LLM_Engineering_OLD/week8/community_contributions/kachaje-andela-genai-bootcamp-w8/price-is-right

Price Is Right - Host-Based Setup

A simplified host-based microservices implementation of "The Price is Right" deal hunting system.

Overview

This setup runs all services directly on the host without Docker containers, using a shared Python virtual environment and direct Ollama connection.

Prerequisites

• Python 3.11+
• Ollama running on port 11434
• Required Ollama models: llama3.2 and llama3.2:3b-instruct-q4_0

Quick Start

1. Install dependencies:

   pip install -r requirements.txt
   # or with uv:
   uv pip install -r requirements.txt
   

2. Start all services:

   python service_manager.py start
   

3. Access the UI:

   • Main UI: http://localhost:7860
   • Notification Receiver: http://localhost:7861

4. Stop all services:

   python service_manager.py stop
   

Service Architecture

Service	Port	Description
Scanner Agent	8001	Scans for deals from RSS feeds
Specialist Agent	8002	Fine-tuned LLM price estimation
Frontier Agent	8003	RAG-based price estimation
Random Forest Agent	8004	ML model price prediction
Ensemble Agent	8005	Combines all price estimates
Planning Agent	8006	Orchestrates deal evaluation
Notification Service	8007	Sends deal alerts
Notification Receiver	8008	Receives and displays alerts
UI	7860	Main web interface

Service Management

Start Services

# Start all services
python service_manager.py start

# Start specific service
python service_manager.py start scanner

Stop Services

# Stop all services
python service_manager.py stop

# Stop specific service
python service_manager.py stop scanner

Check Status

python service_manager.py status

Restart Service

python service_manager.py restart scanner

Data Files

• data/models/ - Contains .pkl model files (immediately accessible)
• data/vectorstore/ - ChromaDB vector store
• data/memory.json - Deal memory storage
• logs/ - Service log files

Key Features

• No Docker overhead - Services start instantly
• Direct file access - .pkl files load immediately
• Single environment - All services share the same Python environment
• Direct Ollama access - No proxy needed
• Easy debugging - Direct process access and logs

Troubleshooting

1. Port conflicts: Check if ports are already in use

   python service_manager.py status
   

2. Ollama connection issues: Ensure Ollama is running on port 11434

   ollama list
   

3. Service logs: Check individual service logs in logs/ directory

4. Model loading: Ensure required models are available

   ollama pull llama3.2
   ollama pull llama3.2:3b-instruct-q4_0
   

Development

• All services are in services/ directory
• Shared code is in shared/ directory
• Service manager handles process lifecycle
• Logs are written to logs/ directory