To optimize query performance in Haystack, focus on three key areas: indexing efficiency, retriever configuration, and query design. Start by ensuring your documents are processed and stored in a way that minimizes latency during retrieval. Next, choose the right retriever model and tune its parameters for your specific data. Finally, structure your queries to reduce complexity and leverage built-in optimizations. Each step directly impacts how quickly and accurately Haystack returns results.
First, optimize your indexing pipeline. Use a document store like FAISS or Milvus for vector-based retrievers, as they are designed for fast similarity searches. Split long documents into smaller chunks (e.g., 200-300 words) with overlap to maintain context while avoiding oversized text blocks. For example, a 1,000-word article could be divided into four 250-word chunks with 50-word overlaps. Include metadata like dates or categories to enable filtering later. If using Elasticsearch with a sparse retriever like BM25, ensure your fields are properly analyzed (e.g., keyword vs. text mappings) to balance precision and recall. Preprocessing steps like removing redundant whitespace or normalizing text casing also reduce index size and improve retrieval speed.
Second, configure your retriever for efficiency. For dense retrievers (e.g., sentence-transformers), use lighter models like “multi-qa-MiniLM-L6-dot-v1” instead of larger ones like “all-mpnet-base-v2” if latency is critical. Adjust the top_k parameter to return only the necessary results—lower values (e.g., top_k=10) reduce computation. For hybrid approaches combining sparse and dense retrievers, use Haystack’s EnsembleRetriever with weighted scores to avoid redundant processing. If using a pipeline with multiple components, cache embeddings or intermediate results using Haystack’s DocumentStore to avoid recalculating them. For example, precompute embeddings during indexing rather than at query time.
Third, structure queries strategically. Use filters to narrow the search space—for instance, restrict results to documents from the last 30 days using metadata filters. Simplify natural language queries by removing unnecessary words (e.g., “What’s the 2023 revenue of Company X?” becomes “2023 revenue Company X”). For keyword-heavy retrievers, add synonyms or controlled vocabularies to your index to handle variations (e.g., “car” and “automobile”). Test different combinations of retrievers and rankers—sometimes a fast first-stage retriever (like BM25) paired with a lightweight reranker (like CrossEncoder) provides better speed/accuracy tradeoffs than a single complex model. Monitor performance with Haystack’s benchmarking tools to identify bottlenecks.