Augor

Using embeddings to find meaningful patterns in financial news

What is this?

Augor is a Flutter desktop application that aggregates financial news from multiple RSS sources and uses OpenAI embeddings to cluster related articles about the same business events. Instead of manually tracking dozens of news sources, Augor automatically groups stories so you can see what's actually happening in markets.

Current Features

RSS Feed Aggregation

• Supports RSS and Atom feeds
• Pre-configured with 20+ major business news sources (Reuters, Bloomberg, WSJ, etc)
• Add custom feeds through the settings interface
• Enable/disable feeds individually

AI-Powered Processing

• Generates embeddings using OpenAI's text-embedding-3-small model
• Filters articles for business relevance using keyword similarity
• Groups related articles about the same event using cosine similarity
• Exports results as JSON for further analisis

Settings Management

• Configure OpenAI API key
• Manage RSS feed sources
• Set custom storage location for output files

Technical Stack

• Flutter - Cross-platform desktop app (macOS, Windows, Linux)
• shadcn_flutter - UI component library
• OpenAI API - Text embeddings for semantic similarity
• Provider - State managment
• go_router - Navigation

Setup

1. Clone the repository
2. Install dependencies:

   flutter pub get
   

3. Create a .env file with your OpenAI API key (or configure it in the app settings)
4. Run the app:

   flutter run
   

How it works

1. Aggregate - Fetches articles from all enabled RSS feeds
2. Embed - Generates vector embeddings for article titles and descriptions
3. Filter - Removes articles not relevant to business/finance using keyword matching
4. Cluster - Groups similar articles (cosine similarity >= 0.7) to identify events
5. Export - Saves results to JSON files in your configured storage location

Output Files

The app generates several JSON files in your storage directory:

• aggregated_feed.json - All fetched articles
• enriched_aggregated_feed.json - Articles with embeddings
• relevant_aggregated_feed.json - Filtered relevant articles
• grouped_relevant_aggregated_feed.json - Articles clustered by event
• readable_*.json - Human-readable versions without embeddings

Author

Benjamin Watt Supervisor: Panagiotis Kanellopoulos University of Essex