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Ragie

Ragie

Ragie accelerates AI app development with easy APIs, supporting data sources like Google Drive, and advanced features such as LLM reordering.
Author:LoRA
Inclusion Time:07 Feb 2025
Visits:1308
Pricing Model:Free
Introduction

What is Ragie?

Ragie is a RAG (Retrieval-Augmented Generation) service designed for developers. It offers easy-to-use APIs and SDKs to help developers quickly build and deploy generative AI applications. Key features include advanced functions like LLM reordering, entity extraction, and support for direct connections with popular data sources such as Google Drive and Notion. Ragie ensures data accuracy and reliability through automatic synchronization.

Ragie is ideal for developers needing to rapidly develop and deploy generative AI applications, particularly those looking to leverage existing data sources for intelligent search and information retrieval services. It is well-suited for scenarios requiring handling large volumes of data and quick results.

Example Usage:

Glue Company used Ragie to complete a project in three weeks that would have otherwise taken three months.

Key Features:

Simple and user-friendly APIs and SDKs for quick integration.

Advanced functionalities including LLM reordering and entity extraction.

Direct connections to data sources like Google Drive, Notion, and Confluence.

Automatic data synchronization to ensure up-to-date and accurate information.

Upcoming embedded connectors for direct data connection within applications.

Focus on RAG technology to allow developers to concentrate on application development.

Getting Started:

1. Register and obtain a Ragie API key.

2. Use Ragie’s APIs to upload files or connect data sources.

3. Set up automatic data synchronization for real-time updates.

4. Utilize Ragie’s search APIs for semantic search queries.

5. Customize and optimize AI applications using built-in advanced features like LLM reordering.

6. Integrate embedded connectors (coming soon) for direct data connection within applications.

7. Refer to Ragie documentation and examples for further customization and optimization.

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