Merge branch 'master' into zamilmajdy/code-validation

autogpt/autogpt/agent_factory/configurators.py

@@ -104,5 +104,5 @@ def create_agent_state(
             allow_fs_access=not app_config.restrict_to_workspace,
             use_functions_api=app_config.openai_functions,
         ),
-        history=Agent.default_settings.history.copy(deep=True),
+        history=Agent.default_settings.history.model_copy(deep=True),
     )

autogpt/autogpt/agent_factory/profile_generator.py

@@ -137,7 +137,7 @@ class AgentProfileGeneratorConfiguration(SystemConfiguration):
                     required=True,
                 ),
             },
-        ).dict()
+        ).model_dump()
     )
 
 
@@ -156,7 +156,7 @@ def __init__(
         self._model_classification = model_classification
         self._system_prompt_message = system_prompt
         self._user_prompt_template = user_prompt_template
-        self._create_agent_function = CompletionModelFunction.parse_obj(
+        self._create_agent_function = CompletionModelFunction.model_validate(
             create_agent_function
         )
 
@@ -222,7 +222,7 @@ async def generate_agent_profile_for_task(
     AIConfig: The AIConfig object tailored to the user's input
     """
     agent_profile_generator = AgentProfileGenerator(
-        **AgentProfileGenerator.default_configuration.dict()  # HACK
+        **AgentProfileGenerator.default_configuration.model_dump()  # HACK
     )
 
     prompt = agent_profile_generator.build_prompt(task)

autogpt/autogpt/agents/agent.py

@@ -106,7 +106,9 @@ def __init__(
         super().__init__(settings)
 
         self.llm_provider = llm_provider
-        prompt_config = prompt_strategy_class.default_configuration.copy(deep=True)
+        prompt_config = prompt_strategy_class.default_configuration.model_copy(
+            deep=True
+        )
         prompt_config.use_functions_api = (
             settings.config.use_functions_api
             # Anthropic currently doesn't support tools + prefilling :(
@@ -167,7 +169,7 @@ async def propose_action(self) -> OneShotAgentActionProposal:
         constraints = await self.run_pipeline(DirectiveProvider.get_constraints)
         best_practices = await self.run_pipeline(DirectiveProvider.get_best_practices)
 
-        directives = self.state.directives.copy(deep=True)
+        directives = self.state.directives.model_copy(deep=True)
         directives.resources += resources
         directives.constraints += constraints
         directives.best_practices += best_practices

autogpt/autogpt/agents/prompt_strategies/one_shot.py

@@ -35,17 +35,16 @@ class AssistantThoughts(ModelWithSummary):
         "you can leave it empty",
     )
     observations: str = Field(
-        ..., description="Relevant observations from your last actions (if any)"
+        description="Relevant observations from your last actions (if any)"
     )
-    text: str = Field(..., description="Thoughts")
-    reasoning: str = Field(..., description="Reasoning behind the thoughts")
-    self_criticism: str = Field(..., description="Constructive self-criticism")
+    text: str = Field(description="Thoughts")
+    reasoning: str = Field(description="Reasoning behind the thoughts")
+    self_criticism: str = Field(description="Constructive self-criticism")
     plan: list[str] = Field(
-        ...,
         description="Short list that conveys the long-term plan, "
         "considering the progress on your task so far",
     )
-    speak: str = Field(..., description="Summary of thoughts, to say to user")
+    speak: str = Field(description="Summary of thoughts, to say to user")
 
     def summary(self) -> str:
         return self.text
@@ -105,7 +104,9 @@ def __init__(
         logger: Logger,
     ):
         self.config = configuration
-        self.response_schema = JSONSchema.from_dict(OneShotAgentActionProposal.schema())
+        self.response_schema = JSONSchema.from_dict(
+            OneShotAgentActionProposal.model_json_schema()
+        )
         self.logger = logger
 
     @property
@@ -192,7 +193,7 @@ def build_system_prompt(
         )
 
     def response_format_instruction(self, use_functions_api: bool) -> tuple[str, str]:
-        response_schema = self.response_schema.copy(deep=True)
+        response_schema = self.response_schema.model_copy(deep=True)
         assert response_schema.properties
         if use_functions_api and "use_tool" in response_schema.properties:
             del response_schema.properties["use_tool"]
@@ -284,6 +285,8 @@ def parse_response_content(
                 raise InvalidAgentResponseError("Assistant did not use a tool")
             assistant_reply_dict["use_tool"] = response.tool_calls[0].function
 
-        parsed_response = OneShotAgentActionProposal.parse_obj(assistant_reply_dict)
+        parsed_response = OneShotAgentActionProposal.model_validate(
+            assistant_reply_dict
+        )
         parsed_response.raw_message = response.copy()
         return parsed_response

autogpt/autogpt/app/agent_protocol_server.py

@@ -314,7 +314,7 @@ async def execute_step(self, task_id: str, step_request: StepRequestBody) -> Ste
                             ""
                             if tool_result is None
                             else (
-                                orjson.loads(tool_result.json())
+                                orjson.loads(tool_result.model_dump_json())
                                 if not isinstance(tool_result, ActionErrorResult)
                                 else {
                                     "error": str(tool_result.error),
@@ -327,7 +327,7 @@ async def execute_step(self, task_id: str, step_request: StepRequestBody) -> Ste
                 if last_proposal and tool_result
                 else {}
             ),
-            **assistant_response.dict(),
+            **assistant_response.model_dump(),
         }
 
         task_cumulative_cost = agent.llm_provider.get_incurred_cost()
@@ -451,15 +451,17 @@ def _get_task_llm_provider(self, task: Task, step_id: str = "") -> MultiProvider
         """
         task_llm_budget = self._task_budgets[task.task_id]
 
-        task_llm_provider_config = self.llm_provider._configuration.copy(deep=True)
+        task_llm_provider_config = self.llm_provider._configuration.model_copy(
+            deep=True
+        )
         _extra_request_headers = task_llm_provider_config.extra_request_headers
         _extra_request_headers["AP-TaskID"] = task.task_id
         if step_id:
             _extra_request_headers["AP-StepID"] = step_id
         if task.additional_input and (user_id := task.additional_input.get("user_id")):
             _extra_request_headers["AutoGPT-UserID"] = user_id
 
-        settings = self.llm_provider._settings.copy()
+        settings = self.llm_provider._settings.model_copy()
         settings.budget = task_llm_budget
         settings.configuration = task_llm_provider_config
         task_llm_provider = self.llm_provider.__class__(

autogpt/autogpt/app/config.py

@@ -5,15 +5,15 @@
 import os
 import re
 from pathlib import Path
-from typing import Any, Optional, Union
+from typing import Optional, Union
 
 import forge
 from forge.config.base import BaseConfig
 from forge.llm.providers import CHAT_MODELS, ModelName
 from forge.llm.providers.openai import OpenAICredentials, OpenAIModelName
 from forge.logging.config import LoggingConfig
 from forge.models.config import Configurable, UserConfigurable
-from pydantic import SecretStr, validator
+from pydantic import SecretStr, ValidationInfo, field_validator
 
 logger = logging.getLogger(__name__)
 
@@ -91,15 +91,15 @@ class AppConfig(BaseConfig):
         default=AZURE_CONFIG_FILE, from_env="AZURE_CONFIG_FILE"
     )
 
-    @validator("openai_functions")
-    def validate_openai_functions(cls, v: bool, values: dict[str, Any]):
-        if v:
-            smart_llm = values["smart_llm"]
+    @field_validator("openai_functions")
+    def validate_openai_functions(cls, value: bool, info: ValidationInfo):
+        if value:
+            smart_llm = info.data["smart_llm"]
             assert CHAT_MODELS[smart_llm].has_function_call_api, (
                 f"Model {smart_llm} does not support tool calling. "
                 "Please disable OPENAI_FUNCTIONS or choose a suitable model."
             )
-        return v
+        return value
 
 
 class ConfigBuilder(Configurable[AppConfig]):