You generate embeddings with text-embedding-ada-002 by sending text input to the OpenAI Embeddings API and receiving a fixed-length numerical vector in response. The process is straightforward: you provide a string (or a list of strings), specify the model as text-embedding-ada-002, and the API returns a 1536-dimensional vector for each input. This vector represents the semantic meaning of the text and can be used for similarity search, clustering, or classification.
In practical terms, most developers generate embeddings during two phases. The first is an offline or batch phase, where documents, articles, or records are embedded once and stored for later use. The second is a query-time phase, where user input such as a search query is embedded on demand. For example, you might embed a product description catalog ahead of time, then embed each incoming user query and compare it against stored vectors to find relevant products. The API response is typically a JSON payload containing an array of floating-point values, which you can store directly without additional processing.
Once generated, embeddings are usually stored in a vector database such as Milvus or Zilliz Cloud. These systems are designed to handle large volumes of vectors and perform fast similarity search. A common setup is to generate embeddings in your application code, write them to Milvus along with metadata, and then query Milvus using an embedded user query. This architecture keeps embedding generation simple while allowing search and retrieval to scale efficiently. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002