# Overview We at AI Planet are excited to introduce [BeyondLLM](https://github.com/aiplanethub/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](http://twitter.com/karpathy/status/1733299213503787018)," underscoring the inherent capabilities of language models. Done talking, lets build.