Development

aixplain/factories/agent_factory/__init__.py

@@ -1,7 +1,4 @@
-__author__ = "lucaspavanelli"
-
-"""
-Copyright 2024 The aiXplain SDK authors
+"""Copyright 2024 The aiXplain SDK authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -21,6 +18,8 @@
     Agent Factory Class
 """
 
+__author__ = "lucaspavanelli"
+
 import json
 import logging
 import warnings
@@ -49,6 +48,19 @@
 from aixplain.enums import DatabaseSourceType
 
 
+def to_literal_text(x):
+    """Convert value to literal text, escaping braces for string formatting.
+
+    Args:
+        x: Value to convert (dict, list, or any other type)
+
+    Returns:
+        str: Escaped string representation
+    """
+    s = json.dumps(x, ensure_ascii=False, indent=2) if isinstance(x, (dict, list)) else str(x)
+    return s.replace("{", "{{").replace("}", "}}")
+
+
 class AgentFactory:
     """Factory class for creating and managing agents in the aiXplain system.
 
@@ -90,9 +102,11 @@ def create(
             api_key (Text, optional): team/user API key. Defaults to config.TEAM_API_KEY.
             supplier (Union[Dict, Text, Supplier, int], optional): owner of the agent. Defaults to "aiXplain".
             version (Optional[Text], optional): version of the agent. Defaults to None.
+            tasks (List[WorkflowTask], optional): Deprecated. Use workflow_tasks instead. Defaults to None.
             workflow_tasks (List[WorkflowTask], optional): list of tasks for the agent. Defaults to [].
             output_format (OutputFormat, optional): default output format for agent responses. Defaults to OutputFormat.TEXT.
             expected_output (Union[BaseModel, Text, dict], optional): expected output. Defaults to None.
+
         Returns:
             Agent: created Agent
         """
@@ -133,7 +147,8 @@ def create(
 
         if tasks is not None:
             warnings.warn(
-                "The 'tasks' parameter is deprecated and will be removed in a future version. " "Use 'workflow_tasks' instead.",
+                "The 'tasks' parameter is deprecated and will be removed in a future version. "
+                "Use 'workflow_tasks' instead.",
                 DeprecationWarning,
                 stacklevel=2,
             )
@@ -144,8 +159,8 @@ def create(
         payload = {
             "name": name,
             "assets": [build_tool_payload(tool) for tool in tools],
-            "description": description,
-            "instructions": instructions or description,
+            "description": to_literal_text(description),
+            "instructions": to_literal_text(instructions) if instructions is not None else description,
             "supplier": supplier,
             "version": version,
             "llmId": llm_id,
@@ -228,6 +243,17 @@ def create_workflow_task(
         expected_output: Text,
         dependencies: Optional[List[Text]] = None,
     ) -> WorkflowTask:
+        """Create a new workflow task for an agent.
+
+        Args:
+            name (Text): Name of the task
+            description (Text): Description of what the task does
+            expected_output (Text): Expected output format or content
+            dependencies (Optional[List[Text]], optional): List of task names this task depends on. Defaults to None.
+
+        Returns:
+            WorkflowTask: Created workflow task object
+        """
         dependencies = [] if dependencies is None else list(dependencies)
         return WorkflowTask(
             name=name,
@@ -238,6 +264,11 @@ def create_workflow_task(
 
     @classmethod
     def create_task(cls, *args, **kwargs):
+        """Create a workflow task (deprecated - use create_workflow_task instead).
+
+        .. deprecated::
+            Use :meth:`create_workflow_task` instead.
+        """
         warnings.warn(
             "The 'create_task' method is deprecated and will be removed in a future version. "
             "Use 'create_workflow_task' instead.",
@@ -351,7 +382,7 @@ def create_sql_tool(
         tables: Optional[List[Text]] = None,
         enable_commit: bool = False,
     ) -> SQLTool:
-        """Create a new SQL tool
+        """Create a new SQL tool.
 
         Args:
             name (Text): name of the tool
@@ -361,6 +392,7 @@ def create_sql_tool(
             schema (Optional[Text], optional): database schema description
             tables (Optional[List[Text]], optional): table names to work with (optional)
             enable_commit (bool, optional): enable to modify the database (optional)
+
         Returns:
             SQLTool: created SQLTool
 
@@ -403,7 +435,9 @@ def create_sql_tool(
             # Already the correct type, no conversion needed
             pass
         else:
-            raise SQLToolError(f"Source type must be either a string or DatabaseSourceType enum, got {type(source_type)}")
+            raise SQLToolError(
+                f"Source type must be either a string or DatabaseSourceType enum, got {type(source_type)}"
+            )
 
         database_path = None  # Final database path to pass to SQLTool
 

aixplain/factories/team_agent_factory/__init__.py

@@ -227,10 +227,13 @@ def _setup_llm_and_tool(
         team_agent = build_team_agent(payload=internal_payload, agents=agent_list, api_key=api_key)
         team_agent.validate(raise_exception=True)
         response = "Unspecified error"
+        inspectors=team_agent.inspectors
+        inspector_targets=team_agent.inspector_targets
         try:
             payload["inspectors"] = [
                 inspector.model_dump(by_alias=True) for inspector in inspectors
-            ]  # convert Inspector object to dict
+            ]
+            payload["inspectorTargets"] = inspector_targets
             logging.debug(f"Start service for POST Create TeamAgent  - {url} - {headers} - {json.dumps(payload)}")
             r = _request_with_retry("post", url, headers=headers, json=payload)
             response = r.json()

aixplain/factories/team_agent_factory/utils.py

@@ -10,11 +10,12 @@
 from aixplain.modules.agent.agent_task import AgentTask
 from aixplain.modules.agent.tool.model_tool import ModelTool
 from aixplain.modules.team_agent import TeamAgent, InspectorTarget
-from aixplain.modules.team_agent.inspector import Inspector
+from aixplain.modules.team_agent.inspector import Inspector, InspectorAction, InspectorAuto, InspectorPolicy, InspectorOutput
 from aixplain.factories.agent_factory import AgentFactory
 from aixplain.factories.model_factory import ModelFactory
 from aixplain.modules.model.model_parameters import ModelParameters
 from aixplain.modules.agent.output_format import OutputFormat
+from aixplain.modules.model.response import ModelResponse
 
 GPT_4o_ID = "6646261c6eb563165658bbb1"
 SUPPORTED_TOOLS = ["llm", "website_search", "website_scrape", "website_crawl", "serper_search"]
@@ -154,6 +155,51 @@ def get_cached_model(model_id: str) -> any:
                 elif tool["description"] == "mentalist":
                     mentalist_llm = llm
 
+    resolved_model_id = payload.get("llmId", None)
+    if not resolved_model_id:
+        resolved_model_id = GPT_4o_ID
+    has_quality_check = any(
+        (getattr(ins, "name", "") or "").lower() == "qualitycheckinspector"
+        for ins in inspectors
+    )
+    if not has_quality_check:
+        try:
+            def process_response(model_response: ModelResponse, input_content: str) -> InspectorOutput:
+                critiques = model_response.data
+                action = InspectorAction.RERUN
+                return InspectorOutput(critiques=critiques, content_edited=input_content, action=action)
+
+            default_inspector = Inspector(
+                name="QualityCheckInspector",
+                model_id=resolved_model_id,
+                model_params={"prompt": "Analyze content to ensure correctness of response"},
+                policy=process_response
+            )
+
+            inspectors = [default_inspector] + inspectors
+            inspector_targets = payload.get("inspectorTargets", inspector_targets if 'inspector_targets' in locals() else [])
+            if isinstance(inspector_targets, (str, InspectorTarget)):
+                inspector_targets = [inspector_targets]
+            normalized = []
+            for t in inspector_targets:
+                if isinstance(t, InspectorTarget):
+                    normalized.append(t)
+                elif isinstance(t, str):
+                    try:
+                        normalized.append(InspectorTarget(t.lower()))
+                    except Exception:
+                        logging.warning(f"Ignoring unknown inspector target: {t!r}")
+                else:
+                    logging.warning(f"Ignoring inspector target with unexpected type: {type(t)}")
+
+            if InspectorTarget.STEPS not in normalized:
+                normalized.append(InspectorTarget.STEPS)
+
+            inspector_targets = normalized 
+
+        except Exception as e:
+            logging.warning(f"Failed to add default QualityCheckInspector: {e}")
+
     team_agent = TeamAgent(
         id=payload.get("id", ""),
         name=payload.get("name", ""),

aixplain/modules/agent/__init__.py

@@ -325,6 +325,7 @@ def run(
             output_format (OutputFormat, optional): response format. If not provided, uses the format set during initialization.
             expected_output (Union[BaseModel, Text, dict], optional): expected output. Defaults to None.
             trace_request (bool, optional): return the request id for tracing the request. Defaults to False.
+
         Returns:
             Dict: parsed output from model
         """
@@ -427,6 +428,7 @@ def run_async(
             output_format (ResponseFormat, optional): response format. Defaults to TEXT.
             evolve (Union[Dict[str, Any], EvolveParam, None], optional): evolve the agent configuration. Can be a dictionary, EvolveParam instance, or None.
             trace_request (bool, optional): return the request id for tracing the request. Defaults to False.
+
         Returns:
             dict: polling URL in response
         """
@@ -490,7 +492,7 @@ def run_async(
         input_data = process_variables(query, data, parameters, self.instructions)
         if expected_output is None:
             expected_output = self.expected_output
-        if expected_output is not None and issubclass(expected_output, BaseModel):
+        if expected_output is not None and isinstance(expected_output, type) and issubclass(expected_output, BaseModel):
             expected_output = expected_output.model_json_schema()
         expected_output = normalize_expected_output(expected_output)
         # Use instance output_format if none provided

aixplain/modules/model/llm_model.py

@@ -1,7 +1,4 @@
-__author__ = "lucaspavanelli"
-
-"""
-Copyright 2024 The aiXplain SDK authors
+"""Copyright 2024 The aiXplain SDK authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -20,6 +17,8 @@
 Description:
     Large Language Model Class
 """
+
+__author__ = "lucaspavanelli"
 import time
 import logging
 import traceback
@@ -63,7 +62,7 @@ def __init__(
         function: Optional[Function] = None,
         is_subscribed: bool = False,
         cost: Optional[Dict] = None,
-        temperature: float = 0.001,
+        temperature: Optional[float] = None,
         function_type: Optional[FunctionType] = FunctionType.AI,
         **additional_info,
     ) -> None:
@@ -79,14 +78,16 @@ def __init__(
             function (Function, optional): Model's AI function. Must be Function.TEXT_GENERATION.
             is_subscribed (bool, optional): Whether the user is subscribed. Defaults to False.
             cost (Dict, optional): Cost of the model. Defaults to None.
-            temperature (float, optional): Default temperature for text generation. Defaults to 0.001.
+            temperature (Optional[float], optional): Default temperature for text generation. Defaults to None.
             function_type (FunctionType, optional): Type of the function. Defaults to FunctionType.AI.
             **additional_info: Any additional model info to be saved.
 
         Raises:
             AssertionError: If function is not Function.TEXT_GENERATION.
         """
-        assert function == Function.TEXT_GENERATION, "LLM only supports large language models (i.e. text generation function)"
+        assert function == Function.TEXT_GENERATION, (
+            "LLM only supports large language models (i.e. text generation function)"
+        )
         super().__init__(
             id=id,
             name=name,
@@ -112,12 +113,13 @@ def run(
         history: Optional[List[Dict]] = None,
         temperature: Optional[float] = None,
         max_tokens: int = 128,
-        top_p: float = 1.0,
+        top_p: Optional[float] = None,
         name: Text = "model_process",
         timeout: float = 300,
         parameters: Optional[Dict] = None,
         wait_time: float = 0.5,
         stream: bool = False,
+        response_format: Optional[Text] = None,
     ) -> Union[ModelResponse, ModelResponseStreamer]:
         """Run the LLM model synchronously to generate text.
 
@@ -138,8 +140,8 @@ def run(
                 Defaults to None.
             max_tokens (int, optional): Maximum number of tokens to generate.
                 Defaults to 128.
-            top_p (float, optional): Nucleus sampling parameter. Only tokens with cumulative
-                probability < top_p are considered. Defaults to 1.0.
+            top_p (Optional[float], optional): Nucleus sampling parameter. Only tokens with cumulative
+                probability < top_p are considered. Defaults to None.
             name (Text, optional): Identifier for this model run. Useful for logging.
                 Defaults to "model_process".
             timeout (float, optional): Maximum time in seconds to wait for completion.
@@ -150,6 +152,8 @@ def run(
                 Defaults to 0.5.
             stream (bool, optional): Whether to stream the model's output tokens.
                 Defaults to False.
+            response_format (Optional[Union[str, dict, BaseModel]], optional):
+                Specifies the desired output structure or format of the model’s response.
 
         Returns:
             Union[ModelResponse, ModelResponseStreamer]: If stream=False, returns a ModelResponse
@@ -166,9 +170,13 @@ def run(
         parameters.setdefault("context", context)
         parameters.setdefault("prompt", prompt)
         parameters.setdefault("history", history)
-        parameters.setdefault("temperature", temperature if temperature is not None else self.temperature)
+        temp_value = temperature if temperature is not None else self.temperature
+        if temp_value is not None:
+            parameters.setdefault("temperature", temp_value)
         parameters.setdefault("max_tokens", max_tokens)
-        parameters.setdefault("top_p", top_p)
+        if top_p is not None:
+            parameters.setdefault("top_p", top_p)
+        parameters.setdefault("response_format", response_format)
 
         if stream:
             return self.run_stream(data=data, parameters=parameters)
@@ -210,9 +218,10 @@ def run_async(
         history: Optional[List[Dict]] = None,
         temperature: Optional[float] = None,
         max_tokens: int = 128,
-        top_p: float = 1.0,
+        top_p: Optional[float] = None,
         name: Text = "model_process",
         parameters: Optional[Dict] = None,
+        response_format: Optional[Text] = None,
     ) -> ModelResponse:
         """Run the LLM model asynchronously to generate text.
 
@@ -233,12 +242,14 @@ def run_async(
                 Defaults to None.
             max_tokens (int, optional): Maximum number of tokens to generate.
                 Defaults to 128.
-            top_p (float, optional): Nucleus sampling parameter. Only tokens with cumulative
-                probability < top_p are considered. Defaults to 1.0.
+            top_p (Optional[float], optional): Nucleus sampling parameter. Only tokens with cumulative
+                probability < top_p are considered. Defaults to None.
             name (Text, optional): Identifier for this model run. Useful for logging.
                 Defaults to "model_process".
             parameters (Optional[Dict], optional): Additional model-specific parameters.
                 Defaults to None.
+            response_format (Optional[Text], optional): Desired output format specification.
+                Defaults to None.
 
         Returns:
             ModelResponse: A response object containing:
@@ -261,9 +272,13 @@ def run_async(
         parameters.setdefault("context", context)
         parameters.setdefault("prompt", prompt)
         parameters.setdefault("history", history)
-        parameters.setdefault("temperature", temperature if temperature is not None else self.temperature)
+        temp_value = temperature if temperature is not None else self.temperature
+        if temp_value is not None:
+            parameters.setdefault("temperature", temp_value)
         parameters.setdefault("max_tokens", max_tokens)
-        parameters.setdefault("top_p", top_p)
+        if top_p is not None:
+            parameters.setdefault("top_p", top_p)
+        parameters.setdefault("response_format", response_format)
         payload = build_payload(data=data, parameters=parameters)
         response = call_run_endpoint(payload=payload, url=url, api_key=self.api_key)
         return ModelResponse(

aixplain/modules/team_agent/inspector.py

@@ -58,7 +58,7 @@ class InspectorOutput(BaseModel):
 
 class InspectorAuto(str, Enum):
     """A list of keywords for inspectors configured automatically in the backend."""
-
+    ALIGNMENT = "alignment" 
     CORRECTNESS = "correctness"
 
     def get_name(self) -> Text:

tests/functional/agent/agent_functional_test.py

@@ -122,7 +122,7 @@ def test_python_interpreter_tool(delete_agents_and_team_agents, AgentFactory):
     assert len(response["data"]["intermediate_steps"]) > 0
     intermediate_step = response["data"]["intermediate_steps"][0]
     assert len(intermediate_step["tool_steps"]) > 0
-    assert intermediate_step["tool_steps"][0]["tool"] == "Custom Code Tool"
+    assert intermediate_step["tool_steps"][0]["tool"] == "Python Code Interpreter Tool"
     agent.delete()