pydantic bump

swarms/memory/schemas.py

@@ -12,7 +12,7 @@ class TaskInput(BaseModel):
         description=(
             "The input parameters for the task. Any value is allowed."
         ),
-        example='{\n"debug": false,\n"mode": "benchmarks"\n}',
+        examples=['{\n"debug": false,\n"mode": "benchmarks"\n}'],
     )
 
 
@@ -20,17 +20,17 @@ class Artifact(BaseModel):
     artifact_id: str = Field(
         ...,
         description="Id of the artifact",
-        example="b225e278-8b4c-4f99-a696-8facf19f0e56",
+        examples=["b225e278-8b4c-4f99-a696-8facf19f0e56"],
     )
     file_name: str = Field(
-        ..., description="Filename of the artifact", example="main.py"
+        ..., description="Filename of the artifact", examples=["main.py"]
     )
     relative_path: Optional[str] = Field(
         None,
         description=(
             "Relative path of the artifact in the agent's workspace"
         ),
-        example="python/code/",
+        examples=["python/code/"],
     )
 
 
@@ -41,7 +41,7 @@ class ArtifactUpload(BaseModel):
         description=(
             "Relative path of the artifact in the agent's workspace"
         ),
-        example="python/code/",
+        examples=["python/code/"],
     )
 
 
@@ -52,7 +52,7 @@ class StepInput(BaseModel):
             "Input parameters for the task step. Any value is"
             " allowed."
         ),
-        example='{\n"file_to_refactor": "models.py"\n}',
+        examples=['{\n"file_to_refactor": "models.py"\n}'],
     )
 
 
@@ -63,7 +63,7 @@ class StepOutput(BaseModel):
             "Output that the task step has produced. Any value is"
             " allowed."
         ),
-        example='{\n"tokens": 7894,\n"estimated_cost": "0,24$"\n}',
+        examples=['{\n"tokens": 7894,\n"estimated_cost": "0,24$"\n}'],
     )
 
 
@@ -71,9 +71,9 @@ class TaskRequestBody(BaseModel):
     input: Optional[str] = Field(
         None,
         description="Input prompt for the task.",
-        example=(
+        examples=[(
             "Write the words you receive to the file 'output.txt'."
-        ),
+        )],
     )
     additional_input: Optional[TaskInput] = None
 
@@ -82,15 +82,15 @@ class Task(TaskRequestBody):
     task_id: str = Field(
         ...,
         description="The ID of the task.",
-        example="50da533e-3904-4401-8a07-c49adf88b5eb",
+        examples=["50da533e-3904-4401-8a07-c49adf88b5eb"],
     )
     artifacts: List[Artifact] = Field(
         [],
         description="A list of artifacts that the task has produced.",
-        example=[
+        examples=[[
             "7a49f31c-f9c6-4346-a22c-e32bc5af4d8e",
             "ab7b4091-2560-4692-a4fe-d831ea3ca7d6",
-        ],
+        ]],
     )
 
 
@@ -98,7 +98,7 @@ class StepRequestBody(BaseModel):
     input: Optional[str] = Field(
         None,
         description="Input prompt for the step.",
-        example="Washington",
+        examples=["Washington"],
     )
     additional_input: Optional[StepInput] = None
 
@@ -113,17 +113,17 @@ class Step(StepRequestBody):
     task_id: str = Field(
         ...,
         description="The ID of the task this step belongs to.",
-        example="50da533e-3904-4401-8a07-c49adf88b5eb",
+        examples=["50da533e-3904-4401-8a07-c49adf88b5eb"],
     )
     step_id: str = Field(
         ...,
         description="The ID of the task step.",
-        example="6bb1801a-fd80-45e8-899a-4dd723cc602e",
+        examples=["6bb1801a-fd80-45e8-899a-4dd723cc602e"],
     )
     name: Optional[str] = Field(
         None,
         description="The name of the task step.",
-        example="Write to file",
+        examples=["Write to file"],
     )
     status: Status = Field(
         ..., description="The status of the task step."
@@ -131,11 +131,11 @@ class Step(StepRequestBody):
     output: Optional[str] = Field(
         None,
         description="Output of the task step.",
-        example=(
+        examples=[(
             "I am going to use the write_to_file command and write"
             " Washington to a file called output.txt"
             " <write_to_file('output.txt', 'Washington')"
-        ),
+        )],
     )
     additional_output: Optional[StepOutput] = None
     artifacts: List[Artifact] = Field(

swarms/models/anthropic.py

@@ -24,7 +24,7 @@ from langchain.callbacks.manager import (
     CallbackManagerForLLMRun,
 )
 from langchain.llms.base import LLM
-from pydantic import Field, SecretStr, root_validator
+from pydantic import model_validator, ConfigDict, Field, SecretStr
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.output import GenerationChunk
 from langchain.schema.prompt import PromptValue
@@ -260,7 +260,8 @@ class _AnthropicCommon(BaseLanguageModel):
     count_tokens: Optional[Callable[[str], int]] = None
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
+    @classmethod
     def build_extra(cls, values: Dict) -> Dict:
         extra = values.get("model_kwargs", {})
         all_required_field_names = get_pydantic_field_names(cls)
@@ -269,7 +270,8 @@ class _AnthropicCommon(BaseLanguageModel):
         )
         return values
 
-    @root_validator()
+    @model_validator()
+    @classmethod
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
         values["anthropic_api_key"] = convert_to_secret_str(
@@ -376,14 +378,10 @@ class Anthropic(LLM, _AnthropicCommon):
             prompt = f"{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}"
             response = model(prompt)
     """
+    model_config = ConfigDict(populate_by_name=True, arbitrary_types_allowed=True)
 
-    class Config:
+    @model_validator()
-        """Configuration for this pydantic object."""
+    @classmethod
-
-        allow_population_by_field_name = True
-        arbitrary_types_allowed = True
-
-    @root_validator()
     def raise_warning(cls, values: Dict) -> Dict:
         """Raise warning that this class is deprecated."""
         warnings.warn(

swarms/models/cohere_chat.py

@@ -16,7 +16,7 @@ from langchain.callbacks.manager import (
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.load.serializable import Serializable
-from pydantic import Extra, Field, root_validator
+from pydantic import model_validator, ConfigDict, Field
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
@@ -85,7 +85,8 @@ class BaseCohere(Serializable):
     user_agent: str = "langchain"
     """Identifier for the application making the request."""
 
-    @root_validator()
+    @model_validator()
+    @classmethod
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
         try:
@@ -145,11 +146,7 @@ class Cohere(LLM, BaseCohere):
 
     max_retries: int = 10
     """Maximum number of retries to make when generating."""
-
+    model_config = ConfigDict(extra="forbid")
-    class Config:
-        """Configuration for this pydantic object."""
-
-        extra = Extra.forbid
 
     @property
     def _default_params(self) -> Dict[str, Any]:

swarms/models/dalle3.py

@@ -13,7 +13,7 @@ from cachetools import TTLCache
 from dotenv import load_dotenv
 from openai import OpenAI
 from PIL import Image
-from pydantic import validator
+from pydantic import field_validator
 from termcolor import colored
 
 load_dotenv()
@@ -92,7 +92,8 @@ class Dalle3:
 
         arbitrary_types_allowed = True
 
-    @validator("max_retries", "time_seconds")
+    @field_validator("max_retries", "time_seconds")
+    @classmethod
     def must_be_positive(cls, value):
         if value <= 0:
             raise ValueError("Must be positive")

swarms/models/eleven_labs.py

@@ -3,7 +3,7 @@ from enum import Enum
 from typing import Any, Dict, Union
 
 from langchain.utils import get_from_dict_or_env
-from pydantic import root_validator
+from pydantic import model_validator
 
 from swarms.tools.tool import BaseTool
 
@@ -59,7 +59,8 @@ class ElevenLabsText2SpeechTool(BaseTool):
         " Italian, French, Portuguese, and Hindi. "
     )
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
+    @classmethod
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key exists in environment."""
         _ = get_from_dict_or_env(

swarms/models/fastvit.py

@@ -20,6 +20,8 @@ class ClassificationResult(BaseModel):
     class_id: List[StrictInt]
     confidence: List[StrictFloat]
 
+    # TODO[pydantic]: We couldn't refactor the `validator`, please replace it by `field_validator` manually.
+    # Check https://docs.pydantic.dev/dev-v2/migration/#changes-to-validators for more information.
     @validator("class_id", "confidence", pre=True, each_item=True)
     def check_list_contents(cls, v):
         assert isinstance(v, int) or isinstance(

swarms/models/kosmos2.py

@@ -20,6 +20,8 @@ class Detections(BaseModel):
         ), "All fields must have the same length."
         return values
 
+    # TODO[pydantic]: We couldn't refactor the `validator`, please replace it by `field_validator` manually.
+    # Check https://docs.pydantic.dev/dev-v2/migration/#changes-to-validators for more information.
     @validator(
         "xyxy", "class_id", "confidence", pre=True, each_item=True
     )

swarms/models/openai_embeddings.py

@@ -16,7 +16,7 @@ from typing import (
 )
 
 import numpy as np
-from pydantic import BaseModel, Extra, Field, root_validator
+from pydantic import model_validator, ConfigDict, BaseModel, Field
 from tenacity import (
     AsyncRetrying,
     before_sleep_log,
@@ -186,7 +186,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
 
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model: str = "text-embedding-ada-002"
     deployment: str = model  # to support Azure OpenAI Service custom deployment names
     openai_api_version: Optional[str] = None
@@ -227,13 +227,10 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
     """Whether to show a progress bar when embedding."""
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for `create` call not explicitly specified."""
+    model_config = ConfigDict(extra="forbid")
 
-    class Config:
+    @model_validator(mode="before")
-        """Configuration for this pydantic object."""
+    @classmethod
-
-        extra = Extra.forbid
-
-    @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
@@ -264,7 +261,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         values["model_kwargs"] = extra
         return values
 
-    @root_validator()
+    @model_validator()
+    @classmethod
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
         values["openai_api_key"] = get_from_dict_or_env(

swarms/models/openai_function_caller.py

@@ -2,7 +2,7 @@ from typing import Any, Dict, List, Optional, Union
 
 import openai
 import requests
-from pydantic import BaseModel, validator
+from pydantic import field_validator, BaseModel
 from tenacity import (
     retry,
     stop_after_attempt,
@@ -78,7 +78,8 @@ class FunctionSpecification(BaseModel):
     parameters: Dict[str, Any]
     required: Optional[List[str]] = None
 
-    @validator("parameters")
+    @field_validator("parameters")
+    @classmethod
     def check_parameters(cls, params):
         if not isinstance(params, dict):
             raise ValueError("Parameters must be a dictionary.")

swarms/models/openai_models.py

@@ -37,6 +37,7 @@ from langchain.utils.utils import build_extra_kwargs
 from importlib.metadata import version
 
 from packaging.version import parse
+from pydantic import model_validator, ConfigDict
 
 logger = logging.getLogger(__name__)
 
@@ -247,13 +248,10 @@ class BaseOpenAI(BaseLLM):
         """Initialize the OpenAI object."""
         data.get("model_name", "")
         return super().__new__(cls)
+    model_config = ConfigDict(populate_by_name=True)
 
-    class Config:
+    @model_validator(mode="before")
-        """Configuration for this pydantic object."""
+    @classmethod
-
-        allow_population_by_field_name = True
-
-    @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = get_pydantic_field_names(cls)
@@ -263,7 +261,8 @@ class BaseOpenAI(BaseLLM):
         )
         return values
 
-    @root_validator()
+    @model_validator()
+    @classmethod
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
         values["openai_api_key"] = get_from_dict_or_env(
@@ -758,7 +757,8 @@ class AzureOpenAI(BaseOpenAI):
     openai_api_type: str = ""
     openai_api_version: str = ""
 
-    @root_validator()
+    @model_validator()
+    @classmethod
     def validate_azure_settings(cls, values: Dict) -> Dict:
         values["openai_api_version"] = get_from_dict_or_env(
             values,
@@ -847,7 +847,8 @@ class OpenAIChat(BaseLLM):
     disallowed_special: Union[Literal["all"], Collection[str]] = "all"
     """Set of special tokens that are not allowed。"""
 
-    @root_validator(pre=True)
+    @model_validator(mode="before")
+    @classmethod
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
         all_required_field_names = {
@@ -865,7 +866,8 @@ class OpenAIChat(BaseLLM):
         values["model_kwargs"] = extra
         return values
 
-    @root_validator()
+    @model_validator()
+    @classmethod
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
         openai_api_key = get_from_dict_or_env(

swarms/models/palm.py

@@ -15,6 +15,7 @@ from tenacity import (
     stop_after_attempt,
     wait_exponential,
 )
+from pydantic import model_validator
 
 logger = logging.getLogger(__name__)
 
@@ -104,7 +105,8 @@ class GooglePalm(BaseLLM, BaseModel):
     """Number of chat completions to generate for each prompt. Note that the API may
        not return the full n completions if duplicates are generated."""
 
-    @root_validator()
+    @model_validator()
+    @classmethod
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate api key, python package exists."""
         google_api_key = get_from_dict_or_env(

swarms/models/ssd_1b.py

@@ -9,7 +9,7 @@ import backoff
 import torch
 from diffusers import StableDiffusionXLPipeline
 from PIL import Image
-from pydantic import validator
+from pydantic import field_validator
 from termcolor import colored
 from cachetools import TTLCache
 
@@ -72,7 +72,8 @@ class SSD1B:
 
         arbitrary_types_allowed = True
 
-    @validator("max_retries", "time_seconds")
+    @field_validator("max_retries", "time_seconds")
+    @classmethod
     def must_be_positive(cls, value):
         if value <= 0:
             raise ValueError("Must be positive")

swarms/models/timm.py

@@ -2,17 +2,14 @@ from typing import List
 
 import timm
 import torch
-from pydantic import BaseModel
+from pydantic import ConfigDict, BaseModel
 
 
 class TimmModelInfo(BaseModel):
     model_name: str
     pretrained: bool
     in_chans: int
-
+    model_config = ConfigDict(strict=True)
-    class Config:
-        # Use strict typing for all fields
-        strict = True
 
 
 class TimmModel:

swarms/tools/tool.py

@@ -30,11 +30,10 @@ from langchain.callbacks.manager import (
 
 from langchain.load.serializable import Serializable
 from pydantic import (
-    BaseModel,
+    model_validator, BaseModel,
     Extra,
     Field,
     create_model,
-    root_validator,
     validate_arguments,
 )
 from langchain.schema.runnable import (
@@ -192,6 +191,8 @@ class ChildTool(BaseTool):
     ] = False
     """Handle the content of the ToolException thrown."""
 
+    # TODO[pydantic]: The `Config` class inherits from another class, please create the `model_config` manually.
+    # Check https://docs.pydantic.dev/dev-v2/migration/#changes-to-config for more information.
     class Config(Serializable.Config):
         """Configuration for this pydantic object."""
 
@@ -276,7 +277,8 @@ class ChildTool(BaseTool):
                 }
         return tool_input
 
-    @root_validator()
+    @model_validator()
+    @classmethod
     def raise_deprecation(cls, values: Dict) -> Dict:
         """Raise deprecation warning if callback_manager is used."""
         if values.get("callback_manager") is not None:

swarms/utils/serializable.py

@@ -1,7 +1,7 @@
 from abc import ABC
 from typing import Any, Dict, List, Literal, TypedDict, Union, cast
 
-from pydantic import BaseModel, PrivateAttr
+from pydantic import ConfigDict, BaseModel, PrivateAttr
 
 
 class BaseSerialized(TypedDict):
@@ -64,9 +64,7 @@ class Serializable(BaseModel, ABC):
         constructor.
         """
         return {}
-
+    model_config = ConfigDict(extra="ignore")
-    class Config:
-        extra = "ignore"
 
     _lc_kwargs = PrivateAttr(default_factory=dict)