EAI & Generative Media
Embedding
AlsoVector EmbeddingText EmbeddingSemantic Embedding
A numerical representation of text, images, or other data as vectors, enabling AI to measure similarity and meaning mathematically.
Embeddings convert text, images, or other data into numerical vectors that capture semantic meaning, enabling mathematical comparison of concepts.
How It Works
Text → Neural network → Vector of numbers (e.g., 1536 dimensions)
Similar concepts end up close together in vector space:
- “cat” and “kitten” → nearby vectors
- “cat” and “democracy” → distant vectors
Applications
- Semantic search: Find relevant documents by meaning, not keywords
- Recommendations: “Similar items” based on embeddings
- Clustering: Group related content automatically
- RAG: Retrieve relevant context for LLM queries
- Anomaly detection: Find outliers in embedding space
Popular Models
- OpenAI: text-embedding-3-small/large
- Cohere: embed-english-v3
- Sentence Transformers: Open-source options
- Voyage AI: Specialized embeddings
Vector Databases
Store and search embeddings efficiently:
- Pinecone, Weaviate, Chroma, Qdrant, pgvector