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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Embeddings
Embeddings represent content as numeric vectors for tasks such as similarity and retrieval. Their usefulness depends on the representation, the comparison method, and how the resulting candidates support the application’s real task.
Use the field notes below to connect embeddings 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 Embeddings
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.