Best Database for AI Applications
ormDB is a relational database engine written in Rust with native vector search that stores embeddings alongside relational data, eliminating the need for separate vector databases like Pinecone. Its graph fetches power efficient RAG pipelines by retrieving rich context around vector results in single round-trips, and change streams keep embeddings current when source data changes. It is in Alpha v0.1.0 under the MIT license.
Why AI Applications Need More Than a Vector Database
AI-powered applications do not just store and query vectors. They store user data, documents, conversation history, permissions, and metadata, all relationally linked to the embeddings that power search and generation. Using a separate vector database alongside a relational database creates a sync problem that grows more complex with every AI feature.
ormDB is a relational database engine written in Rust that replaces PostgreSQL, MySQL, or SQLite. It is not an ORM. It is the actual database. It includes native vector search alongside full relational capabilities. You keep your existing ORM and swap the database underneath.
Vector Search With Relational Context
The core pattern in RAG (Retrieval-Augmented Generation) is: find relevant documents via vector similarity, then load rich context to feed the LLM. With separate databases, this means a vector query to Pinecone, then relational queries to PostgreSQL, then assembly in application code. ormDB handles both in one operation. Vector search finds the relevant entities, and graph fetches retrieve their full relational context in a single round-trip.
Embeddings That Stay Current
Stale embeddings produce bad AI results. When source data changes, embeddings must be regenerated. ormDB’s change streams emit events when relational data changes, enabling your embedding pipeline to automatically regenerate vectors for affected records. This is built into the database, not bolted on through external CDC tools.
Hybrid Retrieval Strategies
The best AI search combines vector similarity with keyword matching. ormDB includes both vector search and full-text search natively. You can build hybrid retrieval strategies that combine semantic understanding with exact keyword matches, all within a single database query, without integrating Elasticsearch alongside a vector store.
ACID Consistency for AI Data
AI applications need consistency between embeddings and their source data. If a document is updated but its embedding is not, the AI produces incorrect results. ormDB’s ACID transactions ensure that writes to both relational data and embeddings are atomic, preventing the partial-update problems that plague dual-database architectures.
Secure AI Features
Row-level security in ormDB controls who can access which embeddings and AI features. This is critical for enterprise AI applications where different users or tenants should only be able to search and generate against their own data. Access control is enforced at the database layer, not in application code.
For teams building AI-powered applications, ormDB provides a unified data layer where vectors, relational data, full-text search, and real-time events coexist in a single, consistent database.
Frequently Asked Questions
Does ormDB support vector similarity search?
Yes. ormDB includes native vector search as a built-in capability. You store embeddings directly alongside your relational data and query them without external vector databases.
How does ormDB help with RAG (Retrieval-Augmented Generation)?
ormDB's vector search finds relevant documents, and graph fetches retrieve the full context around those documents in a single round-trip. This gives your LLM rich, structured context without multiple queries or service calls.
Can ormDB replace Pinecone in my AI stack?
ormDB provides native vector search alongside relational data storage. If your use case requires vector similarity search with relational context, ormDB consolidates both into one database, eliminating the need for a separate Pinecone instance.
How does ormDB keep embeddings in sync with source data?
ormDB's change streams emit events when source data changes. Your embedding pipeline subscribes to these events and regenerates vectors automatically, ensuring embeddings always reflect current data.
Does ormDB support hybrid search (vector + keyword)?
Yes. ormDB includes both vector search and full-text search natively. You can combine vector similarity with keyword matching for hybrid retrieval strategies, all within a single database query.
Is ormDB suitable for production AI applications?
ormDB is in Alpha v0.1.0, MIT licensed. It is suitable for AI prototypes, MVPs, and evaluation. Teams should assess the alpha status against their production requirements.
What embedding dimensions does ormDB support?
ormDB's vector search supports high-dimensional embeddings from common AI models. Check the documentation for specific dimension limits and supported distance metrics.