Merge pull request #250 from evelynmitchell/master

.github/workflows/docker-compose.yml

@@ -0,0 +1,40 @@
+---
+# This is a github action to run docker-compose
+# docker-compose.yml
+# to run the docker build in the top level directory
+# to run the docker build in the tests directory and run the tests with pytest
+# docker-compose run --rm app pytest
+on:
+  push:
+    branches: [ main ]
+    paths:
+        - 'docker-compose.yml'
+        - 'Dockerfile'
+        - 'tests/**'
+        - 'app/**'
+        - 'app.py'
+        - 'requirements.txt'
+        - 'README.md'
+        - '.github/workflows/**'
+        - '.github/workflows/docker-compose.yml'
+        - '.github/workflows/main.yml'
+        - '.github/workflows/python-app.yml'
+        - '.github/workflows/python-app.yml'
+        - '.github/workflows'
+
+name: Docker Compose
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      # Add your build and test steps here
+
+      - name: Build and run docker services
+        run: |
+          docker-compose build
+          docker-compose up -d
+          docker-compose run --rm app pytest

.github/workflows/test.yml

@@ -10,6 +10,7 @@ on:
 env:
   POETRY_VERSION: "1.4.2"
 
+jobs:
   test:
     runs-on: ubuntu-latest
     strategy:
@@ -46,7 +47,7 @@ env:
             make extended_tests
           fi
         shell: bash
-    name: Python ${{ matrix.python-version }} ${{ matrix.test_type }}
+
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}

.github/workflows/testing.yml

@@ -16,7 +16,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.x
+          python-version: 3.11
 
       - name: Install dependencies
         run: |

Dockerfile

@@ -2,6 +2,8 @@
 # ==================================
 # Use an official Python runtime as a parent image
 FROM python:3.9-slim
+RUN apt-get update && apt-get -y install libgl1-mesa-dev libglib2.0-0; apt-get clean
+RUN pip install opencv-contrib-python-headless
 
 # Set environment variables
 ENV PYTHONDONTWRITEBYTECODE 1

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "2.5.7"
+version = "2.5.8"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]
@@ -52,11 +52,11 @@ ratelimit = "*"
 beautifulsoup4 = "*"
 cohere = "*"
 huggingface-hub = "*"
-pydantic = "1.10.12"
+pydantic = "2.*"
 tenacity = "*"
 Pillow = "*"
 chromadb = "*"
-opencv-python-headless = "*"
+opencv-python-headless
 tabulate = "*"
 termcolor = "*"
 black = "*"

requirements.txt

@@ -17,7 +17,7 @@ faiss-cpu
 openai==0.28.0
 attrs
 datasets
-pydantic==1.10.12
+pydantic>2
 soundfile
 huggingface-hub
 google-generativeai

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 langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.output import GenerationChunk
 from langchain.schema.prompt import PromptValue
@@ -219,21 +219,13 @@ def build_extra_kwargs(
 
     return extra_kwargs
 
-
-def convert_to_secret_str(value: Union[SecretStr, str]) -> SecretStr:
-    """Convert a string to a SecretStr if needed."""
-    if isinstance(value, SecretStr):
-        return value
-    return SecretStr(value)
-
-
 class _AnthropicCommon(BaseLanguageModel):
     client: Any = None  #: :meta private:
     async_client: Any = None  #: :meta private:
-    model: str = Field(default="claude-2", alias="model_name")
+    model: str ="claude-2"
     """Model name to use."""
 
-    max_tokens_to_sample: int = Field(default=256, alias="max_tokens")
+    max_tokens_to_sample: int =256
     """Denotes the number of tokens to predict per generation."""
 
     temperature: Optional[float] = None
@@ -253,14 +245,14 @@ class _AnthropicCommon(BaseLanguageModel):
 
     anthropic_api_url: Optional[str] = None
 
-    anthropic_api_key: Optional[SecretStr] = None
+    anthropic_api_key: Optional[str] = None
 
     HUMAN_PROMPT: Optional[str] = None
     AI_PROMPT: Optional[str] = None
     count_tokens: Optional[Callable[[str], int]] = None
-    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    model_kwargs: Dict[str, Any] = {}
 
-    @root_validator(pre=True)
+    @classmethod
     def build_extra(cls, values: Dict) -> Dict:
         extra = values.get("model_kwargs", {})
         all_required_field_names = get_pydantic_field_names(cls)
@@ -269,13 +261,11 @@ class _AnthropicCommon(BaseLanguageModel):
         )
         return values
 
-    @root_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(
+        values["anthropic_api_key"] = get_from_dict_or_env(
-            get_from_dict_or_env(
                 values, "anthropic_api_key", "ANTHROPIC_API_KEY"
-            )
         )
         # Get custom api url from environment.
         values["anthropic_api_url"] = get_from_dict_or_env(
@@ -376,14 +366,8 @@ class Anthropic(LLM, _AnthropicCommon):
             prompt = f"{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}"
             response = model(prompt)
     """
-
+    
-    class Config:
+    @classmethod
-        """Configuration for this pydantic object."""
-
-        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

@@ -38,6 +38,7 @@ from importlib.metadata import version
 
 from packaging.version import parse
 
+
 logger = logging.getLogger(__name__)
 
 
@@ -248,12 +249,8 @@ class BaseOpenAI(BaseLLM):
         data.get("model_name", "")
         return super().__new__(cls)
 
-    class Config:
+  
-        """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 +260,8 @@ class BaseOpenAI(BaseLLM):
         )
         return values
 
-    @root_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 +756,7 @@ class AzureOpenAI(BaseOpenAI):
     openai_api_type: str = ""
     openai_api_version: str = ""
 
-    @root_validator()
+    @classmethod
     def validate_azure_settings(cls, values: Dict) -> Dict:
         values["openai_api_version"] = get_from_dict_or_env(
             values,
@@ -847,7 +845,7 @@ class OpenAIChat(BaseLLM):
     disallowed_special: Union[Literal["all"], Collection[str]] = "all"
     """Set of special tokens that are not allowed。"""
 
-    @root_validator(pre=True)
+    @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 +863,8 @@ class OpenAIChat(BaseLLM):
         values["model_kwargs"] = extra
         return values
 
-    @root_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

@@ -29,14 +29,7 @@ from langchain.callbacks.manager import (
 )
 
 from langchain.load.serializable import Serializable
-from pydantic import (
+
-    BaseModel,
-    Extra,
-    Field,
-    create_model,
-    root_validator,
-    validate_arguments,
-)
 from langchain.schema.runnable import (
     Runnable,
     RunnableConfig,
@@ -48,62 +41,9 @@ class SchemaAnnotationError(TypeError):
     """Raised when 'args_schema' is missing or has an incorrect type annotation."""
 
 
-def _create_subset_model(
-    name: str, model: BaseModel, field_names: list
-) -> Type[BaseModel]:
-    """Create a pydantic model with only a subset of model's fields."""
-    fields = {}
-    for field_name in field_names:
-        field = model.__fields__[field_name]
-        fields[field_name] = (field.outer_type_, field.field_info)
-    return create_model(name, **fields)  # type: ignore
-
 
-def _get_filtered_args(
-    inferred_model: Type[BaseModel],
-    func: Callable,
-) -> dict:
-    """Get the arguments from a function's signature."""
-    schema = inferred_model.schema()["properties"]
-    valid_keys = signature(func).parameters
-    return {
-        k: schema[k]
-        for k in valid_keys
-        if k not in ("run_manager", "callbacks")
-    }
 
 
-class _SchemaConfig:
-    """Configuration for the pydantic model."""
-
-    extra: Any = Extra.forbid
-    arbitrary_types_allowed: bool = True
-
-
-def create_schema_from_function(
-    model_name: str,
-    func: Callable,
-) -> Type[BaseModel]:
-    """Create a pydantic schema from a function's signature.
-    Args:
-        model_name: Name to assign to the generated pydandic schema
-        func: Function to generate the schema from
-    Returns:
-        A pydantic model with the same arguments as the function
-    """
-    # https://docs.pydantic.dev/latest/usage/validation_decorator/
-    validated = validate_arguments(func, config=_SchemaConfig)  # type: ignore
-    inferred_model = validated.model  # type: ignore
-    if "run_manager" in inferred_model.__fields__:
-        del inferred_model.__fields__["run_manager"]
-    if "callbacks" in inferred_model.__fields__:
-        del inferred_model.__fields__["callbacks"]
-    # Pydantic adds placeholder virtual fields we need to strip
-    valid_properties = _get_filtered_args(inferred_model, func)
-    return _create_subset_model(
-        f"{model_name}Schema", inferred_model, list(valid_properties)
-    )
-
 
 class ToolException(Exception):
     """An optional exception that tool throws when execution error occurs.
@@ -131,7 +71,7 @@ class BaseTool(RunnableSerializable[Union[str, Dict], Any]):
         if args_schema_type is not None:
             if (
                 args_schema_type is None
-                or args_schema_type == BaseModel
+                # or args_schema_type == BaseModel
             ):
                 # Throw errors for common mis-annotations.
                 # TODO: Use get_args / get_origin and fully
@@ -168,11 +108,10 @@ class ChildTool(BaseTool):
     verbose: bool = False
     """Whether to log the tool's progress."""
 
-    callbacks: Callbacks = Field(default=None, exclude=True)
+    callbacks:  Callbacks = None
     """Callbacks to be called during tool execution."""
-    callback_manager: Optional[BaseCallbackManager] = Field(
+    # TODO: I don't know how to remove Field here
-        default=None, exclude=True
+    callback_manager: Optional[BaseCallbackManager]  = None
-    )
     """Deprecated. Please use callbacks instead."""
     tags: Optional[List[str]] = None
     """Optional list of tags associated with the tool. Defaults to None
@@ -192,9 +131,11 @@ 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."""
-
+        model_config = {}
         arbitrary_types_allowed = True
 
     @property
@@ -214,7 +155,8 @@ class ChildTool(BaseTool):
     # --- Runnable ---
 
     @property
-    def input_schema(self) -> Type[BaseModel]:
+    # TODO 
+    def input_schema(self):
         """The tool's input schema."""
         if self.args_schema is not None:
             return self.args_schema
@@ -276,7 +218,7 @@ class ChildTool(BaseTool):
                 }
         return tool_input
 
-    @root_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:
@@ -671,9 +613,7 @@ class StructuredTool(BaseTool):
     """Tool that can operate on any number of inputs."""
 
     description: str = ""
-    args_schema: Type[BaseModel] = Field(
+   
-        ..., description="The tool schema."
-    )
     """The input arguments' schema."""
     func: Optional[Callable[..., Any]]
     """The function to run when the tool is called."""

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)
 

tests/Dockerfile

@@ -2,6 +2,8 @@
 # -==================
 # Use an official Python runtime as a parent image
 FROM python:3.9-slim
+RUN apt-get update && apt-get -y install libgl1-mesa-dev libglib2.0-0; apt-get clean
+RUN pip install opencv-contrib-python-headless
 
 # Set environment variables to make Python output unbuffered and disable the PIP cache
 ENV PYTHONDONTWRITEBYTECODE 1