This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
https://github.com/NirDiamant/RAG_Techniques
https://github.com/NirDiamant/RAG_Techniques/tree/main/all_rag_techniques
Perplexica is an open-source AI-powered searching tool or an AI-powered search engine that goes deep into the internet to find answers. Inspired by Perplexity AI, it’s an open-source option that not just searches the web but understands your questions. It uses advanced machine learning algorithms like similarity searching and embeddings to refine results and provides clear answers with sources cited.
Using SearxNG to stay current and fully open source, Perplexica ensures you always get the most up-to-date information without compromising your privacy.
RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
RAG Foundry is a library designed to improve LLMs ability to use external information by fine-tuning models on specially created RAG-augmented datasets. The library helps create the data for training, given a RAG technique, helps easily train models using parameter-efficient finetuning (PEFT), and finally can help users measure the improved performance using various, RAG-specific metrics. The library is modular, workflows are customizable using configuration files.
In deepeval, a metric serves as a standard of measurement for evaluating the performance of an LLM output based on a specific criteria of interest. Essentially, while the metric acts as the ruler, a test case represents the thing you’re trying to measure. deepeval offers a range of default metrics for you to quickly get started with, such as:
deepeval also offers conversational metrics, which are metrics used to evaluate conversations instead of individual, granular LLM interactions. These include: