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

Workflow overview

Why this workflow matters

Supports knowledge capture and document intelligence use cases.

🎯 CV Keyword Optimizer An AI-powered AlekSystem 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? | Step | Model | Why | |------|-------|-----| | Keyword Extraction | Ollama llama3.1:8b (local) | Free, private, no API costs. Reasoning about which keywords actually matter for ATS | | Text Rewriting | Groq llama-3.3-70b-versatile (cloud) | Larger model = better at natural language. Produces find/replace pairs that read naturally | 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 Go to console.groq.com Sign up / log in Navigate to API Keys → Create a new key 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 AlekSystem docker compose up -d AlekSystem 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 Open AlekSystem at http://localhost:5678 Go to Workflows → Import from File Select cv-keyword-optimizer.json You'll see credential warnings on some nodes — that's expected 6. Set Up Google Credentials In AlekSystem, go to Settings → Credentials Create a Google Docs OAuth2 credential Follow AlekSystem's OAuth2 setup guide for Google Required scopes: https://www.googleapis.com/auth/documents Create a Google Drive OAuth2 credential Required scopes: https://www.googleapis.com/auth/drive Click each node with a ⚠️ warning → select your credential from the dropdown 7. Activate & Use Toggle the workflow Active Open the form URL shown in the trigger node (or go to http://localhost:5678/form/cv-keyword-optimizer-form) Fill in: Google Docs CV Link (required) Job Posting URL or Job Description (at least one) Submit and wait ~30-60 seconds Get your optimized CV link + detailed changelog Project Structure cv-generator/ ├── cv-keyword-optimizer.json # AlekSystem workflow definition ├── docker-compose.yml # AlekSystem container config ├── .env # Environment variables (not committed) ├── .env.example # Template for .env ├── .gitignore # Ignores .env └── README.md # This file Troubleshooting | Issue | Solution | |-------|----------| | Ollama connection refused | Make sure ollama serve is running. AlekSystem reaches it via host.docker.internal:11434 | | Groq 429 rate limit | Free tier has limits. Wait a minute and retry | | Scraping fails on LinkedIn | LinkedIn blocks scrapers. Paste the JD manually instead | | Google Docs auth error | Re-check OAuth2 credentials in AlekSystem. Ensure correct scopes | | Replacements don't apply | The AI's "find" text must exactly match the CV. Check the Changes Summary for what was attempted | | Empty response from Ollama | Model may still be loading. First run takes longer. Timeout is set to 5 min |

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