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│ URL                  │ Title                │ Published Date   │ Summary                                                      │
╞══════════════════════╪══════════════════════╪══════════════════╪══════════════════════════════════════════════════════════════╡
│ https://tech.dentsus │ LlamaIndexを使ってロ │ 2024-01-22       │ The webpage provides a step-by-step guide on how to          │
│ oken.com/entry/2024/ │ ーカル環境でRAGを実  │                  │ implement Retrieval-Augmented Generation (RAG) using the     │
│ 01/22/LlamaIndex%E3% │ 行する方法           │                  │ LlamaIndex library, aiming for local deployment of LLM. It   │
│ 82%92%E4%BD%BF%E3%81 │                      │                  │ explains why utilizing LLM in a local environment can be     │
│ %A3%E3%81%A6%E3%83%A │                      │                  │ beneficial, such as dealing with confidential data or        │
│ D%E3%83%BC%E3%82%AB% │                      │                  │ restricted internet access.   The instruction includes       │
│ E3%83%AB%E7%92%B0%E5 │                      │                  │ setting up the necessary environment using WSL, Dev          │
│ %A2%83%E3%81%A7RAG%E │                      │                  │ Container, and installing required libraries. Additionally,  │
│ 3%82%92%E5%AE%9F%E8% │                      │                  │ it describes the process of building a RAG system using      │
│ A1%8C%E3%81%99%E3%82 │                      │                  │ LlamaIndex, including loading data, initializing models, and │
│ %8B%E6%96%B9%E6%B3%9 │                      │                  │ handling querying and responding tasks. The page also        │
│ 5                    │                      │                  │ explores areas for improvement, discussing optimizations     │
│                      │                      │                  │ like minimizing query response time, selecting relevant      │
│                      │                      │                  │ contexts, and tweaking hardware and software configurations. │
│                      │                      │                  │ Finally, it encourages readers to try out the RAG            │
│                      │                      │                  │ implementation and appreciate the convenience of LlamaIndex  │
│                      │                      │                  │ while acknowledging the complexity involved in constructing  │
│                      │                      │                  │ effective RAG systems. The page is authored by Yamashita     │
│                      │                      │                  │ Tsuyoshi and reviewed by Wakamoto Ryosuke, using Shodo for   │
│                      │                      │                  │ documentation.                                               │
├──────────────────────┼──────────────────────┼──────────────────┼──────────────────────────────────────────────────────────────┤
│ https://blog.llamain │ LlamaIndex Blog      │ 2024-01-23       │ The LlamaIndex Blog is a hub for news, updates, and guides   │
│ dex.ai/?gi=a117797fb │                      │                  │ related to LlamaIndex, a search engine and platform for      │
│ bc8                  │                      │                  │ building and deploying AI-powered applications. This blog    │
│                      │                      │                  │ features release updates, community showcases, and guides on │
│                      │                      │                  │ using LlamaIndex. Articles range from introducing new        │
│                      │                      │                  │ features to exploring building various systems using         │
│                      │                      │                  │ LlamaIndex. Some notable topics covered in the blog include  │
│                      │                      │                  │ building a secure Multi-Tenancy RAG System, enhancing        │
│                      │                      │                  │ accessibility in AI with LlamaIndex and GPT3.5, and          │
│                      │                      │                  │ introducing Query Pipelines.                                 │
├──────────────────────┼──────────────────────┼──────────────────┼──────────────────────────────────────────────────────────────┤
│ https://levelup.gitc │ Live Indexing for    │ 2024-01-08       │ * The task of processing and answering questions from PDFs   │
│ onnected.com/live-   │ RAG: A Guide For     │                  │ is difficult for AI systems due to complex information, such │
│ indexing-for-rag-a-  │ Real-Time Indexing   │                  │ as nested tables, figures, and equations. * RAG frameworks   │
│ guide-for-real-time- │ Using LlamaIndex and │                  │ and large language models (LLMs) have evolved to create      │
│ indexing-using-      │ AWS                  │                  │ fully-stack applications, enabling a chat-with-PDFs          │
│ llamaindex-and-aws-5 │                      │                  │ application with minimal code. * Creating an enterprise RAG  │
│ 1353083ace4?gi=472c9 │                      │                  │ application requires addressing challenges such as re-       │
│ 89ddb71&source=rss   │                      │                  │ indexing and live updates of data sources.                   │
│ ----5517fd7b58a6---4 │                      │                  │                                                              │
├──────────────────────┼──────────────────────┼──────────────────┼──────────────────────────────────────────────────────────────┤
│ https://dev.to/lgram │ Create Your Own      │ 2024-01-13       │ This article explains how to create a chatbot using Next.js, │
│ mel/create-your-own- │ Local Chatbot with   │                  │ Llama.cpp, and ModelFusion.   Here's a concise summary:  1.  │
│ local-chatbot-with-  │ Next.js, Llama.cpp,  │                  │ **Setup:**    - Clone and build Llama.cpp, an LLM inference  │
│ nextjs-llamacpp-and- │ and ModelFusion      │                  │ engine.    - Download the OpenHermes 2.5 Mistral model from  │
│ modelfusion-461j     │                      │                  │ HuggingFace.    - Start the Llama.cpp server.  2. **Next.js  │
│                      │                      │                  │ Project:**    - Create a Next.js project.    - Install       │
│                      │                      │                  │ required libraries: Vercel AI SDK, ModelFusion, and          │
│                      │                      │                  │ ModelFusion Vercel AI SDK Integration.  3. **API Route:**    │
│                      │                      │                  │ - Create a POST API route in Next.js to handle chat          │
│                      │                      │                  │ interactions.    - Initialize a ModelFusion text generation  │
│                      │                      │                  │ model with the OpenHermes model.    - Create a ModelFusion   │
│                      │                      │                  │ chat prompt from Vercel AI SDK messages and call the model.  │
│                      │                      │                  │ - Return the streaming response using the                    │
│                      │                      │                  │ ModelFusionTextStream adapter.  4. **Chat Interface:**    -  │
│                      │                      │                  │ Create a dedicated Chat page using the useChat hook from     │
│                      │                      │                  │ Vercel AI SDK to render chat messages.    - Update global    │
│                      │                      │                  │ styles for improved readability.  5. **Run the               │
│                      │                      │                  │ Application:**    - Run the development server and navigate  │
│                      │                      │                  │ to http://localhost:3000 to interact with the chatbot.  This │
│                      │                      │                  │ chatbot is functional, leveraging these technologies to      │
│                      │                      │                  │ provide real-time responses to user messages. The code is a  │
│                      │                      │                  │ starting point for further exploration and customization.    │
├──────────────────────┼──────────────────────┼──────────────────┼──────────────────────────────────────────────────────────────┤
│ https://blog.llamain │ A Cheat Sheet and    │ 2024-01-05       │ This article provides a comprehensive overview of Retrieval- │
│ dex.ai/a-cheat-      │ Some Recipes For     │                  │ Augmented Generation (RAG) systems, focusing on the advanced │
│ sheet-and-some-      │ Building Advanced    │                  │ techniques and strategies used to build effective RAG        │
│ recipes-for-         │ RAG                  │                  │ systems that can handle complex queries using external       │
│ building-advanced-   │                      │                  │ knowledge bases. It covers success requirements, various     │
│ rag-803a9d94c41b     │                      │                  │ techniques for Retrieval and Generation components, and      │
│                      │                      │                  │ includes a RAG Cheat Sheet for reference. The techniques     │
│                      │                      │                  │ include Chunk-Size Optimization, Structured External         │
│                      │                      │                  │ knowledge, Sparse-Attention Mechanism, Referring and Fine-   │
│                      │                      │                  │ tuning on Predictions. The article also addresses the        │
│                      │                      │                  │ challenges encountered in implementing these techniques.     │
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