Tech companies like Langflow_AI, Nvidia, and Cohere are introducing new tools and models for building RAG (Retrieval-Augmented Generation) applications. These tools aim to simplify the process with pre-built components and advanced models like Llama3-70B. Developers can now create fully local RAG pipelines with a better understanding, using frameworks like HyDE, Astra DB, and various AI technologies.
Build Retrieval Augmented Generation(RAG) using Llama-3 in just 4 lines of code:
Another fantastic 🧵 by @akshay_pachaar on how to build a local RAG web app, featuring @Cohere's ⌘R, @Ollama, @Streamlit, @Llama_Index, @qdrant_engine, and @LightningAI! 👏 https://t.co/SzKjtNV1j7
Nvidia Publishes A Competitive Llama3-70B Quality Assurance (QA) / Retrieval-Augmented Generation (RAG) Fine-Tune Model Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B are the two versions of this state-of-the-art model that come with 8 billion and 70 billion parameters,… https://t.co/kZWAt44wUo
There's an easier (& way more visually appealing) way to build RAG apps. Enter: @Langflow_AI, an open-source framework with pre-built components for any kind of app, API, or data source. @TejasKumar_ shows you how to get started with Langflow + Astra DB: https://t.co/jTKQb7N7uJ
Fully Local RAG 101 with Llama 3 + @ollama + @llama_index This article by @pavan_mantha1 is a great handbook for creating a fully local RAG pipeline with @llama_index (with a HyDE layer). It is lower-level than our “5 lines of code” Quickstart, giving you a better understanding… https://t.co/d1Nioa7K8y
5 real-world examples of building LLM Apps with RAG in 30 lines of Python Code (step-by-step instructions):