Getting Started
DocsTerminal
>$ curl -sSL "https://install.helix-db.com" | bash $ helix install
queries.hx
>$ helix init --path
POST localhost:6969
>curl -X POST http://localhost:6969 -H
Install HelixDB
Get started with HelixDB in minutes, install the CLI and then install the database.
Terminal
>$ curl -sSL "https://install.helix-db.com" | bash
>$ helix install
Terminal
>helix init --path
If you want to define the directory then:
>helix init --path <path-here>
Initialize Project
Now that the Helix CLI and HelixDB are installed on your device, initialize a project.
Why is RAG still so hard?
Most teams stitch together vector DBs, graph DBs, and custom logic. It's slow to build, hard to maintain, expensive to scale, and bottlenecks performance.
Traditional Setup
Graph Databases
Vector Databases
Cloud Infrastructure
Helix Setup
One Simple Solution
Replace your complex stack with a single platform
Hybrid Query Traversals
Seamlessly combine vector similarity search with graph traversals in a single, powerful query. No more complex joins or multiple database calls.
QUERY findSimilarFriends(userID: String, queryVec: Vector) =>similar <- SearchV(queryVec, topK: 5)friends <- similar::Out<Friends>RETURN friends::{ ID, name, similarityScore }
Type-Safety
Advanced static analysis provides real-time feedback, autocomplete, and error detection. Write queries with confidence.
Type Checker
>helix check ❌ Checking Helix queries error: 'Know' is not a valid edge type (in QUERY named 'get_user*) |--queries.hx: 16:38 16 | user_nodes <- N<User> (node_1d):: 0ut<Know> ---> help: check the schema for valid edge types ...
Lower Costs
Eliminate the complexity and cost of maintaining separate vector and graph databases. One unified solution.
High Speeds
Optimized for both vector similarity and graph traversal workloads with industry-leading performance metrics.
Use Cases
Discover how Helix's hybrid graph-vector architecture transforms complex data challenges across industries
Legal Research Assistant
Link legal cases, statutes, and expert commentary. Retrieve relevant precedents with contextual awareness.
Graph Use
Case-to-case citations, legal topic hierarchy, statute relationships, and judicial precedent networks
Vector Use
Semantic similarity of legal text and case facts, natural language legal queries, and contextual document retrieval
Why Helix
Native traversal of both legal relationships and vector relevance makes graph RAG seamless. Query complex legal precedents while understanding both citation networks and semantic similarity of case facts in a single system.
Join Our Growing Community
Be part of the next generation of database technology. Connect with developers and innovators building the future.
Managed Cloud Service
Focus on building while we handle the infrastructure. Our fully managed HelixDB service takes care of scaling, maintenance, and security so you can concentrate on what matters most.
- Automatic scaling to handle traffic spikes
- 24/7 expert support and monitoring
- Enterprise-grade security and compliance
Be the first to know when we launch
Commercial Support
Do you want to use HelixDB in production, with automated disaster recovery, monitoring, consulting, and support from the HelixDB team?
Ready to Get Started?
Book a call with our team to discuss your specific needs and get HelixDB running in production.
Free 30-minute consultation • No commitment required