Embeddings
Embeddings are numerical representations of text, images, or other data. They help computers compare meaning.
Simple idea: embeddings turn meaning into numbers.
Why It Matters
Embeddings make semantic search possible. This means the system can find related content even when the exact words are different.
Example:
- Search: “data leak through AI tools”
- Related result: “employees pasted customer PII into ChatGPT”
The words are different, but the meaning is close.
How It Works
- Take a piece of text.
- Send it to an embedding model.
- The model returns a vector, which is a list of numbers.
- Store that vector in a Vector Database.
- Compare vectors to find similar meaning.
Common Uses
| Use case | Example |
|---|---|
| Semantic search | Search company docs by meaning |
| RAG | Retrieve useful context before answering |
| Clustering | Group similar tickets or documents |
| Deduplication | Find repeated questions or content |
| Recommendations | Suggest related notes or articles |
Important Concepts
- Vector: list of numbers representing meaning.
- Similarity: how close two vectors are.
- Chunking: splitting documents into useful parts.
- Metadata: extra fields like source, date, author, permissions.
CookieYes Ideas
- Find similar compliance questions.
- Cluster customer support tickets.
- Search cookie documentation by meaning.
- Retrieve policy text for RAG.
Mozilor Ideas
- Search WebYes accessibility fixes.
- Cluster WooCommerce support issues.
- Find similar product improvement ideas.
- Retrieve agent playbooks.