api/GETTING_STARTED.md

RagBot API - Getting Started (5 Minutes)

Follow these steps to get your API running in 5 minutes:

---

Prerequisites

Before starting, ensure you have:

1. Python 3.11+ installed

   python --version
   

2. A free API key from one of:

   • Groq — Recommended (fast, free LLaMA 3.3-70B)
   • Google Gemini — Alternative

3. RagBot dependencies installed

   # From RagBot root directory
   pip install -r requirements.txt
   

4. .env configured in project root with your API key:

   GROQ_API_KEY=gsk_...
   LLM_PROVIDER=groq
   

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🚀 Step 1: Install API Dependencies (30 seconds)

# Navigate to api directory
cd C:\Users\admin\OneDrive\Documents\GitHub\RagBot\api

# Install FastAPI and dependencies
pip install -r requirements.txt

Expected output:

Successfully installed fastapi-0.109.0 uvicorn-0.27.0 ...

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🚀 Step 2: Start the API (10 seconds)

# Make sure you're in the api/ directory
python -m uvicorn app.main:app --reload --port 8000

Expected output:

INFO:     Started server process
INFO:     Waiting for application startup.
🚀 Starting RagBot API Server
✅ RagBot service initialized successfully
✅ API server ready to accept requests
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000

⚠️ Wait 10-30 seconds for initialization (loading vector store)

---

✅ Step 3: Verify It's Working (30 seconds)

Option A: Use the Test Script

# In a NEW PowerShell window (keep API running)
cd C:\Users\admin\OneDrive\Documents\GitHub\RagBot\api
.\test_api.ps1

Option B: Manual Test

# Health check
curl http://localhost:8000/api/v1/health

# Get example analysis
curl http://localhost:8000/api/v1/example

Option C: Browser

Open: http://localhost:8000/docs

---

🎉 Step 4: Test Your First Request (1 minute)

Test Natural Language Analysis

# PowerShell
$body = @{
    message = "My glucose is 185 and HbA1c is 8.2"
    patient_context = @{
        age = 52
        gender = "male"
    }
} | ConvertTo-Json

Invoke-RestMethod -Uri "http://localhost:8000/api/v1/analyze/natural" `
    -Method Post -Body $body -ContentType "application/json"

Expected: JSON response with disease prediction, safety alerts, recommendations

---

🔗 Step 5: Integrate with Your Backend (2 minutes)

Your Backend Code (Node.js/Express Example)

// backend/routes/analysis.js
const axios = require('axios');

app.post('/api/analyze', async (req, res) => {
  try {
    // Get user input from your frontend
    const { biomarkerText, patientInfo } = req.body;
    
    // Call RagBot API on localhost
    const response = await axios.post('http://localhost:8000/api/v1/analyze/natural', {
      message: biomarkerText,
      patient_context: patientInfo
    });
    
    // Send results to your frontend
    res.json(response.data);
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

Your Frontend Code (React Example)

// frontend/components/BiomarkerAnalysis.jsx
async function analyzeBiomarkers(userInput) {
  // Call YOUR backend (which calls RagBot API)
  const response = await fetch('/api/analyze', {
    method: 'POST',
    headers: {'Content-Type': 'application/json'},
    body: JSON.stringify({
      biomarkerText: userInput,
      patientInfo: { age: 52, gender: 'male' }
    })
  });
  
  const result = await response.json();
  
  // Display results
  console.log('Disease:', result.prediction.disease);
  console.log('Confidence:', result.prediction.confidence);
  console.log('Summary:', result.conversational_summary);
  
  return result;
}

---

📋 Quick Reference

API Endpoints You'll Use Most:

1. Natural Language (Recommended)

   POST /api/v1/analyze/natural
   Body: {"message": "glucose 185, HbA1c 8.2"}
   

2. Structured (If you have exact values)

   POST /api/v1/analyze/structured
   Body: {"biomarkers": {"Glucose": 185, "HbA1c": 8.2}}
   

3. Health Check

   GET /api/v1/health
   

---

🐛 Troubleshooting

Issue: "Connection refused"

Problem: Ollama not running
Fix:

ollama serve

Issue: "Vector store not loaded"

Problem: Missing vector database
Fix:

cd C:\Users\admin\OneDrive\Documents\GitHub\RagBot
python scripts/setup_embeddings.py

Issue: "Port 8000 in use"

Problem: Another app using port 8000
Fix:

# Use different port
python -m uvicorn app.main:app --reload --port 8001

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📖 Next Steps

1. Read the docs: http://localhost:8000/docs
2. Try all endpoints: See README.md
3. Integrate: Connect your frontend to your backend
4. Deploy: Use Docker when ready (docker-compose.yml)

---

🎊 You're Done!

Your RagBot is now accessible via REST API at http://localhost:8000

Test it right now:

curl http://localhost:8000/api/v1/health

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Need Help?

• Full docs: README.md
• Quick reference: QUICK_REFERENCE.md
• Implementation details: IMPLEMENTATION_COMPLETE.md

Have fun! 🚀