google-gemini-file-search

# Google Gemini File Search

**Status**: Production Ready | **Last Verified**: 2025-11-18

---

## What Is File Search?

Google Gemini File Search is **fully managed RAG** (Retrieval-Augmented Generation):
- Upload documents → Automatic chunking + embeddings + vector search + citations
- **No vector database setup** required
- **100+ file formats** supported (PDF, Word, Excel, code, Markdown, JSON, etc.)
- **Built-in grounding** with citation metadata
- **Cost-effective**: $0.15/1M tokens (one-time indexing), free storage + queries

**Key difference from other RAG:**
- Cloudflare Vectorize: You manage chunking/embeddings
- OpenAI Files API: Tied to Assistants API threads
- File Search: Fully managed, standalone RAG

---

## Quick Start (5 Minutes)

### 1. Get API Key & Install

Get API key: https://aistudio.google.com/apikey (Free tier: 1 GB storage, 1,500 requests/day)

```bash
bun add @google/genai
```

**Version:** 0.21.0+ | **Node.js:** 18+

### 2. Basic Example

```typescript
import { GoogleGenerativeAI } from '@google/genai';
import fs from 'fs';

const ai = new GoogleGenerativeAI(process.env.GOOGLE_AI_API_KEY);

// Create store
const fileStore = await ai.fileSearchStores.create({
  config: { displayName: 'my-knowledge-base' }
});

// Upload document
const operation = await ai.fileSearchStores.uploadToFileSearchStore({
  name: fileStore.name,
  file: fs.createReadStream('./manual.pdf'),
  config: {
    displayName: 'Installation Manual',
    chunkingConfig: {
      whiteSpaceConfig: {
        maxTokensPerChunk: 500,
        maxOverlapTokens: 50
      }
    }
  }
});

// Poll until done
while (!operation.done) {
  await new Promise(resolve => setTimeout(resolve, 1000));
  operation = await ai.operations.get({ name: operation.name });
}

// Query documents
const model = ai.getGenerativeModel({
  model: 'gemini-2.5-pro',  // Only 2.5 Pro/Flash supported
  tools: [{
    fileSearchTool: {
      fileSearchStores: [fileStore.name]
    }
  }]
});

const result = await