embedding

**Embeddings** are **dense vector representations that capture semantic meaning** — transforming text, images, or data into fixed-dimensional numerical vectors where similar items are close together and dissimilar items are far apart, enabling semantic search, clustering, classification, and recommendation systems. **What Are Embeddings?** - **Format**: Fixed-length float arrays (e.g., 384, 768, 1536 dimensions). - **Property**: Semantically similar inputs produce similar vectors. - **Distance**: Cosine similarity or Euclidean distance measures relatedness. - **Models**: E5, BGE, OpenAI ada-002, Cohere embed, Jina embeddings. **Types of Embeddings** - **Word Embeddings**: Word2Vec, GloVe — individual word vectors. - **Sentence Embeddings**: E5, BGE, all-MiniLM — full sentence meaning. - **Document Embeddings**: Longer text representations. - **Multimodal**: CLIP — shared space for text and images. **Applications** - **Semantic Search**: Find relevant documents by meaning, not just keywords. - **RAG**: Retrieve context for LLM generation. - **Clustering**: Group similar items automatically. - **Classification**: Use as features for downstream ML models. - **Recommendation**: Find similar products, content, or users. **Top Models (2025)** - **OpenAI text-embedding-3-large**: 3072d, strong general purpose. - **BGE-M3**: Multilingual, multi-granularity, open-source. - **E5-Mistral-7B**: Instruction-tuned, state-of-the-art on MTEB. - **Jina Embeddings v3**: 8K context, multilingual. - **Cohere Embed v3**: Strong multilingual with compression. Embeddings are **the bridge between human language and machine computation** — enabling AI systems to understand meaning, find relevance, and make connections across vast amounts of unstructured data.