How do I integrate Haystack with vector databases like FAISS or Milvus?

To integrate Haystack with vector databases like FAISS or Milvus, you’ll use Haystack’s built-in document store classes and configure them to work with your chosen database. Haystack provides dedicated classes such as FAISSDocumentStore and MilvusDocumentStore, which handle the connection and operations for these databases. The process involves initializing the document store, indexing your data with embeddings, and querying it using Haystack’s retrieval pipelines. Both FAISS and Milvus require embeddings (vector representations) of your text data, which you can generate using models like Sentence Transformers before storing them in the database.

First, set up the document store. For FAISS, install the faiss library and Haystack, then initialize the FAISSDocumentStore. FAISS operates in-memory, so you’ll need to save the index to disk manually if persistence is required. For Milvus, start a Milvus server (e.g., via Docker) and configure the MilvusDocumentStore with parameters like host, port, and index settings. For example, initializing a Milvus store might involve specifying index_params for vector similarity metrics like cosine distance. Both stores require embeddings for your documents: you can generate these using Haystack’s EmbeddingRetriever with a pre-trained model, then store them alongside your text data.

Next, index your documents. Convert your text data into embeddings using a retriever model (e.g., sentence-transformers/all-MiniLM-L6-v2). For FAISS, you’d write documents and their embeddings to the FAISSDocumentStore using document_store.write_documents(). For Milvus, the process is similar, but the database handles scalability and distributed storage automatically. After indexing, create a retrieval pipeline with the EmbeddingRetriever linked to your document store. When querying, the retriever converts the query text into an embedding and searches the vector database for the closest matches. For example, a pipeline might return the top-5 documents relevant to a user’s question.

Finally, consider scalability and use cases. FAISS is ideal for smaller, single-node applications due to its in-memory design, while Milvus supports distributed deployments and larger datasets. Haystack abstracts much of the complexity, allowing you to switch between databases with minimal code changes. For instance, swapping FAISS for Milvus primarily involves modifying the document store initialization and adjusting index parameters. Ensure your embedding model matches the one used during indexing to maintain consistency between stored vectors and query results. This approach lets you focus on building search pipelines without deep expertise in vector database internals.