We now understand that large language models (LLMs) offer a broad array of use cases. We have also explored their accessibility and usage of LLMs through open-source models and libraries. Additionally, we've developed our own Retrieval-Augmented Generation (RAG) application by uploading a PDF file to provide context to the LLM.

At AI Planet, we built BeyondLLM: an open-source framework that simplifies the development of RAG and LLM applications, including evaluations, in just 5–7 lines of code. Previous modules have shown how LangChain and LlamaIndex make the RAG creation process easier. However, BeyondLLM further streamlines the development and experimentation of RAG applications.

Why should we use BeyondLLM?

Building a robust Retrieval-Augmented Generation (RAG) system requires integrating multiple components and managing related hyperparameters. BeyondLLM provides an ideal framework for rapidly experimenting with RAG applications.

With components like source and auto_retriever that support numerous parameters, most integration tasks are automated, reducing the need for manual coding exceedingly.

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.

The certain goal is to minimize or eliminate hallucinations (when the LLM confidently provides incorrect answers) within the framework. To achieve this, we've created the Advanced RAG section, which supports rapid experimentation for building RAG pipelines with reduced risks of hallucinations.

Core Components

Now, let's discuss the core components, before we actually start testing out the features discussed above.