diff --git a/examples/function-calling/function-calling-anthropic-async-stream.py b/examples/function-calling/function-calling-anthropic-async-stream.py new file mode 100644 index 000000000..953855da0 --- /dev/null +++ b/examples/function-calling/function-calling-anthropic-async-stream.py @@ -0,0 +1,208 @@ +# +# Copyright (c) 2024-2026, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +"""Example: async function call with intermediate updates. + +The ``track_current_location`` tool simulates a GPS tracker reporting the +device's position during a road trip from San Francisco to San Diego. It +sends two intermediate updates (via ``params.result_callback`` with +``is_final=False``) as the vehicle passes through cities along the way, then +delivers the final destination (via ``params.result_callback``). Each update +returns the same structure with a different city: + + Update 1 – {gps, city: "San Francisco"} ← trip start + Update 2 – {gps, city: "Los Angeles"} ← passing through + Final – {gps, city: "San Diego"} ← destination reached + +Because the function is registered with ``cancel_on_interruption=False``, the +LLM can keep talking while the trip is in progress; each position update +arrives as a developer message so the LLM can narrate the journey to the user. +""" + +import asyncio +import os + +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 ( + FunctionCallResultProperties, + LLMRunFrame, + TTSSpeakFrame, +) +from pipecat.pipeline.pipeline import Pipeline +from pipecat.pipeline.runner import PipelineRunner +from pipecat.pipeline.task import PipelineParams, PipelineTask +from pipecat.processors.aggregators.llm_context import LLMContext +from pipecat.processors.aggregators.llm_response_universal import ( + LLMContextAggregatorPair, + LLMUserAggregatorParams, +) +from pipecat.runner.types import RunnerArguments +from pipecat.runner.utils import create_transport +from pipecat.services.anthropic.llm import AnthropicLLMService +from pipecat.services.cartesia.tts import CartesiaTTSService +from pipecat.services.deepgram.stt import DeepgramSTTService +from pipecat.services.llm_service import FunctionCallParams +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 track_current_location(params: FunctionCallParams): + """Simulate a GPS tracker reporting position during a road trip. + + Step 1 – San Francisco (trip start) (update) + Step 2 – Los Angeles (passing through) (update) + Step 3 – San Diego (destination) (final result) + """ + + # First update: initial city estimate. + gps = {"lat": 37.7310, "lng": -122.4527} + await params.result_callback( + {"gps": gps, "city": "San Francisco"}, + properties=FunctionCallResultProperties(is_final=False), + ) + + # Second update: revised city estimate. + await asyncio.sleep(10) + gps = {"lat": 33.96003, "lng": -118.40639} + await params.result_callback( + {"gps": gps, "city": "Los Angeles"}, + properties=FunctionCallResultProperties(is_final=False), + ) + + # Final result: confirmed city. + await asyncio.sleep(10) + gps = {"lat": 32.743569, "lng": -117.20466} + await params.result_callback({"gps": gps, "city": "San Diego"}) + + +# 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, + ), + "twilio": lambda: FastAPIWebsocketParams( + audio_in_enabled=True, + audio_out_enabled=True, + ), + "webrtc": lambda: TransportParams( + audio_in_enabled=True, + audio_out_enabled=True, + ), +} + + +async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): + logger.info(f"Starting bot") + + stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY")) + + tts = CartesiaTTSService( + api_key=os.getenv("CARTESIA_API_KEY"), + settings=CartesiaTTSService.Settings( + voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady + ), + ) + + llm = AnthropicLLMService( + api_key=os.getenv("ANTHROPIC_API_KEY"), + settings=AnthropicLLMService.Settings( + 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. " + "You have access to a function that starts tracking the user's location and " + "provides regular updates on it. When you receive the final location, tell the user " + "the destination has been reached." + ), + ), + ) + + # cancel_on_interruption=False makes this an async function call: the LLM + # continues the conversation immediately and receives updates/result later. + llm.register_function( + "track_current_location", + track_current_location, + cancel_on_interruption=False, + timeout_secs=30, + ) + + @llm.event_handler("on_function_calls_started") + async def on_function_calls_started(service, function_calls): + await tts.queue_frame(TTSSpeakFrame("Sure, tracking your location now.")) + + location_function = FunctionSchema( + name="track_current_location", + description="Start tracking the user's current GPS location, reporting position updates until the user reaches their destination.", + properties={}, + required=[], + ) + tools = ToolsSchema(standard_tools=[location_function]) + + context = LLMContext(tools=tools) + user_aggregator, assistant_aggregator = LLMContextAggregatorPair( + context, + user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()), + ) + + pipeline = Pipeline( + [ + transport.input(), + stt, + user_aggregator, + llm, + tts, + transport.output(), + assistant_aggregator, + ] + ) + + task = PipelineTask( + pipeline, + params=PipelineParams( + enable_metrics=True, + enable_usage_metrics=True, + ), + ) + + @transport.event_handler("on_client_connected") + async def on_client_connected(transport, client): + logger.info(f"Client connected") + # Kick off the conversation. + context.add_message( + {"role": "developer", "content": "Please introduce yourself to the user."} + ) + 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()