home / skills / secondsky / claude-skills / google-gemini-file-search

This skill enables fully managed RAG with Gemini 2.5, indexing 100+ file formats, citations, and cost predictability for document Q&A and knowledge bases.

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SKILL.md
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---
name: google-gemini-file-search
description: Google Gemini File Search for managed RAG with 100+ file formats. Use for document Q&A, knowledge bases, or encountering immutability errors, quota issues, polling failures. Supports Gemini 3 Pro/Flash (Gemini 2.5 legacy).
license: MIT
metadata:
  version: "2.0.0"
  last_verified: "2025-11-18"
  production_tested: true
  token_savings: "~65%"
  errors_prevented: 8
  templates_included: 0
  references_included: 2
---

# 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 model.generateContent('How do I install the product?');
console.log(result.response.text());

// Get citations
const grounding = result.response.candidates[0].groundingMetadata;
if (grounding) {
  console.log('Sources:', grounding.groundingChunks);
}
```

**Load `references/setup-guide.md` for complete walkthrough with batch uploads, error handling, and production checklist.**

---

## Critical Rules

### Always Do

1. **Use delete + re-upload** for updates (documents are immutable)
2. **Calculate 3x storage** (embeddings + metadata = ~3x file size)
3. **Configure chunking** (500 tokens for technical docs, 800 for prose)
4. **Poll operations** until `done: true` (with timeout)
5. **Use force: true** when deleting stores with documents
6. **Use Gemini 2.5 models** only (2.5-pro or 2.5-flash)
7. **Keep metadata under 20 fields** per document
8. **Estimate indexing costs** ($0.15/1M tokens one-time)

### Never Do

1. **Never try to update** documents (no PATCH API exists)
2. **Never assume storage = file size** (it's 3x)
3. **Never skip chunking config** (defaults may not be optimal)
4. **Never upload without polling** (operation may still be processing)
5. **Never delete without force** if store has documents
6. **Never use Gemini 1.5 models** (File Search requires 2.5)
7. **Never exceed 20 metadata fields** (hard limit)
8. **Never upload large files without cost estimate**

---

## Top 3 Errors Prevented

### Error 1: Document Immutability

**Problem:** Trying to update existing document

**Solution:** Delete + re-upload pattern

```typescript
// Find and delete old version
const docs = await ai.fileSearchStores.documents.list({
  parent: fileStore.name
});
const oldDoc = docs.documents.find(d => d.displayName === 'manual.pdf');
if (oldDoc) {
  await ai.fileSearchStores.documents.delete({
    name: oldDoc.name,
    force: true
  });
}

// Upload new version
await ai.fileSearchStores.uploadToFileSearchStore({
  name: fileStore.name,
  file: fs.createReadStream('manual-v2.pdf'),
  config: { displayName: 'manual.pdf' }
});
```

### Error 2: Storage Quota Exceeded

**Problem:** Storage calculation wrong (3x multiplier)

**Solution:** Estimate before upload

```typescript
const fileSize = fs.statSync('data.pdf').size;
const estimatedStorage = fileSize * 3;  // Embeddings + metadata

if (estimatedStorage > 1e9) {
  console.warn('⚠️ May exceed free tier 1 GB limit');
}
```

### Error 3: Model Compatibility

**Problem:** Using wrong model version

**Solution:** Use Gemini 2.5 only

```typescript
// ✅ CORRECT
const model = ai.getGenerativeModel({
  model: 'gemini-2.5-pro',  // or gemini-2.5-flash
  tools: [{ fileSearchTool: { fileSearchStores: [storeName] } }]
});

// ❌ WRONG
const model = ai.getGenerativeModel({
  model: 'gemini-1.5-pro',  // Not supported!
  tools: [{ fileSearchTool: { fileSearchStores: [storeName] } }]
});
```

**Load `references/error-catalog.md` for all 8 errors with detailed solutions including chunking, operation polling, metadata limits, and force delete requirements.**

---

## When to Use File Search

### Use File Search When:

- Want fully managed RAG (no vector DB)
- Cost predictability matters (one-time indexing)
- Need 100+ file format support
- Citations are important (built-in grounding)
- Simple deployment is priority
- Documents are relatively static

### Use Alternatives When:

**Cloudflare Vectorize** - Global edge performance, custom embeddings, real-time R2 updates
**OpenAI Files API** - Assistants API, conversational threads, very large collections (10,000+)

---

## Common Patterns

### Pattern 1: Customer Support Knowledge Base

```typescript
// Upload support docs with metadata
await ai.fileSearchStores.uploadToFileSearchStore({
  name: fileStore.name,
  file: fs.createReadStream('troubleshooting.pdf'),
  config: {
    displayName: 'Troubleshooting Guide',
    customMetadata: {
      doc_type: 'support',
      category: 'troubleshooting',
      language: 'en'
    }
  }
});
```

### Pattern 2: Batch Document Upload

```typescript
const files = ['doc1.pdf', 'doc2.md', 'doc3.docx'];
const uploadPromises = files.map(file =>
  ai.fileSearchStores.uploadToFileSearchStore({
    name: fileStore.name,
    file: fs.createReadStream(file),
    config: { displayName: file }
  })
);
const operations = await Promise.all(uploadPromises);

// Poll all operations
for (const op of operations) {
  let operation = op;
  while (!operation.done) {
    await new Promise(resolve => setTimeout(resolve, 1000));
    operation = await ai.operations.get({ name: operation.name });
  }
  console.log('✅', operation.response.displayName);
}
```

### Pattern 3: Document Update Flow

```typescript
// 1. List existing documents
const docs = await ai.fileSearchStores.documents.list({
  parent: fileStore.name
});

// 2. Delete old version
const oldDoc = docs.documents.find(d => d.displayName === 'manual.pdf');
if (oldDoc) {
  await ai.fileSearchStores.documents.delete({
    name: oldDoc.name,
    force: true
  });
}

// 3. Upload new version
const operation = await ai.fileSearchStores.uploadToFileSearchStore({
  name: fileStore.name,
  file: fs.createReadStream('manual-v2.pdf'),
  config: {
    displayName: 'manual.pdf',
    customMetadata: {
      version: '2.0',
      updated_at: new Date().toISOString()
    }
  }
});

// 4. Poll until done
while (!operation.done) {
  await new Promise(resolve => setTimeout(resolve, 1000));
  operation = await ai.operations.get({ name: operation.name });
}
```

**Load `references/setup-guide.md` for additional patterns including code documentation search and internal knowledge bases.**

---

## When to Load References

### Load `references/setup-guide.md` when:
- First-time File Search setup
- Need step-by-step walkthrough with all configuration options
- Configuring batch upload strategies
- Production deployment checklist
- Complete API initialization patterns

### Load `references/error-catalog.md` when:
- Encountering any of 8 common errors
- Need detailed error solutions with code examples
- Prevention checklist required
- Troubleshooting upload/query issues
- Understanding chunking, metadata, or cost calculation problems

---

## Supported File Formats

**100+ formats including:**
- **Documents**: PDF, Word (.docx), Excel (.xlsx), PowerPoint (.pptx)
- **Text**: Markdown (.md), Plain text (.txt), JSON, CSV
- **Code**: Python, JavaScript, TypeScript, Java, C++, Go, Rust, etc.

**Not supported:** Images in PDFs (text extraction only), Audio files, Video files

---

## Pricing

**Indexing (one-time):** $0.15 per 1M tokens
**Storage:** Free (10 GB - 1 TB depending on tier)
**Query embeddings:** Free (retrieved context counts as input tokens)

**Example:** 1,000-page document ≈ 500k tokens → Indexing cost: $0.075 → Storage: ~1.5 GB (3x multiplier)

---

## Chunking Guidelines

**Technical docs:** 500 tokens/chunk, 50 overlap
**Prose:** 800 tokens/chunk, 80 overlap
**Legal:** 300 tokens/chunk, 30 overlap

```typescript
chunkingConfig: {
  whiteSpaceConfig: {
    maxTokensPerChunk: 500,  // Smaller = more precise
    maxOverlapTokens: 50     // 10% overlap recommended
  }
}
```

---

## Resources

**References** (`references/`):
- `setup-guide.md` - Complete setup walkthrough (authentication, store creation, file upload, batch patterns, production checklist)
- `error-catalog.md` - All 8 documented errors with solutions (immutability, storage, chunking, metadata, costs, polling, force delete, model compatibility)

**Official Documentation**:
- **File Search Overview**: https://ai.google.dev/api/file-search
- **API Reference**: https://ai.google.dev/api/file-search/documents
- **Blog Post**: https://blog.google/technology/developers/file-search-gemini-api/

---

**Questions? Issues?**

1. Check `references/setup-guide.md` for complete setup
2. Review `references/error-catalog.md` for all 8 errors
3. Verify model version (must be Gemini 2.5)
4. Check storage calculation (3x file size)

Overview

This skill integrates Google Gemini File Search for managed RAG workflows that index and query 100+ file formats. It handles automatic chunking, embeddings, vector search, and grounded citations without requiring a separate vector database. Use it for document Q&A, knowledge bases, and production-ready ingestion with error prevention guidance.

How this skill works

The skill creates a managed file store, uploads files (automatic chunking + embeddings), and exposes the store as a tool for Gemini 2.5 models. Indexing is a one-time operation (charged per tokens) and returns an operation you must poll until done. Queries run through Gemini 2.5-pro/flash and return grounded results with citation metadata.

When to use it

  • Building a knowledge base or customer support doc search without managing a vector DB
  • Document Q&A where built-in citations and grounding matter
  • Projects with many file types (PDF, Word, Excel, Markdown, code, JSON, CSV)
  • When cost predictability is required (one-time indexing pricing)
  • Static or infrequently changing document collections

Best practices

  • Always delete and re-upload documents to update content (documents are immutable)
  • Estimate storage at ~3x file size to account for embeddings and metadata
  • Configure chunking per content type (500 tokens for technical, 800 for prose)
  • Poll indexing operations until done, and implement a timeout/retry strategy
  • Keep custom metadata under 20 fields and use force:true when deleting stores with documents
  • Only use Gemini 2.5 models (2.5-pro or 2.5-flash) with File Search

Example use cases

  • Customer support KB: upload manuals, troubleshooting guides, and provide grounded answers with source links
  • Internal engineering docs search: index Markdown and code for documentation Q&A
  • Batch migration: batch upload many files and poll operations for each to confirm indexing
  • Document update flow: list documents, force-delete old versions, and re-upload new files
  • Compliance review: index legal or policy documents with conservative chunking for precise citations

FAQ

Which Gemini models are supported?

File Search requires Gemini 2.5 models (gemini-2.5-pro or gemini-2.5-flash). Gemini 1.5 is not supported.

How do I update a document that's already indexed?

You must delete the existing document (use force:true) and re-upload the new version; documents are immutable.