Version-controlled vector indexing

Why Vectory

• Snapshots — Immutable index snapshots in O(1) (structural sharing).
• Scalable reads — A snapshot is a value. Hand it to any number of workers—no connection pool, no coordinator.
• Reproducibility — Query historical index states for audits, debugging, and compliance workflows.
• Verification — Merkle-hashed indices for verifiable, content-addressed retrieval states.
• Storage — Pluggable persistence via Konserve with backends tailored to your environment.

Performance

Benchmarks are published with methodology and reproducible workloads. The goal is to be explicit about throughput, recall, and operational tradeoffs.

• Reads — QPS and tail latency (p95/p99), plus recall vs. index settings.
• Writes — Insert throughput, background compaction costs, and snapshot creation overhead.
• Method — Workloads + datasets + exact configs are documented (no “magic numbers”).

Integrations

First-class JVM integrations ship alongside the core API.

• Spring AI — Drop-in integration for Spring-based RAG and retrieval pipelines.
• LangChain4j — Adapter for LangChain4j-based applications and evaluators.
• APIs — Full Java API and full Clojure API.

Deployment

Commercial

We offer evaluation support, integration work, and enterprise support. If you need procurement-friendly paperwork, we’ll make it easy.

FAQ

• Production-ready? — We’ll share a concrete readiness checklist and help you evaluate quickly.
• Upgrades? — We optimize for explicit versioning and reproducible state transitions.
• Security? — Offline-friendly, minimal dependencies, and transparent benchmarking methodology.