Principle:Neuml Txtai Index Persistence
| Knowledge Sources | |
|---|---|
| Domains | Semantic_Search, NLP, Information_Retrieval |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
Index persistence is the process of serializing an in-memory vector search index and its associated metadata to durable storage so that it can be reloaded and reused across sessions.
Description
Building a vector index from a large document corpus is a computationally expensive operation that may take minutes to hours depending on corpus size and hardware. Index persistence eliminates the need to repeat this work by saving the fully constructed index to disk or cloud storage. Once saved, an index can be reloaded in seconds, enabling fast startup for search applications.
A persisted index is not a single monolithic file but a directory of components, each representing a distinct part of the search engine state. These components include the index configuration (the blueprint that was used to build the index), the ANN embeddings (the dense vector data structure), ID mappings (the link between internal offsets and external document identifiers), the document database (original content and metadata), the scoring index (sparse term data), subindexes, and graph structures. Each component is serialized independently, allowing partial saves and loads.
Index persistence also supports compressed archives (tar.gz, tar.bz2, tar.xz, zip) for efficient distribution, and cloud storage integration for deploying indexes to remote systems. These features make it possible to build an index on one machine and serve it from another, or to version and distribute indexes as artifacts in a CI/CD pipeline.
Usage
Use index persistence after building or updating an index to preserve the work for future sessions. This is essential for production deployments where the search engine must restart quickly, for distributing pre-built indexes to other machines, and for creating backups of the index state.
Theoretical Basis
1. Component-Based Serialization: A vector search index I is decomposed into independent components:
I = {C, A, R, D, B, S, X, G}
where C = configuration, A = ANN index, R = reducer, D = ID mappings, B = database, S = scoring, X = subindexes, G = graph. Each component c has a save(path) and load(path) operation:
save(I, path) = for each c in I: c.save(path / name_c)
2. Configuration Determinism: The saved configuration C must be sufficient to reconstruct all factories and parameters used to build the index. This means that load(save(I, p), p) = I (round-trip identity), ensuring that the loaded index behaves identically to the saved one.
3. Archive Compression: For distribution, the component directory is packed into a single archive:
archive(path) -> path.tar.gz | path.zip
The compression ratio depends on the data but is typically significant for sparse scoring data and document databases, while dense ANN data (which is effectively random floating-point values) compresses less efficiently.
4. Cloud Abstraction: The persistence layer supports a pluggable cloud backend with a uniform interface:
cloud.save(local_path) -> remote_uri cloud.load(remote_uri) -> local_path
This abstracts away the details of S3, GCS, Azure Blob Storage, or other backends.