What are the best practices for configuring a document store in Haystack?

Configuring a document store in Haystack effectively requires careful consideration of the storage type, preprocessing steps, and indexing strategies. Start by selecting a document store that aligns with your use case. For example, Elasticsearch is ideal for keyword-heavy search, while FAISS or Milvus better suit vector-based semantic search. Hybrid stores like Weaviate handle both text and vectors. Evaluate factors like scalability, latency, and integration with Haystack pipelines. For instance, if you need real-time updates, Elasticsearch’s near-instant indexing is advantageous. If your application relies on dense vector embeddings (e.g., from a transformer model), a vector database like Milvus provides efficient similarity search. Always test the store’s performance with your specific data volume and query patterns before finalizing.

Next, focus on data preprocessing and metadata management. Clean and normalize text (e.g., removing HTML tags, lowercasing) before ingestion, and split large documents into smaller chunks to improve retrieval accuracy. Haystack’s PreProcessor class can handle tasks like splitting by sentence or word count. For metadata, define fields that support filtering—such as dates, categories, or user IDs—and ensure they’re indexed properly. For example, if you’re storing product manuals, include metadata like product_version or language to enable faceted search. Use Haystack’s Document objects to attach metadata consistently, and avoid overloading with irrelevant fields, which can slow down queries. If using Elasticsearch, explicitly map metadata types (e.g., date or keyword) in the index settings to prevent automatic type detection errors.

Finally, optimize indexing and maintenance. Configure index settings for your document store—for Elasticsearch, adjust shard counts based on data size, and enable replicas for reliability. For vector stores, tune parameters like nlist in FAISS to balance speed and accuracy. Regularly update documents and embeddings to reflect new data, and implement versioning to track changes. Use Haystack pipelines to automate indexing workflows, such as rerunning OCR on updated PDFs. Monitor performance with tools like Kibana (for Elasticsearch) or Prometheus, and set alerts for issues like high latency or index failures. Secure the document store with access controls (e.g., Elasticsearch’s role-based permissions) and encrypt data in transit. For example, enable HTTPS and authentication in Milvus to protect sensitive data. Regularly back up indices and test recovery procedures to avoid data loss.