

Ragie
Overview :
Ragie is a Retrieval-Augmented Generation (RAG) as a service product targeted at developers, providing easy-to-use APIs and SDKs to help them quickly launch and implement generative AI applications. Ragie features advanced capabilities such as LLM re-ranking, summary indexing, and entity extraction to ensure accurate and reliable information. It supports direct connections to popular data sources like Google Drive and Notion, along with automatic syncing to keep data current. Led by Craft Ventures, Ragie offers a straightforward pricing strategy with no setup fees or hidden costs.
Target Users :
Ragie is primarily designed for developers who need to quickly develop and deploy generative AI applications, especially those looking to leverage existing data sources for intelligent search and information retrieval services. It is suitable for business scenarios that require handling large volumes of data and obtaining results swiftly.
Use Cases
Glue Company completed a project that originally required three months in just three weeks using Ragie.
With Ragie, developers can quickly integrate intelligent document search functionality into their applications.
Businesses can leverage Ragie to connect internal data sources for automated document management and information retrieval.
Features
Provides easy-to-use APIs and SDKs for rapid onboarding.
Built-in advanced features such as LLM re-ranking, summary indexing, and entity extraction.
Supports direct connections to data sources like Google Drive, Notion, and Confluence.
Automatically synchronizes data to ensure accuracy and reliability.
Upcoming embedded connectors will allow users to connect their own data directly within applications.
Focuses on RAG technology, enabling developers to concentrate on application development.
How to Use
Register and obtain an API key for Ragie.
Upload files or connect data sources using the provided Ragie API.
Set up automatic data synchronization to ensure real-time updates.
Utilize Ragie's retrieval API for semantic search queries.
Employ built-in advanced features as needed, such as LLM re-ranking and summary indexing.
Integrate upcoming embedded connectors that allow users to connect data directly within their applications.
Further customize and optimize AI applications based on Ragie's documentation and examples.
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