SWIRL is an open-source, federated search platform that enables organizations to perform AI-powered searches across their internal knowledge sources — including Microsoft 365, SharePoint, Confluence, GitHub, Jira, and more — without replicating or moving data. Built with Retrieval-Augmented Generation (RAG), SWIRL connects directly to existing data stores, respects native permissions, and delivers relevant answers with source attribution. This eliminates the complexity of vector databases, ETL pipelines, and cloud data migration while maintaining enterprise-grade security. It’s designed for IT teams, knowledge workers, and developers who need fast, accurate search across siloed systems without compromising data governance.
Unlike traditional search tools that require indexing and data movement, SWIRL operates as a query federation layer. It translates user queries into native search syntax for each connected source, retrieves results in real time, re-ranks them using NLP models like spaCy and NLTK, and presents unified, context-aware answers. This makes it ideal for organizations with strict data residency policies or those seeking to avoid the cost and risk of cloud-based RAG solutions.
What You Get
- Federated Search Across 100+ Connectors - SWIRL integrates with Microsoft 365, SharePoint, Confluence, GitHub, Jira, Arxiv, Google News, and more — all without moving data. Each connector uses native APIs to query live data sources.
- No Vector Database Required - SWIRL performs RAG without storing embeddings or requiring vector DB setup. Queries are processed in real time using on-the-fly relevance ranking via spaCy and NLTK.
- Real-Time Query Transformation - Automatically rewrites search syntax (e.g., converts
NOT term to -term) and handles operators like AND, OR, and + across incompatible search providers.
- Built-in Result Re-Ranking - Uses cosine similarity with spaCy’s large language model and NLTK to re-rank results by contextual relevance, not just keyword match.
- Duplicate Detection & Deduplication - Removes duplicate results using configurable cosine similarity thresholds or field-based matching.
- Result Mixers & Sorting - Combine results using relevance, date, or round-robin (stack) strategies; filter for new items only in subscribe mode.
- SQLite3 & PostgreSQL Support - Results are optionally stored for analytics, post-processing, or audit trails using standard SQL databases.
- Query Pipelining with Processors - Extend search behavior with custom processors to transform queries, responses, or results before delivery.
- Search Subscription & Real-Time Monitoring - Subscribe to searches to receive continuous updates when new results appear, ideal for monitoring tickets or documents.
- Spell Correction & Stopword Handling - Uses TextBlob for spell correction and NLTK to filter common stopwords based on language configuration.
- Search Expiration Service - Automatically purge old search results to manage storage usage in production deployments.
- Extensible Connector & Mixer Architecture - Add new data sources or result ranking logic via modular Python components without modifying core code.
Common Use Cases
- Building a unified internal knowledge base - A company with data in SharePoint, Confluence, and Google Drive uses SWIRL to let employees ask natural language questions like ‘How do we onboard new hires?’ and get answers pulled directly from HR docs with clickable source links.
- Creating a customer support assistant - Support teams query SWIRL to find answers in past tickets and knowledge articles, then draft responses using the exact wording from internal documents — ensuring consistency and compliance.
- Developer productivity enhancement - Engineers search across GitHub repos, Jira tickets, and API docs to find code examples or bug fixes without switching tools — reducing context-switching time by up to 7.5 hours per week.
- Enterprise search with strict data governance - A financial institution uses SWIRL to allow secure, permission-aware search across internal wikis and compliance databases without ever moving sensitive data out of their on-premises environment.
- Problem: Teams waste time searching across disconnected tools → Solution: SWIRL unifies search - Employees spend hours toggling between Confluence, Teams, and Jira. SWIRL provides a single search bar that returns results from all systems with source attribution and relevance ranking.
- Team: DevOps & IT governance teams → Use Case: Deploying secure AI search - These teams use SWIRL to roll out enterprise RAG in under 2 minutes using Docker, avoiding weeks of infrastructure work and data migration projects.
Under The Hood
Swirl Search is an extensible, AI-powered search platform designed to unify diverse data sources into a cohesive and intelligent search experience. It emphasizes modular architecture, robust integration capabilities, and developer-friendly extensibility for enterprise use cases.
Architecture
The system follows a layered architecture that clearly separates concerns between presentation, business logic, and data access layers. This design enables a high degree of modularity and supports cross-cutting concerns through middleware patterns.
- Modular components are structured as independent units, allowing for easy addition of new search providers and data connectors.
- The architecture supports extensibility via configurable query processors and result transformers.
- Middleware patterns are leveraged to handle authentication, request routing, and other shared functionalities.
Tech Stack
Built primarily with Python and Django, the platform integrates advanced machine learning and natural language processing tools alongside a wide array of data connectors.
- The tech stack includes extensive use of Python-based frameworks and libraries for search and data processing.
- Django serves as the core web framework, enabling rapid development and scalable API endpoints.
- A variety of data connectors and search engines are integrated to support diverse enterprise needs.
Code Quality
The codebase reflects a mature Python application with consistent practices and strong emphasis on extensibility and testability.
- A substantial test suite covers authentication flows, API interactions, and user access scenarios across multiple environments.
- Try/except blocks are widely used to ensure graceful handling of runtime exceptions and maintain system stability.
- Code follows standard Python conventions with clear separation between core logic and external integrations.
What Makes It Unique
Swirl Search distinguishes itself through its plugin-like architecture and deep integration capabilities that simplify the unification of disparate data sources.
- Its modular connector design allows seamless addition and configuration of new search providers with minimal code changes.
- The platform enables enterprise-grade AI-powered search without requiring extensive customization or deep technical knowledge.
- It uniquely addresses the challenge of integrating heterogeneous data sources into a single, intelligent search interface.