What are Embeddings?
Embeddings are numerical representations of data - such as words, sentences, images, or other objects - in a continuous vector space. They capture semantic relationships, allowing similar items to have similar vector representations, enabling machines to understand and process meaning.
How Embeddings Work
- Input data (text, image, etc.) is processed
- A neural network encodes the input
- Output is a fixed-size vector (e.g., 768 or 1536 dimensions)
- Similar inputs produce similar vectors
- Vector operations enable semantic comparisons
Types of Embeddings
Text Embeddings
- Word embeddings (Word2Vec, GloVe)
- Sentence embeddings (BERT, OpenAI)
- Document embeddings
Other Modalities
- Image embeddings (CLIP, ResNet)
- Audio embeddings
- Multi-modal embeddings
Applications
- Semantic search
- Recommendation systems
- Clustering and classification
- Retrieval-Augmented Generation (RAG)
- Anomaly detection
- Similarity matching
Vector Databases
Embeddings are typically stored in specialized vector databases (Pinecone, Weaviate, Milvus) that enable efficient similarity search.