Files
2025-11-30 08:24:57 +08:00

2.1 KiB

Google Gemini File Search Templates

This directory contains working example projects demonstrating different deployment patterns for Gemini File Search.

Templates

🚧 basic-node-rag/ (TO BE IMPLEMENTED)

Minimal Node.js/TypeScript example for learning and prototyping.

Features:

  • Simple TypeScript setup
  • Create store → Upload documents → Query → Display citations
  • Single-file example (~200 lines)
  • Perfect for understanding core concepts

Use When:

  • Learning File Search API
  • Quick prototyping
  • Building CLI tools

🚧 cloudflare-worker-rag/ (TO BE IMPLEMENTED)

Edge deployment with Cloudflare Workers + R2 integration.

Features:

  • Cloudflare Workers with @cloudflare/vite-plugin
  • R2 integration for document storage
  • Edge API endpoints (upload, query)
  • Hybrid architecture (Gemini File Search + Cloudflare edge)
  • Wrangler configuration

Use When:

  • Building global edge applications
  • Integrating with Cloudflare stack (D1, R2, KV)
  • Need low-latency worldwide

🚧 nextjs-docs-search/ (TO BE IMPLEMENTED)

Full-stack Next.js application with UI.

Features:

  • Next.js 14+ App Router
  • Document upload UI with drag-and-drop
  • Real-time search interface
  • Citation rendering with source links
  • Metadata filtering UI
  • Tailwind CSS + shadcn/ui
  • TypeScript throughout

Use When:

  • Building production documentation sites
  • Creating knowledge base UIs
  • Need full-stack app with frontend

Structure

Each template includes:

  • README.md - Setup and deployment instructions
  • package.json - Dependencies and scripts
  • tsconfig.json - TypeScript configuration
  • .env.example - Environment variables template
  • src/ - Source code
  • Working example with sample data

Development Status

Completed: 0/3 templates (0%)

Priority:

  1. basic-node-rag (foundational example)
  2. nextjs-docs-search (most practical for users)
  3. cloudflare-worker-rag (advanced integration)

Notes

All templates demonstrate:

  • Proper error handling from SKILL.md
  • Recommended chunking configurations
  • Metadata schema best practices
  • Operation polling patterns
  • Cost-aware implementations