242 lines
8.1 KiB
Python
242 lines
8.1 KiB
Python
#
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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 asyncio
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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 (
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LLMRunFrame,
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TTSSpeakFrame,
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)
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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_context import LLMContext
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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.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.llm import OpenAILLMService
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from pipecat.services.openai.stt import OpenAISTTService
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from pipecat.services.openai.tts import OpenAITTSService
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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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# Simulate a long-running API call, so we can test async function calls.
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await asyncio.sleep(20)
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await params.result_callback({"conditions": "nice", "temperature": "75"})
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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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# 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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system_prompt = """
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You are a helpful assistant in a voice conversation.
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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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You can call tools asynchronously. Tool calls follow a lifecycle and may complete later, out of order, or after the user has changed their request.
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You must follow these rules strictly:
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1. Tool Call Lifecycle
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- When you call a tool, a message with role "tool" and content {"type": "async_tool", "status": "started"} will appear.
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- The tool result will arrive later as a message with role "developer" and content:
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{
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"type": "async_tool",
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"tool_call_id": "...",
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"status": "finished",
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"result": "..."
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}
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- Do NOT assume the result until you see "status": "finished".
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2. Handling Multiple Tool Calls
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- You may issue multiple tool calls in parallel.
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- Results may arrive in any order.
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- You must track tool_call_id to match results correctly.
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3. Incremental Updates
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- If results are still pending, you may inform the user that you're waiting.
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- Do NOT hallucinate missing tool results.
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- Do NOT invoke asking for the same information that has already been requested and you are waiting for the result.
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4. Changing User Intent
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- The user may change their request while tools are still running.
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- If a request is no longer relevant, ignore its tool results when they arrive.
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- Do NOT present outdated or cancelled information.
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5. Responding to Results
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- When tool results arrive:
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- Extract and summarize the relevant information.
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- Combine results if multiple tool calls are related.
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- Provide a clear and helpful answer.
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6. Tool Usage Guidelines
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- Always call tools when required instead of guessing.
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- Ensure arguments are correct and reflect the latest user request.
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7. Example Behavior
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- If the user asks for multiple locations, call tools for each.
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- If the user changes from "Florida" to "California", ignore Florida's result if it arrives later.
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Your goal is to behave like a reliable orchestrator of asynchronous tool calls while providing clear, accurate, and up-to-date responses to the user.
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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 = OpenAISTTService(
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api_key=os.getenv("OPENAI_API_KEY"),
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settings=OpenAISTTService.Settings(
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model="gpt-4o-transcribe",
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prompt="Expect words related weather, such as temperature and conditions. And restaurant names.",
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),
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)
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tts = OpenAITTSService(
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api_key=os.getenv("OPENAI_API_KEY"),
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settings=OpenAITTSService.Settings(
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voice="ballad",
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),
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instructions="Please speak clearly and at a moderate pace.",
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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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settings=OpenAILLMService.Settings(
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system_instruction=system_prompt,
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),
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)
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# You can also 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(
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"get_current_weather",
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fetch_weather_from_api,
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cancel_on_interruption=False,
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is_async=True,
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timeout_secs=30,
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)
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llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
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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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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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tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
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context = LLMContext(tools=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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)
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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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