How does Haystack manage indexing and search time?

Haystack manages indexing and search time through a combination of modular components, optimized data structures, and configurable pipelines. At its core, Haystack separates the indexing process (preparing data for search) from the search process (querying that data), allowing developers to fine-tune each stage for performance. The framework supports multiple document stores (e.g., Elasticsearch, FAISS, or SQL databases) and provides tools to preprocess, vectorize, and organize data efficiently. This flexibility ensures that indexing and search can be tailored to specific use cases, balancing speed, accuracy, and resource usage.

For indexing, Haystack uses document stores to persist data and pipelines to preprocess documents. For example, when indexing a large text corpus, a pipeline might split documents into smaller chunks, generate embeddings using a model like BERT, and store both the text and embeddings in a vector database like FAISS. This preprocessing step ensures that search operations can leverage fast similarity comparisons. Haystack also supports batch processing during indexing, which reduces overhead when handling large datasets. Developers can further optimize indexing by choosing lightweight preprocessing steps (e.g., skipping metadata extraction) or adjusting parameters like chunk size to align with their performance goals.

At search time, Haystack relies on retrievers and query pipelines to execute efficient lookups. For instance, a dense retriever like DensePassageRetriever uses precomputed embeddings to find semantically similar documents quickly, while a sparse retriever like BM25 relies on keyword matching for faster exact-term searches. Hybrid approaches combine both methods to improve recall without sacrificing speed. Query pipelines can also include caching mechanisms for frequent queries or use GPU acceleration for embedding generation. By decoupling retrieval from ranking (e.g., using a separate reranker component), Haystack ensures that computationally expensive steps are applied only to the most relevant candidates, reducing overall latency. This modular design lets developers experiment with trade-offs—for example, prioritizing speed with FAISS or accuracy with cross-encoders—without rewriting the entire search stack.