VERCEL_SETUP.md

Vercel Setup - Quick Reference

📋 Pre-Deployment Checklist

• GitHub repository with code
• Google Gemini API key (Get one)
• Vercel account (Sign up free)

🚀 Deployment Steps

1. Import Project to Vercel

1. Go to vercel.com
2. Click "Add New..." → "Project"
3. Select "Import Git Repository"
4. Choose your GitHub repository
5. Vercel auto-detects configuration from vercel.json

2. Configure Environment Variables

Add these in Vercel Dashboard → Settings → Environment Variables:

Required

GOOGLE_API_KEY=AIza...your-key-here

Optional (with defaults)

EMBEDDING_MODEL=all-MiniLM-L6-v2
RELEVANCE_THRESHOLD=0.5
TOP_K_CHUNKS=3
LLM_MODEL=gemini-1.5-flash
LLM_TEMPERATURE=0.3

Important: Set for all environments (Production, Preview, Development)

3. Deploy

1. Click "Deploy"
2. Wait 2-3 minutes
3. Visit your URL: https://your-app.vercel.app

✅ Post-Deployment Testing

Test Questions

Should Answer:

"How do I create a new project in TaskFlow?"
"How do I add a team member?"
"What are the subscription plans?"

Should Escalate:

"What's the weather today?"
"Tell me a joke"
"How do I cook pasta?"

Verify

• Frontend loads correctly
• Can submit questions
• Answers appear in chat
• Confidence explanations show
• Escalations work for out-of-scope questions

🔧 Common Issues

Build Fails

Error: "Module not found"

# Solution: Ensure requirements.txt includes all dependencies
pip freeze > requirements.txt
git add requirements.txt
git commit -m "Update dependencies"
git push

API Returns 500

Error: Function crashes

# Solution: Check Vercel function logs
1. Go to Vercel Dashboard → Deployments
2. Click on latest deployment
3. View Function Logs
4. Look for Python errors

Frontend Can't Reach API

Error: "Failed to fetch"

# Solution: Test API directly
curl -X POST https://your-app.vercel.app/api/ask \
  -H "Content-Type: application/json" \
  -d '{"question":"test"}'

# Should return JSON response

Slow Responses

Issue: First request takes 5-10 seconds

This is normal! First request loads embedding model.
Subsequent requests are fast (1-3 seconds).

📊 Monitoring

View Logs

Vercel Dashboard → Your Project → Deployments → Function Logs

Key Metrics

• Function execution time: Should be <5s
• Error rate: Should be <5%
• Memory usage: Should be <1GB

Enable Analytics

Vercel Dashboard → Analytics → Enable Web Analytics

🔄 Updating

Update Code

git add .
git commit -m "Update feature"
git push
# Vercel auto-deploys

Update Help Articles

vim data/articles/getting-started.md
git add data/articles/
git commit -m "Update help articles"
git push
# Vector store rebuilds automatically

Update Environment Variables

1. Vercel Dashboard → Settings → Environment Variables
2. Edit variable
3. Redeploy (automatic or manual)

💰 Cost Tracking

Free Tier Limits

• 100GB bandwidth/month
• 100 hours function execution/month
• 6,000 function invocations/day

Monitor Usage

Vercel Dashboard → Usage

Expected Usage (100-500 requests/day)

• Function time: ~1-2s per request
• Daily: 100-1000s = 0.03-0.3 hours
• Monthly: 1-9 hours
• Well within free tier

🆘 Getting Help

Resources

• Full Deployment Guide
• README
• Vercel Docs
• Vercel Community

Support Channels

1. Check function logs in Vercel Dashboard
2. Review DEPLOYMENT.md troubleshooting section
3. Open GitHub issue
4. Vercel support (Pro plan)

🎯 Next Steps

After successful deployment:

• Test with various questions
• Enable Vercel Analytics
• Set up custom domain (optional)
• Monitor performance
• Share with users!

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Quick Links:

• Vercel Dashboard
• Google AI Studio
• Full Documentation