📄Overview
Last updated
Last updated
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.
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.
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.
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.
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.
