To use Haystack for structured data extraction from documents, you’ll typically build a pipeline that processes unstructured text and applies natural language processing (NLP) models to identify specific fields. Haystack provides modular components like document converters, preprocessors, and extractors that work together. Start by converting your documents (PDFs, Word files, etc.) into plain text using Haystack’s FileTypeClassifier and TextConverter. Then, split the text into manageable chunks with the PreProcessor to avoid overwhelming models with large inputs. Finally, use an extraction component like the QuestionAnsweringExtractor or EntityExtractor to pull structured data by querying the text or identifying named entities.
For example, if you’re extracting invoice details, you might configure a pipeline that asks targeted questions like, “What is the invoice date?” or “What is the total amount due?” using a question-answering model. Haystack’s ExtractiveQAPipeline can map these questions to answers in the text, returning results in a structured format like JSON. Alternatively, the EntityExtractor can identify predefined entities (e.g., dates, amounts, names) using spaCy or a custom model. If your documents contain tables, use the TableTextConverter to preserve tabular structure, enabling extraction of rows or columns as key-value pairs. Each step is configurable, allowing you to swap models (like switching from BERT to RoBERTa) or adjust chunk sizes based on document complexity.
Customization is key for accurate extraction. If pre-trained models don’t cover your use case, fine-tune a Hugging Face model on your dataset and integrate it via Haystack’s TransformersReader. For validation, add post-processing steps to check extracted data against rules (e.g., date formats) or cross-reference fields for consistency. If errors occur, use Haystack’s evaluation tools to analyze pipeline performance and retrain models. For large-scale workflows, connect Haystack to a document store like Elasticsearch to manage and query extracted data efficiently. By combining these components, you can transform unstructured documents into structured datasets for analysis, reporting, or integration with other systems.