📄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.

Build-Experiment-Evaluate-Repeat

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.