📄 Overview

We at AI Planet are excited to introduce BeyondLLM, an open-source framework designed to streamline the development of RAG and LLM applications, complete with evaluations, all in just 5-7 lines of code.

Yes, you read that correctly. Only 5-7 lines of code.

Let's understand what and why one needs BeyondLLM.

Why BeyondLLM?

Easily build RAG and Evals in 5 lines of code

  • Building a robust RAG (Retrieval-Augmented Generation) system involves integrating various components and managing associated hyperparameters. BeyondLLM offers an optimal framework for quickly experimenting with RAG applications.

  • With components like source and auto_retriever, which support several parameters, most of the integration work is automated, eliminating the need for manual coding.

  • Additionally, we are actively working on enhancing features such as hyperparameter tuning for RAG applications, addressing the next key aspect of our development roadmap.

Customised Evaluation Support

  • The evaluation of RAG in the market largely relies on the OpenAI API Key and closed-source LLMs. However, with BeyondLLM, you have the flexibility to select any LLM for evaluating both LLMs and embeddings.

  • We offer support for 2 evaluation metrics for embeddings: Hit rate and MRR (Mean Reciprocal Rank), allowing users to choose the most suitable model based on their specific needs.

  • Additionally, we provide 4 evaluation metrics for assessing Large Language Models across various criteria, in line with current research standards.

Various Custom LLMs support tailoring the basic needs

  • HuggingFace: Easily accessible for everyone to access Open Source LLMs

  • Ollama: Run LLMs locally

  • Gemini: (default LLM): Run Multimodal applications

  • OpenAI: Powerful chat model LLM with best quality response

  • Azure: For 32K large context good response quality support.

Reduce LLM Hallucination

  • Certainly, the primary objective is to minimize or eliminate hallucinations within the RAG framework.

  • To support this goal, we've developed the Advanced RAG section, facilitating rapid experimentation for constructing RAG pipelines with reduced hallucination risks.

  • BeyondLLM features, including source and auto_retriever, incorporate functionalities such as Markdown splitter, chunking strategies, Re-ranking (Cross encoders and flag embedding) and Hybrid Search, enhancing the reliability of RAG applications.

  • It's worth noting Andrej Karpathy's insight: "Hallucination is a LLM's greatest feature and not a bug," underscoring the inherent capabilities of language models.

Done talking, lets build.

Next🔧 Installation

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