Dev to Test

.pre-commit-config.yaml

@@ -8,18 +8,26 @@ repos:
         pass_filenames: false
         types: [python]
         always_run: true
-  
+
   - repo: https://github.com/psf/black
-    rev: 22.10.0
+    rev: 25.1.0
     hooks:
       - id: black
         language_version: python3
         args: # arguments to configure black
           - --line-length=128
-  
+
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v2.0.0 # Use the latest version
+    rev: v5.0.0 # Use the latest version
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-merge-conflict
+      - id: check-added-large-files
+
+  - repo: https://github.com/pycqa/flake8
+    rev: 7.2.0
     hooks:
       - id: flake8
         args: # arguments to configure flake8
-          - --ignore=E402,E501,E203,W503
+          - --ignore=E402,E501,E203,W503

aixplain/enums/__init__.py

@@ -20,4 +20,5 @@
 from .asset_status import AssetStatus
 from .index_stores import IndexStores
 from .function_type import FunctionType
+from .evolve_type import EvolveType
 from .code_interpreter import CodeInterpreterModel

aixplain/enums/evolve_type.py

@@ -0,0 +1,6 @@
+from enum import Enum
+
+
+class EvolveType(str, Enum):
+    TEAM_TUNING = "team_tuning"
+    INSTRUCTION_TUNING = "instruction_tuning"

aixplain/factories/agent_factory/__init__.py

@@ -28,7 +28,7 @@
 
 from aixplain.enums.function import Function
 from aixplain.enums.supplier import Supplier
-from aixplain.modules.agent import Agent, AgentTask, Tool
+from aixplain.modules.agent import Agent, Tool, WorkflowTask
 from aixplain.modules.agent.output_format import OutputFormat
 from aixplain.modules.agent.tool.model_tool import ModelTool
 from aixplain.modules.agent.tool.pipeline_tool import PipelineTool
@@ -67,7 +67,8 @@ def create(
         api_key: Text = config.TEAM_API_KEY,
         supplier: Union[Dict, Text, Supplier, int] = "aiXplain",
         version: Optional[Text] = None,
-        tasks: List[AgentTask] = [],
+        tasks: List[WorkflowTask] = None,
+        workflow_tasks: List[WorkflowTask] = [],
         output_format: Optional[OutputFormat] = None,
         expected_output: Optional[Union[BaseModel, Text, dict]] = None,
     ) -> Agent:
@@ -80,15 +81,15 @@ def create(
 
         Args:
             name (Text): name of the agent
-            description (Text): description of the agent role.
+            description (Text): description of the agent instructions.
             instructions (Text): instructions of the agent.
             llm (Optional[Union[LLM, Text]], optional): LLM instance to use as an object or as an ID.
             llm_id (Optional[Text], optional): ID of LLM to use if no LLM instance provided. Defaults to None.
             tools (List[Union[Tool, Model]], optional): list of tool for the agent. Defaults to [].
             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[AgentTask], optional): list of tasks for the agent. Defaults to [].
+            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:
@@ -127,6 +128,16 @@ def create(
         elif isinstance(supplier, Supplier):
             supplier = supplier.value["code"]
 
+        if tasks is not None:
+            warnings.warn(
+                "The 'tasks' parameter is deprecated and will be removed in a future version. " "Use 'workflow_tasks' instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            workflow_tasks = tasks if workflow_tasks is None or workflow_tasks == [] else workflow_tasks
+
+        workflow_tasks = workflow_tasks or []
+
         payload = {
             "name": name,
             "assets": [build_tool_payload(tool) for tool in tools],
@@ -136,7 +147,7 @@ def create(
             "version": version,
             "llmId": llm_id,
             "status": "draft",
-            "tasks": [task.to_dict() for task in tasks],
+            "tasks": [task.to_dict() for task in workflow_tasks],
             "tools": [],
         }
 
@@ -206,32 +217,30 @@ def create_from_dict(cls, dict: Dict) -> Agent:
         return agent
 
     @classmethod
-    def create_task(
+    def create_workflow_task(
         cls,
         name: Text,
         description: Text,
         expected_output: Text,
-        dependencies: Optional[List[Text]] = None,
-    ) -> AgentTask:
-        """Create a new task for an agent.
-
-        Args:
-            name (Text): Name of the task.
-            description (Text): Description of what the task should accomplish.
-            expected_output (Text): Description of the expected output format.
-            dependencies (Optional[List[Text]], optional): List of task names that must
-                complete before this task can start. Defaults to None.
-
-        Returns:
-            AgentTask: Created task object.
-        """
-        return AgentTask(
+        dependencies: Optional[List[Text]] = [],
+    ) -> WorkflowTask:
+        return WorkflowTask(
             name=name,
             description=description,
             expected_output=expected_output,
             dependencies=dependencies,
         )
 
+    @classmethod
+    def create_task(cls, *args, **kwargs):
+        warnings.warn(
+            "The 'create_task' method is deprecated and will be removed in a future version. "
+            "Use 'create_workflow_task' instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        return cls.create_workflow_task(*args, **kwargs)
+
     @classmethod
     def create_model_tool(
         cls,

aixplain/factories/model_factory/utils.py

@@ -58,10 +58,14 @@ def create_model_from_response(response: Dict) -> Model:
     additional_kwargs = {}
     attributes = response.get("attributes", None)
     if attributes:
-        embedding_model = next((item["code"] for item in attributes if item["name"] == "embeddingmodel"), None)
+        embedding_model = next(
+            (item.get("code") for item in attributes if item.get("name") == "embeddingmodel" and "code" in item), None
+        )
         if embedding_model:
             additional_kwargs["embedding_model"] = embedding_model
-        embedding_size = next((item["value"] for item in attributes if item["name"] == "embeddingSize"), None)
+        embedding_size = next(
+            (item.get("value") for item in attributes if item.get("name") == "embeddingSize" and "value" in item), None
+        )
         if embedding_size:
             additional_kwargs["embedding_size"] = embedding_size
 
@@ -87,7 +91,7 @@ def create_model_from_response(response: Dict) -> Model:
         ModelClass = IndexModel
     elif function_type == FunctionType.INTEGRATION:
         ModelClass = Integration
-    elif function_type == FunctionType.CONNECTION :
+    elif function_type == FunctionType.CONNECTION:
         ModelClass = ConnectionTool
     elif function_type == FunctionType.MCP_CONNECTION:
         ModelClass = MCPConnection
@@ -297,11 +301,52 @@ def get_model_from_ids(model_ids: List[str], api_key: Optional[str] = None) -> L
         raise Exception(f"{message}")
     if 200 <= r.status_code < 300:
         models = []
-        for item in resp["items"]:
-            item["api_key"] = config.TEAM_API_KEY
-            if api_key is not None:
-                item["api_key"] = api_key
-            models.append(create_model_from_response(item))
+
+        def process_items(items):
+            """Helper function to process model items and add API key"""
+            for item in items:
+                item["api_key"] = config.TEAM_API_KEY
+                if api_key is not None:
+                    item["api_key"] = api_key
+                models.append(create_model_from_response(item))
+
+        # Check if pagination is needed ( pageNumber: 0 indicates pagination required)
+        if "pageTotal" in resp:
+            # Handle paginated response - need to fetch all pages
+            page_number = 1
+            total_fetched = resp.get("pageTotal", 0)
+            page_items = resp.get("items", [])
+            total_items = resp.get("total", 0)
+            process_items(page_items)
+
+            while True:
+                # Make request for current page
+                paginated_url = urljoin(config.BACKEND_URL, f"sdk/models?ids={','.join(model_ids)}&pageNumber={page_number}")
+                logging.info(f"Fetching page {page_number} - {paginated_url}")
+                page_r = _request_with_retry("get", paginated_url, headers=headers)
+                page_resp = page_r.json()
+
+                if not (200 <= page_r.status_code < 300):
+                    error_message = f"Model GET Error: Failed to retrieve models page {page_number}. Status Code: {page_r.status_code}. Error: {page_resp}"
+                    logging.error(error_message)
+                    raise Exception(error_message)
+
+                # Process items from current page
+                page_items = page_resp.get("items", [])
+                if not page_items:
+                    break
+
+                process_items(page_items)
+                total_fetched += len(page_items)
+
+                if total_fetched >= total_items:
+                    break
+
+                page_number += 1
+        else:
+            # Handle non-paginated response (original logic)
+            process_items(resp["items"])
+
         return models
     else:
         error_message = f"Model GET Error: Failed to retrieve models {model_ids}. Status Code: {r.status_code}. Error: {resp}"

aixplain/factories/team_agent_factory/__init__.py

@@ -145,35 +145,39 @@ def _get_llm_safely(llm_id: str, llm_type: str) -> LLM:
             try:
                 return get_llm_instance(llm_id, api_key=api_key)
             except Exception:
-                raise Exception(f"TeamAgent Onboarding Error: LLM {llm_id} does not exist for {llm_type}. To resolve this, set the following LLM parameters to a valid LLM object or LLM ID: llm, supervisor_llm, mentalist_llm.")
+                raise Exception(
+                    f"TeamAgent Onboarding Error: LLM {llm_id} does not exist for {llm_type}. To resolve this, set the following LLM parameters to a valid LLM object or LLM ID: llm, supervisor_llm, mentalist_llm."
+                )
 
-        def _setup_llm_and_tool(llm_param: Optional[Union[LLM, Text]], 
-                              default_id: Text, 
-                              llm_type: str, 
-                              description: str,
-                              tools: List[Dict]) -> LLM:
+        def _setup_llm_and_tool(
+            llm_param: Optional[Union[LLM, Text]], default_id: Text, llm_type: str, description: str, tools: List[Dict]
+        ) -> LLM:
             """Helper to set up an LLM and add its tool configuration."""
             llm_instance = None
             # Set up LLM
             if llm_param is None:
                 llm_instance = _get_llm_safely(default_id, llm_type)
             else:
                 llm_instance = _get_llm_safely(llm_param, llm_type) if isinstance(llm_param, str) else llm_param
-            
+
             # Add tool configuration
             if llm_instance is not None:
-                tools.append({
-                    "type": "llm",
-                    "description": description,
-                    "parameters": llm_instance.get_parameters().to_list() if llm_instance.get_parameters() else None,
-                })
+                tools.append(
+                    {
+                        "type": "llm",
+                        "description": description,
+                        "parameters": llm_instance.get_parameters().to_list() if llm_instance.get_parameters() else None,
+                    }
+                )
             return llm_instance, tools
 
         # Set up LLMs and their tools
         tools = []
         llm, tools = _setup_llm_and_tool(llm, llm_id, "Main LLM", "main", tools)
         supervisor_llm, tools = _setup_llm_and_tool(supervisor_llm, llm_id, "Supervisor LLM", "supervisor", tools)
-        mentalist_llm, tools = _setup_llm_and_tool(mentalist_llm, llm_id, "Mentalist LLM", "mentalist", tools) if use_mentalist else None
+        mentalist_llm, tools = (
+            _setup_llm_and_tool(mentalist_llm, llm_id, "Mentalist LLM", "mentalist", tools) if use_mentalist else (None, [])
+        )
 
         team_agent = None
         url = urljoin(config.BACKEND_URL, "sdk/agent-communities")
@@ -201,9 +205,8 @@ def _setup_llm_and_tool(llm_param: Optional[Union[LLM, Text]],
             "supplier": supplier,
             "version": version,
             "status": "draft",
-            "tools": [],
-            "instructions": instructions,
             "tools": tools,
+            "instructions": instructions,
         }
         # Store the LLM objects directly in the payload for build_team_agent
         internal_payload = payload.copy()

aixplain/factories/team_agent_factory/inspector_factory.py

@@ -4,6 +4,8 @@
 and monitor team agent operations. Inspectors can be created from existing models
 or using automatic configurations.
 
+WARNING: This feature is currently in private beta.
+
 Example:
     Create an inspector from a model with adaptive policy::
 
@@ -19,7 +21,7 @@
 """
 
 import logging
-from typing import Dict, Optional, Text, Union
+from typing import Dict, Optional, Text, Union, Callable
 from urllib.parse import urljoin
 
 from aixplain.enums.asset_status import AssetStatus
@@ -31,7 +33,7 @@
 from aixplain.utils.file_utils import _request_with_retry
 
 
-INSPECTOR_SUPPORTED_FUNCTIONS = [Function.GUARDRAILS, Function.TEXT_GENERATION]
+INSPECTOR_SUPPORTED_FUNCTIONS = [Function.GUARDRAILS, Function.TEXT_GENERATION, Function.UTILITIES]
 
 
 class InspectorFactory:
@@ -48,7 +50,7 @@ def create_from_model(
         name: Text,
         model: Union[Text, Model],
         model_config: Optional[Dict] = None,
-        policy: InspectorPolicy = InspectorPolicy.ADAPTIVE,  # default: doing something dynamically
+        policy: Union[InspectorPolicy, Callable] = InspectorPolicy.ADAPTIVE,  # default: doing something dynamically
     ) -> Inspector:
         """Create a new inspector agent from an onboarded model.
 
@@ -62,11 +64,10 @@ def create_from_model(
                 to use for the inspector.
             model_config (Optional[Dict], optional): Configuration parameters for
                 the inspector model (e.g., prompts, thresholds). Defaults to None.
-            policy (InspectorPolicy, optional): Action to take upon negative feedback:
-                - WARN: Log warning but continue execution
-                - ABORT: Stop execution on negative feedback
-                - ADAPTIVE: Dynamically decide based on context
-                Defaults to InspectorPolicy.ADAPTIVE.
+            policy: Action to take upon negative feedback (WARN/ABORT/ADAPTIVE)
+                or a callable function. If callable, must have name "process_response",
+                arguments "model_response" and "input_content" (both strings), and
+                return InspectorAction. Defaults to ADAPTIVE.
 
         Returns:
             Inspector: Created and configured inspector agent.
@@ -124,7 +125,7 @@ def create_auto(
         cls,
         auto: InspectorAuto,
         name: Optional[Text] = None,
-        policy: InspectorPolicy = InspectorPolicy.ADAPTIVE,
+        policy: Union[InspectorPolicy, Callable] = InspectorPolicy.ADAPTIVE,
     ) -> Inspector:
         """Create a new inspector agent using automatic configuration.
 
@@ -136,11 +137,10 @@ def create_auto(
             auto (InspectorAuto): Pre-configured automatic inspector instance.
             name (Optional[Text], optional): Name for the inspector. If not provided,
                 uses the name from the auto configuration. Defaults to None.
-            policy (InspectorPolicy, optional): Action to take upon negative feedback:
-                - WARN: Log warning but continue execution
-                - ABORT: Stop execution on negative feedback
-                - ADAPTIVE: Dynamically decide based on context
-                Defaults to InspectorPolicy.ADAPTIVE.
+            policy: Action to take upon negative feedback (WARN/ABORT/ADAPTIVE)
+                or a callable function. If callable, must have name "process_response",
+                arguments "model_response" and "input_content" (both strings), and
+                return InspectorAction. Defaults to ADAPTIVE.
 
         Returns:
             Inspector: Created and configured inspector agent using automatic