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Retrieval - OpenAI API

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The Retrieval API allows you to perform semantic search over your data, which is a technique that surfaces semantically similar results — even when they match few or no keywords. Retrieval is useful on its own, but is especially powerful when combined with our models to synthesize responses. The Retrieval API is powered by vector stores, which serve as indices for your data. This guide will cover how to perform semantic search, and go into the details of vector stores. Create vector store and upload files. Send search query to get relevant results. To learn how to use the results with our models, check out the synthesizing responses section. Semantic search is a technique that leverages vector embeddings to surface semantically relevant results. Importantly, this includes results with few or no shared keywords, which classical search techniques might miss. For example, let's look at potential results for "When did we go to the moon?": (Jaccard used for keyword, cosine with text-embeddi

The Retrieval API allows you to perform semantic search over your data, which is a technique that surfaces semantically similar results — even when they match few or no keywords. Retrieval is useful on its own, but is especially powerful when combined with our models to synthesize responses. The Retrieval API is powered by vector stores , which serve as indices for your data. This guide will cover how to perform semantic search, and go into the details of vector stores. Quickstart Create vector store and upload files. Create vector store with files python 1 2 3 4 5 6 7 8 9 10 11 from openai im

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