Gemini File Search API

Google's managed RAG service that hides chunking, embedding, and storage behind four HTTP operations: create store, upload file, import file into store, and query. Priced at 15 cents per million tokens indexed, free storage, pay-per-query for chat model usage. Strengths: near-zero pipeline setup. Weaknesses: no native deduplication on updates, chunk-based retrieval fails on whole-document questions, stores uploaded files on Google servers (PII/GDPR/HIPAA implications).

Related entities

Source references

  • [src-006] Nate Herk cluster — Nate Herk — RAG and data ingestion cluster (5 videos)

– Videos referenced: irg-2IfAjpo

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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Keep reading from this thread

From 500 indexed pages and articles.

  1. Wiki concept RAG Data Pipelines The operational layer around a RAG knowledge base — trigger, inputs, processing, destination — that keeps the vector store fresh. Related by retrieval
  2. Wiki concept RAG Retrieval Methods Framework of four retrieval patterns for AI agents: (1) database filters for structured rows when the answer lives in a small subset, (2) Related by search
  3. Wiki concept Multimodal Embeddings Embedding models that place text, images, video, audio, and documents into a single shared vector space, allowing cross-modal retrieval from one query. Related by gemini