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A new technology called Retrieval-Augmented Text Generation (RAG) is gaining attention in the data processing and AI communities. RAG systems aim to enhance Large Language Models (LLMs) by integrating external information through pre-retrieval, retrieval, post-retrieval, and generation stages. The RAG Module is seen as a significant development in this field, presenting challenges and opportunities for knowledge storage and structured output generation. Various articles and tutorials are emerging to explore the potential and challenges of RAG applications, emphasizing the importance of performance, scalability, and robustness in building production-ready systems.
Awesome RAG app demo using @Langflow_AI & DataStax Astra DB! 💥 See how YouTube creator @TechWithTimm tapped the power of #Langflow to spin up a restaurant chatbot without writing a single line of code. #RAGApplications #VectorDB https://t.co/ilK0kVIUtC
Advanced Retrieval-Augmented Generation: From Theory to LlamaIndex Implementation https://t.co/yjJaJbUS5r #AI #MachineLearning #DeepLearning #LLMs #DataScience https://t.co/zxR2GJVm8c
🐍📰 Build an LLM RAG Chatbot With LangChain In this step-by-step tutorial, you'll leverage LLMs to build your own retrieval-augmented generation (RAG) chatbot using synthetic data with LangChain and Neo4j #python https://t.co/NqRUoexZxq
[CL] RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation C Jin, Z Zhang, X Jiang, F Liu, X Liu, X Liu, X Jin [Peking University] (2024) https://t.co/8R7hW0Gj3C - RAG introduces long sequence generation due to knowledge injection, leading to high… https://t.co/Mb4TSx1OjD
What is Retrieval Augmented Generation? How it Works & Use Cases https://t.co/8Vv4yHDSyd
Kick off your weekend project with this hands-on RAG guide by @stephenbtl using @ollama, @LangChainAI, #Milvus, and #Llama3. #RAG #Vectordatabase #GenAI #developers https://t.co/dEWUcr8Duq https://t.co/wwrJPrBlxq
Learn how to Improve LLMs with RAG by following along this beginner-friendly introduction with Python code, courtest of @ShawhinT. https://t.co/NalpqqZYFc
Prototyping a RAG application is easy, but making it performant, robust, and scalable to a large knowledge corpus is hard. Learn 12 challenges in building production-ready RAG-based LLM applications with solutions ➡️: https://t.co/6PoLhoWb9v #RAGChallenges #RetrievalStage https://t.co/xNeO0WNpnJ
[IR] A Survey on Retrieval-Augmented Text Generation for Large Language Models https://t.co/e0lgw1DjWf - RAG combines retrieval methods and deep learning to address limitations of LLMs by dynamically integrating external information. - The RAG framework consists of 4… https://t.co/gkjOSpHdn7
Prototyping a RAG application is easy, but making it performant, robust, and scalable to a large knowledge corpus is hard. Explore different challenges faced during the retrieval stage of the RAG pipeline. Dive deeper and Learn the 12 challenges in building production-ready… https://t.co/YYe6UUaWvc
🔥 Check out this new article introducing Retrieval Augmented and Guided Generation (RAGG). This article combines txtai with the great outlines library to generate structured output. See how knowledge can be stored as Pydantic models! https://t.co/YK2MY3Cihv
This update is shaking up the RAG and vector database ecosystem. It's akin to OpenAI handing us the keys to a supercar in the realm of data processing. However, the question arises: how will we keep pace within this rapidly evolving circle? 🏎️💨 To see how the RAG Module within… https://t.co/Le2f9sV0yQ
A Survey on Retrieval-Augmented Text Generation for LLMs Presents a comprehensive overview of the RAG domain, its evolution, and challenges. It includes a detailed discussion of four important aspects of RAG systems: pre-retrieval, retrieval, post-retrieval, and generation. If… https://t.co/bPPkkavmtk