How do I use Haystack with different types of document stores?

Haystack allows you to work with multiple document stores by providing a unified interface, making it straightforward to switch between storage systems based on your needs. Document stores in Haystack hold your data (like text, metadata, or embeddings) and enable efficient retrieval during search operations. To use different stores, you first install the required dependencies for your chosen storage backend (e.g., Elasticsearch, FAISS, or Weaviate), initialize the document store with specific parameters, and then integrate it into your Haystack pipeline. Each store has unique strengths—Elasticsearch excels at keyword search, FAISS handles vector similarity, and Weaviate supports hybrid search—so your choice depends on your use case.

For example, to use Elasticsearch, you’d start by running an Elasticsearch server locally or connecting to a cloud instance. In Haystack, you’d initialize an ElasticsearchDocumentStore with the host and port, then write documents into it using write_documents(). For vector-based stores like FAISS, you’d use the FAISSDocumentStore and pair it with an embedding model (e.g., sentence-transformers/all-MiniLM-L6-v2) to convert text into vectors. After adding documents, you’d save the FAISS index to disk for reuse. Weaviate, another option, offers a hybrid approach by supporting both keyword and vector search. You’d configure it with a Docker container, initialize the WeaviateDocumentStore, and define a data schema if needed. Each store requires minimal code changes—Haystack’s standardized methods (like get_all_documents() or query()) work across backends.

When choosing a document store, consider factors like scalability, search type (keyword vs. semantic), and infrastructure requirements. Elasticsearch suits text-heavy applications needing BM25 retrieval, while FAISS is ideal for semantic search with precomputed embeddings. Weaviate simplifies hybrid setups but requires more resources. Haystack pipelines abstract the differences between stores: a Retriever component (e.g., BM25Retriever for Elasticsearch or EmbeddingRetriever for FAISS) connects to the document store, letting you swap backends without rewriting query logic. For instance, you could start with an in-memory InMemoryDocumentStore for testing, then migrate to PostgreSQL or Milvus for production without altering your pipeline code. This flexibility ensures you can optimize for performance, cost, or ease of use as your project evolves.