Embeddings don't exist in isolation — they power entire retrieval architectures when paired with vector databases like Pinecone, Weaviate, or pgvector. Understanding the full embedding stack, from model choice to index configuration to query optimization, is critical for building production AI systems.

A production embedding pipeline has four components. First, the embedding model: choices include OpenAI's text-embedding-3-large (high accuracy, paid API), Cohere Embed (strong multilingual), or open-source alternatives like BGE or E5-mistral for self-hosted deployments. Model choice affects vector dimensionality, latency, and cost. Second, the vector database: purpose-built stores like Pinecone and Weaviate support Approximate Nearest Neighbor (ANN) search using algorithms like HNSW (Hierarchical Navigable Small World), which enables sub-millisecond similarity search across millions of vectors. PostgreSQL with the pgvector extension offers a simpler managed alternative for smaller scales. Third, chunking strategy: documents must be split into chunks before embedding — chunk size and overlap significantly impact retrieval quality. Common strategies include fixed-size, sentence-boundary, and semantic chunking. Fourth, reranking: after ANN retrieval, a cross-encoder reranker (like Cohere Rerank) scores the top-k results against the original query for higher precision. This full stack — embed, index, retrieve, rerank — underpins every modern RAG system.

Embedding Models and Vector Databases: The Full Stack