ALACTIC AGI Framework

🎯 Overview

ALACTIC AGI Framework is an enterprise-grade AI dataset processing platform designed for scalable data acquisition, validation, and structuring. Built with production-ready monitoring and enterprise observability.

Key Features

Automated Data Acquisition: Web-scale scraping with Scrapy integration
Intelligent Data Validation: AI-powered quality scoring and duplicate detection
Structured Data Output: Multiple formats (JSON, Parquet, HDF5) for ML pipelines
Enterprise Monitoring: Prometheus + Grafana + AlertManager integration
Production Scalability: Handles 100M+ sources with distributed processing
Windows-First Design: Optimized for enterprise Windows environments

Architecture Overview

Component	Technology	Purpose	Port
Core Framework	Python 3.10+	Main processing engine	5000
Web Crawler	Scrapy	Data acquisition	-
Search Engine	Apache Solr 9	Data indexing & retrieval	8983
API Server	Node.js	REST API interface	3000
Monitoring	Prometheus + Grafana	Observability & alerting	9090, 3001

📋 System Requirements

🖥️ Operating System

Windows 10/11 (Recommended)
Windows Server 2019/2022
Linux (Ubuntu 20.04+, CentOS 8+)
macOS 10.15+ (Development only)

🔧 Software Dependencies

Python 3.10 or higher
Node.js 18.0+ (for API server)
Java 17+ (for Apache Solr)
Git 2.30+ (for source control)

💻 Hardware Specifications

Minimum: 8GB RAM, 4 CPU cores
Recommended: 16GB RAM, 8 CPU cores
Enterprise: 32GB+ RAM, 16+ CPU cores
Storage: 100GB+ available space

🌐 Network Requirements

Internet connectivity for data scraping
Open ports: 3000, 5000, 8080, 8983, 9090
Firewall exceptions for HTTP/HTTPS
DNS resolution for external APIs

Enterprise Environments: For production deployments processing 1M+ documents daily, we recommend 32GB+ RAM and SSD storage for optimal performance.

🚀 Installation Guide

Prerequisites Installation

Python 3.10+ Setup

PowerShell

# Download Python 3.10+ from python.org
# Or use Windows Package Manager
winget install Python.Python.3.10

# Verify installation
python --version
# Expected output: Python 3.10.x or higher
                        

Node.js Installation

PowerShell

# Download from nodejs.org or use package manager
winget install OpenJS.NodeJS

# Verify installation
node --version
npm --version
                        

Java 17+ Installation

PowerShell

# Install Java 17 (required for Apache Solr)
winget install Eclipse.Temurin.17.JDK

# Verify installation
java -version
# Expected: openjdk version "17.x.x" or higher
                        

Framework Download & Setup

Git

# Clone the ALACTIC AGI repository
git clone https://github.com/AlacticAI/alactic-agi.git
cd alactic-agi

# Create and activate virtual environment
python -m venv .venv

# Windows activation
.venv\Scripts\activate

# Linux/macOS activation
source .venv/bin/activate
                        

Note: Always use a virtual environment to avoid dependency conflicts with other Python projects.

Dependencies Installation

PowerShell

# Install Python dependencies
pip install -r requirements.txt

# Install monitoring dependencies (optional)
pip install prometheus_client grafana-api psutil

# Verify core installations
python -c "import scrapy; print('Scrapy installed successfully')"
python -c "import requests; print('Requests installed successfully')"
                        

Key Dependencies Installed:

Package	Version	Purpose
scrapy	2.10+	Web crawling framework
requests	2.31+	HTTP client library
beautifulsoup4	4.12+	HTML/XML parsing
pandas	2.0+	Data manipulation
flask	2.3+	Web framework for APIs

Apache Solr Setup

PowerShell

# Solr is included in the tools directory
cd tools/solr9

# Start Solr (Windows)
.\bin\solr.cmd start

# Start Solr (Linux/macOS)
./bin/solr start

# Create ALACTIC AGI core
.\bin\solr.cmd create -c super_rag

# Verify Solr is running
curl http://localhost:8983/solr/admin/info/system
                        

Success! Solr should now be accessible at http://localhost:8983

Node.js API Server Setup

PowerShell

# Navigate to API directory
cd api

# Install Node.js dependencies
npm install

# Verify dependencies
npm list

# Start API server (development)
npm run dev

# Start API server (production)
npm start
                        

⚙️ Configuration

Core Configuration File

The main configuration is stored in config.ini:

INI

[DEFAULT]
# Core Framework Settings
debug = false
log_level = INFO
max_workers = 4

[DATABASE]
# Solr Configuration
solr_url = http://localhost:8983/solr/super_rag
solr_timeout = 30

[SCRAPING]
# Web Crawling Settings
max_pages = 10000
delay = 1.0
respect_robots = true
user_agent = ALACTIC-AGI/1.0

[API]
# API Server Configuration
host = 0.0.0.0
port = 5000
cors_enabled = true

[MONITORING]
# Monitoring & Observability
metrics_enabled = true
prometheus_port = 8080
grafana_enabled = true
alert_email = support@alacticai.com
                

Environment Variables

For production deployments, use environment variables to override configuration:

PowerShell

# Set environment variables (Windows)
$env:ALACTIC_DEBUG = "false"
$env:ALACTIC_SOLR_URL = "http://production-solr:8983/solr/super_rag"
$env:ALACTIC_LOG_LEVEL = "WARNING"
$env:ALACTIC_MAX_WORKERS = "8"

# Linux/macOS
export ALACTIC_DEBUG=false
export ALACTIC_SOLR_URL=http://production-solr:8983/solr/super_rag
export ALACTIC_LOG_LEVEL=WARNING
export ALACTIC_MAX_WORKERS=8
                

Solr Configuration

Custom Solr schema for optimal performance:

XML

<!-- solr_config/schema.xml -->
<schema name="alactic-agi" version="1.0">
  <field name="id" type="string" indexed="true" stored="true" required="true" multiValued="false"/>
  <field name="title" type="text_general" indexed="true" stored="true"/>
  <field name="content" type="text_general" indexed="true" stored="true"/>
  <field name="url" type="string" indexed="true" stored="true"/>
  <field name="timestamp" type="pdate" indexed="true" stored="true" default="NOW"/>
  <field name="quality_score" type="pfloat" indexed="true" stored="true"/>
  
  <uniqueKey>id</uniqueKey>
</schema>
                

📊 Enterprise Monitoring Setup

Enterprise Feature: ALACTIC AGI includes comprehensive monitoring with Prometheus, Grafana, and AlertManager for production-grade observability.

Quick Monitoring Setup

Install Monitoring Dependencies

PowerShell

# Install monitoring packages
pip install prometheus_client psutil grafana-api

# Verify installation
python -c "import prometheus_client; print('Monitoring ready!')"
                        

Start Monitoring Demo

PowerShell

# Start the monitoring demonstration
python monitoring_demo.py

# Access monitoring endpoints:
# http://localhost:8080/         - Dashboard
# http://localhost:8080/metrics  - Prometheus metrics
# http://localhost:8080/health   - Health check
                        

Test Core Monitoring

PowerShell

# Run comprehensive monitoring tests
python test_monitoring_core.py

# Expected output: All tests PASSED
# Metrics rate: 500K+ metrics/second
# Memory usage: Stable under load
                        

Monitoring Metrics

Metric Type	Examples	Description
Business Metrics	query_total, documents_processed	Core business KPIs
Performance	query_duration_seconds, api_response_time	System performance tracking
System Health	system_cpu_percent, system_memory_percent	Infrastructure monitoring
Error Tracking	query_error_total, pipeline_failures	Error rates and types

Integration with Existing Systems

Python
from monitoring import MetricsCollector, timed

# Initialize monitoring
metrics = MetricsCollector()

# Track custom business metrics@timed("document_processing")
def process_documents(documents):
    for doc in documents:
        # Your processing logic
        result = process_single_document(doc)
        
        # Record metrics
        metrics.record_counter("documents_processed", 1, 
                             {"type": doc.type, "status": "success"})
        metrics.record_gauge("processing_queue_size", len(queue))
    
    return results
                

🎯 Usage Guide

Quick Start

Python
from alactic_framework import AlacticAGI

# Initialize the framework
agi = AlacticAGI(config_file='config.ini')

# Process a simple query
result = agi.run_pipeline("artificial intelligence datasets")

# Display resultsfor item in result:
    print(f"Title: {item['title']}")
    print(f"URL: {item['url']}")
    print(f"Quality Score: {item['quality_score']}")
                

Advanced Usage Examples

1. Batch Document Processing

Python
import asyncio
from alactic_framework import AlacticAGI

asyncdef batch_process():
    agi = AlacticAGI(retriever_type='async')
    
    queries = [
        "machine learning research papers",
        "natural language processing datasets",
        "computer vision benchmarks"
    ]
    
    # Process multiple queries concurrently
    tasks = [agi.run_pipeline(query) for query in queries]
    results = await asyncio.gather(*tasks)
    
    return results

# Run batch processing
results = asyncio.run(batch_process())
                

2. Custom Data Pipeline

Python
from alactic_framework import AlacticAGI
from monitoring import PerformanceProfiler

class CustomDataPipeline:
    def __init__(self):
        self.agi = AlacticAGI()
        self.profiler = PerformanceProfiler(self.agi.metrics)
    
    def process_with_custom_validation(self, query, min_quality=0.8):
        with self.profiler.profile_operation("custom_pipeline"):
            # Get raw results
            raw_results = self.agi.run_pipeline(query)
            
            # Apply custom filtering
            filtered_results = [
                result for result in raw_results 
                if result.get('quality_score', 0) >= min_quality
            ]
            
            # Record custom metrics
            self.agi.metrics.record_gauge("custom_filter_ratio", 
                                        len(filtered_results) / len(raw_results))
            
            return filtered_results

# Use custom pipeline
pipeline = CustomDataPipeline()
high_quality_results = pipeline.process_with_custom_validation(
    "AI research datasets", min_quality=0.9
)
                

3. API Integration

Python
from flask import Flask, request, jsonify
from alactic_framework import AlacticAGI

app = Flask(__name__)
agi = AlacticAGI()

@app.route('/api/search', methods=['POST'])
def search_endpoint():
    data = request.get_json()
    query = data.get('query')
    limit = data.get('limit', 10)
    
    try:
        results = agi.run_pipeline(query)
        
        # Format response
        response = {
            'query': query,
            'total_results': len(results),
            'results': results[:limit],
            'status': 'success'
        }
        
        return jsonify(response)
        
    except Exception as e:
        return jsonify({'error': str(e), 'status': 'error'}), 500if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=True)
                

Command Line Interface

PowerShell

# Basic query
python alactic_agi.py --query "machine learning datasets"

# Advanced options
python alactic_agi.py --query "AI research" --max-results 100 --format json

# Batch processing from file
python alactic_agi.py --batch-file queries.txt --output results.json

# Start monitoring dashboard
python alactic_agi.py --start-monitoring

# Health check
python alactic_agi.py --health-check
                

🔧 Troubleshooting

Common Issues & Solutions

Before troubleshooting: Always check the logs in alactic_agi.log for detailed error information.

Installation Issues

Problem	Symptoms	Solution
Python version incompatibility	ImportError, syntax errors	Install Python 3.10+ and recreate virtual environment
Scrapy installation fails	pip install errors on Windows	Install Visual C++ Build Tools or use conda
Java not found	Solr fails to start	Install Java 17+ and set JAVA_HOME environment variable
Permission denied	Access errors on file operations	Run as administrator or check folder permissions

Runtime Issues

PowerShell

# Common diagnostic commands

# Check if all services are running
python alactic_agi.py --health-check

# Test Solr connectivity
curl http://localhost:8983/solr/admin/ping

# Check Python dependencies
pip list | grep -E "(scrapy|requests|flask)"

# Monitor system resources
python -c "import psutil; print(f'CPU: {psutil.cpu_percent()}%, Memory: {psutil.virtual_memory().percent}%')"

# Test monitoring system
python test_monitoring_core.py
                

Performance Issues

Slow query responses: Check Solr index size and consider reindexing
High memory usage: Reduce max_workers in config.ini
Network timeouts: Increase timeout values in configuration
Disk space issues: Clear logs and temporary files regularly

Error Code Reference

Error Code	Description	Action
AGI-001	Configuration file not found	Ensure config.ini exists in project root
AGI-002	Solr connection failed	Check if Solr is running on port 8983
AGI-003	Scraping rate limit exceeded	Increase delay in scraping configuration
AGI-004	Memory allocation error	Increase system RAM or reduce batch size

Getting Help

Support Channels:

📧 Email: support@alacticai.com
📖 Documentation: docs.alacticai.com
🐛 Bug Reports: GitHub Issues
💬 Community: Discord Server

📚 API Reference

Core Classes

AlacticAGI Class

Python
class AlacticAGI:
    """
    Main ALACTIC AGI Framework class for AI dataset processing.
    
    Args:
        config_file (str): Path to configuration file
        retriever_type (str): 'sync' or 'async' for retrieval mode
        
    Attributes:
        config: Configuration manager
        metrics: Metrics collection system
        components: Dictionary of initialized components
    """def __init__(self, config_file='config.ini', retriever_type='async'):
        # Initialize framework componentsdef run_pipeline(self, query: str) -> List[Dict]:
        """
        Execute the complete data processing pipeline.
        
        Args:
            query (str): Search query for data acquisition
            
        Returns:
            List[Dict]: Processed and structured data results
            
        Raises:
            AGIException: If pipeline execution fails
        """def get_health_status(self) -> Dict:
        """
        Get comprehensive health status of all components.
        
        Returns:
            Dict: Health status with component details
        """

Monitoring Classes

Python
class MetricsCollector:
    """Enterprise-grade metrics collection system"""def record_counter(self, name: str, value: int = 1, labels: Dict = None):
        """Record counter metric"""def record_gauge(self, name: str, value: float, labels: Dict = None):
        """Record gauge metric"""def record_timer(self, name: str, duration: float, labels: Dict = None):
        """Record timing metric"""class PerformanceProfiler:
    """Performance profiling and analysis"""def profile_operation(self, operation_name: str, labels: Dict = None):
        """Context manager for operation profiling"""@timed(operation_name: str = None)
def decorator_function():
    """Decorator for automatic timing of functions"""

Configuration Parameters

Parameter	Type	Default	Description
debug	boolean	false	Enable debug logging and verbose output
max_workers	integer	4	Maximum concurrent processing threads
solr_url	string	http://localhost:8983/solr/super_rag	Apache Solr endpoint URL
metrics_enabled	boolean	true	Enable enterprise monitoring and metrics

REST API Endpoints

Data Processing

HTTP

POST /api/search
Content-Type: application/json

{
  "query": "machine learning datasets",
  "limit": 50,
  "filters": {
    "quality_min": 0.8,
    "date_from": "2023-01-01"
  }
}

Response:
{
  "query": "machine learning datasets",
  "total_results": 1247,
  "results": [...],
  "status": "success",
  "processing_time": 2.34
}
                

Health & Monitoring

HTTP

GET /api/health
Response:
{
  "status": "healthy",
  "components": {
    "solr": "online",
    "crawler": "operational",
    "monitoring": "enabled"
  },
  "uptime": 86400,
  "version": "1.0.0"
}

GET /api/metrics
Response: Prometheus-formatted metrics

GET /api/stats
Response:
{
  "documents_processed": 1234567,
  "queries_today": 5678,
  "average_response_time": 1.23,
  "system_health": "excellent"
}
                