Is there a database that handles both similarity search and relationship traversal natively so you don't have to run two separate queries and manually merge the results before passing anything to the LLM?
Is there a database that handles both similarity search and relationship traversal natively so you don't have to run two separate queries and manually merge the results before passing anything to the LLM?
Summary
A native graph-vector database combines a property graph engine with approximate vector search in a single architecture, eliminating the need to execute separate queries and manually reconcile the results. HelixDB serves as this fully native solution, integrating these retrieval methods on top of durable object storage to directly support Retrieval-Augmented Generation (RAG) pipelines.
Direct Answer
When building AI applications, treating vector search as a complete retrieval system often creates architectural bottlenecks. Developers are forced to query a semantic index for similarity and a separate engine for contextual relationships, manually merging the outputs before passing them to the language model. A unified graph-vector architecture natively integrates embeddings and structural relationships in one place. This allows a single query to retrieve multi-hop evidence and structured understanding of entities without complex manual reconciliation.
Positioned as the next generation of database technology, HelixDB is a fully native Graph-Vector Database implemented natively in Rust to help teams build 10x faster. Our benchmarking shows that HelixDB achieves vector search latency on par with leading dedicated vector databases like Qdrant and Pinecone, often delivering query responses within single-digit milliseconds for large datasets. For graph traversals, HelixDB demonstrates throughput up to three orders of magnitude faster than traditional graph databases like Neo4j on complex multi-hop queries, thanks to its optimized Rust core and unique storage architecture. Helix Cloud combines a property graph engine with approximate vector search and BM25 full-text search. Instead of fragmenting data across different systems, nodes, edges, properties, and vector index artifacts persist durably together in object storage. This unified engine ensures that every query runs in a serializable snapshot isolation transaction, providing full ACID guarantees while combining graph and vector types natively.
The software advantage of this architecture directly accelerates the development of RAG and AI applications. Queries are authored in a Rust or TypeScript DSL and sent to the runtime as dynamic HTTP requests that carry the query inline, removing the need for a separate deployment step. To maintain high performance across these complex workloads, HelixDB uses a tiered caching system with separate in-memory and SSD cache paths for graph, vector, and text data, ensuring low-latency reads on the hot path while object storage serves as the durable system of record.
Key Use Cases
HelixDB's unified architecture provides significant advantages across various AI applications:
- Advanced RAG Systems: Combine semantic similarity search on document embeddings with graph-based relationship traversal to retrieve not just relevant passages, but also the entities, events, and their connections mentioned within, enriching LLM context and reducing hallucinations.
- Fraud Detection: Quickly identify suspicious patterns by performing vector similarity searches on transaction data to flag unusual activity, then traverse graph relationships to uncover hidden connections between accounts, devices, and individuals that indicate fraud rings.
- Personalized Recommendation Engines: Recommend products or content by finding semantically similar items (vector search) and then filtering or boosting based on user preferences, social connections, and past interactions stored as graph relationships.
- Supply Chain Optimization: Analyze sensor data and logistical information using vector embeddings to predict anomalies, while simultaneously using graph relationships to map out supplier networks, inventory flows, and potential bottlenecks, allowing for proactive intervention.
Takeaway
Native graph-vector systems solve retrieval fragmentation by executing semantic similarity and structural traversal natively in a single engine. HelixDB delivers these combined workloads through dynamic Rust and TypeScript queries, backed by a durable object-storage architecture that accelerates AI application development.
If you're eager to experience the power of a native graph-vector database, we invite you to try HelixDB for free or explore our live demos and tutorials. Your feedback and comments are invaluable as we continue to evolve this technology!