Storing & Updating Embeddings
Understand best practices for storing, indexing, and efficiently updating embeddings in your data pipeline.
Storing & Updating Embeddings
Welcome to Lesson 3! This lesson covers the essential practices for managing embeddings: how to store them effectively, the role of indexing for search performance, and strategies for updating embeddings to keep your data fresh.
These concepts are crucial for building dynamic and responsive AI applications.
Why Persist Embeddings?
Generating embeddings can be computationally intensive and time-consuming. Storing them after creation offers significant benefits:
- Reuse: Avoid re-computing the same embedding for multiple queries.
- Speed: Enable much faster similarity searches.
- Scale: Support larger applications without constant re-generation.
Think of it as caching the 'meaning' of your data for quick access.
All lessons in this course
- Text Embedding Models
- Using Embedding APIs
- Storing & Updating Embeddings
- Chunking Text for Better Embeddings