Use these frameworks to empower your machine-learning work Link

The RAG cheat sheet shared above was greatly inspired by a recent RAG survey paper (“Retrieval-Augmented Generation for Large Language Models: A Survey” Gao, Yunfan, et al. 2023). Link

A comprehensive study of the advanced retrieval augmented generation techniques and algorithms, systemising various approaches. The article comes with a collection of links in my knowledge base referencing various implementations and studies mentioned. Link

Auto Merging Retriever Pack and Small-to-big Retrieval Pack provided by LlamaIndex perform the best for this experiment. Read on to see what went down Link

At their demo day, Open AI reported a series of RAG experiments for a customer that they worked with. While evaluation metics will depend on your specific application, it’s interesting to see what worked and what didn’t for them. Below, we expand on each method mention and show how you can implement each one for yourself. The ability to understand and these methods on your application is critical: from talking to many partners and users, there is no “one-size-fits-all” solution because different problems require different retrieval techniques.

In the ever-evolving landscape of artificial intelligence (AI) and natural language processing (NLP), researchers and developers continue to push the boundaries of what’s possible. One such groundbreaking development is the Auto Generated Agent Chat, a cutting-edge system that employs Retrieval Augmented Generation (RAG) to transform group conversations. This technology combines the strengths of both retrieval-based and generative models, offering a unique and efficient solution for enhancing communication in group settings.

Large Language Models(LLM) have taken the NLP community AI community the Whole World by storm. Here is a curated list of papers about large language models, especially relating to ChatGPT. It also contains frameworks for LLM training, tools to deploy LLM, courses and tutorials about LLM and all publicly available LLM checkpoints and APIs. Link

Curated list of best courses, books, resources on large language model Link

A Semantic Router is an advanced layer in the realm of chatbots and natural language processing. Think of it as a fuzzy yet deterministic interface layered over your chatbots or any system that processes natural language. Its primary function? To serve as a super-fast decision-making layer for Large Language Models (LLMs). Link

How to store the conversation history in memory and include it within our prompt. How to transform the input question such that it retrieves the relevant information from our vector database. Link

Understanding how LLM agents work by building it at 3 levels of complexity Link

Retrieval Augmented Generation (RAG) emerges as a solution to bridge this gap, allowing LLMs to access external knowledge sources. This article delves into RAG, examines its elements, and constructs a usable RAG workflow that harnesses the potential of LlamaIndex, a knowledge graph. Link

Eden AI is revolutionizing the AI landscape by uniting the best AI providers, empowering users to unlock limitless possibilities and tap into the true potential of artificial intelligence. With an all-in-one comprehensive and hassle-free platform, it allows users to deploy AI features to production lightning fast, enabling effortless access to the full breadth of AI capabilities via a single API. (website: https://edenai.co/ ) Link Story Generation Eden AI x LangChain: Harnessing LLMs, Embeddings, and AI

Finetune Mistral, Llama 2-5x faster with 50% less memory! Link

In this tutorial, I am going to keep developing multi-agent LLM applications in the AutoGen framework and using decent open-source language models with function calls to see whether they can generate and execute a function task like calculation for currency exchange. Link