To perform incremental updates to a document store in Haystack, you use the update_documents method provided by most document store implementations. This method allows you to add new documents or update existing ones without rebuilding the entire index. When you call update_documents, the system checks each document’s ID: if the ID already exists in the store, the document is updated; if not, it’s added as a new entry. For example, if you’re working with ElasticsearchDocumentStore, you can pass a list of updated or new documents to this method, and it will handle the changes efficiently.
A critical step is ensuring your documents have stable, unique identifiers. Haystack requires each document to have an id field, which you should explicitly define to avoid collisions or unintended overwrites. For instance, if your documents originate from a database, use the database record’s primary key as the id. If you’re processing files, generate IDs based on file paths or checksums. Without explicit IDs, Haystack may create hashes based on document content, but this can lead to duplicates if content changes slightly (e.g., a typo fix). For example, when adding a document, you might set Document(id="doc_123", content="...") to ensure consistent identification.
Considerations vary depending on the document store type. For vector databases like FAISS or Milvus, updating documents may require re-embedding the content using your chosen embedding model. If you modify a document’s text, you must regenerate its vector representation and update both the metadata and the vector index. Additionally, some stores (e.g., InMemoryDocumentStore) lack native support for incremental updates, requiring manual checks for existing IDs. For large-scale updates, batch processing is recommended to avoid memory issues. Always test your update workflow with a subset of data first to ensure IDs and content are handled correctly, especially when integrating with pipelines that include retrievers or rankers.