NeuraPHP

NeuraPHP brings local AI embeddings to PHP—no APIs, no subscriptions, no external services. Run fast, private, personalized text embeddings directly on your machine, saving money while keeping full control of your data. Simple to install, effortless to use, and built for real‑world PHP apps.

Features

• Local embeddings — No API calls, no network latency, no data leaving your server
• PHP FFI — Direct memory access to a C library, no separate process
• Fluent API — Neuraphp::make()->model(ModelReference::fromEnum(Model::AllMiniLML12V2))->embed('text')
• Multiple models — AllMiniLM-L6-v2, AllMiniLM-L12-v2, Paraphrase, BGE, E5, multilingual; or any BERT-compatible HuggingFace model
• Quantization — F32, F16, Q4_0, Q4_1 for speed/quality tradeoffs
• Vector math — Cosine similarity, dot product, Euclidean distance, L2 normalization
• CLI tools — neuraphp install for auto-setup, neuraphp doctor for diagnostics, neuraphp info for configuration
• Laravel integration — Optional service provider and facade
• Framework-agnostic — Works with any PHP 8.3+ project

Docs: Manual Installation · Quick Start · API Reference · Supported Models · Configuration · Laravel Integration · CLI Reference

Add to your project:

composer require b7s/neuraphp

Quick Start

use B7s\Neuraphp\Neuraphp;
use B7s\Neuraphp\ModelReference;
use B7s\Neuraphp\Enums\Model;

// Embed text (uses default model: AllMiniLML6V2)
$result = Neuraphp::make()->embed('Hello world');
echo $result->dimension();  // 384

// Use a specific model
$result = Neuraphp::make()
    ->model(Model::BgeSmallENV15)
    ->embed('Hello world');

// Compare two texts
$similarity = Neuraphp::make()
    ->cosineSimilarity('The cat sat on the mat', 'A feline rested on the rug');

For more examples, custom models, batch embedding, and the full API — see the Advanced Guide

⚠️ Prerequisites: embedding.cpp Library & Model

Neuraphp requires libbert_shared.so (compiled from embedding.cpp) and a GGUF model file to function. There are two ways to set these up:

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Option A: Automatic Installation (Recommended)

Run the installation command - it clones, compiles, and downloads everything for you:

./vendor/bin/neuraphp install

This will:

1. Check prerequisites (git, cmake, make, C++ compiler, Rust, git-lfs)
2. Clone embedding.cpp into a temp directory and compile libbert_shared.so
3. Download the default model (all-MiniLM-L6-v2) from HuggingFace
4. Convert the model to GGUF format (requires Python + torch + transformers)
5. Copy only final artifacts to bin/neuraphp/ in your project root
   • Clean up temp files and create bin/neuraphp/.gitignore so artifacts are never committed

Options:

# Install a specific model (short name or full HuggingFace ID)
./vendor/bin/neuraphp install --model=bge-small-en-v1.5 --quantization=f16

# Install a custom BERT-compatible model
./vendor/bin/neuraphp install --model=custom-org/my-bert-model

# Skip library compilation (if already installed)
./vendor/bin/neuraphp install --skip-library

# Skip model download (if already downloaded)
./vendor/bin/neuraphp install --skip-model

# Force re-download/re-compile
./vendor/bin/neuraphp install --force

# Keep model source files after conversion
./vendor/bin/neuraphp install --keep-source

# Use a specific Python for model conversion
./vendor/bin/neuraphp install --python-path=~/myenv/bin/python3

# Check if Neuraphp is properly configured
./vendor/bin/neuraphp doctor

# Show model and configuration info
./vendor/bin/neuraphp info

# With options
./vendor/bin/neuraphp doctor --library-path=/custom/libbert_shared.so
./vendor/bin/neuraphp info --model=all-MiniLM-L6-v2 --quantization=q4_0

Testing

# Run all tests
composer test

# Run with coverage
composer test:coverage

# Code style
composer pint

# Static analysis
composer stan

# Quality gate
composer catraca

# Run all checks
composer check

License

MIT

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