Can I use Haystack for offline document search or batch processing?

Yes, you can use Haystack for offline document search and batch processing. Haystack is designed to handle document indexing, retrieval, and processing in environments without internet connectivity, provided you configure it to use local resources. For batch processing, its pipeline architecture allows you to process large volumes of documents efficiently by chaining together components like preprocessors, embedders, and document stores. This makes it suitable for scenarios where you need to index or analyze documents in bulk before querying them later.

To set up offline document search, Haystack supports local document stores such as FAISS, SQLite, or InMemoryDocumentStore. For example, you can use FAISS (a vector similarity library) to store document embeddings locally, enabling semantic search without relying on external services. You’d first index documents by converting them into embeddings using a local model (e.g., sentence-transformers/all-MiniLM-L6-v2), then store those embeddings in FAISS. Once indexed, you can query the document store offline using natural language. This works well for applications like internal knowledge bases or archived data analysis, where real-time connectivity isn’t required.

For batch processing, Haystack’s Pipeline class lets you define workflows for tasks like document cleaning, splitting, or enrichment. For instance, you might create a pipeline that reads PDFs from a folder, extracts text, splits it into chunks, generates embeddings, and saves results to a local database. This is useful for preprocessing large datasets before deployment or periodic updates. Additionally, you can parallelize parts of the pipeline (e.g., using multiprocessing) to speed up bulk operations. Since Haystack is a Python library, it integrates with offline workflows in scripts or scheduled jobs, making it adaptable to environments where data cannot leave a secure, air-gapped system.