What are Embeddings? Vector Representations Explained

Embeddings are numerical vector representations of text that capture semantic meaning. Similar concepts have similar vectors, enabling semantic search and comparison.

How Embeddings Work

Text is converted to a fixed-size vector (e.g., 1536 dimensions)
Similar meanings produce similar vectors
Cosine similarity measures relatedness
Stored in vector databases for fast retrieval

Embedding Use Cases

Semantic search
RAG document retrieval
Clustering and classification
Recommendation systems

Which embedding model should I use?

OpenAI's text-embedding-3-small is popular. For open-source, consider sentence-transformers. Match embedding model to your use case and latency requirements.