use a single FunctionCallParams parameter for function calls

Author: aconchillo

guides/fundamentals/end-pipeline.mdx

@@ -35,11 +35,11 @@ await task.queue_frame(EndFrame())
 - Push an `EndTaskFrame` upstream from inside your pipeline. For example, inside a function call:
 
 ```python
-async def end_conversation(function_name, tool_call_id, args, llm, context, result_callback):
-    await llm.push_frame(TTSSpeakFrame("Have a nice day!"))
+async def end_conversation(params: FunctionCallParams):
+    await params.llm.push_frame(TTSSpeakFrame("Have a nice day!"))
 
     # Signal that the task should end after processing this frame
-    await llm.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
+    await params.llm.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
 ```
 
 ### How Graceful Shutdown Works

guides/fundamentals/function-calling.mdx

@@ -201,10 +201,10 @@ Register handlers for your functions using the LLM service's [`register_function
 llm = OpenAILLMService(api_key="your-api-key", model="gpt-4")
 
 # Main function handler - called to execute the function
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
     # Fetch weather data from your API
     weather_data = {"conditions": "sunny", "temperature": "75"}
-    await result_callback(weather_data)
+    await params.result_callback(weather_data)
 
 # Register the function
 llm.register_function(
@@ -243,12 +243,13 @@ pipeline = Pipeline([
 
 ### Handler Parameters
 
-- `function_name`: Name of the called function
-- `tool_call_id`: Unique identifier for the function call
-- `args`: Arguments passed by the LLM
-- `llm`: Reference to the LLM service
-- `context`: Current conversation context
-- `result_callback`: Async function to return results
+- `params`: FunctionCallParams
+  - `function_name`: Name of the called function
+  - `arguments`: Arguments passed by the LLM
+  - `tool_call_id`: Unique identifier for the function call
+  - `llm`: Reference to the LLM service
+  - `context`: Current conversation context
+  - `result_callback`: Async function to return results
 
 ### Return Values
 
@@ -287,18 +288,18 @@ Controls whether the LLM should generate a response after the function call:
 ### Example Usage
 
 ```python
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+async def fetch_weather_from_api(params: FunctionCallParams):
     # Fetch weather data
     weather_data = {"conditions": "sunny", "temperature": "75"}
 
     # Don't run LLM after this function call
     properties = FunctionCallResultProperties(run_llm=False)
 
-    await result_callback(weather_data, properties=properties)
+    await params.result_callback(weather_data, properties=properties)
 
-async def query_database(function_name, tool_call_id, args, llm, context, result_callback):
+async def query_database(params: FunctionCallParams):
     # Query database
-    results = await db.query(args["query"])
+    results = await db.query(params.arguments["query"])
 
     async def on_update():
         await notify_system("Database query complete")
@@ -309,7 +310,7 @@ async def query_database(function_name, tool_call_id, args, llm, context, result
         on_context_updated=on_update
     )
 
-    await result_callback(results, properties=properties)
+    await params.result_callback(results, properties=properties)
 ```
 
 ## Next steps

server/frameworks/rtvi/rtvi-processor.mdx

@@ -150,12 +150,14 @@ Handle LLM function calls with client interaction:
 
 ```python
 await processor.handle_function_call(
-    function_name=function_name,
-    tool_call_id=tool_call_id,
-    arguments=arguments,
-    llm=llm,
-    context=context,
-    result_callback=result_callback
+    FunctionCallParams(
+      function_name=function_name,
+      arguments=arguments,
+      tool_call_id=tool_call_id,
+      llm=llm,
+      context=context,
+      result_callback=result_callback
+   )
 )
 ```
 

server/services/llm/azure.mdx

@@ -184,8 +184,8 @@ context = OpenAILLMContext(
 )
 
 # Register function handlers
-async def fetch_weather(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "nice", "temperature": "75"})
+async def fetch_weather(params: FunctionCallParams):
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 llm.register_function("get_current_weather", fetch_weather)
 

server/services/llm/cerebras.mdx

@@ -108,8 +108,8 @@ Infer whether to use Fahrenheit or Celsius automatically based on the location,
 )
 
 # Register function handlers
-async def fetch_weather(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "nice", "temperature": "75"})
+async def fetch_weather(params: FunctionCallParams):
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 llm.register_function("get_current_weather", fetch_weather)
 

server/services/llm/deepseek.mdx

@@ -144,8 +144,8 @@ context = OpenAILLMContext(
 )
 
 # Register function handlers
-async def fetch_weather(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "nice", "temperature": "75"})
+async def fetch_weather(params: FunctionCallParams):
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 llm.register_function("get_current_weather", fetch_weather)
 

server/services/llm/grok.mdx

@@ -108,8 +108,8 @@ context = OpenAILLMContext(
 )
 
 # Register function handlers
-async def fetch_weather(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "nice", "temperature": "75"})
+async def fetch_weather(params: FunctionCallParams):
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 llm.register_function("get_current_weather", fetch_weather)
 

server/services/llm/groq.mdx

@@ -107,8 +107,8 @@ context = OpenAILLMContext(
 )
 
 # Register function handlers
-async def fetch_weather(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "nice", "temperature": "75"})
+async def fetch_weather(params: FunctionCallParams):
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 llm.register_function("get_current_weather", fetch_weather)
 

server/services/llm/nim.mdx

@@ -116,8 +116,8 @@ context = OpenAILLMContext(
 )
 
 # Register function handlers
-async def fetch_weather(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "nice", "temperature": "75"})
+async def fetch_weather(params: FunctionCallParams):
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 llm.register_function("get_current_weather", fetch_weather)
 

server/services/llm/openai.mdx

@@ -75,7 +75,7 @@ Range: [-2.0, 2.0] where higher values reduce repetition more.
 <ParamField path="temperature" type="float" optional>
 Controls randomness/creativity in the output. Lower values are more
   deterministic, higher values more creative.
-  
+
 Range: [0.0, 2.0]
 
 </ParamField>
@@ -178,7 +178,7 @@ OpenAI's API requires a specific format for conversation history and tools. The
 <ParamField path="messages" type="List[ChatCompletionMessageParam]" optional>
 Initial list of conversation messages. Each message should be an object with
   at least a "role" (user, assistant, or system) and "content" fields.
-  
+
 Defaults to an empty list.
 
 </ParamField>
@@ -384,8 +384,8 @@ context = OpenAILLMContext(
 )
 
 # Register function handlers
-async def fetch_weather(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "nice", "temperature": "75"})
+async def fetch_weather(params: FunctionCallParams):
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 llm.register_function("get_current_weather", fetch_weather)
 

server/services/llm/openrouter.mdx

@@ -148,8 +148,8 @@ context = OpenAILLMContext(
 )
 
 # Register function handlers
-async def fetch_weather(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "nice", "temperature": "75"})
+async def fetch_weather(params: FunctionCallParams):
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 llm.register_function(get_current_weather, fetch_weather)
 

server/services/llm/qwen.mdx

@@ -116,8 +116,8 @@ context = OpenAILLMContext(
 )
 
 # Register function handlers
-async def fetch_weather(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "sunny", "temperature": "22°C"})
+async def fetch_weather(params: FunctionCallParams):
+    await params.result_callback({"conditions": "sunny", "temperature": "22°C"})
 
 llm.register_function("get_current_weather", fetch_weather)
 

server/services/llm/together.mdx

@@ -156,8 +156,8 @@ context = OpenAILLMContext(
 )
 
 # Register function handlers
-async def fetch_weather(function_name, tool_call_id, args, llm, context, result_callback):
-    await result_callback({"conditions": "nice", "temperature": "75"})
+async def fetch_weather(params: FunctionCallParams):
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
 
 llm.register_function("get_current_weather", fetch_weather)