👥 Tailor your Google Docs CV to any job using Ollama and Groq

⚡ 85 views · 👥 HR & Recruitment

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Description

🎯 CV Keyword Optimizer

An AI-powered n8n workflow that automatically tailors your resume to any job description by injecting relevant keywords — without touching your formatting, layout, or design.

How It Works

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        User Input (Form)                        │
│         CV Google Docs Link  +  Job URL or Pasted JD            │
└──────────────────────────┬──────────────────────────────────────┘
                           │
                           ▼
┌──────────────────────────────────────────────────────────────────┐
│  1. Read CV from Google Docs API                                 │
│  2. Extract full CV text (handles tables, paragraphs, etc.)      │
└──────────────────────────┬───────────────────────────────────────┘
                           │
                           ▼
┌──────────────────────────────────────────────────────────────────┐
│  3. Get Job Description                                          │
│     ├── URL provided? → Scrape job page, strip HTML to text      │
│     │     └── Scrape failed? → Fall back to manual JD            │
│     └── No URL? → Use manually pasted JD directly                │
└──────────────────────────┬───────────────────────────────────────┘
                           │
                           ▼
┌──────────────────────────────────────────────────────────────────┐
│  4. 🏠 Local Ollama (llama3.1:8b)                                │
│     Analyzes JD + CV → Extracts & ranks 10-20 ATS keywords      │
│     Output: keyword, priority, target bullet, reason             │
└──────────────────────────┬───────────────────────────────────────┘
                           │
                           ▼
┌──────────────────────────────────────────────────────────────────┐
│  5. ☁️ Groq API (Llama 3.3 70B)                                  │
│     Takes ranked keywords + CV → Produces find/replace pairs     │
│     Naturally weaves keywords into experience bullet points      │
└──────────────────────────┬───────────────────────────────────────┘
                           │
                           ▼
┌──────────────────────────────────────────────────────────────────┐
│  6. Copy original CV via Google Drive API                        │
│     (preserves ALL formatting, tables, styles)                   │
│                                                                  │
│  7. Apply replacements via Google Docs batchUpdate API            │
│     (replaceAllText — formatting stays intact)                   │
└──────────────────────────┬───────────────────────────────────────┘
                           │
                           ▼
┌──────────────────────────────────────────────────────────────────┐
│  8. Output                                                       │
│     ✅ New Google Doc link                                       │
│     📋 Changelog: original text → updated text + keywords added  │
└──────────────────────────────────────────────────────────────────┘

Why Two AI Models?

What Gets Modified

• ✅ Experience/work bullet points
• ✅ Skills/technical skills lines
• ❌ Name, contact info, education, dates, company names, job titles — never touched

Formatting Preservation

The workflow copies your original Google Doc (not recreates it), then uses replaceAllText to swap text in-place. This means:

• ✅ Tables, columns, fonts, colors — all preserved
• ✅ Bold, italic, underline — all preserved
• ✅ Custom spacing, margins — all preserved
• ✅ Original doc is untouched (changes go to the copy)

Setup Steps

Prerequisites

• Docker installed
• Ollama installed locally
• A Groq API key (free tier works)
• Google account with Docs & Drive access

1. Install & Start Ollama

# macOS
brew install ollama

# Start the Ollama server
ollama serve

# Pull the model (in another terminal)
ollama pull llama3.1:8b

Verify it’s running:

curl http://localhost:11434/api/tags

2. Get a Groq API Key

1. Go to console.groq.com
2. Sign up / log in
3. Navigate to API Keys → Create a new key
4. Copy the key (starts with gsk_...)

3. Configure Environment

cd cv-generator

# Create .env from template
cp .env.example .env

# Edit .env and add your Groq key
# GROQ_API_KEY=gsk_your_key_here

4. Start n8n

docker compose up -d

n8n will be available at http://localhost:5678

Default credentials:

• Username: admin
• Password: changeme

> ⚠️ Change these in docker-compose.yml for production use.

5. Import the Workflow

1. Open n8n at http://localhost:5678
2. Go to Workflows → Import from File
3. Select cv-keyword-optimizer.json
4. You’ll see credential warnings on some nodes — that’s expected

6. Set Up Google Credentials

1. In n8n, go to Settings → Credentials
2. Create a Google Docs OAuth2 credential
   • Follow n8n’s OAuth2 setup guide for Google
   • Required scopes: https://www.googleapis.com/auth/documents
3. Create a Google Drive OAuth2 credential
   • Required scopes: https://www.googleapis.com/auth/drive
4. Click each node with a ⚠️ warning → select your credential from the dropdown

7. Activate & Use

1. Toggle the workflow Active
2. Open the form URL shown in the trigger node (or go to http://localhost:5678/form/cv-keyword-optimizer-form)
3. Fill in:
   • Google Docs CV Link (required)
   • Job Posting URL or Job Description (at least one)
4. Submit and wait ~30-60 seconds
5. Get your optimized CV link + detailed changelog

Project Structure

cv-generator/
├── cv-keyword-optimizer.json   # n8n workflow definition
├── docker-compose.yml          # n8n container config
├── .env                        # Environment variables (not committed)
├── .env.example                # Template for .env
├── .gitignore                  # Ignores .env
└── README.md                   # This file

Troubleshooting

🔗 Nodes Used

HTTP Request, Google Drive, Google Docs, Basic LLM Chain, Ollama Chat Model, n8n Form Trigger

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup