From 124863175ac693aa7e9af4a2b5ba3c4ade13b2af Mon Sep 17 00:00:00 2001 From: Paul Kompfner Date: Mon, 27 Apr 2026 11:14:50 -0400 Subject: [PATCH] Add example demonstrating usage of `tool_resources` --- .../function-calling-tool-resources.py | 260 ++++++++++++++++++ 1 file changed, 260 insertions(+) create mode 100644 examples/function-calling/function-calling-tool-resources.py diff --git a/examples/function-calling/function-calling-tool-resources.py b/examples/function-calling/function-calling-tool-resources.py new file mode 100644 index 000000000..ac1a8df8e --- /dev/null +++ b/examples/function-calling/function-calling-tool-resources.py @@ -0,0 +1,260 @@ +# +# Copyright (c) 2024-2026, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +"""Example demonstrating ``PipelineTask(tool_resources=...)``. + +``tool_resources`` is an application-defined bag of anything you want every +tool handler in a session to share by reference: database handles, HTTP +clients, feature flags, per-user state, observability clients, in-memory +caches — whatever fits your app. Pipecat passes it through untouched as +``FunctionCallParams.tool_resources``. + +This example uses a small ``ToolCallLogger`` as a stand-in for that "shared +thing". A real app might just as easily pass a Postgres pool, a Redis +client, a Stripe SDK instance, or any combination thereof. The mechanics +shown here — construct once, hand to the task, read it from each handler, +inspect it after the session — are the same regardless of what you put in. + +We bundle resources in a typed ``SessionResources`` dataclass and cast back +to it at the top of each handler. Pipecat doesn't care what type you pass +(a plain dict works too), but a typed container gives you autocomplete and +refactor safety instead of dict-by-string-key lookups. +""" + +import json +import os +from collections.abc import Mapping +from dataclasses import dataclass +from datetime import UTC, datetime, timezone +from typing import Any, cast + +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, 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.cartesia.tts import CartesiaTTSService +from pipecat.services.deepgram.stt import DeepgramSTTService +from pipecat.services.llm_service import FunctionCallParams +from pipecat.services.openai.responses.llm import OpenAIResponsesLLMService +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) + + +class ToolCallLogger: + """Stand-in shared resource — swap for whatever your app actually needs.""" + + def __init__(self): + """Initialize the logger with an empty list of recorded calls.""" + self._calls: list[dict[str, Any]] = [] + + def log_tool_call(self, function_name: str, arguments: Mapping[str, Any]) -> None: + """Record a tool call invocation. + + Args: + function_name: The name of the tool being invoked. + arguments: The arguments passed to the tool. + """ + entry = { + "timestamp": datetime.now(UTC).isoformat(), + "function_name": function_name, + "arguments": dict(arguments), + } + self._calls.append(entry) + logger.info(f"[ToolCallLogger] {function_name} called with {dict(arguments)}") + + def dump(self) -> str: + """Return all recorded tool calls as a JSON string.""" + return json.dumps(self._calls, indent=2) + + +@dataclass +class SessionResources: + """Typed container for everything the tool handlers in this session share. + + Add fields here as the app grows (e.g. ``db: AsyncConnection``, + ``http: httpx.AsyncClient``). Handlers ``cast()`` ``params.tool_resources`` + to this type to get autocomplete and refactor safety. + """ + + tool_call_logger: ToolCallLogger + + +async def fetch_weather_from_api(params: FunctionCallParams): + resources = cast(SessionResources, params.tool_resources) + resources.tool_call_logger.log_tool_call(params.function_name, params.arguments) + await params.result_callback({"conditions": "nice", "temperature": "75"}) + + +async def fetch_restaurant_recommendation(params: FunctionCallParams): + resources = cast(SessionResources, params.tool_resources) + resources.tool_call_logger.log_tool_call(params.function_name, params.arguments) + await params.result_callback({"name": "The Golden Dragon"}) + + +# 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.environ["DEEPGRAM_API_KEY"]) + + tts = CartesiaTTSService( + api_key=os.environ["CARTESIA_API_KEY"], + settings=CartesiaTTSService.Settings( + voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady + ), + ) + + llm = OpenAIResponsesLLMService( + api_key=os.environ["OPENAI_API_KEY"], + settings=OpenAIResponsesLLMService.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. Respond to what the user said in a creative, helpful, and brief way.", + ), + ) + + # You can also register a function_name of None to get all functions + # sent to the same callback with an additional function_name parameter. + llm.register_function("get_current_weather", fetch_weather_from_api) + llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation) + + @llm.event_handler("on_connection_error") + async def on_connection_error(service, error): + logger.error(f"LLM connection error: {error}") + + @llm.event_handler("on_function_calls_started") + async def on_function_calls_started(service, function_calls): + # Avoid appending this filler message to the LLM context — it would + # alter the conversation history and prevent + # OpenAIResponsesLLMService's previous_response_id optimization from + # matching, forcing a full context resend. + await tts.queue_frame(TTSSpeakFrame("Let me check on that.", append_to_context=False)) + + 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 user's 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"], + ) + tools = ToolsSchema(standard_tools=[weather_function, restaurant_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, + ] + ) + + # Keep a local handle so we can read collected state after the session + # ends; Pipecat never copies or clears the object. + tool_call_logger = ToolCallLogger() + resources = SessionResources(tool_call_logger=tool_call_logger) + + task = PipelineTask( + pipeline, + params=PipelineParams( + enable_metrics=True, + enable_usage_metrics=True, + ), + idle_timeout_secs=runner_args.pipeline_idle_timeout_secs, + tool_resources=resources, + ) + + @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) + + # The session has ended; read whatever state the handlers built up. + logger.info(f"Tool calls logged during session:\n{tool_call_logger.dump()}") + + +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()