Best Retrieval-Augmented Generation (RAG) Software for Restack

Find and compare the best Retrieval-Augmented Generation (RAG) software for Restack in 2025

Use the comparison tool below to compare the top Retrieval-Augmented Generation (RAG) software for Restack on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Vertex AI Reviews

    Vertex AI

    Google

    Free ($300 in free credits)
    713 Ratings
    See Software
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    Vertex AI Search is an innovative and robust enterprise search platform offered by Google Cloud, crafted to provide search experiences that mirror Google's high standards across various platforms, including websites, intranets, and bespoke applications. This solution utilizes cutting-edge technologies such as advanced crawling, document comprehension, and generative AI to ensure highly pertinent search outcomes. It effortlessly integrates with existing corporate infrastructures and features real-time updates, vector search capabilities, and RAG (Retrieval Augmented Generation) to enhance generative AI functionalities. Vertex AI Search is specifically designed for sectors like retail, healthcare, and media, delivering tailored solutions that significantly boost search effectiveness and enhance customer interaction.
  • 2
    Llama 3.1 Reviews
    Introducing an open-source AI model that can be fine-tuned, distilled, and deployed across various platforms. Our newest instruction-tuned model comes in three sizes: 8B, 70B, and 405B, giving you options to suit different needs. With our open ecosystem, you can expedite your development process using a diverse array of tailored product offerings designed to meet your specific requirements. You have the flexibility to select between real-time inference and batch inference services according to your project's demands. Additionally, you can download model weights to enhance cost efficiency per token while fine-tuning for your application. Improve performance further by utilizing synthetic data and seamlessly deploy your solutions on-premises or in the cloud. Take advantage of Llama system components and expand the model's capabilities through zero-shot tool usage and retrieval-augmented generation (RAG) to foster agentic behaviors. By utilizing 405B high-quality data, you can refine specialized models tailored to distinct use cases, ensuring optimal functionality for your applications. Ultimately, this empowers developers to create innovative solutions that are both efficient and effective.
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