Merge pull request #266 from kyegomez/revert-250-master

Revert "pydantic bump fix for #249 "

.github/workflows/test.yml

@@ -10,7 +10,6 @@ on:
 env:
   POETRY_VERSION: "1.4.2"
 
-jobs:
   test:
     runs-on: ubuntu-latest
     strategy:
@@ -47,7 +46,7 @@ jobs:
             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: 3.11
+          python-version: 3.x
 
       - name: Install dependencies
         run: |

Dockerfile

@@ -2,8 +2,6 @@
 # ==================================
 # 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 = "2.5.8"
+version = "2.5.7"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <[email protected]>"]
@@ -52,11 +52,11 @@ ratelimit = "*"
 beautifulsoup4 = "*"
 cohere = "*"
 huggingface-hub = "*"
-pydantic = "2.*"
+pydantic = "1.10.12"
 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>2
+pydantic==1.10.12
 soundfile
 huggingface-hub
 google-generativeai

swarms/memory/schemas.py

@@ -12,25 +12,25 @@ class TaskInput(BaseModel):
         description=(
             "The input parameters for the task. Any value is allowed."
         ),
-        examples=['{\n"debug": false,\n"mode": "benchmarks"\n}'],
+        example='{\n"debug": false,\n"mode": "benchmarks"\n}',
     )
 
 
 class Artifact(BaseModel):
     artifact_id: str = Field(
         ...,
         description="Id of the artifact",
-        examples=["b225e278-8b4c-4f99-a696-8facf19f0e56"],
+        example="b225e278-8b4c-4f99-a696-8facf19f0e56",
     )
     file_name: str = Field(
-        ..., description="Filename of the artifact", examples=["main.py"]
+        ..., description="Filename of the artifact", example="main.py"
     )
     relative_path: Optional[str] = Field(
         None,
         description=(
             "Relative path of the artifact in the agent's workspace"
         ),
-        examples=["python/code/"],
+        example="python/code/",
     )
 
 
@@ -41,7 +41,7 @@ class ArtifactUpload(BaseModel):
         description=(
             "Relative path of the artifact in the agent's workspace"
         ),
-        examples=["python/code/"],
+        example="python/code/",
     )
 
 
@@ -52,7 +52,7 @@ class StepInput(BaseModel):
             "Input parameters for the task step. Any value is"
             " allowed."
         ),
-        examples=['{\n"file_to_refactor": "models.py"\n}'],
+        example='{\n"file_to_refactor": "models.py"\n}',
     )
 
 
@@ -63,17 +63,17 @@ class StepOutput(BaseModel):
             "Output that the task step has produced. Any value is"
             " allowed."
         ),
-        examples=['{\n"tokens": 7894,\n"estimated_cost": "0,24$"\n}'],
+        example='{\n"tokens": 7894,\n"estimated_cost": "0,24$"\n}',
     )
 
 
 class TaskRequestBody(BaseModel):
     input: Optional[str] = Field(
         None,
         description="Input prompt for the task.",
-        examples=[(
+        example=(
             "Write the words you receive to the file 'output.txt'."
-        )],
+        ),
     )
     additional_input: Optional[TaskInput] = None
 
@@ -82,23 +82,23 @@ class Task(TaskRequestBody):
     task_id: str = Field(
         ...,
         description="The ID of the task.",
-        examples=["50da533e-3904-4401-8a07-c49adf88b5eb"],
+        example="50da533e-3904-4401-8a07-c49adf88b5eb",
     )
     artifacts: List[Artifact] = Field(
         [],
         description="A list of artifacts that the task has produced.",
-        examples=[[
+        example=[
             "7a49f31c-f9c6-4346-a22c-e32bc5af4d8e",
             "ab7b4091-2560-4692-a4fe-d831ea3ca7d6",
-        ]],
+        ],
     )
 
 
 class StepRequestBody(BaseModel):
     input: Optional[str] = Field(
         None,
         description="Input prompt for the step.",
-        examples=["Washington"],
+        example="Washington",
     )
     additional_input: Optional[StepInput] = None
 
@@ -113,29 +113,29 @@ class Step(StepRequestBody):
     task_id: str = Field(
         ...,
         description="The ID of the task this step belongs to.",
-        examples=["50da533e-3904-4401-8a07-c49adf88b5eb"],
+        example="50da533e-3904-4401-8a07-c49adf88b5eb",
     )
     step_id: str = Field(
         ...,
         description="The ID of the task step.",
-        examples=["6bb1801a-fd80-45e8-899a-4dd723cc602e"],
+        example="6bb1801a-fd80-45e8-899a-4dd723cc602e",
     )
     name: Optional[str] = Field(
         None,
         description="The name of the task step.",
-        examples=["Write to file"],
+        example="Write to file",
     )
     status: Status = Field(
         ..., description="The status of the task step."
     )
     output: Optional[str] = Field(
         None,
         description="Output of the task step.",
-        examples=[(
+        example=(
             "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 @@
     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
@@ -219,13 +219,21 @@ 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 ="claude-2"
+    model: str = Field(default="claude-2", alias="model_name")
     """Model name to use."""
 
-    max_tokens_to_sample: int =256
+    max_tokens_to_sample: int = Field(default=256, alias="max_tokens")
     """Denotes the number of tokens to predict per generation."""
 
     temperature: Optional[float] = None
@@ -245,14 +253,14 @@ class _AnthropicCommon(BaseLanguageModel):
 
     anthropic_api_url: Optional[str] = None
 
-    anthropic_api_key: Optional[str] = None
+    anthropic_api_key: Optional[SecretStr] = None
 
     HUMAN_PROMPT: Optional[str] = None
     AI_PROMPT: Optional[str] = None
     count_tokens: Optional[Callable[[str], int]] = None
-    model_kwargs: Dict[str, Any] = {}
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
 
-    @classmethod
+    @root_validator(pre=True)
     def build_extra(cls, values: Dict) -> Dict:
         extra = values.get("model_kwargs", {})
         all_required_field_names = get_pydantic_field_names(cls)
@@ -261,11 +269,13 @@ def build_extra(cls, values: Dict) -> Dict:
         )
         return values
 
-    @classmethod
+    @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
-        values["anthropic_api_key"] = get_from_dict_or_env(
+        values["anthropic_api_key"] = convert_to_secret_str(
+            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(
@@ -366,8 +376,14 @@ class Anthropic(LLM, _AnthropicCommon):
             prompt = f"{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}"
             response = model(prompt)
     """
-
-    @classmethod
+
+    class Config:
+        """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.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.load.serializable import Serializable
-from pydantic import model_validator, ConfigDict, Field
+from pydantic import Extra, Field, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
@@ -85,8 +85,7 @@ class BaseCohere(Serializable):
     user_agent: str = "langchain"
     """Identifier for the application making the request."""
 
-    @model_validator()
-    @classmethod
+    @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
         try:
@@ -146,7 +145,11 @@ 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 dotenv import load_dotenv
 from openai import OpenAI
 from PIL import Image
-from pydantic import field_validator
+from pydantic import validator
 from termcolor import colored
 
 load_dotenv()
@@ -92,8 +92,7 @@ class Config:
 
         arbitrary_types_allowed = True
 
-    @field_validator("max_retries", "time_seconds")
-    @classmethod
+    @validator("max_retries", "time_seconds")
     def must_be_positive(cls, value):
         if value <= 0:
             raise ValueError("Must be positive")

swarms/models/eleven_labs.py

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