Moving Beyond Traditional Search with Swirl

The Problem with Traditional Search

The traditional approach is the lift and shift of data from one container to another. It is a big problem in many cases. Creating inverted indexes is widely used in traditional search engines to enable quick information retrieval. However, this method can be computationally expensive, particularly when identifying and integrating new data into these indexes. As businesses grow and their data becomes more complex and voluminous, these traditional systems often struggle to keep up.

Additionally, enterprises are now generating new data types at an unprecedented rate—the shift towards distributed, cloud-based pools of information compounds these difficulties.

Traditional enterprise information access systems rely on periodically updated inverted indexes and are not well-suited for such dynamic and heterogeneous data environments. They cannot easily accommodate the continuous influx of new data types or the decentralized nature of cloud-based information systems.

This results in inefficiencies and delays in data retrieval, which can hinder decision-making and operational workflows within an organization.

Swirl 3.0 provides a simple and elegant solution to this problem by connecting to various data sources and searching them simultaneously.

Swirl 3.0 Features

Swirl is built on the Python Django stack and provides a user-friendly interface called Galaxy UI. It can be run in Docker or as a managed service in Microsoft Azure. Swirl enables users to leverage AI-powered re-ranking capabilities while maintaining data security and privacy.

Swirl’s search technology transforms how businesses access information across their applications and data stores. By utilizing advanced Large Language Models, Swirl quickly sifts through data from multiple sources, such as Salesforce and Microsoft365, providing users with the most relevant results and insights.

The benefits of Swirl’s approach are clear:

• Users receive finely-tuned search results tailored to their specific needs.
• Without the hassle of moving data or reindexing content.

Key Points:

• Swirl uses LLM technology for analyzing and ranking search results from diverse sources like data silos, Salesforce, and Microsoft etc.
• The Swirl search enhances relevance ranking in near-real time and contextualizes results for targeted queries.
• The system allows customization of the LLM for specific subject areas, and user feedback confirms the effectiveness of Swirl’s relevance ranking.
• Swirl minimizes the need for reindexing, eliminates content movement to search infrastructure, and efficiently manages relevance ranking and deduplication.

Connectors:

A broad and general overview of the list of available connectors can be found on our GitHub Page. If you wish to have any created on demand and priority, please get in touch with the Swirl support team at support@swirl.today.

Internal Working and Use Cases

Swirl integrates advanced content processing and analytics. It uses APIs (application programming interfaces) to locate and rank content from multiple sources, with controls to boost certain content.

Swirl’s framework allows fast finding and streaming information into a data pipeline for various search-based applications, such as Retrieval Augmented Generation (RAG) and fine-tuning Large Language Models.

It provides access to information within an organization’s data silos, solving traditional cost, complexity, and development problems associated with enterprise search solutions. Swirl embraces standards-based authentication mechanisms like OAuth2 to eliminate permission and security issues.

Tools like Swirl become indispensable as organizations grow and diversify their digital assets. Stay tuned as we explore how AI-driven solutions are shaping the future of information access and management.

Swirl is Open Source

Swirl is an open-source search platform. What this means for you:

• It’s a self-hosted, non-restrictive software with a permissive Apache 2.0 license.
• Software Developers can contribute to the project’s development, understanding the search ecosystem deeply while learning about Swirl in depth.
• If you want to learn more about Swirl, please join our Slack Community to talk more about Swirl.