contextual-retrieval

Contextual retrieval locates precise chunks while automatically supplying parent context—surrounding sections that preserve meaning. This technique works by indexing documents hierarchically, then returning child matches paired with their parent sections, making it ideal for contracts, technical manuals, and research papers where exact answers need framing.

Hierarchical chunking associates small text segments with their parent sections, enabling precise matches without losing context. Markdown-aware chunking preserves document structure—headings, code blocks, tables—so child chunks can be retrieved together with parent context, improving both retrieval accuracy and comprehension.

Yes. Contextual retrieval is built to work with Milvus as the vector database. Index your documents with hierarchical chunks in Milvus, then query against it to retrieve child matches combined with parent context in a single end-to-end workflow.

Context-window expansion retrieves not only matching chunks but also adjacent sections from the parent document. This enriches search results by providing background information, so answers are grounded in their full context rather than isolated snippets.

Use contextual retrieval when exact answers must be understood within their document structure—legal contracts, technical specifications, research papers. Flat chunking loses hierarchy; contextual retrieval preserves parent-child relationships so readers get both precision and surrounding context.