BeyondLLM is an open-source toolkit designed for building, evaluating, and observing Retrieval-Augmented Generation (RAG) applications. It targets developers and researchers working with Large Language Models (LLMs), aiming to simplify RAG system development, reduce hallucinations, and enhance reliability through automated integration and customizable evaluation.

How It Works

BeyondLLM streamlines RAG pipelines by abstracting complex data ingestion, retrieval, and generation steps. It supports various data sources (like YouTube videos) and allows integration with different LLMs and embedding models (e.g., OpenAI). The framework includes built-in evaluation metrics for both embeddings (hit rate, MRR) and LLM responses (context relevancy, answer relevancy, groundness), providing a comprehensive approach to RAG system quality assurance. An observability feature monitors LLM performance, including latency and cost for supported models.

Quick Start & Requirements

• Primary install: pip install beyondllm
• Prerequisites: OpenAI API key (for custom LLM/embedding examples), Google API key (for YouTube data source example).
• Documentation: beyondllm.aiplanet.com
• Demo: Available on Google Colab.

Highlighted Details

• Build RAG applications in as few as 5 lines of code.
• Supports custom LLMs and embedding models.
• Includes automated evaluation for retrieval and generation components.
• Observability feature for monitoring LLM performance (latency, cost).

Maintenance & Community

• Active community engagement encouraged via Discord.
• Contributions are welcomed for features, infrastructure, and documentation.
• Acknowledgements include HuggingFace, LlamaIndex, OpenAI, and Google Gemini.

Licensing & Compatibility

• Licensed under the Apache License, version 2.0.
• Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The observability feature currently only monitors OpenAI LLM models. While the project supports various LLMs and embeddings, specific integrations might require custom implementation or may not be fully optimized.