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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
