Ethan B. Holland

Over 52,500 manually organized AI links and counting

a humanoid robot wearing a auto mechanic overalls, holding a blue terry cloth rag, stands proudly over a large book that is polished and shiny --ar 16:9 --style raw

Retrieval-Augmented Generation (RAG) News: Week Ending 04/19/2024

April 19, 2024

a humanoid robot wearing a auto mechanic overalls, holding a blue terry cloth rag, stands proudly over a large book that is polished and shiny –ar 16:9 –style raw

“This is huge for RAG applications! I talk to half a dozen companies every week. Everyone wants to implement RAG solutions with their data. But hallucinations are horrible. Up to 35%, especially with complex documents. But something new just happened: @eyelevelai released…” / X – https://twitter.com/svpino/status/1780571442096087224

“RAG From Scratch on FreeCodeCamp We recently released a playlist of videos that explain RAG fundamentals and highlights over a dozen advanced RAG papers / methods. This tutorial is now free on @freeCodeCamp! Thanks @beaucarnes and team. Video: https://twitter.com/LangChainAI/status/1780629875533181271

Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer – YouTube – https://www.youtube.com/watch?v=sVcwVQRHIc8

Snowflake Launches Practical Text-Embedding Model for Retrieval use Cases – https://www.snowflake.com/blog/introducing-snowflake-arctic-embed-snowflakes-state-of-the-art-text-embedding-family-of-models/

“❄️ Snowflake Arctic Embed ↗️ Yesterday @SnowflakeDB shipped their open source Arctic family of embedding models. You can use them with our @huggingface Embeddings connector to use these exceptionally performant models locally. Python docs: https://twitter.com/LangChainAI/status/1780650806896947547

“The new snowflake-arctic-embed family of text embedding models from ❄️ @SnowflakeDB are super efficient options. ( Just 23M, 33M, 110M, 137M & 335M param ) 📌 Apache 2.0 license: Full commercial use allowed 📌 4 models with 512 sequence length and 1 model with 8192 sequence… https://twitter.com/rohanpaul_ai/status/1780621521230111181

“Build RAG, Function Calling, and Agents with @llama_index and @MistralAI 8x22b – the most powerful @MistralAI model to date, and the best model in its (active) parameter class so far! Features: 64k token context, multilingual, coding, function calling, math, and more. Uses 39B… https://twitter.com/jerryjliu0/status/1780763995781378338

“How Faithful are RAG Models? This new paper aims to quantify the tug-of-war between RAG and LLMs’ internal prior. It focuses on GPT-4 and other LLMs on question answering for the analysis. It finds that providing correct retrieved information fixes most of the model… https://twitter.com/omarsar0/status/1780613738585903182

“I built an entire RAG application without writing any code. This is one of the easiest ways to start building AI applications. I used Langflow. It’s open-source. It’s a visual interface for building and deploying AI applications. I built a couple of workflows to show you how… https://twitter.com/svpino/status/1779843933276672195

Easy 100% Local RAG Tutorial (Ollama) + Full Code – YouTube – https://www.youtube.com/watch?v=Oe-7dGDyzPM

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Be Sure To Read This Week’s Main Post:

This week’s executive overview and top links are here:

AI News #28: Week Ending 04/12/2024 with Executive Summary and Top 48 Links

The post you just read is an deep dive extension of my weekly newsletter, This Week In AI, an executive summary of the top things to know in AI. Each week, I create an accessible overview for laypeople to feel confident they are conversant with the week’s AI developments. I include a curated list of must-click links of the week, to offer everyone a hands-on opportunity to explore the most intriguing updates in artificial intelligence across various categories, including robotics, imagery, video, AR/VR, science, ethics, and more. Beyond the overview, I post these topic-based deeper dives (below). If you haven’t read this week’s overview, I recommend starting there.

Credits/Sources

Most of these weekly links come from just a few prolific oversharing sources. Please follow them, as they work hard to find the news each week and they make it a lot easier for me to compile.