To implement versioning for indexed documents in LlamaIndex, you need a system that tracks changes to documents over time while maintaining efficient retrieval of specific versions. The core approach involves storing multiple versions of a document with metadata to identify revisions and designing queries to target the correct version. This can be achieved by combining document metadata (like version numbers or timestamps) with a storage strategy that preserves historical data instead of overwriting it. For example, each time a document is updated, you could index the new version separately and tag it with a version identifier, ensuring older versions remain accessible.
A practical implementation might involve two key steps. First, when adding documents to LlamaIndex, include metadata fields such as version (e.g., an incrementing integer) and last_updated (a timestamp). For instance, when indexing a document titled “ProjectPlan,” you could store it with version: 1 and later add an updated version with version: 2. Second, use a storage backend (like a database or file system) that retains all versions. For example, instead of overwriting a document in a vector store, append a new entry with a unique ID (e.g., doc123_v2) and updated metadata. This ensures each version is stored independently and can be retrieved using metadata filters. LlamaIndex’s SimpleDirectoryReader can be configured to process versioned files by parsing filenames (e.g., document_v2.txt) to extract version info automatically.
To retrieve specific versions, use LlamaIndex’s query engine with metadata filters. For example, when querying, you could specify version >= 2 to exclude outdated drafts or use last_updated to fetch the most recent version. If using a vector database like Pinecone, you could filter documents by metadata fields during search. Additionally, you could create a wrapper class to manage version history—tracking the latest version and mapping queries to the appropriate document ID. For deletion or rollback scenarios, maintain a separate registry (e.g., a JSON file) linking document IDs to their versions and status (active/archived). This approach balances flexibility with minimal overhead, letting you scale versioning without complicating core indexing logic.