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│ URL                  │ Title                │ Published Date   │ Summary                                                      │
╞══════════════════════╪══════════════════════╪══════════════════╪══════════════════════════════════════════════════════════════╡
│ https://tech.dentsus │ LlamaIndexを使ってロ │ 2024-01-22       │ The article talks about how to implement Retrieval-Augmented │
│ oken.com/entry/2024/ │ ーカル環境でRAGを実  │                  │ Generation (RAG) using the LlamaIndex library in a local     │
│ 01/22/LlamaIndex%E3% │ 行する方法           │                  │ environment. Reasons for choosing local environment for LLM  │
│ 82%92%E4%BD%BF%E3%81 │                      │                  │ utilization is discussed. LlamaIndex benefits and features   │
│ %A3%E3%81%A6%E3%83%A │                      │                  │ along with the required environment setup are also           │
│ D%E3%83%BC%E3%82%AB% │                      │                  │ mentioned. A detailed  step-by-step guide to implement RAG   │
│ E3%83%AB%E7%92%B0%E5 │                      │                  │ using LlamaIndex is provided with sample questions and       │
│ %A2%83%E3%81%A7RAG%E │                      │                  │ answers. The article highlights aspects of this              │
│ 3%82%92%E5%AE%9F%E8% │                      │                  │ implementation that can be further improved in terms of      │
│ A1%8C%E3%81%99%E3%82 │                      │                  │ reducing time and increasing accuracy. Additionally, using   │
│ %8B%E6%96%B9%E6%B3%9 │                      │                  │ more RAM and processing power is suggested. Overall, the     │
│ 5                    │                      │                  │ article explores the convenience of using LlamaIndex for RAG │
│                      │                      │                  │ implementation while highlighting areas for improvement to   │
│                      │                      │                  │ build a more robust RAG system.                              │
├──────────────────────┼──────────────────────┼──────────────────┼──────────────────────────────────────────────────────────────┤
│ https://blog.llamain │ LlamaIndex Blog      │ 2024-01-23       │ The LlamaIndex blog offers updates on releases, guides, and  │
│ dex.ai/?gi=a117797fb │                      │                  │ community showcases. The recent posts include a newsletter   │
│ bc8                  │                      │                  │ from January 23rd, news about building a secure multi-       │
│                      │                      │                  │ tenancy RAG system, using LlamaIndex and GPT3.5 to build an  │
│                      │                      │                  │ AI voice assistant, and launching a free course on advanced  │
│                      │                      │                  │ RAG certification. Additionally, there are introductions to  │
│                      │                      │                  │ new features like query pipelines and discussions on scaling │
│                      │                      │                  │ LlamaIndex with AWS and Hugging Face.                        │
├──────────────────────┼──────────────────────┼──────────────────┼──────────────────────────────────────────────────────────────┤
│ https://levelup.gitc │ Live Indexing for    │ 2024-01-08       │ The page discusses the challenges and solutions in           │
│ onnected.com/live-   │ RAG: A Guide For     │                  │ processing and analyzing PDF documents using AI systems. The │
│ indexing-for-rag-a-  │ Real-Time Indexing   │                  │ author highlights the difficulty in extracting meaningful    │
│ guide-for-real-time- │ Using LlamaIndex and │                  │ information from PDFs due to their complex structure and the │
│ indexing-using-      │ AWS                  │                  │ presence of various elements like tables, figures,           │
│ llamaindex-and-aws-5 │                      │                  │ equations, and photos.  The author also mentions the rise of │
│ 1353083ace4?gi=472c9 │                      │                  │ Retrieval-Augmented Generation (RAG) frameworks and Large    │
│ 89ddb71&source=rss   │                      │                  │ Language Models (LLMs) in 2022 and the evolution of the      │
│ ----5517fd7b58a6---4 │                      │                  │ ecosystem for creating full-stack applications. They         │
│                      │                      │                  │ specifically highlight LlamaIndex as a prominent RAG         │
│                      │                      │                  │ framework that simplifies the creation of chat applications  │
│                      │                      │                  │ for interacting with PDFs.  The page further mentions that   │
│                      │                      │                  │ although creating a basic RAG application is relatively      │
│                      │                      │                  │ simple, developing an enterprise-grade RAG application       │
│                      │                      │                  │ requires addressing challenges related to live data          │
│                      │                      │                  │ indexing, updates, real-time inference, and security.        │
├──────────────────────┼──────────────────────┼──────────────────┼──────────────────────────────────────────────────────────────┤
│ https://dev.to/lgram │ Create Your Own      │ 2024-01-13       │ Sure, here's a brief summary of the webpage's content:       │
│ mel/create-your-own- │ Local Chatbot with   │                  │ **Title: Create Your Own Local Chatbot with Next.js,         │
│ local-chatbot-with-  │ Next.js, Llama.cpp,  │                  │ Llama.cpp, and ModelFusion**  - The blog post provides a     │
│ nextjs-llamacpp-and- │ and ModelFusion      │                  │ step-by-step guide to building a local chatbot using         │
│ modelfusion-461j     │                      │                  │ Next.js, Llama.cpp, and ModelFusion.  - Llama.cpp is an LLM  │
│                      │                      │                  │ (large language model) inference engine that allows running  │
│                      │                      │                  │ LLMs like OpenHermes 2.5 Mistral locally.  - The Vercel AI   │
│                      │                      │                  │ SDK is leveraged to manage stream forwarding and rendering,  │
│                      │                      │                  │ while ModelFusion is utilized for integrating Llama.cpp with │
│                      │                      │                  │ the SDK.  - Instructions are provided for setting up         │
│                      │                      │                  │ Llama.cpp, downloading the OpenHermes 2.5 Mistral model, and │
│                      │                      │                  │ starting the Llama.cpp server.  - The creation of the        │
│                      │                      │                  │ Next.js project and installation of required libraries are   │
│                      │                      │                  │ outlined.  - Detailed steps for creating an API route for    │
│                      │                      │                  │ the chatbot are explained.  - The process of adding the chat │
│                      │                      │                  │ interface to the frontend and cleaning up global styles is   │
│                      │                      │                  │ described.  - The user can run the chatbot application       │
│                      │                      │                  │ locally and interact with it via a user-friendly chat page.  │
│                      │                      │                  │ - The code serves as a starting point for developing AI      │
│                      │                      │                  │ projects using these tools.                                  │
├──────────────────────┼──────────────────────┼──────────────────┼──────────────────────────────────────────────────────────────┤
│ https://blog.llamain │ A Cheat Sheet and    │ 2024-01-05       │ This web page provides information on Retrieval-Augmented    │
│ dex.ai/a-cheat-      │ Some Recipes For     │                  │ Generation (RAG) systems. RAG involves retrieving data from  │
│ sheet-and-some-      │ Building Advanced    │                  │ an external knowledge database and sending it with a user    │
│ recipes-for-         │ RAG                  │                  │ query to an LLM for response generation. A basic RAG         │
│ building-advanced-   │                      │                  │ involves retrieval, an external knowledge database, and a    │
│ rag-803a9d94c41b     │                      │                  │ generation component. The success of a RAG system depends on │
│                      │                      │                  │ the retrieval and generation components meeting requirements │
│                      │                      │                  │ such as relevance and usefulness of answers. To achieve      │
│                      │                      │                  │ these requirements, advanced techniques can be used in       │
│                      │                      │                  │ Retrieval and Generation. Techniques for Retrieval include   │
│                      │                      │                  │ chunk-size optimization and using structured external        │
│                      │                      │                  │ knowledge, while techniques for Generation include LM        │
│                      │                      │                  │ adapters, knowledge-aware training objectives, and answer    │
│                      │                      │                  │ merging/reranking.                                           │
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