What is HelixDB?

HelixDB offers a unified graph and vector database solution engineered in Rust, specifically aimed at accelerating the development process for Retrieval-Augmented Generation (RAG) and various AI applications. By integrating graph relationships and vector embeddings within a single system, it eliminates the need for managing multiple, disparate databases, thereby simplifying the overall system architecture and reducing operational complexity.

Performance is enhanced through its Rust-based optimization and a unique approach where queries, written in its intuitive HelixQL language, are compiled directly into the database. This process transforms queries that combine graph traversal and vector similarity into instantly available, high-performance API endpoints, bypassing common parsing overhead. The platform supports type safety for compile-time error checking and seamless integration of native vector operations within queries, providing a powerful alternative to traditional query languages like Cypher or Gremlin.

Features

Unified Graph-Vector Database: Combines graph relationships and vector embeddings in one system.
Simplified Architecture: Replaces complex multi-database stacks with a single platform.
Faster Development: Enables quicker development cycles with a unified approach.
High Performance: Optimized in Rust and compiles queries directly for speed.
HelixQL: Intuitive query language for combined graph/vector operations with less code.
Native Vector Support: Seamlessly integrates vector operations within queries.
Type Safety: Provides compile-time error checking and IDE support.
Instant API Endpoints: Transforms queries into optimized, high-performance microservices.

Use Cases

Code Documentation Search: Search documentation effectively from codebases for AI agents.
Semantic Search: Perform natural language searches across the entire database.
Code Based Indexing: Store code context alongside file structures and dependencies.
Knowledge Base Creation: Build searchable knowledge bases from technical documentation.
AI Coding Agent Support: Retrieve relevant code snippets for AI agents.
Vector & Graph RAG: Build Retrieval-Augmented Generation systems using vectors or graphs.
Hybrid RAG: Combine vector and graph RAG for enhanced retrieval accuracy.
Part Lookup: Find relevant product parts and compatible components.