Google Apps Script Turns Drive into Serverless AI Vector Search Engine via Apache Iceberg

A developer has published a serverless multimodal vector search architecture that uses Google Apps Script, Apache Iceberg, and BigQuery to eliminate the need for dedicated vector databases like Pinecone or Milvus. The system, built around an engine called IcebergApp.js, ingests heterogeneous Google Drive content — including Docs, Sheets, Slides, binary images, and web-fetched files — into a unified Apache Iceberg table stored as open Parquet files on Google Cloud Storage. Gemini's text-embedding-004 API generates 768-dimensional vector embeddings on the fly, while BigQuery executes cosine distance queries directly against the Parquet files, returning ranked results in 0.3 to 0.5 seconds. Each search result retains the original Google Drive file ID, enabling one-click navigation back to the live collaborative document. The approach is presented as a zero-maintenance, zero-subscription alternative to conventional RAG pipelines, avoiding vendor lock-in while keeping embeddings within core analytical storage.