Groq Customer Service Pipeline Template

AI customer service pipeline powered by Groq's lightning-fast inference

Transform customer interactions with sub-second response times, intelligent safety moderation, and professional tone validation - all running on Groq's high-performance infrastructure.

🚀 Quick Start (5 minutes to running)

1. Prerequisites

• Python 3.8+ (check with python --version)
• Groq API Key - Get yours free here

2. Installation

# Clone and enter the project
git clone https://github.com/benank/groq-customer-service-template
cd groq-customer-service-template

# Install dependencies
pip install -r requirements.txt

3. Configuration

Set your Groq API key using one of these methods:

Option A: Environment Variable (Recommended)

export GROQ_API_KEY="your_groq_api_key_here"

Option B: Create .env file

# Create .env file in project root
echo "GROQ_API_KEY=your_groq_api_key_here" > .env

Option C: Copy from template

# Copy the example file and edit it
cp .env.example .env
# Then edit .env with your API key

4. Run the Demo

python main.py

5. Access the Web Interface

Open http://localhost:5001 in your browser for the human review interface.

That's it! The pipeline is now running with:

• ✅ AI-powered customer service responses
• ✅ Real-time safety moderation
• ✅ Professional tone validation
• ✅ Human review workflow
• ✅ Performance monitoring

Overview

This application demonstrates a complete end-to-end customer service pipeline using Groq API for ultra-fast AI responses. Built as a template that you can fork, customize, and deploy.

Key Features:

• Lightning-Fast Responses: Fast AI processing with Groq's optimized inference
• Multi-Stage Safety: LlamaGuard-powered content moderation at every step
• Professional Tone Validation: Automatic detection and correction of unprofessional language
• Human-in-the-Loop Review: Web-based interface for quality control and oversight
• Real-Time Pipeline Monitoring: Live logging and performance tracking

Configuration Options

The pipeline is highly customizable through environment variables. Create a .env file or set these in your environment:

# Required
GROQ_API_KEY=your_groq_api_key_here

# Optional Customization
COMPANY_NAME="Your Company"
COMPANY_DOMAIN="customer service"
BRAND_VOICE="professional and empathetic"

# Performance Tuning
MAX_PIPELINE_MS=200
REQUEST_TIMEOUT=30
MAX_RETRIES=3

# Web Interface
WEB_UI_PORT=5001

# Logging
LOG_LEVEL=INFO

Architecture

Tech Stack:

• Frontend: Flask web interface with real-time updates
• Backend: Python async pipeline with modular agent architecture
• AI Infrastructure: Groq API with Llama models

Pipeline Stages:

1. Initial Safety Check - LlamaGuard content moderation
2. Response Generation - Professional customer service responses
3. Human Review - Web-based quality control interface
4. Final Safety Validation - Post-review content verification
5. Tone Analysis - Professional language standards enforcement
6. Conditional Rewrite - Automatic improvement for failed tone validation

Groq Models Used:

• Safety: meta-llama/Llama-Guard-4-12B
• Response Generation: meta-llama/llama-4-maverick-17b-128e-instruct
• Tone Validation: meta-llama/llama-4-scout-17b-16e-instruct
• Content Rewriting: meta-llama/llama-4-maverick-17b-128e-instruct

Project Structure

├── main.py                # Demo entry point with test scenarios
├── pipeline_demo.py       # Core pipeline orchestration
├── human_loop.py          # Web interface and human review system
├── config.py              # Model configurations and prompts
├── base.py                # Abstract agent base class
├── guard_agent.py         # LlamaGuard safety moderation
├── response_agent.py      # Customer response generation
├── tone_agent.py          # Professional tone validation
├── rewrite_agent.py       # Content improvement agent
├── utils.py               # Utility classes and functions
├── review.html            # Web interface
├── requirements.txt       # Python dependencies
├── .env.example           # Environment configuration template
└── README.md              # This file

Core Components

Pipeline Agents

GuardAgent: LlamaGuard-4-12B powered safety moderation with comprehensive taxonomy coverage including violence, hate speech, inappropriate content, and professional standards.

ResponseAgent: Llama-4-Maverick-17B generates empathetic, professional customer service responses following best practices for acknowledgment, ownership, and solution-oriented communication.

ToneAgent: Llama-4-Scout-17B validates professional language standards, detecting casual expressions, unprofessional terminology, and ensuring appropriate business tone.

RewriteAgent: Llama-4-Maverick-17B automatically improves responses that fail tone validation, maintaining factual accuracy while enhancing professionalism.

Human Review Interface

• Real-time pipeline step logging
• Interactive response editing and approval
• Performance metrics and timing data
• Thread-safe review workflow
• Customer context preservation

Demo Features

Test Scenarios

Includes challenging customer service scenarios:

• Standard delivery inquiries
• Escalated complaints requiring careful handling
• Hostile customer interactions testing safety systems
• Complex multi-issue requests

Real-Time Monitoring

• Step-by-step pipeline progress tracking
• AI processing latency measurement
• Human review time monitoring
• Performance optimization insights

Safety & Compliance

• LlamaGuard taxonomy enforcement (O1-O6 categories)
• Professional language detection and correction
• Content moderation at multiple pipeline stages
• Audit trail for all interactions

API Usage

Basic Pipeline Execution

from pipeline_demo import GroqCustomerServiceDemo

# Initialize and start the pipeline
demo = GroqCustomerServiceDemo()
demo.start_web_interface()

# Process individual customer inquiries
customer_input = "My order was supposed to arrive yesterday but I haven't received anything."
result = await demo.process_single_scenario(customer_input)

Individual Agent Usage

from guard_agent import GuardAgent
from response_agent import ResponseAgent

# Initialize Groq agents
guard = GuardAgent()
response_gen = ResponseAgent()

# Process customer input
safety_result, latency = await guard.check_safety(customer_input)
if safety_result.passes:
    response, response_time = await response_gen.generate_response(customer_input)

Customization

This template is designed as a foundation for your customer service needs:

Model Configuration

• Update Groq models: Modify model selections in config.py
• Adjust performance targets: Set latency and quality thresholds
• Customize safety taxonomy: Extend LlamaGuard categories for your domain

Professional Standards

• Industry-specific language: Update tone validation rules in config.py
• Brand voice alignment: Customize response generation prompts
• Escalation triggers: Configure when to route to human agents

Web Interface

• Styling and branding: Customize template.html
• Workflow integration: Extend human review process
• Analytics and reporting: Add custom metrics and dashboards

Performance Optimization

Groq Advantages

• Ultra-low latency: 10-50ms response times vs 200-2000ms with other providers
• High throughput: Handle concurrent requests efficiently
• Cost effective: Optimized pricing for large workloads
• Consistent performance: Predictable response times under load

Troubleshooting

Common Issues

"Configuration Error: GROQ_API_KEY must be provided"

• Solution: Set your API key using one of the methods in step 3 above
• Verify: echo $GROQ_API_KEY should show your key

"Cannot connect to Groq API"

• Check your internet connection
• Verify your API key is valid at Groq Console
• Ensure no firewall is blocking the connection

"ModuleNotFoundError"

• Run: pip install -r requirements.txt
• Use Python 3.8+ (python --version)

Web interface not loading

• Check if port 5001 is available
• Try a different port: WEB_UI_PORT=5002 python main.py

Getting Help

If you encounter issues:

1. Check the console output for error messages
2. Verify all requirements are installed
3. Ensure your Groq API key is valid
4. Check the Groq Community Forum for support

Next Steps

For Developers

• Create your free GroqCloud account: Access official API docs, the playground for experimentation, and more resources via Groq Console
• Build and customize: Fork this repo and start customizing to build out your own application
• Dive deep: by learning more about Groq capabilities in our documentation.
• Get support: Connect with other developers building on Groq, chat with our team, and submit feature requests on our Groq Developer Forum

For Founders and Business Leaders

• See enterprise capabilities: This template showcases AI that can handle realtime business workloads
• Discuss your needs: Contact our team to explore how Groq can accelerate your AI initiatives

License

This project is licensed under the MIT License - see the LICENSE file for details.

Credits

Created by Jordan Hagan.