To manage indexing and updating documents in Haystack, you need to understand how the framework handles document storage and modification. Haystack uses a DocumentStore (like Elasticsearch, FAISS, or Weaviate) to index and retrieve documents. Indexing involves converting raw data (text, PDFs, etc.) into structured Document objects with metadata and content, then storing them in the DocumentStore. For updates, you typically re-index the modified document, as many DocumentStores don’t support partial updates. This means deleting the old version and inserting the new one, ensuring consistency.
For indexing, start by preprocessing your data. Use Haystack’s PreProcessor to split large texts into smaller chunks, clean content, or extract metadata. For example, you might create a pipeline that reads files, converts them to Document objects, processes them, and writes to the DocumentStore:
from haystack import Pipeline
from haystack.document_stores import ElasticsearchDocumentStore
from haystack.nodes import PreProcessor, FileTypeClassifier
document_store = ElasticsearchDocumentStore()
preprocessor = PreProcessor(split_length=200)
file_classifier = FileTypeClassifier()
index_pipeline = Pipeline()
index_pipeline.add_node(file_classifier, name="classifier", inputs=["File"])
index_pipeline.add_node(preprocessor, name="preprocessor", inputs=["classifier"])
index_pipeline.add_node(document_store, name="document_store", inputs=["preprocessor"])
This pipeline processes files, splits text, and stores results. For updates, retrieve the document ID, delete it using document_store.delete_documents(ids=["doc_id"]), then re-index the revised document. Ensure your documents have unique IDs (set via Document.id) to target them accurately during updates.
Maintenance is critical for performance. Schedule periodic re-indexing if your data changes frequently. Use versioning in metadata (e.g., last_updated) to track document states. For large datasets, optimize by batching operations or using asynchronous tasks. Monitor your DocumentStore’s health—Elasticsearch, for instance, provides tools to check index size or shard distribution. If using embeddings, regenerate them during re-indexing to keep vector searches accurate. Always test updates in a staging environment to avoid breaking production search functionality.