Haystack integrates with external data sources by converting them into structured documents and storing them in search-optimized databases. The process involves three main steps: data extraction, preprocessing, and ingestion into a document store. For databases, you’d query tables or collections to retrieve records, while files like PDFs or CSVs require parsing tools to extract text. Haystack provides built-in components (e.g., SQLDatabase, FileTypeRouter) to handle these tasks, ensuring raw data is transformed into Document objects with text content and metadata for later retrieval.
For example, to use a SQL database, you might connect via SQLAlchemy, run a query, and map results to Haystack Document objects. Each row could become a document with columns stored as metadata (e.g., author or date). For files, a pipeline could route PDFs to PDFToTextConverter, split text into chunks with PreProcessor, and add metadata like file names. CSV data might be loaded with pandas, then converted into documents row-by-row. These steps ensure unstructured or semi-structured data becomes searchable in Haystack’s document stores (e.g., Elasticsearch, Weaviate).
Once data is ingested, you build pipelines for tasks like question answering. A typical pipeline includes a retriever (e.g., BM25Retriever for keyword search) and a reader (e.g., TransformersReader for answer extraction). To keep data fresh, implement incremental updates: schedule periodic SQL queries for new rows or use file watchers to reprocess updated documents. Haystack’s flexibility lets you mix data sources—for instance, combining database content with crawled web pages—while maintaining a unified search interface. This approach avoids vendor lock-in and adapts to most data ecosystems.