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GitHub - Starofall/S3HyperSync: S3HyperSync is a high-performance, memory-efficient, and cost-effective tool for synchronizing files between S3-compatible storage services.

Jul 10, 2024 18:40 • github.com GitHub

S3HyperSync is a high-performance, memory-efficient, and cost-effective tool for synchronizing files between S3-compatible storage services. - Starofall/S3HyperSync

Tailkits

Jul 10, 2024 18:39 • tailkits.com Tailkits

250+ Free & Premium Tailwind CSS templates, UI Kits, and components to build modern websites and landing pages with pre-built component libraries.

GitHub - dotenvx/dotenvx: a better dotenv–from the creator of `dotenv`

Jul 10, 2024 18:38 • github.com GitHub

a better dotenv–from the creator of `dotenv`. Contribute to dotenvx/dotenvx development by creating an account on GitHub.

GitHub - darrenburns/posting: The modern API client that lives in your terminal.

Jul 10, 2024 18:26 • github.com GitHub

The modern API client that lives in your terminal. - darrenburns/posting

GitHub - szimek/sharedrop: Easy P2P file transfer powered by WebRTC - inspired by Apple AirDrop

Jul 8, 2024 07:11 • github.com GitHub

Easy P2P file transfer powered by WebRTC - inspired by Apple AirDrop - szimek/sharedrop

GitHub - RamiAwar/dataline: Chat with your data - AI data analysis and visualization on CSV, Postgres, MySQL, Snowflake, SQLite...

Jul 8, 2024 07:03 • github.com GitHub

Chat with your data - AI data analysis and visualization on CSV, Postgres, MySQL, Snowflake, SQLite... - RamiAwar/dataline

Searching for Best Practices in Retrieval-Augmented Generation

Jul 8, 2024 06:51 • arxiv.org arXiv.org

Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, particularly in specialized domains. While many RAG approaches have been proposed to enhance large language models through query-dependent retrievals, these approaches still suffer from their complex implementation and prolonged response times. Typically, a RAG workflow involves multiple processing steps, each of which can be executed in various ways. Here, we investigate existing RAG approaches and their potential combinations to identify optimal RAG practices. Through extensive experiments, we suggest several strategies for deploying RAG that balance both performance and efficiency. Moreover, we demonstrate that multimodal retrieval techniques can significantly enhance question-answering capabilities about visual inputs and accelerate the generation of multimodal content using a "retrieval as generation" strategy.