VectorAI DB

What is VectorAI DB?

Actian VectorAI DB is a portable, local-first vector database designed for AI systems that need semantic and hybrid search beyond the cloud. It stores vector embeddings and supports similarity search so applications can retrieve results by meaning rather than keywords.

The product’s core purpose is to help teams deploy vector search in environments where cloud dependencies and network latency are constraints—such as edge devices, on-premises systems, and disconnected facilities—while keeping retrieval fast and predictable.

Key Features

• Local-first vector database for edge and on-premises: Enables semantic search without relying on cloud vector database calls.
• Real-time retrieval targeting low latency: The site highlights sub-100ms behavior (including “13 milliseconds p99 latency”) aimed at real-time AI applications.
• Offline operation with later sync: Supports disconnected environments where reliable internet connectivity cannot be assumed.
• Portable deployment model across environments: The same architecture is positioned for development-to-production movement, from embedded devices to enterprise deployments.
• Data control for regulated and residency needs: The site states on-premises deployment can support GDPR and HIPAA-style data residency requirements by avoiding third-party cloud processing.
• Developer-oriented collection and query flow: Examples show creating collections with a defined vector size, upserting points with payloads, and searching by an embedding vector.

How to Use VectorAI DB

1. Install using Docker:
   • Run docker pull actian/vectorai-db
   • Start the service with docker run -d -p 50051:50051 actian/vectorai-db
2. Create a collection and set vector parameters (e.g., vector size and distance metric) to match your embedding model.
3. Insert vectors as points, including an id and optional payload metadata.
4. Search by similarity: generate an embedding for a query and run a vector search to return the closest matches (with similarity scores).

The documentation walkthrough also emphasizes building a small app first and then deploying the same setup across target environments (laptop, embedded devices, or on-prem).

Use Cases

• Embedded edge AI that performs semantic lookup: Deploy on devices such as Raspberry Pi-class systems to run meaning-based search where internet access is limited or unavailable.
• Factory edge deployments for production optimization: Run vector search on edge servers in disconnected environments to support tasks like predictive maintenance, quality inspection, or production optimization.
• Air-gapped retrieval for regulated environments: Use a local-first setup for environments where data must stay within controlled infrastructure and cloud services are not permitted.
• Healthcare on-prem clinical and record search: Keep patient-related data on-premises while running semantic retrieval for clinical decision support, medical imaging workflows, and record search (as described in the site’s use cases).
• Hybrid or multi-site platform retrieval: Manage vector search across distributed sites (edge plus optional cloud) using a consistent database approach from prototype to production.

FAQ

What is VectorAI DB used for?

It is used as a vector database to store embeddings and run semantic (and hybrid) search close to where your AI application runs, including edge and on-premises environments.

How do I start using VectorAI DB?

A typical start is installing the server with Docker, creating a collection with vector configuration (vector size and distance), upserting vectors with metadata, and running similarity search using embeddings for queries.

Does it support disconnected or offline environments?

The site states it is designed for environments where it can work offline and sync when a connection is available.

How does VectorAI DB differ from cloud vector databases?

The site frames the difference as local-first operation and reduced dependence on network round-trips, which can otherwise add query latency for real-time applications.

Is VectorAI DB tied to a specific embedding model?

The examples show using an embed() function from the developer’s model, but the site does not specify a single required model; configuration should match your embedding vector size.

Alternatives

• Self-managed vector databases for on-prem: Instead of a local-first approach from the same vendor, consider other vector database systems you can deploy on your own infrastructure; the key difference is whether the product is optimized for consistent edge/on-prem operations.
• Hybrid search stacks (vector search + keyword search): If you need combined keyword and semantic retrieval, look for platforms that support both retrieval modes and can run in your deployment environment.
• Edge inference plus local retrieval services: For constrained environments, you can pair edge model inference with an on-device or edge-hosted retrieval component; the tradeoff is architecture complexity versus a single database-based workflow.
• Cloud-hosted vector databases: Cloud solutions can be simpler to start, but the site highlights latency and deployment restrictions as reasons to choose local-first deployments for edge and disconnected scenarios.