Developers should chunk long documents for jina-embeddings-v2-base-en in a way that balances context preservation with semantic focus. Although the model supports up to 8192 tokens, embedding entire documents in a single vector is often not ideal if the content covers multiple topics. A good starting point is to split documents into logical sections, such as headings, subsections, or paragraphs that each focus on a single idea.
In practice, many teams use chunk sizes ranging from a few hundred to a couple thousand tokens, depending on document structure. Overlapping chunks can help preserve context between sections, but too much overlap increases storage and indexing cost when embeddings are stored in a vector database such as Milvus or Zilliz Cloud. Each chunk is embedded separately and stored with metadata indicating its position in the original document.
The goal of chunking is to improve retrieval precision. When a user query is embedded and searched against the database, smaller, focused chunks are more likely to match the specific intent of the query. Developers should experiment with chunk size and boundaries using real queries and evaluate retrieval quality. jina-embeddings-v2-base-en provides the flexibility to handle long text, but good chunking strategy remains essential for high-quality semantic search and RAG results.
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