How does Haystack handle document versioning?

Haystack does not include built-in document versioning capabilities, but it provides flexible patterns to implement version control through custom logic or integration with external systems. The framework focuses on document processing and retrieval, leaving storage and version management to the underlying database or external tools. Developers can handle versioning by designing their document storage layer to track revisions, timestamps, or unique identifiers for document updates.

One common approach is to use a database with native versioning features as Haystack’s document store. For example, if you use PostgreSQL with a version history table, each document update can be recorded as a new entry with a timestamp or version number. Haystack’s Document objects can store metadata fields like version_id or last_modified, which you can update manually when changes occur. When ingesting documents via pipelines, you could add a preprocessing step to check for existing versions, compare content, and append new versions to the store. For instance, if a document with ID doc_123 is updated, you might save it as doc_123_v2 and retain the old version in the database, queryable via metadata filters.

Another strategy involves combining Haystack with external version control systems like Git or cloud storage services (e.g., AWS S3 object versioning). For instance, you could store raw documents in a Git repository linked to your Haystack pipeline, using commit hashes as version markers. When retrieving documents, the pipeline could fetch specific versions from Git before processing them. When using Haystack’s indexing pipelines, you might also implement logic to detect changes in source files (e.g., via file hashes) and trigger reindexing only for modified documents. However, this requires careful synchronization between the versioned storage and Haystack’s document stores (e.g., Elasticsearch, Weaviate) to avoid stale data.

The choice of method depends on performance needs and complexity. For simple use cases, adding version metadata and filtering queries by version_id or timestamp may suffice. For large-scale systems, integrating with dedicated versioned databases or object stores is more sustainable. Haystack’s modular design allows developers to plug in these solutions without altering core retrieval logic, ensuring that versioning remains a storage-layer concern rather than a framework limitation.