Creating a new example for async stream using Anthropic.

examples/function-calling/function-calling-anthropic-async-stream.py

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+#
+# 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()