LlamaIndex does not natively support document version control, but it can work effectively with external version control systems or custom implementations. The library focuses on indexing and querying data for LLM applications, leaving versioning to be managed through existing tools or workflows. Developers need to handle document changes outside LlamaIndex and explicitly update indexes when new versions are available.
For example, a common approach is to integrate Git for tracking document revisions. Suppose you store Markdown files in a Git repository that your LlamaIndex pipeline processes. Each time documents change, Git commits capture the updates. Your code could reference specific Git commit hashes when rebuilding LlamaIndex indexes, ensuring queries use the correct document version. Similarly, cloud storage systems like AWS S3 offer object versioning—you could design a pipeline where LlamaIndex indexes update automatically when new S3 object versions are detected, using event triggers from services like AWS Lambda.
Another strategy involves separating index storage by version. If you frequently update documents like API specifications, you could generate a new LlamaIndex index for each major version (v1, v2, etc.) and store them in separate directories or cloud paths. Queries would then route to the appropriate index based on the requested version. For smaller-scale projects, a simple file-naming convention (e.g., manual_2023Q1.json vs. manual_2023Q2.json) paired with periodic full re-indexing might suffice. This avoids complex tooling while maintaining version alignment between source documents and vector indexes.
The key consideration is that LlamaIndex indexes don’t automatically stay synchronized with document changes—developers must rebuild or duplicate indexes when source data updates. This makes version control primarily an architectural decision rather than a library feature. Teams should assess their update frequency and query requirements to choose between versioned indexes, on-demand re-indexing, or hybrid approaches using external triggers.