text-embedding-3-large handles long documents best when they are split into smaller, semantically coherent chunks before embedding. While the model can process long text inputs, embedding an entire document as a single vector often produces diluted representations that mix multiple topics together.
In practice, developers usually split long documents into sections, paragraphs, or sliding windows of text with overlap. Each chunk is embedded separately, allowing the system to retrieve the most relevant part of a document in response to a query. For example, a long technical guide might be split by headings, with each section embedded independently. This approach improves recall and makes search results easier to interpret and display.
Once chunked embeddings are generated, they are stored in a vector database such as Milvus or Zilliz Cloud along with metadata linking each chunk back to its source document. At query time, you retrieve the top matching chunks and then aggregate or rerank them at the document level if needed. This pattern is widely used in production semantic search and retrieval systems because it balances accuracy, performance, and maintainability when working with long-form content.
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