Source code for langchain.chains.prompt_selector

from abc import ABC, abstractmethod
from typing import Callable, List, Tuple

from langchain_core.language_models import BaseLanguageModel
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.language_models.llms import BaseLLM
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field


[docs] class BasePromptSelector(BaseModel, ABC): """Base class for prompt selectors."""
[docs] @abstractmethod def get_prompt(self, llm: BaseLanguageModel) -> BasePromptTemplate: """Get default prompt for a language model."""
[docs] class ConditionalPromptSelector(BasePromptSelector): """Prompt collection that goes through conditionals.""" default_prompt: BasePromptTemplate """Default prompt to use if no conditionals match.""" conditionals: List[ Tuple[Callable[[BaseLanguageModel], bool], BasePromptTemplate] ] = Field(default_factory=list) """List of conditionals and prompts to use if the conditionals match."""
[docs] def get_prompt(self, llm: BaseLanguageModel) -> BasePromptTemplate: """Get default prompt for a language model. Args: llm: Language model to get prompt for. Returns: Prompt to use for the language model. """ for condition, prompt in self.conditionals: if condition(llm): return prompt return self.default_prompt
[docs] def is_llm(llm: BaseLanguageModel) -> bool: """Check if the language model is a LLM. Args: llm: Language model to check. Returns: True if the language model is a BaseLLM model, False otherwise. """ return isinstance(llm, BaseLLM)
[docs] def is_chat_model(llm: BaseLanguageModel) -> bool: """Check if the language model is a chat model. Args: llm: Language model to check. Returns: True if the language model is a BaseChatModel model, False otherwise. """ return isinstance(llm, BaseChatModel)