From 2df231143a9ae9053afa24a98c190fedcab71f66 Mon Sep 17 00:00:00 2001 From: Paul Kompfner Date: Wed, 3 Sep 2025 09:31:49 -0400 Subject: [PATCH] Add foundational example using Anthropic with universal `LLMContext` --- ...ion-calling-anthropic-universal-context.py | 165 ++++++++++++++++++ 1 file changed, 165 insertions(+) create mode 100644 examples/foundational/14z-function-calling-anthropic-universal-context.py diff --git a/examples/foundational/14z-function-calling-anthropic-universal-context.py b/examples/foundational/14z-function-calling-anthropic-universal-context.py new file mode 100644 index 000000000..b11d9e268 --- /dev/null +++ b/examples/foundational/14z-function-calling-anthropic-universal-context.py @@ -0,0 +1,165 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + + +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 LLMRunFrame +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 +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.network.fastapi_websocket import FastAPIWebsocketParams +from pipecat.transports.services.daily import DailyParams + +load_dotenv(override=True) + + +async def get_weather(params: FunctionCallParams): + location = params.arguments["location"] + await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.") + + +async def fetch_restaurant_recommendation(params: FunctionCallParams): + await params.result_callback({"name": "The Golden Dragon"}) + + +# We store functions so objects (e.g. SileroVADAnalyzer) don't get +# instantiated. The function will be called when the desired transport gets +# selected. +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")) + + tts = CartesiaTTSService( + api_key=os.getenv("CARTESIA_API_KEY"), + voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady + ) + + llm = AnthropicLLMService( + api_key=os.getenv("ANTHROPIC_API_KEY"), + model="claude-3-7-sonnet-latest", + ) + llm.register_function("get_weather", get_weather) + llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation) + + weather_function = FunctionSchema( + name="get_weather", + description="Get the current weather", + properties={ + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + }, + required=["location"], + ) + 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"], + ) + tools = ToolsSchema(standard_tools=[weather_function, restaurant_function]) + + # todo: test with very short initial user message + + # messages = [{"role": "system", + # "content": "You are a helpful assistant who can report the weather in any location in the universe. Respond concisely. Your response will be turned into speech so use only simple words and punctuation."}, + # {"role": "user", + # "content": " Start the conversation by introducing yourself."}] + + messages = [{"role": "user", "content": "Say 'hello' to start the conversation."}] + + context = LLMContext(messages, tools) + context_aggregator = LLMContextAggregatorPair(context) + + pipeline = Pipeline( + [ + transport.input(), # Transport user input + stt, + context_aggregator.user(), # User spoken responses + llm, # LLM + tts, # TTS + transport.output(), # Transport bot output + context_aggregator.assistant(), # Assistant spoken responses and tool context + ] + ) + + 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()