prompt-llm

A module that provides abstractions and implementations for working with Large Language Models (LLMs) from various providers.

Overview

The prompt-llm module defines core abstractions for working with Large Language Models (LLMs) in a provider-agnostic way. It includes:

• LLMCapability: A sealed class hierarchy that defines various capabilities that LLMs can support, such as:

• Temperature adjustment for controlling response randomness

• Tools integration for external system interaction

• Vision processing for handling visual data

• Embedding generation for vector representations

• Completion for text generation tasks

• Schema support for structured data (JSON with Simple and Full variants)

• Speculation for exploratory responses

• LLMProvider: A sealed class hierarchy that defines supported LLM providers, including:

• Google

• OpenAI

• Anthropic

• Meta

• Alibaba

• OpenRouter

• LLModel: A data class that represents a specific LLM with its provider, unique identifier, and supported capabilities.

• OllamaModels: A collection of predefined LLM configurations for specific models from providers like Meta and Alibaba, with their respective capabilities.

This module serves as a foundation for interacting with different LLM providers in a unified way, allowing applications to work with various models while abstracting away provider-specific details.

Example of usage

// Creating a custom LLM configuration
val myCustomModel = LLModel(
    provider = LLMProvider.OpenAI,
    id = "gpt-4-turbo",
    capabilities = listOf(
        LLMCapability.Temperature,
        LLMCapability.Tools,
        LLMCapability.Schema.JSON.Full
    )
)

// Using a predefined model
val metaModel = OllamaModels.Meta.LLAMA_3_2

// Checking if a model supports a specific capability
val supportsTools = myCustomModel.capabilities.contains(LLMCapability.Tools) // true