LLMService: A Principled Framework for Building LLM Applications

LLMService is a Python framework designed to build applications using large language models (LLMs) with a strong emphasis on good software development practices. It aims to be a more structured and robust alternative to frameworks like LangChain.

Key Features:

• Modularity and Separation of Concerns: It promotes a clear separation between different parts of your application, making it easier to manage and extend.
• Robust Error Handling: Features like retries with exponential backoff and custom exception handling ensure reliable interactions with LLM providers.
• Prompt Management (Proteas): A sophisticated system for defining, organizing, and reusing prompt templates from YAML files.
• Result Monad Design: Provides a structured way to handle results and errors, giving users control over event handling.
• Rate-Limit Aware Asynchronous Requests & Batching: Efficiently handles requests to LLMs, respecting rate limits and supporting batch processing for better performance.
• Extensible Base Class: Provides a BaseLLMService class that users can subclass to implement their custom service logic, keeping LLM-specific logic separate from the rest of the application.

How it Works (Simplified):

1. Define Prompts: You create a prompts.yaml file to define reusable prompt "units" with placeholders.
2. Create Custom Service: You subclass BaseLLMService and define methods that orchestrate the LLM interaction. This involves:
   • Crafting the full prompt by combining prompt units and filling placeholders.
   • Calling the generation_engine to invoke the LLM.
   • Receiving a generation_result object containing the LLM's output and other relevant information.
3. Use the Service: Your main application interacts with your custom service to get LLM-generated content.

In essence, LLMService provides a structured, error-resilient, and modular way to build LLM-powered applications, encouraging best practices in software development.