Category: Building AI Assistants
Parent item: Knowledge and Training
When organizations evaluate AI assistants for storing and accessing institutional information, one of the most critical technical questions involves data storage methodology. This guide explains LearnWise's approach to storing institutional information and addresses the key question: Should we copy data completely into hosting or use a vector database?
LearnWise uses a vector database approach hosted on AWS infrastructure. This architecture is specifically designed to optimize AI assistant performance while maintaining security and data integrity.
A vector database is a specialized data storage system optimized for handling embeddings—mathematical representations of text, images, or other data that AI models can efficiently process and compare.
Here's how it works:
Aspect | Vector Database (LearnWise Approach) | Traditional Data Copying |
---|---|---|
Search Efficiency | Semantic search across all content simultaneously | Keyword-based search, slower queries |
Storage Optimization | Compressed vector representations | Full data duplication |
Update Speed | Incremental updates to changed content only | Full data re-sync required |
Privacy & Security | Data processed into mathematical representations | Complete data copies stored |
Scalability | Handles large datasets efficiently | Performance degrades with data volume |