94 lines
2.9 KiB
Python
94 lines
2.9 KiB
Python
# Copyright (c) 2023 - 2025, AG2ai, Inc., AG2ai open-source projects maintainers and core contributors
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#
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# SPDX-License-Identifier: Apache-2.0
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from typing import TYPE_CHECKING, Any
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from pydantic import BaseModel
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from .context_str import ContextStr
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if TYPE_CHECKING:
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# Avoid circular import
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from ..conversable_agent import ConversableAgent
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__all__ = ["ContextStrLLMCondition", "LLMCondition", "StringLLMCondition"]
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class LLMCondition(BaseModel):
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"""Protocol for conditions evaluated by an LLM."""
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def get_prompt(self, agent: "ConversableAgent", messages: list[dict[str, Any]]) -> str:
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"""Get the prompt text for LLM evaluation.
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Args:
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agent: The agent evaluating the condition
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messages: The conversation history
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Returns:
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The prompt text to be evaluated by the LLM
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"""
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raise NotImplementedError("Requires subclasses to implement.")
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class StringLLMCondition(LLMCondition):
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"""Simple string-based LLM condition.
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This condition provides a static string prompt to be evaluated by an LLM.
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"""
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prompt: str
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def __init__(self, prompt: str, **data: Any) -> None:
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"""Initialize with a prompt string as a positional parameter.
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Args:
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prompt: The static prompt string to evaluate
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data: Additional data for the parent class
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"""
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super().__init__(prompt=prompt, **data)
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def get_prompt(self, agent: "ConversableAgent", messages: list[dict[str, Any]]) -> str:
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"""Return the static prompt string.
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Args:
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agent: The agent evaluating the condition (not used)
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messages: The conversation history (not used)
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Returns:
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The static prompt string
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"""
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return self.prompt
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class ContextStrLLMCondition(LLMCondition):
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"""Context variable-based LLM condition.
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This condition uses a ContextStr object with context variable placeholders that
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will be substituted before being evaluated by an LLM.
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"""
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context_str: ContextStr
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def __init__(self, context_str: ContextStr, **data: Any) -> None:
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"""Initialize with a context string as a positional parameter.
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Args:
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context_str: The ContextStr object with variable placeholders
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data: Additional data for the parent class
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"""
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super().__init__(context_str=context_str, **data)
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def get_prompt(self, agent: "ConversableAgent", messages: list[dict[str, Any]]) -> str:
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"""Return the prompt with context variables substituted.
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Args:
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agent: The agent evaluating the condition (provides context variables)
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messages: The conversation history (not used)
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Returns:
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The prompt with context variables substituted
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"""
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result = self.context_str.format(agent.context_variables)
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return result if result is not None else ""
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