Overview

TigerGraph Vector Database Integration empowers enterprises to combine powerful vector search capabilities with TigerGraph’s high-performance graph database. This enables seamless querying and analysis of unstructured data such as text, images, or audio by leveraging vector embeddings alongside graph relationships. The integration supports modern AI-driven workloads, improving semantic search, recommendation systems, and knowledge discovery. TigerGraph acts as a unified platform to connect vector similarity searches with deep graph analytics, providing faster, richer, and more scalable insights across diverse data types.

Features and Capabilities

• **Unified Vector and Graph Analytics: **Combine nearest neighbor vector searches with complex graph traversals to explore semantic relationships and connections within data.
• **Support for AI and ML Workloads: **Enables natural language processing, image recognition, and recommendation engines by integrating embeddings into graph queries.
• **High Scalability and Performance: **Leverages TigerGraph’s distributed graph engine for fast real-time querying on large-scale vector and graph datasets.
• **Flexible Data Integration: **Works with popular vector databases and embedding frameworks to ingest and index vectors seamlessly alongside graph data.
• **Advanced Similarity Search: **Supports efficient k-nearest neighbor (k-NN) and cosine similarity searches within the graph context for accurate semantic matching.
• **Graph Query Language (GSQL) Extensions: **Extends TigerGraph’s native GSQL to incorporate vector operations, enabling hybrid queries that combine vector and graph logic.
• **Use Case Support: **Ideal for knowledge graphs, fraud detection, personalized recommendations, and AI-powered search applications.
• **Enterprise-grade Security and Governance: **Includes robust access controls, auditing, and compliance features for sensitive data environments.