Persisting and reusing Indexes
An important question arises – where exactly can we store the vector embeddings generated during the indexing process?
Storing them is important for multiple reasons:
- Avoid the computational cost of re-embedding documents and rebuilding Indexes in every session. Generating high-quality embeddings for large document collections requires significant processing that can become costly over time. Persisting Indexes preserves these precomputed artifacts
- Enable low-latency processing. Avoiding runtime embedding and indexing by loading the already computed embeddings allows applications to get up and running much faster
- Maintain query consistency and accuracy. Reloading an Index guarantees we reuse the exact vectors and structure used in the previous sessions. This promises consistent and accurate query execution
If we want to avoid regenerating them on each run, these vector embeddings need to reside somewhere – a repository...
