Handling large documents in Haystack requires breaking them into manageable pieces and using efficient retrieval strategies. The core challenge is that most language models have input limits (often 512 tokens) and struggle with long texts. Start by splitting documents into smaller chunks using Haystack’s preprocessing tools. The DocumentSplitter or PreProcessor class can divide documents with options like split_length (e.g., 200 words per chunk) and split_overlap (e.g., 20 words) to preserve context between segments. For example, a 10,000-word document could be split into 50 chunks of 200 words each, with adjacent chunks sharing 20 words to avoid losing key information at boundaries. This ensures each chunk fits model limits while maintaining semantic continuity.
After splitting, manage chunks efficiently using Haystack’s document stores and retrieval pipelines. Stores like ElasticsearchDocumentStore or FAISSDocumentStore index chunks for fast search. When querying, use a retriever (e.g., BM25Retriever or EmbeddingRetriever) to find relevant chunks, then pass them to a reader model like FARMReader for answer extraction. For instance, a question about a specific paragraph in a large contract would first retrieve the relevant chunk containing that paragraph. To avoid redundant or fragmented answers, consider post-processing steps like aggregating results from multiple chunks or using a JoinDocuments node in pipelines to merge overlapping answers. Metadata (e.g., document_id) can help trace chunks back to their source document.
Optimize performance by balancing chunk size and retrieval accuracy. Smaller chunks reduce computational load but may miss broader context. Experiment with chunk lengths (e.g., 128 vs. 512 tokens) and overlap values to find the right trade-off for your use case. For very large datasets, use scalable document stores like Elasticsearch, which handles high-volume searches efficiently. Additionally, leverage Haystack’s caching mechanisms (e.g., SQLDocumentStore with update_existing_documents=True) to avoid reprocessing unchanged documents. If runtime is critical, combine sparse retrievers (fast but less precise) with dense retrievers (slower but more accurate) in a pipeline to prioritize speed without sacrificing relevance. For example, use BM25Retriever for initial filtering and EmbeddingRetriever to refine results.