embed-english-light-v3.0 handles long documents by embedding them as smaller chunks rather than as a single input. Embedding models generally have input length limits, and even when a long input is accepted, a single vector for an entire document is usually too coarse for retrieval. The practical solution is chunking: split long documents into passages, embed each passage, and retrieve the most relevant passages at query time. This approach gives you higher precision because users rarely need an entire manual—they need the right section.
Chunking should follow the structure of your content instead of arbitrary character counts. For example, chunk by headings and paragraphs, target a consistent chunk size, and use small overlap so that boundary sentences remain searchable. Store each chunk with metadata that keeps it grounded: doc_id, section_title, url, offset, and version. Then store the vectors in a vector database such as Milvus or Zilliz Cloud. At query time, embed the user’s question and search for the nearest chunks. You can then return snippets directly, or assemble multiple top chunks into a longer context for a RAG system.
For very long docs, hierarchical retrieval often works better than “flat” chunk search. Create chunk-level vectors for precision and section-level vectors for navigation. The first step finds relevant sections; the second step retrieves the best chunks inside those sections. This reduces noise, keeps context coherent, and helps prevent retrieval from scattering across unrelated parts of the corpus. The key is consistency: once you choose chunking rules, keep them stable so your embeddings and indexes remain predictable and your evaluation results stay meaningful.
For more resources, click here: https://zilliz.com/ai-models/embed-english-light-v3.0