What is Embedding?
Embedding is a technique in machine learning that transforms discrete data such as words, sentences, or entities into continuous dense vectors in a high-dimensional space, where semantically similar items are mapped to nearby points.
Quick Facts
| Created | 2013 by Tomas Mikolov et al. (Word2Vec) |
|---|---|
| Specification | Official Specification |
How It Works
Embeddings capture semantic relationships by representing data as numerical vectors, typically with hundreds or thousands of dimensions. Early approaches like Word2Vec and GloVe learned word embeddings by analyzing word co-occurrence patterns in large text corpora. Modern transformer-based models like BERT and GPT produce contextual embeddings where the same word can have different representations depending on its surrounding context. These dense vector representations enable mathematical operations on semantic meaning, such as calculating cosine similarity to measure how related two concepts are. The latest embedding models like OpenAI's text-embedding-3-large, Cohere's embed-v3, and open-source alternatives like BGE and E5 offer improved performance and multilingual support. Vector databases such as Pinecone, Weaviate, Milvus, Chroma, and Qdrant have emerged as essential infrastructure for storing and querying embeddings at scale, enabling RAG applications and semantic search systems.
Key Characteristics
- High-dimensional dense vectors typically ranging from 128 to 4096 dimensions
- Captures semantic meaning and relationships between data points
- Enables similarity computation through distance metrics like cosine similarity
- Contextual embeddings vary based on surrounding context in transformer models
- Learned representations that encode complex patterns from training data
- Supports arithmetic operations on semantic concepts (e.g., king - man + woman ≈ queen)
Common Use Cases
- Semantic search engines that find conceptually related content
- Retrieval-Augmented Generation (RAG) for grounding LLM responses
- Recommendation systems based on content similarity
- Document clustering and topic modeling
- Anomaly detection through distance-based outlier identification
Example
Loading code...Frequently Asked Questions
What is embedding in machine learning?
Embedding is a technique that converts discrete data (words, sentences, entities) into continuous dense vectors in high-dimensional space. Semantically similar items are mapped to nearby points, enabling mathematical operations on meaning like similarity computation.
What is the difference between Word2Vec and BERT embeddings?
Word2Vec produces static embeddings - each word has one fixed vector regardless of context. BERT produces contextual embeddings - the same word gets different vectors based on surrounding context. BERT captures more nuanced meaning but requires more computation.
How do you use embeddings for semantic search?
Convert documents and queries to embeddings using models like text-embedding-3-small. Store document embeddings in a vector database (Pinecone, Weaviate, Chroma). At search time, embed the query and find nearest neighbors using cosine similarity or Euclidean distance.
What is embedding dimension and how to choose it?
Embedding dimension is the number of values in the vector (e.g., 384, 768, 1536). Higher dimensions capture more information but increase computation and storage costs. Most use cases work well with 384-1536 dimensions. Choose based on accuracy vs. efficiency tradeoff.
What is the role of embeddings in RAG?
In RAG (Retrieval-Augmented Generation), embeddings enable semantic retrieval of relevant documents. The query is embedded, similar documents are retrieved from a vector database, and these documents provide context for the LLM to generate grounded, accurate responses.