Merge pull request #4474 from pipecat-ai/pk/inworld-realtime-tools

Extend cancel_on_interruption=False to Inworld Realtime (best-effort + warning)

examples/realtime/realtime-inworld.py

@@ -28,10 +28,14 @@ Usage:
 """
 
 import os
+import random
+from datetime import datetime
 
 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.frames.frames import LLMRunFrame
 from pipecat.observers.loggers.transcription_log_observer import (
     TranscriptionLogObserver,
@@ -48,6 +52,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.inworld.realtime.llm import InworldRealtimeLLMService
+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
@@ -55,6 +60,43 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
 load_dotenv(override=True)
 
 
+async def fetch_weather_from_api(params: FunctionCallParams):
+    temperature = (
+        random.randint(60, 85)
+        if params.arguments["format"] == "fahrenheit"
+        else random.randint(15, 30)
+    )
+    await params.result_callback(
+        {
+            "conditions": "nice",
+            "temperature": temperature,
+            "location": params.arguments["location"],
+            "format": params.arguments["format"],
+            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
+        }
+    )
+
+
+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 users location.",
+        },
+    },
+    required=["location", "format"],
+)
+
+tools = ToolsSchema(standard_tools=[weather_function])
+
+
 # --- Transport Configuration ---
 
 # No local VAD needed — Inworld's server-side semantic VAD handles turn detection.
@@ -85,7 +127,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # See: https://docs.inworld.ai/router/introduction
     llm = InworldRealtimeLLMService(
         api_key=os.environ["INWORLD_API_KEY"],
-        llm_model="xai/grok-4-1-fast-non-reasoning",
+        llm_model="openai/gpt-4.1-mini",
         voice="Sarah",
         settings=InworldRealtimeLLMService.Settings(
             system_instruction="""You are a helpful and friendly AI assistant powered by Inworld.
@@ -97,9 +139,14 @@ Always be helpful and proactive in offering assistance.""",
         ),
     )
 
-    # Create context with initial message
+    # Note: function calling requires a paid Inworld account and a
+    # function-calling-capable model
+    llm.register_function("get_current_weather", fetch_weather_from_api)
+
+    # Create context with initial message + tools
     context = LLMContext(
         [{"role": "developer", "content": "Say hello and introduce yourself!"}],
+        tools,
     )
 
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(context)