Embedding (AI)
Embedding (AI) is a fixed-length vector representation of data produced by a neural network, where the geometric relationships between vectors reflect semantic relationships between the original inputs; An embedding model, often a transformer encoder, processes input text and produces a single vector by pooling the token representations at its final layer; Generating embeddings is typically the first step in building a RAG pipeline or semantic search feature
Embedding (AI) is a fixed-length vector representation of data produced by a neural network, where the geometric relationships between vectors reflect semantic relationships between the original inputs. Text embeddings encode the meaning of a word, sentence, or document into typically 256 to 4096 floating-point dimensions, enabling mathematical operations over meaning.
How it works
An embedding model, often a transformer encoder, processes input text and produces a single vector by pooling the token representations at its final layer. The model is trained on large corpora using objectives like contrastive learning, which pulls embeddings for semantically similar pairs closer and pushes dissimilar pairs apart. The resulting embedding space captures syntax, semantics, and topic at once.
Key facts
- Dimensionality: Common embedding dimensions are 768 (BERT-sized), 1536 (OpenAI text-embedding-3-small), and 3072 (larger models).
- Similarity metric: Cosine similarity and dot product are the standard distance measures for comparing embeddings.
- Models: OpenAI text-embedding-3, Cohere Embed, and open-weight models like nomic-embed-text are widely used.
- Storage: Embeddings are stored in vector databases, which provide efficient approximate nearest-neighbor search.
For builders
Generating embeddings is typically the first step in building a RAG pipeline or semantic search feature. Selecting an embedding model involves balancing retrieval quality against cost and latency: larger models produce better embeddings but cost more per token. Embeddings are also used for deduplication, clustering, anomaly detection, and intent classification in production systems.
Sources
- Lewis, P., et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv:2005.11401. arxiv.org
- Gao, Y., et al. (2023). Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv:2312.10997. arxiv.org
- Reimers, N., Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv:1908.10084. arxiv.org
- Pinecone. What is a vector database? pinecone.io
- Johnson, J., Douze, M., Jegou, H. (2017). Billion-scale similarity search with GPUs (FAISS). Facebook Research. github.com
