Make JSON output optional in LLM calls

autogpt_platform/backend/backend/blocks/llm.py

@@ -382,7 +382,9 @@ def extract_openai_tool_calls(response) -> list[ToolContentBlock] | None:
     return None
 
 
-def get_parallel_tool_calls_param(llm_model: LlmModel, parallel_tool_calls):
+def get_parallel_tool_calls_param(
+    llm_model: LlmModel, parallel_tool_calls: bool | None
+):
     """Get the appropriate parallel_tool_calls parameter for OpenAI-compatible APIs."""
     if llm_model.startswith("o") or parallel_tool_calls is None:
         return openai.NOT_GIVEN
@@ -393,8 +395,8 @@ async def llm_call(
     credentials: APIKeyCredentials,
     llm_model: LlmModel,
     prompt: list[dict],
-    json_format: bool,
     max_tokens: int | None,
+    force_json_output: bool = False,
     tools: list[dict] | None = None,
     ollama_host: str = "localhost:11434",
     parallel_tool_calls=None,
@@ -407,7 +409,7 @@ async def llm_call(
         credentials: The API key credentials to use.
         llm_model: The LLM model to use.
         prompt: The prompt to send to the LLM.
-        json_format: Whether the response should be in JSON format.
+        force_json_output: Whether the response should be in JSON format.
         max_tokens: The maximum number of tokens to generate in the chat completion.
         tools: The tools to use in the chat completion.
         ollama_host: The host for ollama to use.
@@ -446,7 +448,7 @@ async def llm_call(
             llm_model, parallel_tool_calls
         )
 
-        if json_format:
+        if force_json_output:
             response_format = {"type": "json_object"}
 
         response = await oai_client.chat.completions.create(
@@ -559,7 +561,7 @@ async def llm_call(
             raise ValueError("Groq does not support tools.")
 
         client = AsyncGroq(api_key=credentials.api_key.get_secret_value())
-        response_format = {"type": "json_object"} if json_format else None
+        response_format = {"type": "json_object"} if force_json_output else None
         response = await client.chat.completions.create(
             model=llm_model.value,
             messages=prompt,  # type: ignore
@@ -717,7 +719,7 @@ async def llm_call(
         )
 
         response_format = None
-        if json_format:
+        if force_json_output:
             response_format = {"type": "json_object"}
 
         parallel_tool_calls_param = get_parallel_tool_calls_param(
@@ -769,6 +771,16 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             description="Expected format of the response. If provided, the response will be validated against this format. "
             "The keys should be the expected fields in the response, and the values should be the description of the field.",
         )
+        force_json_output: bool = SchemaField(
+            title="Restrict LLM to pure JSON output",
+            default=False,
+            description=(
+                "Whether to force the LLM to produce a JSON-only response. "
+                "This can increase a model's reliability of outputting valid JSON. "
+                "However, it may also reduce the quality of the response, because it "
+                "prohibits the LLM from reasoning before providing its JSON response."
+            ),
+        )
         list_result: bool = SchemaField(
             title="List Result",
             default=False,
@@ -867,9 +879,9 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         credentials: APIKeyCredentials,
         llm_model: LlmModel,
         prompt: list[dict],
-        json_format: bool,
-        compress_prompt_to_fit: bool,
         max_tokens: int | None,
+        force_json_output: bool = False,
+        compress_prompt_to_fit: bool = True,
         tools: list[dict] | None = None,
         ollama_host: str = "localhost:11434",
     ) -> LLMResponse:
@@ -882,8 +894,8 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             credentials=credentials,
             llm_model=llm_model,
             prompt=prompt,
-            json_format=json_format,
             max_tokens=max_tokens,
+            force_json_output=force_json_output,
             tools=tools,
             ollama_host=ollama_host,
             compress_prompt_to_fit=compress_prompt_to_fit,
@@ -954,7 +966,10 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                     llm_model=llm_model,
                     prompt=prompt,
                     compress_prompt_to_fit=input_data.compress_prompt_to_fit,
-                    json_format=bool(input_data.expected_format),
+                    force_json_output=(
+                        input_data.force_json_output
+                        and bool(input_data.expected_format)
+                    ),
                     ollama_host=input_data.ollama_host,
                     max_tokens=input_data.max_tokens,
                 )

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -523,7 +523,6 @@ class SmartDecisionMakerBlock(Block):
             credentials=credentials,
             llm_model=input_data.model,
             prompt=prompt,
-            json_format=False,
             max_tokens=input_data.max_tokens,
             tools=tool_functions,
             ollama_host=input_data.ollama_host,

autogpt_platform/backend/backend/blocks/test/test_llm.py

@@ -30,7 +30,6 @@ class TestLLMStatsTracking:
                 credentials=llm.TEST_CREDENTIALS,
                 llm_model=llm.LlmModel.GPT4O,
                 prompt=[{"role": "user", "content": "Hello"}],
-                json_format=False,
                 max_tokens=100,
             )
 

autogpt_platform/backend/backend/executor/activity_status_generator.py

@@ -423,7 +423,6 @@ async def _call_llm_direct(
         credentials=credentials,
         llm_model=LlmModel.GPT4O_MINI,
         prompt=prompt,
-        json_format=False,
         max_tokens=150,
         compress_prompt_to_fit=True,
     )