How do I handle large queries in Haystack?

Handling large queries in Haystack requires strategies to manage data volume and maintain performance. The primary approach involves breaking down the query into smaller, manageable chunks and optimizing retrieval components. Haystack’s PreProcessor class can split documents into segments (e.g., 500-word chunks) to avoid overwhelming models with excessive text. For instance, using split_by="word" and split_length=500 ensures each chunk is processable by transformers with token limits. Additionally, asynchronous processing with AsyncPipeline prevents blocking during large operations, allowing parallel execution of tasks like document retrieval or question answering.

Optimizing retrieval methods is critical for efficiency. Use sparse retrievers like BM25Retriever for fast keyword-based filtering before applying slower but more accurate dense retrievers (e.g., EmbeddingRetriever). For example, combine BM25 to narrow results to 1,000 documents, then use a dense retriever to rank the top 100. Adjust top_k parameters to balance speed and accuracy—lower values reduce computational load. For very large datasets, consider approximate nearest neighbor (ANN) libraries like FAISS or Milvus to speed up vector similarity searches. These tools index embeddings in a way that sacrifices minimal accuracy for significant performance gains.

Scaling infrastructure and leveraging caching further improve handling of large queries. Deploy Haystack components in distributed environments using Docker or Kubernetes to parallelize workloads. For instance, run multiple retriever or reader nodes behind a load balancer. Implement caching mechanisms (e.g., Redis) to store frequent query results or precomputed embeddings, reducing redundant computations. Monitor performance with tools like Prometheus to identify bottlenecks—if a reader model struggles with 10,000 documents, adjust chunk sizes or add GPU resources. By combining preprocessing, optimized retrieval, and scalable infrastructure, Haystack can efficiently manage large queries without compromising responsiveness.