Handle incomplete function call arguments from interrupted LLM streams

When a user interruption causes the LLM chunk stream to exit early,
function call arguments may be incomplete JSON. Wrap json.loads() in
try/except JSONDecodeError to skip malformed function calls with a
warning instead of crashing. Fixes #2461.

src/pipecat/services/google/openai/llm.py

@@ -199,7 +199,11 @@ class GoogleLLMOpenAIBetaService(OpenAILLMService):
                     # which currently results in an empty function name('').
                     continue
 
-                arguments = json.loads(arguments)
+                try:
+                    arguments = json.loads(arguments)
+                except json.JSONDecodeError:
+                    logger.warning(f"{self}: Failed to parse function call arguments: {arguments}")
+                    continue
 
                 function_calls.append(
                     FunctionCallFromLLM(

src/pipecat/services/openai/base_llm.py

@@ -567,7 +567,11 @@ class BaseOpenAILLMService(LLMService):
             for function_name, arguments, tool_id in zip(
                 functions_list, arguments_list, tool_id_list
             ):
-                arguments = json.loads(arguments)
+                try:
+                    arguments = json.loads(arguments)
+                except json.JSONDecodeError:
+                    logger.warning(f"{self}: Failed to parse function call arguments: {arguments}")
+                    continue
                 function_calls.append(
                     FunctionCallFromLLM(
                         context=context,

src/pipecat/services/sambanova/llm.py

@@ -245,7 +245,11 @@ class SambaNovaLLMService(OpenAILLMService):  # type: ignore
                 if len(arguments) < 1:
                     continue
 
-                arguments = json.loads(arguments)
+                try:
+                    arguments = json.loads(arguments)
+                except json.JSONDecodeError:
+                    logger.warning(f"{self}: Failed to parse function call arguments: {arguments}")
+                    continue
                 function_calls.append(
                     FunctionCallFromLLM(
                         context=context,