engaging, with a lively and
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playful tone.
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If interacting in a non-English language, start by using the standard accent or dialect familiar to
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the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
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even if you're asked about them.
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You are participating in a voice conversation. Keep your responses concise, short, and to the point
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unless specifically asked to elaborate on a topic.
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You have access to the following tools:
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- get_current_weather: Get the current weather for a given location.
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- get_restaurant_recommendation: Get a restaurant recommendation for a given location.
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Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
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)
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llm = OpenAIRealtimeBetaLLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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session_properties=session_properties,
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)
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# you can either register a single function for all function calls, or specific functions
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# llm.register_function(None, fetch_weather_from_api)
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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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transcript = TranscriptProcessor()
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# Create a standard OpenAI LLM context object using the normal messages format. The
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# OpenAIRealtimeBetaLLMService will convert this internally to messages that the
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# openai WebSocket API can understand.
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context = OpenAILLMContext(
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[{"role": "developer", "content": "Say hello!"}],
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tools,
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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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transport.input(), # Transport user input
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context_aggregator.user(),
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llm, # LLM
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transcript.user(), # Placed after the LLM, as LLM pushes TranscriptionFrames downstream
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transport.output(), # Transport bot output
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transcript.assistant(), # After the transcript output, to time with the audio output
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context_aggregator.assistant(),
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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@transport.event_handler("on_client_connected")
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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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await task.queue_frames([LLMRunFrame()])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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# Register event handler for transcript updates
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@transcript.event_handler("on_transcript_update")
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async def on_transcript_update(processor, frame):
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for msg in frame.messages:
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if isinstance(msg, TranscriptionMessage):
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timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
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line = f"{timestamp}{msg.role}: {msg.content}"
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logger.info(f"Transcript: {line}")
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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@@ -1,214 +0,0 @@
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#
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# Copyright (c) 2024-2026, Daily
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||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
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|
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import os
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from datetime import datetime
|
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|
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from dotenv import load_dotenv
|
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from loguru import logger
|
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|
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from pipecat.adapters.schemas.function_schema import FunctionSchema
|
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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.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.runner.types import RunnerArguments
|
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from pipecat.runner.utils import create_transport
|
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from pipecat.services.llm_service import FunctionCallParams
|
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from pipecat.services.openai_realtime_beta import (
|
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AzureRealtimeBetaLLMService,
|
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InputAudioTranscription,
|
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SessionProperties,
|
||||
)
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
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|
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load_dotenv(override=True)
|
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|
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|
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async def fetch_weather_from_api(params: FunctionCallParams):
|
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temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
|
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await params.result_callback(
|
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{
|
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"conditions": "nice",
|
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"temperature": temperature,
|
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"format": params.arguments["format"],
|
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"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
|
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}
|
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)
|
||||
|
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|
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async def fetch_restaurant_recommendation(params: FunctionCallParams):
|
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await params.result_callback({"name": "The Golden Dragon"})
|
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|
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|
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# Define weather function using standardized schema
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weather_function = FunctionSchema(
|
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name="get_current_weather",
|
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description="Get the current weather",
|
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properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit to use. Infer this from the users location.",
|
||||
},
|
||||
},
|
||||
required=["location", "format"],
|
||||
)
|
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|
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restaurant_function = FunctionSchema(
|
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name="get_restaurant_recommendation",
|
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description="Get a restaurant recommendation",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
},
|
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required=["location"],
|
||||
)
|
||||
|
||||
# Create tools schema
|
||||
tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
session_properties = SessionProperties(
|
||||
input_audio_transcription=InputAudioTranscription(model="whisper-1"),
|
||||
# Set openai TurnDetection parameters. Not setting this at all will turn it
|
||||
# on by default
|
||||
# turn_detection=TurnDetection(silence_duration_ms=1000),
|
||||
# Or set to False to disable openai turn detection and use transport VAD
|
||||
# turn_detection=False,
|
||||
# tools=tools,
|
||||
instructions="""You are a helpful and friendly AI.
|
||||
|
||||
Act like a human, but remember that you aren't a human and that you can't do human
|
||||
things in the real world. Your voice and personality should be warm and engaging, with a lively and
|
||||
playful tone.
|
||||
|
||||
If interacting in a non-English language, start by using the standard accent or dialect familiar to
|
||||
the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
|
||||
even if you're asked about them.
|
||||
-
|
||||
You are participating in a voice conversation. Keep your responses concise, short, and to the point
|
||||
unless specifically asked to elaborate on a topic.
|
||||
|
||||
You have access to the following tools:
|
||||
- get_current_weather: Get the current weather for a given location.
|
||||
- get_restaurant_recommendation: Get a restaurant recommendation for a given location.
|
||||
|
||||
Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
|
||||
)
|
||||
|
||||
llm = AzureRealtimeBetaLLMService(
|
||||
api_key=os.getenv("AZURE_REALTIME_API_KEY"),
|
||||
base_url=os.getenv("AZURE_REALTIME_BASE_URL"),
|
||||
session_properties=session_properties,
|
||||
)
|
||||
|
||||
# you can either register a single function for all function calls, or specific functions
|
||||
# llm.register_function(None, fetch_weather_from_api)
|
||||
llm.register_function("get_current_weather", fetch_weather_from_api)
|
||||
llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
|
||||
|
||||
# Create a standard OpenAI LLM context object using the normal messages format. The
|
||||
# OpenAIRealtimeBetaLLMService will convert this internally to messages that the
|
||||
# openai WebSocket API can understand.
|
||||
context = OpenAILLMContext(
|
||||
[{"role": "developer", "content": "Say hello!"}],
|
||||
# [{"role": "developer", "content": [{"type": "text", "text": "Say hello!"}]}],
|
||||
# [
|
||||
# {
|
||||
# "role": "developer",
|
||||
# "content": [
|
||||
# {"type": "text", "text": "Say"},
|
||||
# {"type": "text", "text": "yo what's up!"},
|
||||
# ],
|
||||
# }
|
||||
# ],
|
||||
tools,
|
||||
)
|
||||
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
context_aggregator.user(),
|
||||
llm, # LLM
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,215 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
from datetime import datetime
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.openai_realtime_beta import (
|
||||
InputAudioNoiseReduction,
|
||||
InputAudioTranscription,
|
||||
OpenAIRealtimeBetaLLMService,
|
||||
SemanticTurnDetection,
|
||||
SessionProperties,
|
||||
)
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def fetch_weather_from_api(params: FunctionCallParams):
|
||||
temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
|
||||
await params.result_callback(
|
||||
{
|
||||
"conditions": "nice",
|
||||
"temperature": temperature,
|
||||
"format": params.arguments["format"],
|
||||
"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
async def fetch_restaurant_recommendation(params: FunctionCallParams):
|
||||
await params.result_callback({"name": "The Golden Dragon"})
|
||||
|
||||
|
||||
weather_function = FunctionSchema(
|
||||
name="get_current_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit to use. Infer this from the users location.",
|
||||
},
|
||||
},
|
||||
required=["location", "format"],
|
||||
)
|
||||
|
||||
restaurant_function = FunctionSchema(
|
||||
name="get_restaurant_recommendation",
|
||||
description="Get a restaurant recommendation",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
},
|
||||
required=["location"],
|
||||
)
|
||||
|
||||
# Create tools schema
|
||||
tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
session_properties = SessionProperties(
|
||||
input_audio_transcription=InputAudioTranscription(),
|
||||
modalities=["text"],
|
||||
# Set openai TurnDetection parameters. Not setting this at all will turn it
|
||||
# on by default
|
||||
turn_detection=SemanticTurnDetection(),
|
||||
# Or set to False to disable openai turn detection and use transport VAD
|
||||
# turn_detection=False,
|
||||
input_audio_noise_reduction=InputAudioNoiseReduction(type="near_field"),
|
||||
# tools=tools,
|
||||
instructions="""You are a helpful and friendly AI.
|
||||
|
||||
Act like a human, but remember that you aren't a human and that you can't do human
|
||||
things in the real world. Your voice and personality should be warm and engaging, with a lively and
|
||||
playful tone.
|
||||
|
||||
If interacting in a non-English language, start by using the standard accent or dialect familiar to
|
||||
the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
|
||||
even if you're asked about them.
|
||||
|
||||
You are participating in a voice conversation. Keep your responses concise, short, and to the point
|
||||
unless specifically asked to elaborate on a topic.
|
||||
|
||||
You have access to the following tools:
|
||||
- get_current_weather: Get the current weather for a given location.
|
||||
- get_restaurant_recommendation: Get a restaurant recommendation for a given location.
|
||||
|
||||
Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
|
||||
)
|
||||
|
||||
llm = OpenAIRealtimeBetaLLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
session_properties=session_properties,
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
)
|
||||
|
||||
# you can either register a single function for all function calls, or specific functions
|
||||
# llm.register_function(None, fetch_weather_from_api)
|
||||
llm.register_function("get_current_weather", fetch_weather_from_api)
|
||||
llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
|
||||
|
||||
# Create a standard OpenAI LLM context object using the normal messages format. The
|
||||
# OpenAIRealtimeBetaLLMService will convert this internally to messages that the
|
||||
# openai WebSocket API can understand.
|
||||
context = OpenAILLMContext(
|
||||
[{"role": "developer", "content": "Say hello!"}],
|
||||
tools,
|
||||
)
|
||||
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
context_aggregator.user(),
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,267 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import glob
|
||||
import json
|
||||
import os
|
||||
from datetime import datetime
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.openai_llm_context import (
|
||||
OpenAILLMContext,
|
||||
)
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.openai_realtime_beta import (
|
||||
InputAudioTranscription,
|
||||
OpenAIRealtimeBetaLLMService,
|
||||
SessionProperties,
|
||||
TurnDetection,
|
||||
)
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
BASE_FILENAME = "/tmp/pipecat_conversation_"
|
||||
|
||||
|
||||
async def fetch_weather_from_api(params: FunctionCallParams):
|
||||
temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
|
||||
await params.result_callback(
|
||||
{
|
||||
"conditions": "nice",
|
||||
"temperature": temperature,
|
||||
"format": params.arguments["format"],
|
||||
"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
async def get_saved_conversation_filenames(params: FunctionCallParams):
|
||||
# Construct the full pattern including the BASE_FILENAME
|
||||
full_pattern = f"{BASE_FILENAME}*.json"
|
||||
|
||||
# Use glob to find all matching files
|
||||
matching_files = glob.glob(full_pattern)
|
||||
logger.debug(f"matching files: {matching_files}")
|
||||
|
||||
await params.result_callback({"filenames": matching_files})
|
||||
|
||||
|
||||
async def save_conversation(params: FunctionCallParams):
|
||||
timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
|
||||
filename = f"{BASE_FILENAME}{timestamp}.json"
|
||||
logger.debug(
|
||||
f"writing conversation to {filename}\n{json.dumps(params.context.messages, indent=4)}"
|
||||
)
|
||||
try:
|
||||
with open(filename, "w") as file:
|
||||
messages = params.context.get_messages_for_persistent_storage()
|
||||
# remove the last message, which is the instruction we just gave to save the conversation
|
||||
messages.pop()
|
||||
json.dump(messages, file, indent=2)
|
||||
await params.result_callback({"success": True})
|
||||
except Exception as e:
|
||||
await params.result_callback({"success": False, "error": str(e)})
|
||||
|
||||
|
||||
async def load_conversation(params: FunctionCallParams):
|
||||
async def _reset():
|
||||
filename = params.arguments["filename"]
|
||||
logger.debug(f"loading conversation from {filename}")
|
||||
try:
|
||||
with open(filename, "r") as file:
|
||||
params.context.set_messages(json.load(file))
|
||||
await params.llm.reset_conversation()
|
||||
await params.llm._create_response()
|
||||
except Exception as e:
|
||||
await params.result_callback({"success": False, "error": str(e)})
|
||||
|
||||
asyncio.create_task(_reset())
|
||||
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"name": "get_current_weather",
|
||||
"description": "Get the current weather",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit to use. Infer this from the users location.",
|
||||
},
|
||||
},
|
||||
"required": ["location", "format"],
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"name": "save_conversation",
|
||||
"description": "Save the current conversation. Use this function to persist the current conversation to external storage.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {},
|
||||
"required": [],
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"name": "get_saved_conversation_filenames",
|
||||
"description": "Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {},
|
||||
"required": [],
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"name": "load_conversation",
|
||||
"description": "Load a conversation history. Use this function to load a conversation history into the current session.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"filename": {
|
||||
"type": "string",
|
||||
"description": "The filename of the conversation history to load.",
|
||||
}
|
||||
},
|
||||
"required": ["filename"],
|
||||
},
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
# We use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
session_properties = SessionProperties(
|
||||
input_audio_transcription=InputAudioTranscription(),
|
||||
# Set openai TurnDetection parameters. Not setting this at all will turn
|
||||
# it on by default
|
||||
turn_detection=TurnDetection(silence_duration_ms=1000),
|
||||
# Or set to False to disable openai turn detection and use transport VAD
|
||||
# turn_detection=False,
|
||||
# tools=tools,
|
||||
instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.
|
||||
|
||||
Act like a human, but remember that you aren't a human and that you can't do human
|
||||
things in the real world. Your voice and personality should be warm and engaging, with a lively and
|
||||
playful tone.
|
||||
|
||||
If interacting in a non-English language, start by using the standard accent or dialect familiar to
|
||||
the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
|
||||
even if you're asked about them.
|
||||
-
|
||||
You are participating in a voice conversation. Keep your responses concise, short, and to the point
|
||||
unless specifically asked to elaborate on a topic.
|
||||
|
||||
Remember, your responses should be short. Just one or two sentences, usually.""",
|
||||
)
|
||||
|
||||
llm = OpenAIRealtimeBetaLLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
session_properties=session_properties,
|
||||
)
|
||||
|
||||
# you can either register a single function for all function calls, or specific functions
|
||||
# llm.register_function(None, fetch_weather_from_api)
|
||||
llm.register_function("get_current_weather", fetch_weather_from_api)
|
||||
llm.register_function("save_conversation", save_conversation)
|
||||
llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
|
||||
llm.register_function("load_conversation", load_conversation)
|
||||
|
||||
context = OpenAILLMContext([], tools)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
context_aggregator.user(),
|
||||
llm, # LLM
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
Reference in New Issue
Block a user