Artificial Intelligence with PHP
  • Getting Started
    • Introduction
    • Audience
    • How to Read This Book
    • Glossary
    • Contributors
    • Resources
    • Changelog
  • Artificial Intelligence
    • Introduction
    • Overview of AI
      • History of AI
      • How Does AI Work?
      • Structure of AI
      • Will AI Take Over the World?
      • Types of AI
        • Limited Memory AI
        • Reactive AI
        • Theory of Mind AI
        • Self-Aware AI
    • AI Capabilities in PHP
      • Introduction to LLM Agents PHP SDK
      • Overview of AI Libraries in PHP
    • AI Agents
      • Introduction to AI Agents
      • Structure of AI Agent
      • Components of AI Agents
      • Types of AI Agents
      • AI Agent Architecture
      • AI Agent Environment
      • Application of Agents in AI
      • Challenges in AI Agent Development
      • Future of AI Agents
      • Turing Test in AI
      • LLM AI Agents
        • Introduction to LLM AI Agents
        • Implementation in PHP
          • Sales Analyst Agent
          • Site Status Checker Agent
    • Theoretical Foundations of AI
      • Introduction to Theoretical Foundations of AI
      • Problem Solving in AI
        • Introduction
        • Types of Search Algorithms
          • Comparison of Search Algorithms
          • Informed (Heuristic) Search
            • Global Search
              • Beam Search
              • Greedy Search
              • Iterative Deepening A* Search
              • A* Search
                • A* Graph Search
                • A* Graph vs A* Tree Search
                • A* Tree Search
            • Local Search
              • Hill Climbing Algorithm
                • Introduction
                • Best Practices and Optimization
                • Practical Applications
                • Implementation in PHP
              • Simulated Annealing Search
              • Local Beam Search
              • Genetic Algorithms
              • Tabu Search
          • Uninformed (Blind) Search
            • Global Search
              • Bidirectional Search (BDS)
              • Breadth-First Search (BFS)
              • Depth-First Search (DFS)
              • Iterative Deepening Depth-First Search (IDDFS)
              • Uniform Cost Search (UCS)
            • Local Search
              • Depth-Limited Search (DLS)
              • Random Walk Search (RWS)
          • Adversarial Search
          • Means-Ends Analysis
      • Knowledge & Uncertainty in AI
        • Knowledge-Based Agents
        • Knowledge Representation
          • Introduction
          • Approaches to KR in AI
          • The KR Cycle in AI
          • Types of Knowledge in AI
          • KR Techniques
            • Logical Representation
            • Semantic Network Representation
            • Frame Representation
            • Production Rules
        • Reasoning in AI
        • Uncertain Knowledge Representation
        • The Wumpus World
        • Applications and Challenges
      • Cybernetics and AI
      • Philosophical and Ethical Foundations of AI
    • Mathematics for AI
      • Computational Theory in AI
      • Logic and Reasoning
        • Classification of Logics
        • Formal Logic
          • Propositional Logic
            • Basics of Propositional Logic
            • Implementation in PHP
          • Predicate Logic
            • Basics of Predicate Logic
            • Implementation in PHP
          • Second-order and Higher-order Logic
          • Modal Logic
          • Temporal Logic
        • Informal Logic
        • Semi-formal Logic
      • Set Theory and Discrete Mathematics
      • Decision Making in AI
    • Key Application of AI
      • AI in Astronomy
      • AI in Agriculture
      • AI in Automotive Industry
      • AI in Data Security
      • AI in Dating
      • AI in E-commerce
      • AI in Education
      • AI in Entertainment
      • AI in Finance
      • AI in Gaming
      • AI in Healthcare
      • AI in Robotics
      • AI in Social Media
      • AI in Software Development
      • AI in Adult Entertainment
      • AI in Criminal Justice
      • AI in Criminal World
      • AI in Military Domain
      • AI in Terrorist Activities
      • AI in Transforming Our World
      • AI in Travel and Transport
    • Practice
  • Machine Learning
    • Introduction
    • Overview of ML
      • History of ML
        • Origins and Early Concepts
        • 19th Century
        • 20th Century
        • 21st Century
        • Coming Years
      • Key Terms and Principles
      • Machine Learning Life Cycle
      • Problems and Challenges
    • ML Capabilities in PHP
      • Overview of ML Libraries in PHP
      • Configuring an Environment for PHP
        • Direct Installation
        • Using Docker
        • Additional Notes
      • Introduction to PHP-ML
      • Introduction to Rubix ML
    • Mathematics for ML
      • Linear Algebra
        • Scalars
          • Definition and Operations
          • Scalars with PHP
        • Vectors
          • Definition and Operations
          • Vectors in Machine Learning
          • Vectors with PHP
        • Matrices
          • Definition and Types
          • Matrix Operations
          • Determinant of a Matrix
          • Inverse Matrices
          • Cofactor Matrices
          • Adjugate Matrices
          • Matrices in Machine Learning
          • Matrices with PHP
        • Tensors
          • Definition of Tensors
          • Tensor Properties
            • Tensor Types
            • Tensor Dimension
            • Tensor Rank
            • Tensor Shape
          • Tensor Operations
          • Practical Applications
          • Tensors in Machine Learning
          • Tensors with PHP
        • Linear Transformations
          • Introduction
          • LT with PHP
          • LT Role in Neural Networks
        • Eigenvalues and Eigenvectors
        • Norms and Distances
        • Linear Algebra in Optimization
      • Calculus
      • Probability and Statistics
      • Information Theory
      • Optimization Techniques
      • Graph Theory and Networks
      • Discrete Mathematics and Combinatorics
      • Advanced Topics
    • Data Fundamentals
      • Data Types and Formats
        • Data Types
        • Structured Data Formats
        • Unstructured Data Formats
        • Implementation with PHP
      • General Data Processing
        • Introduction
        • Storage and Management
          • Data Security and Privacy
          • Data Serialization and Deserialization in PHP
          • Data Versioning and Management
          • Database Systems for AI
          • Efficient Data Storage Techniques
          • Optimizing Data Retrieval for AI Algorithms
          • Big Data Considerations
            • Introduction
            • Big Data Techniques in PHP
      • ML Data Processing
        • Introduction
        • Types of Data in ML
        • Stages of Data Processing
          • Data Acquisition
            • Data Collection
            • Ethical Considerations in Data Preparation
          • Data Cleaning
            • Data Cleaning Examples
            • Data Cleaning Types
            • Implementation with PHP
          • Data Transformation
            • Data Transformation Examples
            • Data Transformation Types
            • Implementation with PHP ?..
          • Data Integration
          • Data Reduction
          • Data Validation and Testing
            • Data Splitting and Sampling
          • Data Representation
            • Data Structures in PHP
            • Data Visualization Techniques
          • Typical Problems with Data
    • ML Algorithms
      • Classification of ML Algorithms
        • By Methods Used
        • By Learning Types
        • By Tasks Resolved
        • By Feature Types
        • By Model Depth
      • Supervised Learning
        • Regression
          • Linear Regression
            • Types of Linear Regression
            • Finding Best Fit Line
            • Gradient Descent
            • Assumptions of Linear Regression
            • Evaluation Metrics for Linear Regression
            • How It Works by Math
            • Implementation in PHP
              • Multiple Linear Regression
              • Simple Linear Regression
          • Polynomial Regression
            • Introduction
            • Implementation in PHP
          • Support Vector Regression
        • Classification
        • Recommendation Systems
          • Matrix Factorization
          • User-Based Collaborative Filtering
      • Unsupervised Learning
        • Clustering
        • Dimension Reduction
        • Search and Optimization
        • Recommendation Systems
          • Item-Based Collaborative Filtering
          • Popularity-Based Recommendations
      • Semi-Supervised Learning
        • Regression
        • Classification
        • Clustering
      • Reinforcement Learning
      • Distributed Learning
    • Integrating ML into Web
      • Open-Source Projects
      • Introduction to EasyAI-PHP
    • Key Applications of ML
    • Practice
  • Neural Networks
    • Introduction
    • Overview of NN
      • History of NN
      • Basic Components of NN
        • Activation Functions
        • Connections and Weights
        • Inputs
        • Layers
        • Neurons
      • Problems and Challenges
      • How NN Works
    • NN Capabilities in PHP
    • Mathematics for NN
    • Types of NN
      • Classification of NN Types
      • Linear vs Non-Linear Problems in NN
      • Basic NN
        • Simple Perceptron
        • Implementation in PHP
          • Simple Perceptron with Libraries
          • Simple Perceptron with Pure PHP
      • NN with Hidden Layers
      • Deep Learning
      • Bayesian Neural Networks
      • Convolutional Neural Networks (CNN)
      • Recurrent Neural Networks (RNN)
    • Integrating NN into Web
    • Key Applications of NN
    • Practice
  • Natural Language Processing
    • Introduction
    • Overview of NLP
      • History of NLP
        • Ancient Times
        • Medieval Period
        • 15th-16th Century
        • 17th-18th Century
        • 19th Century
        • 20th Century
        • 21st Century
        • Coming Years
      • NLP and Text
      • Key Concepts in NLP
      • Common Challenges in NLP
      • Machine Learning Role in NLP
    • NLP Capabilities in PHP
      • Overview of NLP Libraries in PHP
      • Other Popular Tools for NLP
      • Challenges in NLP with PHP
    • Mathematics for NLP
    • NLP Processing Methods
      • NLP Workflow
      • Text Preprocessing
      • Feature Extraction Techniques
      • Distributional Semantics
      • Categories of NLP Models
        • Pure Statistical Models
        • Neural Models
        • Notable Models
      • Advanced NLP Topics
    • Integrating NLP into Web
    • Key Applications of NLP
    • Practice
  • Computer Vision
    • Introduction
  • Overview of CV
    • History of CV
    • Common Use Cases
  • CV Capabilities in PHP
  • Mathematics for CV
  • CV Techniques
  • Integrating CV into Web
  • Key Applications of CV
  • Practice
  • Robotics
    • Introduction
  • Overview of Robotics
    • History and Evolution of Robotics
    • Core Components
      • Sensors (Perception)
      • Actuators (Action)
      • Controllers (Processing and Logic)
    • The Role of AI in Robotics
      • Object Detection and Recognition
      • Path Planning and Navigation
      • Decision Making and Learning
  • Robotics Capabilities in PHP
  • Mathematics for Robotics
  • Building Robotics
  • Integration Robotics into Web
  • Key Applications of Robotics
  • Practice
  • Expert Systems
    • Introduction
    • Overview of ES
      • History of ES
        • Origins and Early ES
        • Milestones in the Evolution of ES
        • Expert Systems in Modern AI
      • Core Components and Architecture
      • Challenges and Limitations
      • Future Trends
    • ES Capabilities in PHP
    • Mathematics for ES
    • Building ES
      • Knowledge Representation Approaches
      • Inference Mechanisms
      • Best Practices for Knowledge Base Design and Inference
    • Integration ES into Web
    • Key Applications of ES
    • Practice
  • Cognitive Computing
    • Introduction
    • Overview of CC
      • History of CC
      • Differences Between CC and AI
    • CC Compatibilities in PHP
    • Mathematics for CC
    • Building CC
      • Practical Implementation
    • Integration CC into Web
    • Key Applications of CC
    • Practice
  • AI Ethics and Safety
    • Introduction
    • Overview of AI Ethics
      • Core Principles of AI Ethics
      • Responsible AI Development
      • Looking Ahead: Ethical AI Governance
    • Building Ethics & Safety AI
      • Fairness, Bias, and Transparency
        • Bias in AI Models
        • Model Transparency and Explainability
        • Auditing, Testing, and Continuous Monitoring
      • Privacy and Security in AI
        • Data Privacy and Consent
        • Safety Mechanisms in AI Integration
        • Preventing and Handling AI Misuse
      • Ensuring AI Accountability
        • Ethical AI in Decision Making
        • Regulations & Compliance
        • AI Risk Assessment
    • Key Applications of AI Ethics
    • Practice
  • Epilog
    • Summing-up
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  1. Artificial Intelligence
  2. AI Agents
  3. LLM AI Agents
  4. Implementation in PHP

Site Status Checker Agent

Coding Site Status Checker Agent in PHP

This agent gives you a status of following:

  • Check if a site is up and running

  • Dig up DNS info

  • Run ping tests

  • Give you the lowdown on why a site might be offline

Step 1: Create Agent class

For this example, let’s create SiteStatusCheckerAgent class.

SiteStatusCheckerAgent class
namespace app\public\include\classes\llmagents\sitestatuschecker;

use app\public\include\classes\llmagents\sitestatuschecker\tools\CheckSiteAvailabilityTool;
use app\public\include\classes\llmagents\sitestatuschecker\tools\GetDnsInfoTool;
use app\public\include\classes\llmagents\sitestatuschecker\tools\PerformPingTestTool;
use LLM\Agents\Agent\Agent;
use LLM\Agents\Agent\AgentAggregate;
use LLM\Agents\Solution\MetadataType;
use LLM\Agents\Solution\Model;
use LLM\Agents\Solution\SolutionMetadata;
use LLM\Agents\Solution\ToolLink;

final class SiteStatusCheckerAgent extends AgentAggregate
{
    public const DEFAULT_MODEL = 'gpt-4o-mini';
    public const NAME = 'site_status_checker';

    public static function create(string $model = self::DEFAULT_MODEL): self
    {
        $agent = new Agent(
            key: self::NAME,
            name: 'Site Status Checker',
            description: 'This agent specializes in checking the online status of websites. It can verify if a given URL is accessible, retrieve basic information about the site, and provide insights on potential issues if a site is offline.',
            instruction: 'You are a website status checking assistant. Your primary goal is to help users determine if a website is online and provide relevant information about its status. Use the provided tools to check site availability, retrieve DNS information, and perform ping tests when necessary. Always aim to give clear, concise responses about a site\'s status and offer potential reasons or troubleshooting steps if a site appears to be offline.',
        );

        $aggregate = new self($agent);

        $aggregate->addMetadata(
        // Instructions
            new SolutionMetadata(
                type: MetadataType::Memory,
                key: 'describe_decisions',
                content: 'Before calling any tools, describe the decisions you are making and why you are making them.',
            ),
            new SolutionMetadata(
                type: MetadataType::Memory,
                key: 'check_availability_first',
                content: 'Always start by checking the site\'s availability before using other tools.',
            ),
            new SolutionMetadata(
                type: MetadataType::Memory,
                key: 'don_not_repeat',
                content: 'Don\'t repeat yourself. If you have already provided something, don\'t repeat it unless necessary.',
            ),
            new SolutionMetadata(
                type: MetadataType::Memory,
                key: 'offline_site_checks',
                content: 'If a site is offline, consider checking DNS information and performing a ping test to gather more data.',
            ),
            new SolutionMetadata(
                type: MetadataType::Memory,
                key: 'explain_technical_terms',
                content: 'Provide clear explanations of technical terms and status codes for users who may not be familiar with them.',
            ),
            new SolutionMetadata(
                type: MetadataType::Memory,
                key: 'suggest_troubleshooting',
                content: 'Suggest common troubleshooting steps if a site appears to be offline.',
            ),

            // Prompts examples
            new SolutionMetadata(
                type: MetadataType::Prompt,
                key: 'google',
                content: 'Check if google.com is online.',
            ),

            new SolutionMetadata(
                type: MetadataType::Prompt,
                key: 'offline_site',
                content: 'Can you check why buggregator.dev is offline?',
            ),

            new SolutionMetadata(
                type: MetadataType::Configuration,
                key: 'max_tokens',
                content: 3000,
            ),
        );

        $aggregate->addAssociation(
            new Model(model: $model)
        );

        // Abbilities of the agent
        $aggregate->addAssociation(new ToolLink(name: CheckSiteAvailabilityTool::NAME));
        $aggregate->addAssociation(new ToolLink(name: GetDnsInfoTool::NAME));
        $aggregate->addAssociation(new ToolLink(name: PerformPingTestTool::NAME));

        return $aggregate;
    }

    public function getInputParamDescription(): array {
        $descriptions = [
            'url' => 'The URL of the website to check',
            'timeout' => 'Maximum time in seconds to wait for response',
            'method' => 'HTTP method to use for the request',
            'headers' => 'Additional HTTP headers to send with the request',
            'followRedirects' => 'Whether to follow HTTP redirects',
            'maxRedirects' => 'Maximum number of redirects to follow',
            'verifySSL' => 'Whether to verify SSL certificates'
        ];

        return $descriptions;
    }

    /**
     * Always require URL parameter for both tools
     * @param $properties
     * @param $required
     * @return void
     */
    public function addRequiredParams(&$properties, &$required) {
        // Always require URL parameter for both tools
        if (!isset($properties['url'])) {
            $properties['url'] = [
                'type' => 'string',
                'description' => 'The URL of the website to check'
            ];
            $required[] = 'url';
        }
    }

    public function getRequiredArgument():string {
        return 'url';
    }
}

Step 2: Create Tools and Associate Then With an Agent

Create CheckSiteAvailabilityTool. This tool checks if a given URL is accessible and returns its HTTP status code and response time.

CheckSiteAvailabilityTool class
namespace app\public\include\classes\llmagents\sitestatuschecker\tools;

use LLM\Agents\Tool\PhpTool;

/**
 * @extends PhpTool<CheckSiteAvailabilityInput>
 */
final class CheckSiteAvailabilityTool extends PhpTool {
    public const NAME = 'check_site_availability';

    public function __construct() {
        parent::__construct(
            name: self::NAME,
            inputSchema: CheckSiteAvailabilityInput::class,
            description: 'This tool checks if a given URL is accessible and returns its HTTP status code and response time.',
        );
    }

    public function execute(object $input): string {
        $ch = \curl_init($input->url);
        \curl_setopt_array($ch, [
            CURLOPT_RETURNTRANSFER => true,
            CURLOPT_HEADER => true,
            CURLOPT_NOBODY => true,
            CURLOPT_FOLLOWLOCATION => true,
            CURLOPT_MAXREDIRS => 10,
            CURLOPT_TIMEOUT => 30,
        ]);

        $startTime = \microtime(true);
        $response = \curl_exec($ch);
        $endTime = \microtime(true);

        $statusCode = \curl_getinfo($ch, CURLINFO_HTTP_CODE);
        $finalUrl = \curl_getinfo($ch, CURLINFO_EFFECTIVE_URL);
        $redirectCount = \curl_getinfo($ch, CURLINFO_REDIRECT_COUNT);
        $responseTime = \round(($endTime - $startTime) * 1000, 2);

        \curl_close($ch);

        $isOnline = $statusCode >= 200 && $statusCode < 400;

        return \json_encode([
            'status_code' => $statusCode,
            'response_time_ms' => $responseTime,
            'is_online' => $isOnline,
            'final_url' => $finalUrl,
            'redirect_count' => $redirectCount,
        ]);
    }
}

Create GetDnsInfoTool. This tool retrieves DNS information for a given domain, including IP addresses and name servers.

GetDnsInfoTool class
namespace app\public\include\classes\llmagents\sitestatuschecker\tools;

use LLM\Agents\Tool\PhpTool;

/**
 * @extends PhpTool<GetDnsInfoInput>
 */
final class GetDnsInfoTool extends PhpTool {
    public const NAME = 'get_dns_info';

    public function __construct() {
        parent::__construct(
            name: self::NAME,
            inputSchema: GetDnsInfoInput::class,
            description: 'This tool retrieves DNS information for a given domain, including IP addresses and name servers.',
        );
    }

    public function execute(object $input): string {
        // Implement the actual DNS info retrieval here
        // This is a placeholder implementation
        $dnsRecords = \dns_get_record(str_ireplace(['https://', 'http://'], '', $input->domain), DNS_A + DNS_NS);

        $ipAddresses = \array_column(array_filter($dnsRecords, fn($record) => $record['type'] === 'A'), 'ip');
        $nameServers = \array_column(array_filter($dnsRecords, fn($record) => $record['type'] === 'NS'), 'target');

        return \json_encode([
            'ip_addresses' => $ipAddresses,
            'name_servers' => $nameServers,
        ]);
    }
}

Create PerformPingTestTool. This tool performs a ping test to a specified host and returns the results, including response times and packet loss.

PerformPingTestTool class
namespace app\public\include\classes\llmagents\sitestatuschecker\tools;

use LLM\Agents\Tool\PhpTool;

/**
 * @extends PhpTool<PerformPingTestInput>
 */
final class PerformPingTestTool extends PhpTool {
    public const NAME = 'perform_ping_test';

    public function __construct() {
        parent::__construct(
            name: self::NAME,
            inputSchema: PerformPingTestInput::class,
            description: 'This tool performs a ping test to a specified host and returns the results, including response times and packet loss.',
        );
    }

    public function execute(object $input): string {
        // Implement the actual ping test here
        // This is a placeholder implementation
        $command = \sprintf('ping -c %d %s', 4, \escapeshellarg($input->host));
        \exec($command, $output, $returnVar);

        $packetLoss = 0;
        $avgRoundTripTime = 0;

        foreach ($output as $line) {
            if (str_contains($line, 'packet loss')) {
                \preg_match('/(\d+(?:\.\d+)?)%/', $line, $matches);
                $packetLoss = $matches[1] ?? 0;
            }

            if (str_contains($line, 'rtt min/avg/max')) {
                \preg_match('/= [\d.]+\/([\d.]+)\/[\d.]+/', $line, $matches);
                $avgRoundTripTime = $matches[1] ?? 0;
            }
        }

        return \json_encode([
            'packet_loss_percentage' => (float)$packetLoss,
            'avg_round_trip_time_ms' => (float)$avgRoundTripTime,
            'success' => $returnVar === 0,
        ]);
    }
}

Step 3: Associate Agent with Tools

Associate SiteStatusCheckerAgent with these tools.

$aggregate->addAssociation(new ToolLink(name: CheckSiteAvailabilityTool::NAME));
$aggregate->addAssociation(new ToolLink(name: GetDnsInfoTool::NAME));
$aggregate->addAssociation(new ToolLink(name: PerformPingTestTool::NAME));

Step 4: Run Code by Executor and Get Result

Now we're ready to run agent and see the result.

use app\public\include\classes\llmagents\AiAgentExecutor;
use app\public\include\classes\llmagents\salesanalysis\SalesAnalysisAgent;

// Usage example:
try {
    // Initialize the checker
    $checker = new AiAgentExecutor(
        aiAgent: SalesAnalysisAgent::class,
        apiKey: OPEN_AI_KEY,
        model: 'gpt-4o-mini',
        finalAnalysis: false,
        debug: true
    );

    $reportPath = 'public/pages/ai-agents/llm-agents/data/IC-Weekly-Sales-Activity-Report-11538.csv';

    // Generate report
    $result = $checker->execute(
        'Generate sales report from report path: ' . $reportPath
    );

    // Output debug results
    $agentDebug ??= '';
    $debugResult = '--';
    if ($agentDebug) {
        $debugLog = $checker->getDebugLog();
        foreach ($debugLog as $key => $message) {
            $debugResult .= humanize($key);
            $debugResult .= "\n=================\n";
            $debugResult .= $message . "\n\n";
        }
    }

    // Output the results
    echo "Sales Analysis:\n";

    // Show conversation history
    echo "Analysis Process:\n";
    foreach ($result['conversation_history'] as $message) {
        if (isset($message->functionCall)) {
            echo "Tool Called: {$message->functionCall->name}\n";
            echo "Arguments: {$message->functionCall->arguments}\n";
        } elseif (!empty($message->content)) {
            echo "\n&nbsp;\nAI: {$message->content}\n";
        }
        echo "\n";
    }

    if (!empty($result['final_analysis'])) {
        echo "\nFinal Analysis:\n{$result['final_analysis']}\n";
    }

} catch (\Exception $e) {
    echo 'Error: ' . $e->getFile() . ' | ' . $e->getLine() . "\n";
    echo 'Error: ' . $e->getMessage() . "\n";
}

Here the result:

Sales Analysis:
Analysis Process:
Tool Called: generate_sales_report
Arguments: {"reportPath":"public/pages/ai-agents/llm-agents/data/IC-Weekly-Sales-Activity-Report-11538.csv"}

Tool Called: analyze_sales_data
Arguments: {"reportPath":"public/pages/ai-agents/llm-agents/data/IC-Weekly-Sales-Activity-Report-11538.csv"}

Tool Called: forecast_future_sales
Arguments: {"reportPath":"public/pages/ai-agents/llm-agents/data/IC-Weekly-Sales-Activity-Report-11538.csv","forecastMethod":"linear","forecastPeriods":4,"timeUnit":"weeks","confidence":95,"seasonality":1}

### AI: The analysis of the sales data extracted from the provided report indicates the following key insights:
---

### Summary of Sales Activity
- **Total Sales:** $5,880,400
- **Total Orders:** 95 (total count of records)
- **Total Units Sold:** 13,304 units
- **Average Order Value:** This information wasn't available directly, but can be calculated as Total Sales divided by Total Orders, yielding approximately $61,000 per order.
- **Unique Customers:** No specific data was provided to determine unique customers.

### Detailed Insights
1. **Sales Performance by Region:**
   - **Northeast:** $983,750 (Variance: -$16,250)
   - **Southeast:** $1,066,300 (Variance: +$116,300)
   - **Midwest:** $734,350 (Variance: +$94,350)
   - **Southwest:** $1,665,000 (Variance: +$65,000)
   - **West Coast:** $1,431,000 (Variance: +$231,000)

   The **West Coast** showed the highest revenue and positive variance from targets, indicating effective sales strategies in that region. The **Southeast** also performed well above its target.

2. **Sales Performance by Sales Representative:**
   - **David Wilson** (West Coast) had the highest individual sales revenue at $1,431,000.
    - **Amanda Rodriguez** (Southwest) followed closely with $1,665,000.
    - **John Smith** (Northeast) and **Sarah Johnson** (Southeast) had mixed results, showing both successes and challenges.

3. **Product Breakdown:**
   - **Enterprise Solutions** contributed the most to revenue at $2,125,000.
    - **Mid-Market Solutions** and **Small Business Package** also made significant contributions, highlighting a diverse product portfolio.

### Recommendations
1. **Focus on High-Performing Regions:** Increase sales efforts in the West Coast and Southwest regions by leveraging successful strategies used there.

2. **Product Strategy:** Given that Enterprise Solutions contribute significantly to sales, consider enhancing marketing and sales tactics for this category to further capitalize on its success.

3. **Sales Training:** For representatives in regions underperforming against their targets, consider additional training or resources to help improve their sales techniques.

4. **Customer Acquisition:** Investigate strategies to attract new customers, particularly in regions like the Northeast, where performance has lagged.

5. **Data Collection Improvement:** To enable future forecasting, it's crucial to ensure more granular data is collected consistently, particularly on unique customers and more detailed sales metrics.

Unfortunately, due to insufficient time series data, forecasting future sales trends was not possible. To assist with forecasting in the future, ensure that historical sales data is collected over a sufficient timeline. 

To try this code yourself, install the example files from the official GitHub repository: https://github.com/apphp/ai-with-php-examples

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Last updated 2 months ago