Created examples for unified format function calling.

2025-03-05 14:12:30 -03:00
parent a840b0e815
commit 2a75373c04
17 changed files with 744 additions and 0 deletions
--- a/examples/unified-format-function-calling/multimodal_base_function_calling.py
+++ b/examples/unified-format-function-calling/multimodal_base_function_calling.py
@@ -0,0 +1,126 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import sys
+from typing import List
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+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 TTSSpeakFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.ai_services import LLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+load_dotenv(override=True)
+
+
+async def start_fetch_weather(function_name, llm, context):
+    """Push a frame to the LLM; this is handy when the LLM response might take a while."""
+    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+    await result_callback({"conditions": "nice", "temperature": "75"})
+
+
+class MultimodalWeatherBot:
+    """Generic base class for setting up and running an LLM-powered bot."""
+
+    def __init__(self, llm: LLMService):
+        """Initialize the base handler with a specific LLM."""
+        self.llm = llm
+
+    @staticmethod
+    def tools() -> ToolsSchema:
+        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"],
+        )
+        return ToolsSchema(standard_tools=[weather_function])
+
+    async def run(self):
+        """Set up and start the processing pipeline."""
+        async with aiohttp.ClientSession() as session:
+            (room_url, token) = await configure(session)
+
+            transport = DailyTransport(
+                room_url,
+                token,
+                "Respond bot",
+                DailyParams(
+                    audio_out_enabled=True,
+                    vad_enabled=True,
+                    vad_analyzer=SileroVADAnalyzer(),
+                    vad_audio_passthrough=True,
+                ),
+            )
+
+            # Register a function_name of None to get all functions
+            # sent to the same callback with an additional function_name parameter.
+            self.llm.register_function(
+                None, fetch_weather_from_api, start_callback=start_fetch_weather
+            )
+
+            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."},
+            ]
+
+            context = OpenAILLMContext(messages, MultimodalWeatherBot.tools())
+            context_aggregator = self.llm.create_context_aggregator(context)
+
+            pipeline = Pipeline(
+                [
+                    transport.input(),
+                    context_aggregator.user(),
+                    self.llm,
+                    transport.output(),
+                    context_aggregator.assistant(),
+                ]
+            )
+
+            task = PipelineTask(
+                pipeline,
+                params=PipelineParams(
+                    allow_interruptions=True,
+                ),
+            )
+
+            @transport.event_handler("on_first_participant_joined")
+            async def on_first_participant_joined(transport, participant):
+                await transport.capture_participant_transcription(participant["id"])
+                await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+            runner = PipelineRunner()
+            await runner.run(task)