LangChain embeddings

LangChain embeddings are vector representations generated through the LangChain framework’s Embeddings interface, which unifies calls to services such as OpenAI, Cohere, Hugging Face, or local sentence-transformer models. A developer passes raw text, and the wrapper returns fixed-length floating-point arrays that capture semantic meaning, enabling similarity search, clustering, or Retrieval-Augmented Generation (RAG). Methods like embed_documents() batch-process large corpora, while embed_query() converts a user prompt into a single vector for top-k search against a vector database such as Qdrant or Chroma. The interface handles rate limits, async streaming, and token counting, and exposes model-specific options—dimension size, context window, truncation strategy—through a typed config. Because every embedding provider implements the same schema, teams can swap models for cost, speed, or language coverage without altering downstream code, making LangChain embeddings the foundation of scalable, data-aware AI pipelines.