ApertureData Resource Library

ApertureDB has its own query language, AQL, using JSON as its native format, because traditional languages like SQL, Cypher, were insufficient for managing, searching, and processing multimodal AI data at scale. AQL allows for expressing complex data types and operations, and ApertureDB also offers simplified interfaces, including SQL and SPARQL wrappers, and natural language access through RAG and MCP. The core idea is to prioritize the problem's solution over existing language barriers, aligning with the evolving needs of AI systems.

This blog shows how to build a knowledge graph using ApertureDB and Gemini 2.5 Flash to power smarter RAG systems. Part 1 covers extracting and storing entities, enabling real-world use cases like semantic search and AI-powered customer support.

ApertureDB AI Workflows are designed to simplify the creation of multimodal AI applications by providing modular, flexible, and purpose-built components for AI pipelines. These workflows automate common AI/ML tasks such as data ingestion, search, and data correlation, integrating with ApertureDB's graph, vector, and multimodal capabilities, and partnering with models and services from other tools.