Fixed structured output parsing

src/llama_cpp_agent/llm_output_settings/settings.py

@@ -616,8 +616,11 @@ def add_all_current_functions_to_heartbeat_list(self, excluded: list[str] = None
     def handle_structured_output(self, llm_output: str, prompt_suffix: str = None):
         if self.output_raw_json_string:
             return llm_output
+
         if prompt_suffix:
             llm_output = llm_output.replace(prompt_suffix, "", 1)
+
+
         if (
                 self.output_type is LlmStructuredOutputType.function_calling
                 or self.output_type is LlmStructuredOutputType.parallel_function_calling
@@ -627,7 +630,7 @@ def handle_structured_output(self, llm_output: str, prompt_suffix: str = None):
 
             return self.handle_function_call(output)
         elif self.output_type == LlmStructuredOutputType.object_instance:
-            output = json.loads(llm_output)
+            output = parse_json_response(llm_output)
             output = self.clean_keys(output)
             model_name = output[self.output_model_name_field_name]
             model_attributes = output[self.output_model_attributes_field_name]
@@ -636,7 +639,7 @@ def handle_structured_output(self, llm_output: str, prompt_suffix: str = None):
                     return model(**model_attributes)
 
         elif self.output_type == LlmStructuredOutputType.list_of_objects:
-            output = json.loads(llm_output)
+            output = parse_json_response(llm_output)
             output = self.clean_keys(output)
             models = []
             for out in output:

src/llama_cpp_agent/prompt_templates.py

@@ -42,23 +42,21 @@
 
 {after_system_instructions}
 
-Your task is to output a JSON object containing the content of a specific model, based on the system instructions provided. Your output should be structured in the following way: 
-
-{thoughts_and_reasoning}
-
-The {model_field_name} field should contain the name of the specific model that you are outputting, based on the system instructions. 
-
-The {fields_field_name} field should contain the actual fields and content of the model you are outputting, filled out according to the system instructions.
-
-Here are the output models you can choose from:
+Your output should be structured as JSON and represent one of the following output models: 
 
 <output_models>
 {output_models}
 </output_models>
 
-Please read the system instructions and output models carefully before proceeding.'''
+You JSON output should have the following fields:
+
+{thoughts_and_reasoning}
+
+The '{model_field_name}' field should contain the name of the specific model that you are outputting, based on the system instructions. 
+
+The '{fields_field_name}' field should contain the actual fields and content of the model you are outputting, filled out according to the system instructions.'''
 
-thoughts_and_reasoning_structured_output = "The {thoughts_and_reasoning_field_name} field should contain your step-by-step reasoning and decision making process as you work through the task. Explain how you are interpreting the instructions and planning your response."
+thoughts_and_reasoning_structured_output = "The '{thoughts_and_reasoning_field_name}' field should contain your step-by-step reasoning and decision making process as you work through the task. Explain how you are interpreting the instructions and planning your response."
 structured_output_templater = PromptTemplate.from_string(structured_output_template)
 structured_output_thoughts_and_reasoning_templater = PromptTemplate.from_string(thoughts_and_reasoning_structured_output)
 general_summarizing_system_prompt = """Your task is to summarize and extract relevant information from a text based on a specific query.