Tuesday Tech Talks: Multimodal Search for Generative AI

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.

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.

This blog compares Vanilla RAG and Graph RAG pipelines built with Gemini and ApertureDB, showing that while traditional retrieval metrics favor pure vector search, LLM-based evaluation often prefers Graph RAG for producing clearer, richer, and more connected answers.

In Part 3 of her blog series, Ayesha Imran moves beyond symbolic structure, adding a semantic layer with Gemini embeddings and ingest vectors into ApertureDB native database to enable the hybrid retrieval that makes GraphRAG possible.