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What is Ragie?

Ragie streamlines the tasks of data ingestion, chunking, and multimodal indexing for both structured and unstructured datasets. By creating direct links to your data sources, it ensures a continually refreshed data pipeline. Its sophisticated features, which include LLM re-ranking, summary indexing, entity extraction, and dynamic filtering, support the deployment of innovative generative AI solutions. Furthermore, it enables smooth integration with popular data sources like Google Drive, Notion, and Confluence, among others. The automatic synchronization capability guarantees that your data is always up to date, providing your application with reliable and accurate information. With Ragie’s connectors, incorporating your data into your AI application is remarkably simple, allowing for easy access from its original source with just a few clicks. The first step in a Retrieval-Augmented Generation (RAG) pipeline is to ingest the relevant data, which you can easily accomplish by uploading files directly through Ragie’s intuitive APIs. This method not only boosts efficiency but also empowers users to utilize their data more effectively, ultimately leading to better decision-making and insights. Moreover, the user-friendly interface ensures that even those with minimal technical expertise can navigate the system with ease.

What is BGE?

BGE, or BAAI General Embedding, functions as a comprehensive toolkit designed to enhance search performance and support Retrieval-Augmented Generation (RAG) applications. It includes features for model inference, evaluation, and fine-tuning of both embedding models and rerankers, facilitating the development of advanced information retrieval systems. Among its key components are embedders and rerankers, which can seamlessly integrate into RAG workflows, leading to marked improvements in the relevance and accuracy of search outputs. BGE supports a range of retrieval strategies, such as dense retrieval, multi-vector retrieval, and sparse retrieval, which enables it to adjust to various data types and retrieval scenarios. Users can conveniently access these models through platforms like Hugging Face, and the toolkit provides an array of tutorials and APIs for efficient implementation and customization of retrieval systems. By leveraging BGE, developers can create resilient and high-performance search solutions tailored to their specific needs, ultimately enhancing the overall user experience and satisfaction. Additionally, the inherent flexibility of BGE guarantees its capability to adapt to new technologies and methodologies as they emerge within the data retrieval field, ensuring its continued relevance and effectiveness. This adaptability not only meets current demands but also anticipates future trends in information retrieval.

Media

Media

Integrations Supported

Baseten
Confluence
Google Drive
Hugging Face
Microsoft OneDrive
Microsoft PowerPoint
Nango
Notion
Salesforce

Integrations Supported

Baseten
Confluence
Google Drive
Hugging Face
Microsoft OneDrive
Microsoft PowerPoint
Nango
Notion
Salesforce

API Availability

Has API

API Availability

Has API

Pricing Information

$500 per month
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Ragie

Date Founded

2024

Company Website

www.ragie.ai/

Company Facts

Organization Name

BGE

Date Founded

2025

Company Location

United States

Company Website

bge-model.com/Introduction/index.html

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