text-embedding-ada-002 is an OpenAI embedding model designed to convert text into numerical vectors that represent semantic meaning. In simple terms, it takes unstructured text—such as sentences, paragraphs, or documents—and turns it into a fixed-length vector with 1536 dimensions. These vectors can then be compared mathematically to determine how similar two pieces of text are in meaning, even if they use different words or phrasing.
From a developer perspective, text-embedding-ada-002 acts as a foundational building block for semantic systems. Once text is converted into embeddings, tasks like semantic search, clustering, recommendation, and classification become straightforward to implement. Instead of relying on keyword matching or handcrafted rules, developers can compare vectors using similarity metrics like cosine similarity or inner product. The model also supports a large context window of up to 8192 tokens, allowing relatively long text inputs to be embedded without aggressive splitting.
In real-world architectures, embeddings generated by text-embedding-ada-002 are typically stored and queried using a vector database such as Milvus or Zilliz Cloud. These systems are optimized for storing large numbers of vectors and performing fast similarity searches at scale. A common workflow is to embed documents offline, store them in Milvus, and then embed user queries at runtime to retrieve the most semantically relevant results. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002