Managing and optimizing resource usage in Haystack requires a combination of efficient pipeline design, component configuration, and monitoring. Start by analyzing your pipeline components—document stores, retrievers, readers, and rankers—to identify bottlenecks. For example, if your retriever processes large document sets, consider using lightweight models or approximate nearest neighbor (ANN) techniques like FAISS to reduce memory and computation. Similarly, limit the number of documents processed at each stage (e.g., top_k parameters) to avoid unnecessary work. For instance, setting retriever.top_k = 50 and reader.top_k = 5 ensures downstream components handle only the most relevant candidates.
Optimize model choices and hardware usage. Replace heavy models with distilled or quantized versions where possible. For example, using a smaller BERT variant like distilbert-base-uncased instead of the full-sized model can cut inference time and memory usage by half while retaining most accuracy. Utilize GPU acceleration for compute-heavy tasks like inference or embedding generation, and ensure batch processing is enabled where applicable. If using Haystack’s REST API, configure worker threads and processes (via uvicorn or gunicorn) to balance CPU/GPU load. For document stores, choose storage backends that match your needs: FAISS for fast vector searches, Elasticsearch for hybrid keyword-vector workflows, or SQLite for lightweight text storage.
Monitor and scale resources based on usage patterns. Use tools like PyTorch Profiler or system utilities (e.g., nvidia-smi, htop) to track CPU/GPU/memory usage. If your pipeline scales horizontally, deploy components like retrievers or readers as separate microservices and load-balance requests. For cloud deployments, autoscaling groups can adjust resources dynamically. Cache frequent queries or precompute embeddings for static datasets to reduce runtime overhead. Finally, test configurations rigorously—A/B test retriever accuracy vs. speed trade-offs, and use Haystack’s benchmarking tools to compare pipelines. By iteratively refining these aspects, you can maintain performance while minimizing resource costs.