nomanur/laravel-markdown-rag

Transform Markdown files into AI-ready context with smart chunking and RAG capabilities for Laravel

0.1.9 2026-03-09 05:28 UTC

Requires

• php: ^8.2
• illuminate/support: ^10.0|^11.0|^12.0
• laravel/ai: ^0.2.5
• livewire/livewire: ^3.0|^4.0

Requires (Dev)

• orchestra/testbench: ^9.16
• phpunit/phpunit: ^10.0|^11.0

MIT

• nomanur rahman

• php
• markdown
• laravel
• ai
• nomanur
• rag
• laravel-markdown-rag

README

Laravel Markdown RAG is a package that allows you to build a Retrieval-Augmented Generation (RAG) system using Markdown files as your knowledge base, powered by Gemini AI.

Installation

You can install the package via composer:

composer require nomanur/laravel-markdown-rag

Publish the package assets and configuration:

php artisan vendor:publish --provider="Nomanur\LaravelMarkdownRAGServiceProvider"
php artisan vendor:publish --provider="Laravel\Ai\AiServiceProvider"

Run the migrations:

php artisan migrate

Configuration

Add the following environment variables to your .env file:

GEMINI_API_KEY=
GEMINI_MODEL=gemini-2.5-flash
AI_EMBEDDING_PROVIDER=gemini
AI_DEFAULT_PROVIDER=gemini
MARKDOWN_INFO_PATH=knowledge-base
MARKDOWN_EMBEDDING_BATCH_SIZE=50
MARKDOWN_AI_RETRY_MAX_ATTEMPTS=3
MARKDOWN_QUERY_REWRITE=false
MARKDOWN_RERANKING=false

• MARKDOWN_CHAT_RATE_LIMIT: 5 (optional) if you want to rate the limit for the chat.
• MARKDOWN_INFO_PATH: (optional) the path to the markdown files in the public directory.
• MARKDOWN_EMBEDDING_BATCH_SIZE: (optional) the number of chunks to process in a single embedding request (default: 50).
• MARKDOWN_AI_RETRY_MAX_ATTEMPTS: (optional) the maximum number of retry attempts for AI requests (default: 3).
• MARKDOWN_QUERY_REWRITE: (optional) whether to enable query rewriting for better retrieval (default: false).
• MARKDOWN_RERANKING: (optional) whether to enable reranking of search results (default: false).

To use the configuration, update the code in config/ai.php with:

'default' => env('AI_DEFAULT_PROVIDER', 'ollama'),
'default_for_images' => env('AI_IMAGE_PROVIDER', 'gemini'),
'default_for_audio' => env('AI_AUDIO_PROVIDER', 'openai'),
'default_for_transcription' => env('AI_TRANSCRIPTION_PROVIDER', 'openai'),
'default_for_embeddings' => env('AI_EMBEDDING_PROVIDER', 'openai'),
'default_for_reranking' => env('AI_RERANKING_PROVIDER', 'cohere'),

'providers' => [
    'gemini' => [
        'driver' => 'gemini',
        'key' => env('GEMINI_API_KEY'),
        'models' => [
            'text' => [
                'default' => env('GEMINI_MODEL', 'gemini-1.5-flash'),
                'cheapest' => env('GEMINI_MODEL_CHEAPEST', 'gemini-1.5-flash'),
                'smartest' => env('GEMINI_MODEL_SMARTEST', 'gemini-2.0-pro-exp-02-05'),
            ],
        ],
    ],
],

Usage

1. Register Routes

Run the following command to register the necessary routes:

php artisan markdownrag:route

2. Setup Knowledge Base

Create a folder for your markdown files within the public directory. By default, this is public/knowledge-base, but you can change it using the MARKDOWN_INFO_PATH environment variable. Add your .md files there.

Example:

public/
└── knowledge-base/
    ├── company/
    │   ├── file1.md
    │   ├── file2.md
    │   └── file3.md
    └── product/
        ├── file1.md
        ├── file2.md
        └── file3.md

3. Indexing

Index your markdown files to make them searchable:

php artisan markdownrag:index

4. Accessing the Interface

You can access the chat interface at the following URL: your-domain.com/markdownrag

Testing

composer test

Changelog

Please see CHANGELOG for more information what has changed recently.

Contributing

Please see CONTRIBUTING for details.

Security

If you discover any security related issues, please email [e-mail] instead of using the issue tracker.

Credits

• nomanur rahman
• All Contributors

License

The MIT License (MIT). Please see License File for more information.