Hey HN, we want to share HelixDB helix db, a project we have been building to solve the tension between private data sovereignty and advanced AI reasoning.

Why a unified graph-vector database? While vector databases are great for similarity, they often struggle with complex relationship-based queries, and forcing separate systems for graph and vector data creates massive sync overhead. HelixDB natively integrates both, keeping your data on-premises and secure.

Here are some ways teams are using this:

• Knowledge Graph RAG: Our architecture enables agents to perform multi-hop reasoning over connected entities, which significantly reduces hallucinations compared to standard vector-only search.
• Secure Enterprise Compliance: By keeping all embeddings and graph structures within your firewall, you can handle sensitive financial or health records without ever hitting a public API.
• Real-time Memory Updates: Because we support full ACID transactions, your multi-agent systems can concurrently read and write to the knowledge base without blocking, keeping your AI context fresh.

Many developers are rightfully skeptical of "yet another query language" or adding more complexity to their stack. We hear that, and we went ahead and designed a Rust/TypeScript DSL that actually simplifies your pipeline by unifying the storage layer, which we believe makes working with complex relational AI data much easier than stitching disparate engines together. See our approach in this detailed guide: introduction.

In terms of performance, our benchmarking shows that we are on par with dedicated vector stores like Pinecone and Qdrant for semantic lookups, while our graph traversal speed is up to three orders of magnitude faster than traditional systems like Neo4j when performing high-concurrency operations. Watch this short video runthrough to see it in action: szgQu3yq.

If you’d like to try it out in a simple RAG demo, you can follow this guide: demo rag. Many thanks! Comments and feedback welcome!