Small Sam

small sam is an intelligent Fantasy Premier League assistant powered by multi-agent AI systems, providing data-driven insights, transfer recommendations, and conversational team management.

🎯 Overview

Small Sam leverages advanced data orchestration, vector search, and multi-agent AI frameworks to deliver comprehensive Fantasy Premier League analysis and recommendations. The system aggregates player statistics, news sentiment, and tactical insights to provide actionable intelligence for FPL managers.

Key Features

🤖 Conversational AI Interface: Natural language querying for player analysis and transfer strategies
📊 Dynamic Dashboards: Real-time player statistics, form analysis, and league comparisons
🎯 Transfer Intelligence: AI-powered recommendations based on form, fixtures, and value metrics
📰 News Integration: Semantic search across injury reports, transfer rumors, and match analysis
👥 Team Management: Comprehensive FPL team tracking with historical performance analysis

🏗️ Architecture

The FPL Assistant implements a microservices architecture with clear separation of concerns:

graph TB
    A[RSS Feeds] --> B[Data Orchestration Service]
    C[FBref Stats] --> B
    D[FPL API] --> B
    
    B --> E[PostgreSQL]
    B --> F[Qdrant Vector DB]
    
    G[FastAPI Backend] --> E
    G --> F
    G --> H[Small Sam Agent Framework]
    
    H --> I[Stats Agent]
    H --> J[News Retrieval Agent]
    H --> K[Reporter Agent]
    H --> L[Reviewer Agent]
    
    M[Next.js Frontend] --> G

Component Overview

Component	Technology	Purpose
Data Orchestration	Python + Celery	RSS scraping, FBref extraction, data enrichment
Agent Framework	LangGraph + OpenAI	Multi-agent reasoning and query processing
Backend API	FastAPI + SQLAlchemy	REST API and business logic coordination
Frontend	Next.js + TypeScript	User interface and state management
Primary Database	PostgreSQL 15+	Structured data storage with JSONB support
Vector Store	Qdrant	Semantic search for news and match reports
Cache Layer	Redis 7+	Multi-tier caching with intelligent TTL

🚀 Quick Start

Prerequisites

Docker & Docker Compose: 20.10+
Python: 3.11+ (for local development)
Node.js: 18+ (for frontend development)
Git: 2.30+

Development Setup

Clone the repository

git clone https://github.com/ribhu97/smallsam.git
cd smallsam

Environment Configuration

cp .env.example .env
# Edit .env with your configuration

Infrastructure Startup

docker-compose up -d postgres redis qdrant

Backend Service

cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
alembic upgrade head
uvicorn app.main:app --reload --port 8000

Data Orchestration Service

cd data-orchestration
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
celery -A app.celery worker --loglevel=info

Frontend Service
```
cd frontend
npm install
npm run dev
```

Agent Framework

cd agent-framework
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python -m app.main

Production Deployment

# Build and deploy all services
docker-compose -f docker-compose.prod.yml up -d

# Initialize database schema
docker-compose exec backend alembic upgrade head

# Verify deployment
curl http://localhost/api/v1/health

📊 Data Pipeline

Data Sources

The system integrates multiple data sources with sophisticated error handling and rate limiting:

FPL Official API

Player statistics and pricing
Gameweek fixtures and results
League standings and transfers

FBref Statistical Data

Advanced player metrics (xG, xA, progressive passes)
Team tactical analysis
Historical performance trends

RSS News Feeds

Injury and transfer news
Match reports and tactical analysis
Manager press conferences

Data Flow Architecture

# Simplified data pipeline workflow
RSS_FEEDS -> Content_Enrichment -> Vector_Embedding -> Qdrant
FPL_API -> Data_Validation -> PostgreSQL
FBREF -> Statistical_Processing -> PostgreSQL

Caching Strategy

The system implements a three-tier caching architecture:

Cache Level	Technology	TTL Strategy	Use Case
L1 - Application	In-Memory	5 minutes	Frequent API responses
L2 - Distributed	Redis	15-60 minutes	Cross-request data sharing
L3 - Database	PostgreSQL	24 hours	Expensive aggregations

🤖 Agent Framework

Multi-Agent Architecture

The Small Sam framework implements a cooperative multi-agent system with specialized responsibilities:

Orchestration Agent

Query interpretation and task decomposition
Agent coordination and context management
Response synthesis and user interaction

Stats Agent

Natural language to PostgreSQL query translation
Statistical analysis and trend identification
Player comparison and ranking operations

News Retrieval Agent

Vector similarity search in Qdrant
Contextual filtering and relevance scoring
Injury and transfer intelligence aggregation

Reporter Agent

Natural language response generation
Data visualization recommendations
Actionable insight synthesis

Reviewer Agent

Response accuracy validation using source attribution
Hallucination detection and quality scoring
Final output approval and user safety

Agent Communication Protocol

# Example agent interaction flow
user_query = "Who should I captain this gameweek?"

orchestrator = OrchestrationAgent()
context = orchestrator.parse_query(user_query)

stats_data = await stats_agent.analyze_captaincy_options(context)
news_context = await news_agent.get_injury_updates(context)
recommendation = await reporter_agent.generate_recommendation(
    stats_data, news_context
)

final_response = await reviewer_agent.validate_and_approve(
    recommendation, source_attribution=True
)

📈 Performance Characteristics

Scalability Metrics

Metric	Target	Current
Concurrent Users	100+	50
API Response Time	<2s (95th percentile)	1.2s
Agent Query Time	<5s (95th percentile)	3.8s
Database Query Time	<500ms (95th percentile)	280ms
System Uptime	99.5%	99.2%

Resource Requirements

Minimum System Requirements

CPU: 4 cores, 2.4GHz
Memory: 8GB RAM
Storage: 50GB SSD
Network: 100Mbps

Recommended Production Setup

CPU: 8 cores, 3.2GHz
Memory: 16GB RAM
Storage: 200GB NVMe SSD
Network: 1Gbps

🧪 Testing

Test Coverage Requirements

The project maintains strict test coverage standards:

Unit Tests: >85% coverage across all modules
Integration Tests: API endpoints and database interactions
End-to-End Tests: Critical user workflows
Load Tests: Performance validation under concurrent load

Running Tests

# Backend unit tests
cd backend
pytest --cov=app --cov-report=html

# Frontend tests
cd frontend
npm run test:coverage

# Integration tests
docker-compose -f docker-compose.test.yml up --abort-on-container-exit

# Load testing
cd load-tests
k6 run --vus 50 --duration 5m api-load-test.js

Test Data Management

# Generate test fixtures
python scripts/generate_test_data.py

# Database test migrations
alembic -c alembic.test.ini upgrade head

# Vector store test data
python scripts/populate_test_vectors.py

🔧 Configuration

Environment Variables

Variable	Description	Default	Required
`DATABASE_URL`	PostgreSQL connection string	-	✅
`REDIS_URL`	Redis connection string	`redis://localhost:6379`	✅
`QDRANT_URL`	Qdrant vector database URL	`http://localhost:6333`	✅
`OPENAI_API_KEY`	OpenAI API key for agents	-	✅
`FPL_API_BASE_URL`	FPL API endpoint	`https://fantasy.premierleague.com/api/`	❌
`LOG_LEVEL`	Logging verbosity	`INFO`	❌
`ENVIRONMENT`	Deployment environment	`development`	❌

Service Configuration

Database Connection Pooling

# SQLAlchemy configuration
engine = create_engine(
    DATABASE_URL,
    pool_size=20,
    max_overflow=30,
    pool_pre_ping=True,
    pool_recycle=3600
)

Redis Configuration

# Redis connection with retry logic
redis_client = Redis.from_url(
    REDIS_URL,
    retry_on_timeout=True,
    health_check_interval=30,
    socket_keepalive=True
)

📚 API Documentation

Core Endpoints

Team Management

GET /api/v1/users/{user_id}/team
GET /api/v1/users/{user_id}/gameweeks
GET /api/v1/users/{user_id}/leagues

Player Data

GET /api/v1/players
GET /api/v1/players/{player_id}/stats
GET /api/v1/players/search?q={query}

Conversational Interface

POST /api/v1/chat/query
GET /api/v1/chat/sessions/{session_id}
DELETE /api/v1/chat/sessions/{session_id}

Agent Query Examples

# Player analysis
curl -X POST "http://localhost:8000/api/v1/chat/query" \
  -H "Content-Type: application/json" \
  -d '{"query": "Who is the best value midfielder under £6m?"}'

# Transfer strategy
curl -X POST "http://localhost:8000/api/v1/chat/query" \
  -H "Content-Type: application/json" \
  -d '{"query": "Should I use my wildcard this gameweek?"}'

🚦 Monitoring & Observability

Health Checks

# Service health validation
curl http://localhost:8000/api/v1/health

# Database connectivity
curl http://localhost:8000/api/v1/health/database

# Agent framework status
curl http://localhost:8000/api/v1/health/agents

Metrics Collection

The system exports metrics in Prometheus format:

Request Latency: API response time percentiles
Error Rates: HTTP 4xx/5xx error frequencies
Agent Performance: Query success rates and processing time
Resource Utilization: CPU, memory, and database connection pools

Logging Strategy

# Structured logging with correlation IDs
logger.info(
    "Agent query processed",
    extra={
        "correlation_id": "req_123456",
        "user_id": "fpl_789",
        "agent": "stats_agent",
        "processing_time_ms": 1250,
        "success": True
    }
)

🤝 Contributing

We welcome contributions from the FPL and software development community! Please read our CONTRIBUTING.md for detailed guidelines on:

Code style and formatting standards
Testing requirements and procedures
Pull request workflow and review process
Issue reporting and feature requests

📄 License

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

🙏 Acknowledgments

Fantasy Premier League: Official API and statistical data
FBref: Advanced football statistics and analytics
LangGraph: Multi-agent framework capabilities
Qdrant: Vector database performance and reliability

📞 Support

Documentation: Wiki
Issues: GitHub Issues
Discussions: GitHub Discussions
Discord: Community Server

⚡ Built with modern AI and data engineering practices for the FPL community

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
agent-framework		agent-framework
backend		backend
data-orchestration		data-orchestration
frontend		frontend
scripts		scripts
.DS_Store		.DS_Store
.env.example		.env.example
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
CONTRIBUTING.md		CONTRIBUTING.md
Makefile		Makefile
README.md		README.md
docker-compose.dev.yml		docker-compose.dev.yml
docker-compose.prod.yml		docker-compose.prod.yml
docker-compose.test.yml		docker-compose.test.yml
docker-compose.yml		docker-compose.yml

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