google-gemini-file-search
Google Gemini File Search Setup
Overview
Google Gemini File Search is a fully managed RAG (Retrieval-Augmented Generation) system that eliminates the need for separate vector databases, custom chunking logic, or embedding generation code. Upload documents (PDFs, Word, Excel, code files, etc.) and query them using natural language—Gemini automatically handles intelligent chunking, embedding with its optimized model, semantic search, and citation generation.
What This Skill Provides:
- Complete setup guide for @google/genai File Search API
- TypeScript/JavaScript SDK configuration patterns
- Working templates for 3 deployment scenarios (Node.js, Cloudflare Workers, Next.js)
- 8 documented common errors with prevention strategies
- Chunking best practices for optimal retrieval
- Cost optimization techniques
- Comparison guide (vs Cloudflare Vectorize, OpenAI Files API, Claude MCP)
Key Features of File Search:
- 100+ File Formats: PDF, Word (.docx), Excel (.xlsx), PowerPoint (.pptx), Markdown, JSON, CSV, code files (Python, JavaScript, TypeScript, Java, C++, Go, Rust, etc.)
- Automatic Embeddings: Uses Google's Gemini Embedding model (no custom embedding code required)
- Semantic Search: Vector-based search understands meaning and context, not just keywords
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