Integrating Haystack with vector databases like FAISS or Milvus involves a few straightforward steps to set up the environment, configure the database, and connect it with Haystack's components. Haystack is a framework designed for building search systems, and when combined with vector databases, it enables efficient retrieval of high-dimensional vectors often used in applications like semantic search or recommendation systems.
Begin by installing the required libraries. If you're using FAISS, you will need to install it alongside Haystack using pip, like so: pip install farm-haystack[faiss]. For Milvus, you’ll need their client library: pip install pymilvus. Once you have everything installed, the next step is to configure your vector database. For example, if you are using FAISS, you can create a new index using the faiss.IndexFlatL2 function to store your vector embeddings. For Milvus, you would set up a collection to define the structure of your stored data type.
Finally, you need to integrate your vector database in the Haystack pipeline. Haystack provides classes like FAISSDocumentStore or MilvusDocumentStore specifically for these databases, allowing you to index your documents into the vectors. You can create a Document object for each item you want to store and use the .write_documents() method to save them. This allows your vectorized data to be easily searchable. After this setup, you can query your vector store and get similar items efficiently using Haystack's retrieval functionality.