How do I configure Haystack to handle large datasets?

To configure HayStack for large datasets, focus on optimizing storage, retrieval, and processing pipelines. HayStack’s scalability largely depends on your choice of DocumentStore, preprocessing strategies, and retrieval configuration. Start by selecting a DocumentStore that supports distributed storage and efficient querying, such as Elasticsearch or Weaviate. These backends handle large volumes of data by indexing documents for fast search and scaling horizontally. For example, Elasticsearch can be tuned for performance by adjusting shard counts, replication settings, and refresh intervals to balance speed and resource usage. If you’re using vector-based retrieval (e.g., with dense embeddings), consider FAISS or Milvus, which are optimized for high-dimensional data and approximate nearest neighbor searches.

Next, optimize data preprocessing. Large datasets often require splitting documents into smaller chunks to avoid exceeding memory limits. Use HayStack’s PreProcessor class with parameters like split_length and split_overlap to break documents into manageable segments. Enable multiprocessing by setting num_processes in your pipeline to parallelize tasks like text splitting or embedding generation. For example, when processing 1 million documents, batching them into groups of 10,000 and using multiple workers can drastically reduce processing time. Additionally, cache intermediate results (e.g., embeddings) to avoid redundant computations during retraining or updates. Tools like Redis or HayStack’s built-in caching mechanisms can help here.

Finally, configure retrievers and pipelines for efficiency. Use sparse retrievers like BM25 (via Elasticsearch) for fast keyword-based filtering to narrow down the dataset before applying slower, dense retrievers. If using a DensePassageRetriever, limit the top_k value to reduce the number of documents processed in downstream tasks. For hybrid retrieval, combine results from multiple retrievers using HayStack’s EnsembleRetriever. Profile your pipeline with tools like Python’s cProfile to identify bottlenecks—for instance, if embedding generation is slow, consider GPU acceleration or model quantization. Test with subsets of your data to validate performance before scaling to the full dataset.