text-embedding-ada-002 performs reasonably well with long documents thanks to its large context window of up to 8192 tokens. This allows developers to embed larger chunks of text than many earlier models, reducing the need to aggressively split documents. For moderately long content such as articles or documentation sections, this can simplify preprocessing.
That said, embedding very long documents as a single vector is usually not ideal. Long documents often contain multiple topics, and a single embedding may blur these distinctions. A common practice is to split long documents into smaller, semantically coherent chunks before embedding. This improves retrieval accuracy because each vector represents a more focused piece of content. Chunking also makes search results easier to present to users.
When dealing with many chunks from long documents, storing embeddings in a vector database such as Milvus or Zilliz Cloud becomes especially important. These systems are designed to handle large numbers of vectors efficiently and allow fast retrieval even when documents are heavily chunked. This approach balances the model’s context window with practical retrieval needs. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002