Tuesday Tech Talks: Multimodal Search for Generative AI

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.

Graph databases are powerful in what they can let us build but there are a lot of misconceptions limiting their adoption. This blog addresses those and shows what's possible.

For human quality AI or better, applications based on classic ML to Gen AI to AI agents, will have to be based on multimodal data since we, as humans, process a combination of text, voice, imagery to, relationships to answer questions or decide what we want to do. We explore what that really means.