examples: update with single FunctionCallParams parameter

examples/foundational/14-function-calling.py

@@ -20,6 +20,7 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 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.llm import OpenAILLMService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
@@ -28,9 +29,9 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/14a-function-calling-anthropic.py

@@ -20,6 +20,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 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 TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -27,9 +28,9 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
+async def get_weather(params: FunctionCallParams):
-    location = arguments["location"]
+    location = params.arguments["location"]
-    await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
+    await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/14b-function-calling-anthropic-video.py

@@ -21,6 +21,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 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 TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -29,23 +30,23 @@ load_dotenv(override=True)
 
 
 # Global variable to store the peer connection ID
-webrtc_peer_id = None
+webrtc_peer_id = ""
 
 
-async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
+async def get_weather(params: FunctionCallParams):
-    location = arguments["location"]
+    location = params.arguments["location"]
-    await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
+    await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
 
 
-async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
+async def get_image(params: FunctionCallParams):
-    question = arguments["question"]
+    question = params.arguments["question"]
     logger.debug(f"Requesting image with user_id={webrtc_peer_id}, question={question}")
 
     # Request the image frame
-    await llm.request_image_frame(
+    await params.llm.request_image_frame(
         user_id=webrtc_peer_id,
-        function_name=function_name,
+        function_name=params.function_name,
-        tool_call_id=tool_call_id,
+        tool_call_id=params.tool_call_id,
         text_content=question,
     )
 
@@ -53,7 +54,7 @@ async def get_image(function_name, tool_call_id, arguments, llm, context, result
     await asyncio.sleep(0.5)
 
     # Return a result to complete the function call
-    await result_callback(
+    await params.result_callback(
         f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
     )
 

examples/foundational/14c-function-calling-together.py

@@ -20,6 +20,7 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.services.together.llm import TogetherLLMService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
@@ -28,9 +29,9 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/14d-function-calling-video.py

@@ -20,6 +20,7 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 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.llm import OpenAILLMService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
@@ -29,23 +30,23 @@ load_dotenv(override=True)
 
 
 # Global variable to store the peer connection ID
-webrtc_peer_id = None
+webrtc_peer_id = ""
 
 
-async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
+async def get_weather(params: FunctionCallParams):
-    location = arguments["location"]
+    location = params.arguments["location"]
-    await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
+    await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
 
 
-async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
+async def get_image(params: FunctionCallParams):
-    question = arguments["question"]
+    question = params.arguments["question"]
     logger.debug(f"Requesting image with user_id={webrtc_peer_id}, question={question}")
 
     # Request the image frame
-    await llm.request_image_frame(
+    await params.llm.request_image_frame(
         user_id=webrtc_peer_id,
-        function_name=function_name,
+        function_name=params.function_name,
-        tool_call_id=tool_call_id,
+        tool_call_id=params.tool_call_id,
         text_content=question,
     )
 
@@ -53,7 +54,7 @@ async def get_image(function_name, tool_call_id, arguments, llm, context, result
     await asyncio.sleep(0.5)
 
     # Return a result to complete the function call
-    await result_callback(
+    await params.result_callback(
         f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
     )
 

examples/foundational/14e-function-calling-gemini.py

@@ -22,6 +22,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -30,24 +31,24 @@ load_dotenv(override=True)
 
 
 # Global variable to store the peer connection ID
-webrtc_peer_id = None
+webrtc_peer_id = ""
 
 
-async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
+async def get_weather(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    location = arguments["location"]
+    location = params.arguments["location"]
-    await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
+    await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
 
 
-async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
+async def get_image(params: FunctionCallParams):
-    question = arguments["question"]
+    question = params.arguments["question"]
     logger.debug(f"Requesting image with user_id={webrtc_peer_id}, question={question}")
 
     # Request the image frame
-    await llm.request_image_frame(
+    await params.llm.request_image_frame(
         user_id=webrtc_peer_id,
-        function_name=function_name,
+        function_name=params.function_name,
-        tool_call_id=tool_call_id,
+        tool_call_id=params.tool_call_id,
         text_content=question,
     )
 
@@ -55,7 +56,7 @@ async def get_image(function_name, tool_call_id, arguments, llm, context, result
     await asyncio.sleep(0.5)
 
     # Return a result to complete the function call
-    await result_callback(
+    await params.result_callback(
         f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
     )
 

examples/foundational/14f-function-calling-groq.py

@@ -21,6 +21,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.groq.llm import GroqLLMService
 from pipecat.services.groq.stt import GroqSTTService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -28,9 +29,9 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/14g-function-calling-grok.py

@@ -20,6 +20,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.grok.llm import GrokLLMService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -27,8 +28,8 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/14h-function-calling-azure.py

@@ -21,6 +21,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.azure.llm import AzureLLMService
 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 TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -28,9 +29,9 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/14i-function-calling-fireworks.py

@@ -21,6 +21,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.fireworks.llm import FireworksLLMService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -28,9 +29,9 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/14j-function-calling-nim.py

@@ -20,6 +20,7 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.services.nim.llm import NimLLMService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
@@ -28,9 +29,9 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/14k-function-calling-cerebras.py

@@ -21,6 +21,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.cerebras.llm import CerebrasLLMService
 from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -28,9 +29,9 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/14l-function-calling-deepseek.py

@@ -21,6 +21,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.deepseek.llm import DeepSeekLLMService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -28,9 +29,9 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/14m-function-calling-openrouter.py

@@ -20,6 +20,7 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.azure.tts import AzureTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.services.openrouter.llm import OpenRouterLLMService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
@@ -28,9 +29,9 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/14o-function-calling-gemini-openai-format.py

@@ -21,6 +21,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
 from pipecat.services.google.llm_openai import GoogleLLMOpenAIBetaService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -28,9 +29,9 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/14p-function-calling-gemini-vertex-ai.py

@@ -21,6 +21,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
 from pipecat.services.google.llm_vertex import GoogleVertexLLMService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -28,9 +29,9 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/14q-function-calling-qwen.py

@@ -20,6 +20,7 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.services.qwen.llm import QwenLLMService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
@@ -28,9 +29,9 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/15-switch-voices.py

@@ -20,6 +20,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.filters.function_filter import FunctionFilter
 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.llm import OpenAILLMService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
@@ -31,10 +32,10 @@ load_dotenv(override=True)
 current_voice = "News Lady"
 
 
-async def switch_voice(function_name, tool_call_id, args, llm, context, result_callback):
+async def switch_voice(params: FunctionCallParams):
     global current_voice
-    current_voice = args["voice"]
+    current_voice = params.arguments["voice"]
-    await result_callback(
+    await params.result_callback(
         {
             "voice": f"You are now using your {current_voice} voice. Your responses should now be as if you were a {current_voice}."
         }

examples/foundational/15a-switch-languages.py

@@ -21,6 +21,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.filters.function_filter import FunctionFilter
 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.llm import OpenAILLMService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
@@ -32,10 +33,12 @@ load_dotenv(override=True)
 current_language = "English"
 
 
-async def switch_language(function_name, tool_call_id, args, llm, context, result_callback):
+async def switch_language(params: FunctionCallParams):
     global current_language
-    current_language = args["language"]
+    current_language = params.arguments["language"]
-    await result_callback({"voice": f"Your answers from now on should be in {current_language}."})
+    await params.result_callback(
+        {"voice": f"Your answers from now on should be in {current_language}."}
+    )
 
 
 async def english_filter(frame) -> bool:

examples/foundational/19-openai-realtime-beta.py

@@ -19,6 +19,7 @@ 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.llm_service import FunctionCallParams
 from pipecat.services.openai_realtime_beta import (
     InputAudioNoiseReduction,
     InputAudioTranscription,
@@ -33,13 +34,13 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    temperature = 75 if args["format"] == "fahrenheit" else 24
+    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
-    await result_callback(
+    await params.result_callback(
         {
             "conditions": "nice",
             "temperature": temperature,
-            "format": args["format"],
+            "format": params.arguments["format"],
             "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
         }
     )

examples/foundational/19a-azure-realtime-beta.py

@@ -19,6 +19,7 @@ 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.llm_service import FunctionCallParams
 from pipecat.services.openai_realtime_beta import (
     AzureRealtimeBetaLLMService,
     InputAudioTranscription,
@@ -31,13 +32,13 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    temperature = 75 if args["format"] == "fahrenheit" else 24
+    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
-    await result_callback(
+    await params.result_callback(
         {
             "conditions": "nice",
             "temperature": temperature,
-            "format": args["format"],
+            "format": params.arguments["format"],
             "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
         }
     )

examples/foundational/20a-persistent-context-openai.py

@@ -23,6 +23,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
 )
 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.llm import OpenAILLMService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
@@ -35,21 +36,19 @@ BASE_FILENAME = "/tmp/pipecat_conversation_"
 tts = None
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    temperature = 75 if args["format"] == "fahrenheit" else 24
+    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
-    await result_callback(
+    await params.result_callback(
         {
             "conditions": "nice",
             "temperature": temperature,
-            "format": args["format"],
+            "format": params.arguments["format"],
             "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
         }
     )
 
 
-async def get_saved_conversation_filenames(
+async def get_saved_conversation_filenames(params: FunctionCallParams):
-    function_name, tool_call_id, args, llm, context, result_callback
-):
     # Construct the full pattern including the BASE_FILENAME
     full_pattern = f"{BASE_FILENAME}*.json"
 
@@ -57,37 +56,39 @@ async def get_saved_conversation_filenames(
     matching_files = glob.glob(full_pattern)
     logger.debug(f"matching files: {matching_files}")
 
-    await result_callback({"filenames": matching_files})
+    await params.result_callback({"filenames": matching_files})
 
 
-async def save_conversation(function_name, tool_call_id, args, llm, context, result_callback):
+async def save_conversation(params: FunctionCallParams):
     timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
     filename = f"{BASE_FILENAME}{timestamp}.json"
-    logger.debug(f"writing conversation to {filename}\n{json.dumps(context.messages, indent=4)}")
+    logger.debug(
+        f"writing conversation to {filename}\n{json.dumps(params.context.messages, indent=4)}"
+    )
     try:
         with open(filename, "w") as file:
-            messages = context.get_messages_for_persistent_storage()
+            messages = params.context.get_messages_for_persistent_storage()
             # remove the last message, which is the instruction we just gave to save the conversation
             messages.pop()
             json.dump(messages, file, indent=2)
-        await result_callback({"success": True})
+        await params.result_callback({"success": True})
     except Exception as e:
-        await result_callback({"success": False, "error": str(e)})
+        await params.result_callback({"success": False, "error": str(e)})
 
 
-async def load_conversation(function_name, tool_call_id, args, llm, context, result_callback):
+async def load_conversation(params: FunctionCallParams):
     global tts
-    filename = args["filename"]
+    filename = params.arguments["filename"]
     logger.debug(f"loading conversation from {filename}")
     try:
         with open(filename, "r") as file:
-            context.set_messages(json.load(file))
+            params.context.set_messages(json.load(file))
             logger.debug(
-                f"loaded conversation from {filename}\n{json.dumps(context.messages, indent=4)}"
+                f"loaded conversation from {filename}\n{json.dumps(params.context.messages, indent=4)}"
             )
         await tts.say("Ok, I've loaded that conversation.")
     except Exception as e:
-        await result_callback({"success": False, "error": str(e)})
+        await params.result_callback({"success": False, "error": str(e)})
 
 
 messages = [

examples/foundational/20b-persistent-context-openai-realtime.py

@@ -23,6 +23,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContext,
 )
 from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.services.openai_realtime_beta import (
     InputAudioTranscription,
     OpenAIRealtimeBetaLLMService,
@@ -38,21 +39,19 @@ load_dotenv(override=True)
 BASE_FILENAME = "/tmp/pipecat_conversation_"
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    temperature = 75 if args["format"] == "fahrenheit" else 24
+    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
-    await result_callback(
+    await params.result_callback(
         {
             "conditions": "nice",
             "temperature": temperature,
-            "format": args["format"],
+            "format": params.arguments["format"],
             "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
         }
     )
 
 
-async def get_saved_conversation_filenames(
+async def get_saved_conversation_filenames(params: FunctionCallParams):
-    function_name, tool_call_id, args, llm, context, result_callback
-):
     # Construct the full pattern including the BASE_FILENAME
     full_pattern = f"{BASE_FILENAME}*.json"
 
@@ -60,48 +59,37 @@ async def get_saved_conversation_filenames(
     matching_files = glob.glob(full_pattern)
     logger.debug(f"matching files: {matching_files}")
 
-    await result_callback({"filenames": matching_files})
+    await params.result_callback({"filenames": matching_files})
 
 
-# async def get_saved_conversation_filenames(
+async def save_conversation(params: FunctionCallParams):
-#     function_name, tool_call_id, args, llm, context, result_callback
-# ):
-#     pattern = re.compile(re.escape(BASE_FILENAME) + "\\d{8}_\\d{6}\\.json$")
-#     matching_files = []
-
-#     for filename in os.listdir("."):
-#         if pattern.match(filename):
-#             matching_files.append(filename)
-
-#     await result_callback({"filenames": matching_files})
-
-
-async def save_conversation(function_name, tool_call_id, args, llm, context, result_callback):
     timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
     filename = f"{BASE_FILENAME}{timestamp}.json"
-    logger.debug(f"writing conversation to {filename}\n{json.dumps(context.messages, indent=4)}")
+    logger.debug(
+        f"writing conversation to {filename}\n{json.dumps(params.context.messages, indent=4)}"
+    )
     try:
         with open(filename, "w") as file:
-            messages = context.get_messages_for_persistent_storage()
+            messages = params.context.get_messages_for_persistent_storage()
             # remove the last message, which is the instruction we just gave to save the conversation
             messages.pop()
             json.dump(messages, file, indent=2)
-        await result_callback({"success": True})
+        await params.result_callback({"success": True})
     except Exception as e:
-        await result_callback({"success": False, "error": str(e)})
+        await params.result_callback({"success": False, "error": str(e)})
 
 
-async def load_conversation(function_name, tool_call_id, args, llm, context, result_callback):
+async def load_conversation(params: FunctionCallParams):
     async def _reset():
-        filename = args["filename"]
+        filename = params.arguments["filename"]
         logger.debug(f"loading conversation from {filename}")
         try:
             with open(filename, "r") as file:
-                context.set_messages(json.load(file))
+                params.context.set_messages(json.load(file))
-                await llm.reset_conversation()
+                await params.llm.reset_conversation()
-                await llm._create_response()
+                await params.llm._create_response()
         except Exception as e:
-            await result_callback({"success": False, "error": str(e)})
+            await params.result_callback({"success": False, "error": str(e)})
 
     asyncio.create_task(_reset())
 

examples/foundational/20c-persistent-context-anthropic.py

@@ -24,6 +24,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
 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 TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -35,21 +36,19 @@ BASE_FILENAME = "/tmp/pipecat_conversation_"
 tts = None
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    temperature = 75 if args["format"] == "fahrenheit" else 24
+    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
-    await result_callback(
+    await params.result_callback(
         {
             "conditions": "nice",
             "temperature": temperature,
-            "format": args["format"],
+            "format": params.arguments["format"],
             "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
         }
     )
 
 
-async def get_saved_conversation_filenames(
+async def get_saved_conversation_filenames(params: FunctionCallParams):
-    function_name, tool_call_id, args, llm, context, result_callback
-):
     # Construct the full pattern including the BASE_FILENAME
     full_pattern = f"{BASE_FILENAME}*.json"
 
@@ -57,38 +56,40 @@ async def get_saved_conversation_filenames(
     matching_files = glob.glob(full_pattern)
     logger.debug(f"matching files: {matching_files}")
 
-    await result_callback({"filenames": matching_files})
+    await params.result_callback({"filenames": matching_files})
 
 
-async def save_conversation(function_name, tool_call_id, args, llm, context, result_callback):
+async def save_conversation(params: FunctionCallParams):
     timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
     filename = f"{BASE_FILENAME}{timestamp}.json"
-    logger.debug(f"writing conversation to {filename}\n{json.dumps(context.messages, indent=4)}")
+    logger.debug(
+        f"writing conversation to {filename}\n{json.dumps(params.context.messages, indent=4)}"
+    )
     try:
         with open(filename, "w") as file:
             # todo: extract 'system' into the first message in the list
-            messages = context.get_messages_for_persistent_storage()
+            messages = params.context.get_messages_for_persistent_storage()
             # remove the last message, which is the instruction we just gave to save the conversation
             messages.pop()
             json.dump(messages, file, indent=2)
-        await result_callback({"success": True})
+        await params.result_callback({"success": True})
     except Exception as e:
-        await result_callback({"success": False, "error": str(e)})
+        await params.result_callback({"success": False, "error": str(e)})
 
 
-async def load_conversation(function_name, tool_call_id, args, llm, context, result_callback):
+async def load_conversation(params: FunctionCallParams):
     global tts
-    filename = args["filename"]
+    filename = params.arguments["filename"]
     logger.debug(f"loading conversation from {filename}")
     try:
         with open(filename, "r") as file:
-            context.set_messages(json.load(file))
+            params.context.set_messages(json.load(file))
             logger.debug(
-                f"loaded conversation from {filename}\n{json.dumps(context.messages, indent=4)}"
+                f"loaded conversation from {filename}\n{json.dumps(params.context.messages, indent=4)}"
             )
         await tts.say("Ok, I've loaded that conversation.")
     except Exception as e:
-        await result_callback({"success": False, "error": str(e)})
+        await params.result_callback({"success": False, "error": str(e)})
 
 
 # Test message munging ...

examples/foundational/20d-persistent-context-gemini.py

@@ -24,6 +24,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -36,37 +37,35 @@ video_participant_id = None
 
 BASE_FILENAME = "/tmp/pipecat_conversation_"
 tts = None
-webrtc_peer_id = None
+webrtc_peer_id = ""
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    temperature = 75 if args["format"] == "fahrenheit" else 24
+    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
-    await result_callback(
+    await params.result_callback(
         {
             "conditions": "nice",
             "temperature": temperature,
-            "format": args["format"],
+            "format": params.arguments["format"],
             "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
         }
     )
 
 
-async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
+async def get_image(params: FunctionCallParams):
-    question = arguments["question"]
+    question = params.arguments["question"]
     logger.debug(f"Requesting image with user_id={webrtc_peer_id}, question={question}")
 
     # Request the image frame
-    await llm.request_image_frame(
+    await params.llm.request_image_frame(
         user_id=webrtc_peer_id,
-        function_name=function_name,
+        function_name=params.function_name,
-        tool_call_id=tool_call_id,
+        tool_call_id=params.tool_call_id,
         text_content=question,
     )
 
 
-async def get_saved_conversation_filenames(
+async def get_saved_conversation_filenames(params: FunctionCallParams):
-    function_name, tool_call_id, args, llm, context, result_callback
-):
     # Construct the full pattern including the BASE_FILENAME
     full_pattern = f"{BASE_FILENAME}*.json"
 
@@ -74,43 +73,43 @@ async def get_saved_conversation_filenames(
     matching_files = glob.glob(full_pattern)
     logger.debug(f"matching files: {matching_files}")
 
-    await result_callback({"filenames": matching_files})
+    await params.result_callback({"filenames": matching_files})
 
 
-async def save_conversation(function_name, tool_call_id, args, llm, context, result_callback):
+async def save_conversation(params: FunctionCallParams):
     timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
     filename = f"{BASE_FILENAME}{timestamp}.json"
     logger.debug(
-        f"writing conversation to {filename}\n{json.dumps(context.get_messages_for_logging(), indent=4)}"
+        f"writing conversation to {filename}\n{json.dumps(params.context.get_messages_for_logging(), indent=4)}"
     )
     try:
         with open(filename, "w") as file:
             # todo: extract 'system' into the first message in the list
-            messages = context.get_messages_for_persistent_storage()
+            messages = params.context.get_messages_for_persistent_storage()
             # remove the last message (the instruction to save the context)
             messages.pop()
             json.dump(messages, file, indent=2)
-        await result_callback({"success": True})
+        await params.result_callback({"success": True})
     except Exception as e:
         logger.debug(f"error saving conversation: {e}")
-        await result_callback({"success": False, "error": str(e)})
+        await params.result_callback({"success": False, "error": str(e)})
 
 
-async def load_conversation(function_name, tool_call_id, args, llm, context, result_callback):
+async def load_conversation(params: FunctionCallParams):
     global tts
-    filename = args["filename"]
+    filename = params.arguments["filename"]
     logger.debug(f"loading conversation from {filename}")
     try:
         with open(filename, "r") as file:
-            context.set_messages(json.load(file))
+            params.context.set_messages(json.load(file))
-        await result_callback(
+        await params.result_callback(
             {
                 "success": True,
                 "message": "The most recent conversation has been loaded. Awaiting further instructions.",
             }
         )
     except Exception as e:
-        await result_callback({"success": False, "error": str(e)})
+        await params.result_callback({"success": False, "error": str(e)})
 
 
 # Test message munging ...

examples/foundational/22b-natural-conversation-proposal.py

@@ -44,6 +44,7 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.processors.user_idle_processor import UserIdleProcessor
 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.llm import OpenAILLMService
 from pipecat.sync.base_notifier import BaseNotifier
 from pipecat.sync.event_notifier import EventNotifier
@@ -196,9 +197,9 @@ class OutputGate(FrameProcessor):
                 break
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/22c-natural-conversation-mixed-llms.py

@@ -45,6 +45,7 @@ from pipecat.processors.user_idle_processor import UserIdleProcessor
 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.services.openai.llm import OpenAILLMService
 from pipecat.sync.base_notifier import BaseNotifier
 from pipecat.sync.event_notifier import EventNotifier
@@ -400,9 +401,9 @@ class OutputGate(FrameProcessor):
                 break
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    await llm.push_frame(TTSSpeakFrame("Let me check on that."))
+    await params.llm.push_frame(TTSSpeakFrame("Let me check on that."))
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/24-stt-mute-filter.py

@@ -21,6 +21,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.filters.stt_mute_filter import STTMuteConfig, STTMuteFilter, STTMuteStrategy
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.deepgram.tts import DeepgramTTSService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
@@ -29,12 +30,12 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
     # Add a delay to test interruption during function calls
     logger.info("Weather API call starting...")
     await asyncio.sleep(5)  # 5-second delay
     logger.info("Weather API call completed")
-    await result_callback({"conditions": "nice", "temperature": "75"})
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/26b-gemini-multimodal-live-function-calling.py

@@ -20,6 +20,7 @@ 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.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -27,13 +28,13 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
-    temperature = 75 if args["format"] == "fahrenheit" else 24
+    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
-    await result_callback(
+    await params.result_callback(
         {
             "conditions": "nice",
             "temperature": temperature,
-            "format": args["format"],
+            "format": params.arguments["format"],
             "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
         }
     )

examples/foundational/33-gemini-rag.py

@@ -64,6 +64,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -107,11 +108,9 @@ Here is the knowledge base you have access to:
 genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
 
 
-async def query_knowledge_base(
+async def query_knowledge_base(params: FunctionCallParams):
-    function_name, tool_call_id, arguments, llm, context, result_callback
-):
     """Query the knowledge base for the answer to the question."""
-    logger.info(f"Querying knowledge base for question: {arguments['question']}")
+    logger.info(f"Querying knowledge base for question: {params.arguments['question']}")
     client = genai.GenerativeModel(
         model_name=RAG_MODEL,
         system_instruction=RAG_PROMPT,
@@ -122,11 +121,11 @@ async def query_knowledge_base(
     )
     # for our case, the first two messages are the instructions and the user message
     # so we remove them.
-    conversation_turns = context.messages[2:]
+    conversation_turns = params.context.messages[2:]
     # convert to standard messages
     messages = []
     for turn in conversation_turns:
-        messages.extend(context.to_standard_messages(turn))
+        messages.extend(params.context.to_standard_messages(turn))
 
     def _is_tool_call(turn):
         if turn.get("role", None) == "tool":
@@ -150,7 +149,7 @@ async def query_knowledge_base(
     end = time.perf_counter()
     logger.info(f"Time taken: {end - start:.2f} seconds")
     logger.info(response.text)
-    await result_callback(response.text)
+    await params.result_callback(response.text)
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/foundational/36-user-email-gathering.py

@@ -18,6 +18,7 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 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.llm import OpenAILLMService
 from pipecat.services.rime.tts import RimeHttpTTSService
 from pipecat.transports.base_transport import TransportParams
@@ -27,8 +28,8 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
 load_dotenv(override=True)
 
 
-async def store_user_emails(function_name, tool_call_id, args, llm, context, result_callback):
+async def store_user_emails(params: FunctionCallParams):
-    print(f"User emails: {args}")
+    print(f"User emails: {params.arguments}")
 
 
 async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):

examples/patient-intake/bot.py

@@ -23,6 +23,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFr
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.processors.logger import FrameLogger
 from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.services.openai.llm import OpenAILLMContext, OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
@@ -84,11 +85,9 @@ class IntakeProcessor:
             ]
         )
 
-    async def verify_birthday(
+    async def verify_birthday(self, params: FunctionCallParams):
-        self, function_name, tool_call_id, args, llm, context, result_callback
+        if params.arguments["birthday"] == "1983-01-01":
-    ):
+            params.context.set_tools(
-        if args["birthday"] == "1983-01-01":
-            context.set_tools(
                 [
                     {
                         "type": "function",
@@ -124,7 +123,7 @@ class IntakeProcessor:
             # await llm.push_frame(sounds["ding2.wav"], FrameDirection.DOWNSTREAM)
             # We don't need the function call in the context, so just return a new
             # system message and let the framework re-prompt
-            await result_callback(
+            await params.result_callback(
                 [
                     {
                         "role": "system",
@@ -134,7 +133,7 @@ class IntakeProcessor:
             )
         else:
             # The user provided an incorrect birthday; ask them to try again
-            await result_callback(
+            await params.result_callback(
                 [
                     {
                         "role": "system",
@@ -143,12 +142,10 @@ class IntakeProcessor:
                 ]
             )
 
-    async def list_prescriptions(
+    async def list_prescriptions(self, params: FunctionCallParams):
-        self, function_name, tool_call_id, args, llm, context, result_callback
-    ):
         print(f"!!! doing start prescriptions")
         # Move on to allergies
-        context.set_tools(
+        params.context.set_tools(
             [
                 {
                     "type": "function",
@@ -176,23 +173,23 @@ class IntakeProcessor:
                 }
             ]
         )
-        context.add_message(
+        params.context.add_message(
             {
                 "role": "system",
                 "content": "Next, ask the user if they have any allergies. Once they have listed their allergies or confirmed they don't have any, call the list_allergies function.",
             }
         )
         print(f"!!! about to await llm process frame in start prescrpitions")
-        await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
+        await params.llm.queue_frame(
+            OpenAILLMContextFrame(params.context), FrameDirection.DOWNSTREAM
+        )
         print(f"!!! past await process frame in start prescriptions")
-        await self.save_data(args, result_callback)
+        await self.save_data(params.arguments, params.result_callback)
 
-    async def list_allergies(
+    async def list_allergies(self, params: FunctionCallParams):
-        self, function_name, tool_call_id, args, llm, context, result_callback
-    ):
         print("!!! doing list allergies")
         # Move on to conditions
-        context.set_tools(
+        params.context.set_tools(
             [
                 {
                     "type": "function",
@@ -220,21 +217,21 @@ class IntakeProcessor:
                 },
             ]
         )
-        context.add_message(
+        params.context.add_message(
             {
                 "role": "system",
                 "content": "Now ask the user if they have any medical conditions the doctor should know about. Once they've answered the question, call the list_conditions function.",
             }
         )
-        await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
+        await params.llm.queue_frame(
-        await self.save_data(args, result_callback)
+            OpenAILLMContextFrame(params.context), FrameDirection.DOWNSTREAM
+        )
+        await self.save_data(params.arguments, params.result_callback)
 
-    async def list_conditions(
+    async def list_conditions(self, params: FunctionCallParams):
-        self, function_name, tool_call_id, args, llm, context, result_callback
-    ):
         print("!!! doing start conditions")
         # Move on to visit reasons
-        context.set_tools(
+        params.context.set_tools(
             [
                 {
                     "type": "function",
@@ -262,26 +259,28 @@ class IntakeProcessor:
                 }
             ]
         )
-        context.add_message(
+        params.context.add_message(
             {
                 "role": "system",
                 "content": "Finally, ask the user the reason for their doctor visit today. Once they answer, call the list_visit_reasons function.",
             }
         )
-        await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
+        await params.llm.queue_frame(
-        await self.save_data(args, result_callback)
+            OpenAILLMContextFrame(params.context), FrameDirection.DOWNSTREAM
+        )
+        await self.save_data(params.arguments, params.result_callback)
 
-    async def list_visit_reasons(
+    async def list_visit_reasons(self, params: FunctionCallParams):
-        self, function_name, tool_call_id, args, llm, context, result_callback
-    ):
         print("!!! doing start visit reasons")
         # move to finish call
-        context.set_tools([])
+        params.context.set_tools([])
-        context.add_message(
+        params.context.add_message(
             {"role": "system", "content": "Now, thank the user and end the conversation."}
         )
-        await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
+        await params.llm.queue_frame(
-        await self.save_data(args, result_callback)
+            OpenAILLMContextFrame(params.context), FrameDirection.DOWNSTREAM
+        )
+        await self.save_data(params.arguments, params.result_callback)
 
     async def save_data(self, args, result_callback):
         logger.info(f"!!! Saving data: {args}")

examples/phone-chatbot/call_transfer.py

@@ -29,7 +29,7 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.filters.function_filter import FunctionFilter
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.llm_service import LLMService
+from pipecat.services.llm_service import FunctionCallParams, LLMService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyDialinSettings, DailyParams, DailyTransport
 
@@ -220,12 +220,7 @@ async def main(
 
     async def terminate_call(
         task: PipelineTask,  # Pipeline task reference
-        function_name,
+        params: FunctionCallParams,
-        tool_call_id,
-        args,
-        llm: LLMService,
-        context: OpenAILLMContext,
-        result_callback,
     ):
         """Function the bot can call to terminate the call."""
         # Create a message to add
@@ -237,16 +232,9 @@ async def main(
         await task.queue_frames([LLMMessagesFrame(messages)])
 
         # Then end the call
-        await llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
+        await params.llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
 
-    async def dial_operator(
+    async def dial_operator(params: FunctionCallParams):
-        function_name: str,
-        tool_call_id: str,
-        args: dict,
-        llm: LLMService,
-        context: dict,
-        result_callback: callable,
-    ):
         """Function the bot can call to dial an operator."""
         dialout_setting = session_manager.call_flow_state.get_current_dialout_setting()
         if call_config_manager.get_transfer_mode() == "dialout":
@@ -306,9 +294,7 @@ async def main(
     llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
 
     # Register functions with the LLM
-    llm.register_function(
+    llm.register_function("terminate_call", lambda params: terminate_call(task, params))
-        "terminate_call", lambda *args, **kwargs: terminate_call(task, *args, **kwargs)
-    )
     llm.register_function("dial_operator", dial_operator)
 
     # Initialize LLM context and aggregator

examples/phone-chatbot/simple_dialin.py

@@ -22,7 +22,7 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.llm_service import LLMService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyDialinSettings, DailyParams, DailyTransport
 
@@ -99,16 +99,14 @@ async def main(
 
     # ------------ FUNCTION DEFINITIONS ------------
 
-    async def terminate_call(
+    async def terminate_call(params: FunctionCallParams):
-        function_name, tool_call_id, args, llm: LLMService, context, result_callback
-    ):
         """Function the bot can call to terminate the call upon completion of a voicemail message."""
         if session_manager:
             # Mark that the call was terminated by the bot
             session_manager.call_flow_state.set_call_terminated()
 
         # Then end the call
-        await llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
+        await params.llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
 
     # Define function schemas for tools
     terminate_call_function = FunctionSchema(

examples/phone-chatbot/simple_dialout.py

@@ -22,7 +22,7 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.llm_service import LLMService
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
@@ -77,11 +77,9 @@ async def main(
 
     # ------------ FUNCTION DEFINITIONS ------------
 
-    async def terminate_call(
+    async def terminate_call(params: FunctionCallParams):
-        function_name, tool_call_id, args, llm: LLMService, context, result_callback
-    ):
         """Function the bot can call to terminate the call upon completion of a voicemail message."""
-        await llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
+        await params.llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
 
     # Define function schemas for tools
     terminate_call_function = FunctionSchema(

examples/phone-chatbot/voicemail_detection.py

@@ -32,7 +32,7 @@ from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.google.google import GoogleLLMContext
 from pipecat.services.google.llm import GoogleLLMService
-from pipecat.services.llm_service import LLMService  # Base LLM service class
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.services.daily import (
     DailyParams,
     DailyTransport,
@@ -98,35 +98,19 @@ class FunctionHandlers:
         self.session_manager = session_manager
         self.prompt = None  # Can be set externally
 
-    async def voicemail_response(
+    async def voicemail_response(self, params: FunctionCallParams):
-        self,
-        function_name,
-        tool_call_id,
-        args,
-        llm: LLMService,
-        context,
-        result_callback,
-    ):
         """Function the bot can call to leave a voicemail message."""
         message = """You are Chatbot leaving a voicemail message. Say EXACTLY this message and then terminate the call:
 
                     'Hello, this is a message for Pipecat example user. This is Chatbot. Please call back on 123-456-7891. Thank you.'"""
 
-        await result_callback(message)
+        await params.result_callback(message)
 
-    async def human_conversation(
+    async def human_conversation(self, params: FunctionCallParams):
-        self,
-        function_name,
-        tool_call_id,
-        args,
-        llm: LLMService,
-        context,
-        result_callback,
-    ):
         """Function called when bot detects it's talking to a human."""
         # Update state to indicate human was detected
         self.session_manager.call_flow_state.set_human_detected()
-        await llm.push_frame(StopTaskFrame(), FrameDirection.UPSTREAM)
+        await params.llm.push_frame(StopTaskFrame(), FrameDirection.UPSTREAM)
 
 
 # ------------ MAIN FUNCTION ------------
@@ -182,12 +166,7 @@ async def main(
     # ------------ FUNCTION DEFINITIONS ------------
 
     async def terminate_call(
-        function_name,
+        params: FunctionCallParams,
-        tool_call_id,
-        args,
-        llm: LLMService,
-        context,
-        result_callback,
         session_manager=None,
     ):
         """Function the bot can call to terminate the call."""
@@ -195,7 +174,7 @@ async def main(
             # Set call terminated flag in the session manager
             session_manager.call_flow_state.set_call_terminated()
 
-        await llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
+        await params.llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
 
     # ------------ VOICEMAIL DETECTION PHASE SETUP ------------
 
@@ -275,7 +254,7 @@ async def main(
         "switch_to_human_conversation", handlers.human_conversation
     )
     voicemail_detection_llm.register_function(
-        "terminate_call", functools.partial(terminate_call, session_manager=session_manager)
+        "terminate_call", lambda params: terminate_call(params, session_manager)
     )
 
     # Set up audio collector for handling audio input