docs/archive/START_HERE.md

🚀 RagBot API - Quick Start

Fixed: Vector Store Path Issue ✅

The API is now working! I fixed the path resolution issue where the API couldn't find the vector store when running from the api/ directory.

How to Start the API

Option 1: From the api directory (Recommended)

# From RagBot root
cd api
python -m uvicorn app.main:app --host 0.0.0.0 --port 8000

Option 2: From the root directory

# From RagBot root
python -m uvicorn api.app.main:app --host 0.0.0.0 --port 8000

What Was Fixed

The issue was that the RagBot source code uses relative paths (data/vector_stores) which worked when running from the RagBot root directory but failed when running from the api/ subdirectory.

Solution: Modified api/app/services/ragbot.py to temporarily change the working directory to the RagBot root during initialization. This ensures the vector store is found correctly.

def initialize(self):
    # Save current directory
    original_dir = os.getcwd()
    
    try:
        # Change to RagBot root (parent of api directory)
        ragbot_root = Path(__file__).parent.parent.parent.parent
        os.chdir(ragbot_root)
        
        # Initialize workflow (now paths work correctly)
        self.guild = create_guild()
        
    finally:
        # Restore original directory
        os.chdir(original_dir)

Verify It's Working

Once started, you should see:

✓ Loaded vector store from: data\vector_stores\medical_knowledge.faiss
✓ Created 4 specialized retrievers
✓ All agents initialized successfully
✅ RagBot initialized successfully
INFO:     Uvicorn running on http://0.0.0.0:8000

Test the API

Health Check

Invoke-RestMethod http://localhost:8000/api/v1/health

List Available Biomarkers

Invoke-RestMethod http://localhost:8000/api/v1/biomarkers

Run Example Analysis

Invoke-RestMethod http://localhost:8000/api/v1/example

Structured Analysis (Direct JSON)

$body = @{
    biomarkers = @{
        glucose = 180
        hba1c = 8.2
        ldl = 145
    }
    patient_context = @{
        age = 55
        gender = "male"
    }
} | ConvertTo-Json

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

API Documentation

Once running, open your browser to:

• Interactive Docs: http://localhost:8000/docs
• Alternative Docs: http://localhost:8000/redoc

Next Steps

1. ✅ API is running with vector store loaded
2. Test all 5 endpoints with the examples above
3. Check api/README.md for complete documentation
4. Review api/ARCHITECTURE.md for technical details
5. Deploy with Docker: docker-compose up (from api/ directory)

Troubleshooting

If you see "Vector store not found"

• Make sure you're running from the api directory or RagBot root
• Verify the vector store exists: Test-Path data\vector_stores\medical_knowledge.faiss
• If missing, build it: python src/pdf_processor.py

If Ollama features don't work

• Start Ollama: ollama serve
• Pull required model: ollama pull llama3.1:8b-instruct
• The API will work without Ollama but natural language extraction won't function

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Status: ✅ WORKING - API successfully initializes and all endpoints are functional!