README

Nalrep (Natural Language Reporting) is a powerful Laravel package that empowers your application with AI-driven reporting capabilities. It allows users to generate complex database reports using simple natural language prompts, converting them into safe, executable Laravel Query Builder code.

📑 Table of Contents

Example Report
Features
How It Works Internally
Installation
Configuration
Security Architecture
Usage
Error Handling
Extensibility
- Custom AI Agents
- Using PromptBuilder Standalone
License

📊 Example Report

Generate beautiful reports from natural language queries like "Top 5 customers by total purchase revenue"

Get clean JSON responses for API integrations: "give me list of all users with count of customers served and sales made"

🚀 Features

Natural Language to SQL: Convert questions like "Show me top selling products last month" into database queries.
Safe Execution: Built-in validation ensures only read-only queries are executed.
Context-Aware: Automatically scans your database schema and Eloquent models to provide the AI with accurate context.
Eloquent Integration: Intelligently uses your application's Eloquent models (e.g., \\App\\Models\\Sale) when available.
Flexible Output: Returns results as JSON or HTML tables. HTML reports can be printed to PDF using browser's print function.
Multi-Provider Support: Works with OpenAI, OpenRouter, and Ollama (local LLMs).
Highly Configurable: Fine-tune schema visibility, model scanning, and auto-imports.

� How It Works Internally

User Prompt
   ↓
PromptBuilder → AI Model
   ↓
JSON Query Definition
   ↓
Validator (read-only, method whitelist)
   ↓
Interpreter → Laravel Query Builder
   ↓
Result (HTML / JSON)

�📦 Installation

Require the package via Composer:
```
composer require nalrep/laravel
```

Publish the configuration file:

php artisan vendor:publish --tag=config --provider="Nalrep\\NalrepServiceProvider"

⚙️ Configuration

The configuration file config/nalrep.php gives you full control over how Nalrep behaves.

1. AI Provider

Choose your AI driver. Supported drivers: openai, openrouter, ollama.

'driver' => env('NALREP_DRIVER', 'openai'),

'openai' => [
    'api_key' => env('OPENAI_API_KEY'),
    'model' => env('NALREP_OPENAI_MODEL', 'gpt-4o-mini'), // Required
],

'openrouter' => [
    'api_key' => env('OPENROUTER_API_KEY'),
    'model' => env('NALREP_OPENROUTER_MODEL', 'openai/gpt-4o-mini'), // Required
],

Example Models:

gpt-4o-mini - Fast and cost-effective (recommended for most use cases)
gpt-4o - More powerful for complex queries
o3-mini - Optimized for code generation

2. Request Timeout

Set the maximum duration for AI requests to prevent hanging processes.

// Timeout in seconds (default: 120)
'timeout' => env('NALREP_TIMEOUT', 120),

3. Schema Exclusion

Control which database tables are visible to the AI. This is crucial for security and token optimization.

// Tables to exclude from the schema sent to the AI
'excluded_laravel_tables' => [
    'migrations', 'failed_jobs', 'password_reset_tokens', 'sessions', 
    'cache', 'cache_locks', 'jobs', 'job_batches', 'sqlite_sequence'
],

'excluded_tables' => [
    'admin_users', 'audit_logs', 'sensitive_data'
],

4. Model Scanning

Nalrep scans your application for Eloquent models to help the AI write cleaner, more "Laravel-like" queries using your actual classes.

// Directories to scan for Eloquent models
'model_paths' => [
    'app/Models',
],

The AI is instructed to use the Fully Qualified Class Name (FQCN) (e.g., \\App\\Models\\User) to avoid "Class Not Found" errors.

5. Common Classes (Auto-Imports)

Define classes that should be automatically available in the generated code execution environment. This prevents common "Class not found" errors.

'common_classes' => [
    'Carbon\\Carbon',
    'Illuminate\\Support\\Facades\\DB',
    // Add any other classes your queries might need
],

These classes will be automatically available when executing queries, allowing the AI to use them without import statements.

6. Frontend Component Settings

Configure the input component behavior and available formats.

'allowed_formats' => ['html', 'json', 'pdf'], // Formats available in the dropdown
'example_prompts' => [
    'Total sales last month',
    'Top 5 customers by revenue',
    'New users this week',
],

7. Safety Settings

Configure the safety guardrails.

'safety' => [
    'allow_destructive' => false, // MUST be false in production. Blocks DELETE, UPDATE, DROP, etc.
    'max_rows' => 1000,           // Limit result set size to prevent memory exhaustion.
],

🔒 Security Architecture

Nalrep takes security seriously. We use a JSON-based Query Interpreter to ensure safe execution.

1. No `eval()`

We do not use PHP's eval() function. Instead, the AI generates a structured JSON definition of the query (e.g., {"method": "where", "args": [...]}). This JSON is parsed and executed by a strict interpreter that only allows valid Query Builder methods.

2. Read-Only Enforcement

The Validator inspects the JSON structure before execution and blocks any destructive methods such as delete, update, insert, drop, or truncate.

3. Schema Filtering

By using excluded_tables, you ensure that the AI never sees the structure of sensitive tables.

4. Static Date Generation

The AI is provided with the current date and generates static date strings (e.g., "2024-01-01"), eliminating the need to execute arbitrary PHP date logic.

💻 Usage

Blade Component

The easiest way to use Nalrep is via the provided Blade component. It renders a simple input form for users to type their request.

<x-nalrep::input />

Customizing the View

You can publish the views to customize the frontend component:

php artisan vendor:publish --tag=views --provider="Nalrep\\NalrepServiceProvider"

This will publish the views to resources/views/vendor/nalrep. You can edit components/input.blade.php to match your application's design.

Programmatic Usage

You can use the Nalrep facade to generate reports programmatically in your controllers or commands.

use Nalrep\\Facades\\Nalrep;

public function report()
{
    $prompt = "Show me the total sales for each product in 2024";
    
    // Returns HTML string of the report table
    $html = Nalrep::generate($prompt, 'html');
    
    return view('reports.show', compact('html'));
}

⚠️ Error Handling

Nalrep provides comprehensive error handling to help developers and users understand what went wrong.

Exception Types

Nalrep throws specific exceptions for different error scenarios:

use Nalrep\Exceptions\{
    NalrepException,           // Base exception
    VaguePromptException,      // Prompt is too unclear
    InvalidPromptException,    // Prompt not related to data reporting
    QueryGenerationException,  // Cannot generate valid query
    InvalidJsonException,      // AI returned malformed JSON
    ValidationException        // Query failed security validation
};

Handling Errors in Your Code

use Nalrep\Facades\Nalrep;
use Nalrep\Exceptions\VaguePromptException;
use Nalrep\Exceptions\InvalidPromptException;

try {
    $report = Nalrep::generate($userPrompt, 'html');
} catch (VaguePromptException $e) {
    // User's query was too vague
    return back()->with('error', $e->getMessage());
} catch (InvalidPromptException $e) {
    // User's query wasn't related to data reporting
    return back()->with('error', 'Please ask for a data report or query.');
} catch (\Nalrep\Exceptions\NalrepException $e) {
    // Any other Nalrep-specific error
    \Log::warning('Nalrep error', ['message' => $e->getMessage()]);
    return back()->with('error', 'Unable to generate report. Please try rephrasing your query.');
}

How Errors Are Surfaced

In Development (APP_DEBUG=true):

Full error messages are displayed
Stack traces are available in logs
JSON responses include detailed error information

In Production (APP_DEBUG=false):

User-friendly error messages are shown
Technical details are logged but not exposed
Generic fallback messages for unexpected errors

AI Error Detection

The AI is instructed to detect and report issues:

Vague Prompts: "Show me data" → AI returns vague_prompt error
Invalid Requests: "What's the weather?" → AI returns invalid_prompt error
Schema Mismatch: "Show sales from products table" (when table doesn't exist) → AI returns query_generation_failed error

Example Error Responses

JSON Format:

{
  "error": true,
  "type": "VaguePromptException",
  "message": "Your query is too vague. Please be more specific about what data you want to see."
}

HTML Format:

<div class="bg-yellow-50 border border-yellow-200 text-yellow-800 px-4 py-3 rounded">
  <strong>Unable to generate report:</strong> Your query is too vague...
</div>

🔌 Extensibility

Custom AI Agents

You can implement your own AI driver by implementing the Nalrep\\Contracts\\Agent interface and registering it in the config.

use Nalrep\\Contracts\\Agent;
use Nalrep\\AI\\PromptBuilder;

class MyCustomAgent implements Agent {
    protected $schema;
    protected $models;
    
    public function setSchema(array $schema): Agent {
        $this->schema = $schema;
        return $this;
    }
    
    public function setModels(array $models): Agent {
        $this->models = $models;
        return $this;
    }
    
    public function generateQuery(string $prompt, string $mode = 'builder'): string {
        // Use the built-in PromptBuilder
        $promptBuilder = new PromptBuilder();
        
        // Optionally add custom instructions
        $promptBuilder->appendCustomInstructions(
            "Always include a summary row at the end of results."
        );
        
        $systemPrompt = $promptBuilder->build(
            $this->schema,
            $this->models,
            date('Y-m-d')
        );
        
        // Send to your custom AI model (e.g., local Ollama, Anthropic, etc.)
        $aiCompletion = $this->myAiService->complete($systemPrompt, $prompt);
        
        // Return the JSON query definition from the AI
        return $aiCompletion;
    }
}

// config/nalrep.php
'driver' => MyCustomAgent::class,

Using PromptBuilder Standalone

Developers can also use the PromptBuilder class directly for custom implementations:

use Nalrep\\AI\\PromptBuilder;

$builder = new PromptBuilder();

// Get just the base prompt
$basePrompt = $builder->getBasePrompt();

// Or build the complete prompt
$fullPrompt = $builder->build($schema, $models, date('Y-m-d'));

// Add custom instructions
$builder->appendCustomInstructions("Focus on performance optimization.");
$customPrompt = $builder->build($schema, $models, date('Y-m-d'));

📄 License

The MIT License (MIT).

nalrep / laravel

Maintainers

Details

README

📑 Table of Contents

📊 Example Report

🚀 Features

� How It Works Internally

�📦 Installation

⚙️ Configuration

1. AI Provider

2. Request Timeout

3. Schema Exclusion

4. Model Scanning

5. Common Classes (Auto-Imports)

6. Frontend Component Settings

7. Safety Settings

🔒 Security Architecture

1. No `eval()`

2. Read-Only Enforcement

3. Schema Filtering

4. Static Date Generation

💻 Usage

Blade Component

Customizing the View

Programmatic Usage

⚠️ Error Handling

Exception Types

Handling Errors in Your Code

How Errors Are Surfaced

AI Error Detection

Example Error Responses

🔌 Extensibility

Custom AI Agents

Using PromptBuilder Standalone

📄 License

nalrep / laravel

Maintainers

Details

README

📑 Table of Contents

📊 Example Report

🚀 Features

� How It Works Internally

�📦 Installation

⚙️ Configuration

1. AI Provider

2. Request Timeout

3. Schema Exclusion

4. Model Scanning

5. Common Classes (Auto-Imports)

6. Frontend Component Settings

7. Safety Settings

🔒 Security Architecture

1. No eval()

2. Read-Only Enforcement

3. Schema Filtering

4. Static Date Generation

💻 Usage

Blade Component

Customizing the View

Programmatic Usage

⚠️ Error Handling

Exception Types

Handling Errors in Your Code

How Errors Are Surfaced

AI Error Detection

Example Error Responses

🔌 Extensibility

Custom AI Agents

Using PromptBuilder Standalone

📄 License

1. No `eval()`