Handling long documents in semantic search requires strategies to manage information density while preserving context. The main challenge is balancing the need to process large amounts of text with the limitations of embedding models, which often have token limits (e.g., 512 or 4k tokens). To address this, developers typically break documents into smaller chunks, use metadata to retain structural context, and apply post-processing techniques to refine results. Let’s explore these steps in detail.
First, document chunking is essential. Split the text into logical segments, such as paragraphs, sections, or fixed-size windows (e.g., 256 tokens). For example, a research paper could be divided by sections like “Abstract,” “Methods,” and “Results.” This ensures each chunk contains a coherent idea while fitting within model limits. When using models like BERT or sentence-transformers, overlapping chunks (e.g., sliding a 128-token window over 256-token segments) can help preserve context between adjacent sections. Tools like LangChain or simple Python scripts can automate this splitting. However, avoid making chunks too small, as they might lose broader meaning—experiment with lengths based on your data and model.
Next, enrich chunks with metadata and structure. Include information like document titles, section headers, or entity tags (e.g., “author: John Doe” or “topic: Machine Learning”) to help the search system understand relationships between chunks. For instance, in a legal document, attaching metadata like “Section 2.1: Liability” lets the system prioritize relevant sections during retrieval. When indexing, combine embeddings of chunk text with metadata filters. This hybrid approach improves precision—imagine searching for “privacy clauses” in contracts and using metadata to focus on sections tagged “Data Protection.” Tools like Elasticsearch or FAISS can store embeddings alongside metadata for efficient filtering.
Finally, refine results with re-ranking and aggregation. After retrieving top chunks, use a cross-encoder model (e.g., a more computationally intensive model like BERT-large) to re-rank them by comparing the query to each chunk in full. This compensates for potential context loss during initial retrieval. For example, a query about “neural network optimization” might initially match chunks mentioning “optimization” in a generic sense, but re-ranking can surface chunks discussing “gradient descent in CNNs.” Additionally, aggregate results from multiple related chunks to construct a comprehensive answer. If a user asks about symptoms of a disease, combine information from “Diagnosis” and “Clinical Presentation” sections of a medical paper. Libraries like Hugging Face’s Transformers or Haystack provide pipelines for these steps.
By combining chunking, metadata enrichment, and post-processing, developers can effectively handle long documents without sacrificing search quality. The key is to maintain context through thoughtful segmentation and leverage metadata and re-ranking to bridge gaps between localized chunks and the document’s broader meaning.