Types

A primary objective of our framework is to simplify its usage for those who wish to employ it without delving into development. We aim to offer them a straightforward and effortless setup process, minimizing the complexities and initializations associated with numerous classes. This approach reduces potential bugs and allows users to focus on their primary task: developing an application using our framework. To ensure uniformity throughout the framework and to effectively manage new LLMs while avoiding incorrect class initializations, we have introduced Types identifiers. These identifiers, at a high level, determine the specific type of llm required. Consequently, CHA users are relieved from the intricate details of initializing LLM objects, integrating them into the CHA, and handling setups.

LLM Types

This enumeration class defines different types of llm. This ensures consistency in case the llm developer decides to change the name of their llm, the end user need not to change their code cause they use the keys. It inherits from the str class and the Enum class in Python’s enum module. Each value in this enumeration represents a specific type of llm. The key naming convention should be all uppercase with underscore, and the value naming convention should be underscore_case: NAME_OF_LLM = this_is_a_sample_llm_name

from enum import Enum


class LLMType(str, Enum):
    OPENAI = "openai"
    ANTHROPIC = "anthropic"

Types

This dictionary is used to map each LLMType value to its corresponding LLM class. It allows for easy retrieval of the appropriate class based on the llm type.

from typing import Dict
from typing import Type

from llms.anthropic import AntropicLLM
from llms.llm import BaseLLM
from llms.llm_types import LLMType
from llms.openai import OpenAILLM

LLM_TO_CLASS: Dict[LLMType, Type[BaseLLM]] = {
    LLMType.OPENAI: OpenAILLM,
    LLMType.ANTHROPIC: AntropicLLM,
}