mirror of
https://github.com/crewAIInc/crewAI.git
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3e97393f58
- add type annotations across utility modules - refactor printer system, agent utils, and imports for consistency - remove unused modules, constants, and redundant patterns - improve runtime type checks, exception handling, and guardrail validation - standardize warning suppression and logging utilities - fix llm typing, threading/typing edge cases, and test behavior
99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Generic, TypeGuard, TypeVar
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from pydantic import BaseModel
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if TYPE_CHECKING:
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from crewai.agent import Agent
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from crewai.llm import LLM
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from crewai.llms.base_llm import BaseLLM
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from crewai.utilities.logger_utils import suppress_warnings
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from crewai.utilities.types import LLMMessage
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T = TypeVar("T", bound=BaseModel)
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def _is_valid_llm(llm: Any) -> TypeGuard[str | LLM | BaseLLM]:
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"""Type guard to ensure LLM is valid and not None.
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Args:
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llm: The LLM to validate
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Returns:
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True if LLM is valid (string or has model attribute), False otherwise
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"""
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return llm is not None and (isinstance(llm, str) or hasattr(llm, "model"))
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class InternalInstructor(Generic[T]):
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"""Class that wraps an agent LLM with instructor for structured output generation.
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Attributes:
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content: The content to be processed
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model: The Pydantic model class for the response
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agent: The agent with LLM
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llm: The LLM instance or model name
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"""
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def __init__(
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self,
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content: str,
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model: type[T],
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agent: Agent | None = None,
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llm: LLM | BaseLLM | str | None = None,
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) -> None:
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"""Initialize InternalInstructor.
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Args:
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content: The content to be processed
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model: The Pydantic model class for the response
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agent: The agent with LLM
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llm: The LLM instance or model name
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"""
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self.content = content
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self.agent = agent
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self.model = model
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self.llm = llm or (agent.function_calling_llm or agent.llm if agent else None)
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with suppress_warnings():
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import instructor
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from litellm import completion
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self._client = instructor.from_litellm(completion)
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def to_json(self) -> str:
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"""Convert the structured output to JSON format.
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Returns:
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JSON string representation of the structured output
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"""
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pydantic_model = self.to_pydantic()
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return pydantic_model.model_dump_json(indent=2)
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def to_pydantic(self) -> T:
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"""Generate structured output using the specified Pydantic model.
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Returns:
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Instance of the specified Pydantic model with structured data
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Raises:
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ValueError: If LLM is not provided or invalid
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"""
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messages: list[LLMMessage] = [{"role": "user", "content": self.content}]
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if not _is_valid_llm(self.llm):
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raise ValueError(
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"LLM must be provided and have a model attribute or be a string"
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)
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if isinstance(self.llm, str):
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model_name = self.llm
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else:
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model_name = self.llm.model
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return self._client.chat.completions.create(
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model=model_name, response_model=self.model, messages=messages
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)
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