Seyah Space - Smart Workflow Automation API

Production-ready AI-powered workflow automation system built for Seyah Space. Demonstrates expertise in FastAPI, async Python, LLM integration, and scalable backend architecture.

🟢 Live API: https://seyah-workflow-automation.onrender.com/docs
🔑 API Key: seyah-demo-key-2026
👨‍ Built by: Bashir (BashOps)

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🚀 Quick Start

Try the live API right now:

1. Go to https://seyah-workflow-automation.onrender.com/docs
2. Click Authorize (top right)
3. Enter API Key: seyah-demo-key-2026
4. Try any endpoint!

Example: Automate a task

curl -X POST "https://seyah-workflow-automation.onrender.com/api/v1/automate/task" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: seyah-demo-key-2026" \
  -d '{
    "description": "Summarize the benefits of FastAPI for building modern APIs"
  }'

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✨ Features

1. Workflow Creation & Management

• Create multi-step automation workflows
• Store workflow definitions in PostgreSQL
• Execute workflows on-demand

2. AI-Powered Task Automation

• Natural language task execution
• Groq LLM integration (Mixtral-8x7b)
• Sub-200ms response times
• Structured JSON output

3. Intelligent Document Processing

• Extract entities from text
• Auto-summarization
• Categorization & tagging
• Key information extraction

4. Workflow Execution Engine

• Sequential step execution
• Progress tracking
• Status monitoring
• Error handling

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🛠️ Tech Stack

Layer	Technology
Framework	FastAPI 0.109.0
Language	Python 3.11+
Database	PostgreSQL / SQLite (Async SQLAlchemy)
AI/LLM	Groq API (Mixtral-8x7b-32768)
Validation	Pydantic V2
Deployment	Render
API Docs	OpenAPI 3.1 / Swagger UI

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📡 API Endpoints

Workflows

• POST /api/v1/workflows/create - Create new workflow
• POST /api/v1/workflows/{id}/execute - Execute workflow

Automation

• POST /api/v1/automate/task - AI-powered task execution
• POST /api/v1/process/document - Intelligent document processing

Health

• GET / - Root health check

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🏗️ Architecture

Database Strategy

The app uses SQLAlchemy async engine with a flexible configuration:

• Development/Demo: SQLite + aiosqlite (zero setup)
• Production: PostgreSQL + asyncpg (one-line toggle)

This allows instant testing without Docker while maintaining production readiness.

LLM Integration

Why Groq?

• 10-50x faster than traditional LLM APIs
• Critical for real-time workflow automation- Cost-effective for high-volume tasks
• Mixtral-8x7b provides excellent reasoning capabilities

Async Architecture

• All endpoints use async/await
• Non-blocking database queries
• High concurrency support
• Optimized for I/O-bound operations

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📦 Installation (Local Setup)

1. Clone the Repository

git clone https://github.com/BashOpsDev/seyah-workflow-automation.git
cd seyah-workflow-automation

2. Create Virtual Environment

python -m venv venv

# Windows
venv\Scripts\activate

# macOS/Linux
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment

# Copy example env file
cp .env.example .env

# Edit .env and add your Groq API key
# Get yours at: https://console.groq.com

.env file:

DATABASE_URL=sqlite+aiosqlite:///./seyah_demo.db
GROQ_API_KEY=your_groq_api_key_here
API_KEY=seyah-demo-key-2026
SECRET_KEY=your_secret_key_here```

### **5. Run the Application**
```bash
python run.py

Server starts at: http://localhost:8000
API Docs: http://localhost:8000/docs

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🧪 Testing

Test Workflow Creation

curl -X POST "http://localhost:8000/api/v1/workflows/create" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: seyah-demo-key-2026" \
  -d '{
    "name": "Daily News Summary",
    "description": "Summarize top AI news daily",
    "steps": [
      {"step_id": "fetch", "action": "scrape", "parameters": {"url": "https://news.ycombinator.com"}},
      {"step_id": "summarize", "action": "llm", "parameters": {"max_length": 500}},
      {"step_id": "email", "action": "send", "parameters": {"to": "user@example.com"}}
    ]
  }'

Test AI Task Automation

curl -X POST "http://localhost:8000/api/v1/automate/task" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: seyah-demo-key-2026" \
  -d '{
    "description": "Explain the benefits of async Python for web development"
  }'

Test Document Processing

curl -X POST "http://localhost:8000/api/v1/process/document" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: seyah-demo-key-2026" \
  -d '{
    "content": "Seyah Space is building AI-powered automation tools using Python and FastAPI to help businesses streamline workflows and increase productivity."
  }'

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🚢 Deployment (Render)

1. Push to GitHub

git add .
git commit -m "Production-ready workflow automation API"
git push origin main

2. Deploy on Render

1. Go to https://render.com
2. Click New → Web Service
3. Connect your GitHub repo
4. Configure:
   • Name: seyah-workflow-automation
   • Region: Choose closest to you
   • Branch: main
   • Runtime: Python 3
   • Build Command: pip install -r requirements.txt
   • Start Command: uvicorn app.main:app --host 0.0.0.0 --port $PORT

3. Add Environment Variables

In Render dashboard, add:

• DATABASE_URL (Render auto-creates PostgreSQL)
• GROQ_API_KEY=your_key_here
• API_KEY=seyah-demo-key-2026
• SECRET_KEY=your_random_secret

4. Deploy

Click Create Web Service → Wait 3-5 minutes → Done!

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📁 Project Structure

🛠️ Tech Stack

• Framework: FastAPI, Python 3.10+
• Database: PostgreSQL / SQLite (via SQLAlchemy Async)
• AI Integration: Groq API
• Validation: Pydantic V2

📦 Installation & Immediate Run

1. Clone & Install Dependencies

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt