HelixDB is the first fully native Graph-Vector Database, built in Rust, designed to accelerate the development of Retrieval-Augmented Generation (RAG) and AI applications by combining the power of graph and vector types natively.

Key Features:

• Unified Graph-Vector Database for AI Retrieval: Provides more relevant context for AI with the fastest and most cost-effective solution.
• Hybrid Query Traversals: Seamlessly combines vector similarity search with graph traversals in a single, powerful query, eliminating complex joins or multiple database calls.
• Type-Safety: Offers advanced static analysis for real-time feedback, autocomplete, and error detection, enabling confident query writing.
• Optimized for Speed: Achieves approximately 2ms for Vector Similarity Search and sub-1ms for Graph Traversals.

Use Cases:

• Legal Research Assistant: Links legal cases, statutes, and expert commentary to retrieve relevant precedents with contextual awareness.
• Financial Intelligence Engine: Analyzes financial data for insights.
• Codebase Q&A Assistant: Provides answers to questions about codebases.
• Sales & Market Research Copilot: Assists with sales and market research.
• Enterprise Knowledge Search: Facilitates searching within enterprise knowledge bases.
• AI Tutor with Custom Curriculum: Creates personalized AI tutoring experiences.
• Memory Layers for LLM Agents: Enhances the memory capabilities of Large Language Model agents.
• Personalized AI Doctor: Supports personalized medical insights.
• Drug Discovery Explorer: Aids in the discovery of new drugs.
• Social Graph + Feed Optimization: Optimizes social graphs and feeds.
• Recommendation Engines: Powers recommendation systems.
• Fraud Detection & Risk Graphs: Identifies fraud and analyzes risk.
• Access Control & Entitlements: Manages access and entitlements.
• Agentic NPCs in Video Games: Enables intelligent non-player characters in games.
• Customer Support Copilot: Assists customer support operations.
• Code Indexing & Retrieval: Indexes and retrieves code efficiently.

Benefits:

• 10x Faster Building: Enables rapid development of AI applications.
• Simplified Stack: Replaces complex setups involving multiple databases (graph, vector, relational) and servers with a single platform.
• Lower Costs: Eliminates the expense and complexity of maintaining separate vector and graph databases, reducing data duplication and operational overhead.
• No Complex Infrastructure: Removes the need for managing complex infrastructure.
• No Multiple Services to Maintain: Consolidates services into one unified solution.
• No Operational Overhead: Reduces the burden of operational management.