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Feat/remove langchain (#1668)
* feat: add initial changes from langchain * feat: remove kwargs of being processed * feat: remove langchain, update uv.lock and fix type_hint * feat: change docs * feat: remove forced requirements for parameter * feat add tests for new structure tool * feat: fix tests and adapt code for args * fix tool calling for langchain tools * doc strings --------- Co-authored-by: Eduardo Chiarotti <dudumelgaco@hotmail.com>
This commit is contained in:
Brandon Hancock (bhancock_ai)
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GitHub
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293305790d
commit
366bbbbea3
@@ -19,6 +19,7 @@ from crewai.agents.agent_builder.utilities.base_token_process import TokenProces
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from crewai.agents.cache.cache_handler import CacheHandler
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from crewai.agents.tools_handler import ToolsHandler
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from crewai.tools import BaseTool
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from crewai.tools.base_tool import Tool
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from crewai.utilities import I18N, Logger, RPMController
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from crewai.utilities.config import process_config
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@@ -106,7 +107,7 @@ class BaseAgent(ABC, BaseModel):
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default=False,
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description="Enable agent to delegate and ask questions among each other.",
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)
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tools: Optional[List[BaseTool]] = Field(
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tools: Optional[List[Any]] = Field(
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default_factory=list, description="Tools at agents' disposal"
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)
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max_iter: Optional[int] = Field(
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@@ -135,6 +136,35 @@ class BaseAgent(ABC, BaseModel):
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def process_model_config(cls, values):
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return process_config(values, cls)
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@field_validator("tools")
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@classmethod
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def validate_tools(cls, tools: List[Any]) -> List[BaseTool]:
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"""Validate and process the tools provided to the agent.
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This method ensures that each tool is either an instance of BaseTool
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or an object with 'name', 'func', and 'description' attributes. If the
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tool meets these criteria, it is processed and added to the list of
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tools. Otherwise, a ValueError is raised.
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"""
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processed_tools = []
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for tool in tools:
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if isinstance(tool, BaseTool):
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processed_tools.append(tool)
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elif (
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hasattr(tool, "name")
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and hasattr(tool, "func")
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and hasattr(tool, "description")
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):
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# Tool has the required attributes, create a Tool instance
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processed_tools.append(Tool.from_langchain(tool))
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else:
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raise ValueError(
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f"Invalid tool type: {type(tool)}. "
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"Tool must be an instance of BaseTool or "
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"an object with 'name', 'func', and 'description' attributes."
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)
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return processed_tools
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@model_validator(mode="after")
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def validate_and_set_attributes(self):
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# Validate required fields
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@@ -1,7 +1,8 @@
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from abc import ABC, abstractmethod
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from inspect import signature
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from typing import Any, Callable, Type, get_args, get_origin
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from pydantic import BaseModel, ConfigDict, Field, validator
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from pydantic import BaseModel, ConfigDict, Field, create_model, validator
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from pydantic import BaseModel as PydanticBaseModel
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from crewai.tools.structured_tool import CrewStructuredTool
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@@ -75,17 +76,47 @@ class BaseTool(BaseModel, ABC):
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)
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@classmethod
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def from_langchain(cls, tool: CrewStructuredTool) -> "BaseTool":
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if cls == Tool:
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if tool.func is None:
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raise ValueError("CrewStructuredTool must have a callable 'func'")
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return Tool(
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name=tool.name,
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description=tool.description,
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args_schema=tool.args_schema,
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func=tool.func,
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)
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raise NotImplementedError(f"from_langchain not implemented for {cls.__name__}")
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def from_langchain(cls, tool: Any) -> "BaseTool":
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"""Create a Tool instance from a CrewStructuredTool.
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This method takes a CrewStructuredTool object and converts it into a
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Tool instance. It ensures that the provided tool has a callable 'func'
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attribute and infers the argument schema if not explicitly provided.
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"""
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if not hasattr(tool, "func") or not callable(tool.func):
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raise ValueError("The provided tool must have a callable 'func' attribute.")
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args_schema = getattr(tool, "args_schema", None)
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if args_schema is None:
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# Infer args_schema from the function signature if not provided
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func_signature = signature(tool.func)
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annotations = func_signature.parameters
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args_fields = {}
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for name, param in annotations.items():
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if name != "self":
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param_annotation = (
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param.annotation if param.annotation != param.empty else Any
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)
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field_info = Field(
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default=...,
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description="",
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)
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args_fields[name] = (param_annotation, field_info)
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if args_fields:
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args_schema = create_model(f"{tool.name}Input", **args_fields)
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else:
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# Create a default schema with no fields if no parameters are found
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args_schema = create_model(
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f"{tool.name}Input", __base__=PydanticBaseModel
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)
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return cls(
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name=getattr(tool, "name", "Unnamed Tool"),
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description=getattr(tool, "description", ""),
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func=tool.func,
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args_schema=args_schema,
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)
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def _set_args_schema(self):
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if self.args_schema is None:
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@@ -136,12 +167,65 @@ class BaseTool(BaseModel, ABC):
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class Tool(BaseTool):
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func: Callable
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"""The function that will be executed when the tool is called."""
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func: Callable
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def _run(self, *args: Any, **kwargs: Any) -> Any:
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return self.func(*args, **kwargs)
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@classmethod
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def from_langchain(cls, tool: Any) -> "Tool":
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"""Create a Tool instance from a CrewStructuredTool.
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This method takes a CrewStructuredTool object and converts it into a
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Tool instance. It ensures that the provided tool has a callable 'func'
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attribute and infers the argument schema if not explicitly provided.
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Args:
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tool (Any): The CrewStructuredTool object to be converted.
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Returns:
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Tool: A new Tool instance created from the provided CrewStructuredTool.
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Raises:
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ValueError: If the provided tool does not have a callable 'func' attribute.
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"""
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if not hasattr(tool, "func") or not callable(tool.func):
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raise ValueError("The provided tool must have a callable 'func' attribute.")
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args_schema = getattr(tool, "args_schema", None)
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if args_schema is None:
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# Infer args_schema from the function signature if not provided
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func_signature = signature(tool.func)
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annotations = func_signature.parameters
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args_fields = {}
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for name, param in annotations.items():
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if name != "self":
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param_annotation = (
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param.annotation if param.annotation != param.empty else Any
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)
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field_info = Field(
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default=...,
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description="",
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)
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args_fields[name] = (param_annotation, field_info)
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if args_fields:
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args_schema = create_model(f"{tool.name}Input", **args_fields)
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else:
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# Create a default schema with no fields if no parameters are found
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args_schema = create_model(
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f"{tool.name}Input", __base__=PydanticBaseModel
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)
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return cls(
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name=getattr(tool, "name", "Unnamed Tool"),
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description=getattr(tool, "description", ""),
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func=tool.func,
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args_schema=args_schema,
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)
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def to_langchain(
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tools: list[BaseTool | CrewStructuredTool],
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