Haystack manages batch processing of documents by utilizing a series of well-structured components that streamline the workflow from document ingestion to embedding creation and indexing. First, it allows you to upload multiple documents at once rather than one at a time. This is particularly useful when handling large datasets or numerous files, as it helps save time and resources. Developers can use built-in functions to load popular document formats like PDF, Word, or plain text in bulk, enabling efficient processing.
Once the documents are ingested, Haystack employs parallel processing to accelerate the extraction of relevant information. This means that if you have a batch of documents, Haystack can handle them simultaneously using different threads or processes. For instance, while one thread extracts text from one document, another can generate embeddings for a separate document in the same batch. This concurrency not only speeds up the overall processing time but also makes better use of available compute resources, which is crucial when working with large volumes of data.
After the processing, the next step typically involves storing the embeddings or processed data in a searchable index. Haystack supports the integration of various databases and indexing systems, such as Elasticsearch or FAISS, which allow for efficient organization and retrieval of indexed documents. Developers can set up these systems to accept batches of processed documents at once, making retrieval faster when users need to query a particular subset of data. By combining these strategies—bulk loading, parallel processing, and effective indexing—Haystack significantly simplifies batch document management for developers.