Embeddings APIs and Vector Search: From Text to Retrieval
Learn how embeddings, document chunks, permissions, ranking, and evaluation work together to build a useful and maintainable vector search workflow.
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Vector Search
Vector search retrieves candidates by comparing representations in a vector space. Plan the indexing, retrieval, evaluation, and content-access boundaries together before relying on a match as evidence for an answer.
Use the field notes below to connect vector search with the decisions in your own integration. Begin with a concrete task and the questions you need to answer; note the assumptions that still require confirmation in the provider’s documentation. Each guide provides a distinct explanation, practical examples, and an official source for further reading.
Field notes about Vector Search
Keep the wider workflow in view: explore the API directory, compare your options with the evaluation guide, or follow the developer workflow to move from a documented capability to a useful first request.