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Ollama-Laravel Package

Ollama-Laravel is a Laravel package that provides seamless integration with the Ollama API. It enables you to harness the power of local AI models for various tasks including text generation, vision analysis, chat completion, embeddings, and more. This package is perfect for developers looking to integrate AI capabilities into their Laravel applications without relying on external API services.

🌟 Features

• Text Generation: Generate content with customizable prompts and models
• Vision Analysis: Analyze images using multimodal models
• Chat Completion: Build conversational AI with message history
• Function Calling: Execute tools and functions through AI
• Streaming Responses: Real-time response streaming
• Model Management: List, copy, delete, and pull models
• Embeddings: Generate vector embeddings for semantic search
• Flexible Configuration: Customizable timeouts, temperature, and more

📋 Requirements

• PHP ^8.2
• Laravel ^11.0
• Ollama server running locally or remotely

If you use Laravel 10.x, please use version V1.0.9

🚀 Installation

⚙️ Configuration

Publish the configuration file:

Update your .env file:

Published config file:

📖 Usage Examples

🔧 Basic Text Generation

Simple Question Answering

Content Creation with Custom Options

Code Generation

🖼️ Vision Analysis

Basic Image Analysis

Product Catalog Analysis

Multiple Image Comparison

Document OCR and Analysis

💬 Chat Completion

Customer Support Bot

Educational Tutor

Code Review Assistant

php function calculateTotal($items) { $total = 0; foreach($items as $item) { $total += $item["price"] * $item["quantity"]; } return $total; }

🔧 Function Calling / Tools

Weather Information System

Database Query Assistant

🌊 Streaming Responses

Real-time Content Generation

Live Chat Implementation

📊 Embeddings for Semantic Search

Document Similarity Search

Product Recommendation System

🛠️ Model Management

List Available Models

Get Model Information

Copy and Manage Models

🏗️ Advanced Use Cases

Content Moderation System

Automated Code Documentation

Multi-language Translation Service

Data Analysis Assistant

🎯 Best Practices

1. Model Selection

• Use llama3.1 for general tasks and reasoning
• Use codellama for code-related tasks
• Use llava models for vision tasks
• Use nomic-embed-text for embeddings

2. Temperature Settings

• 0.1-0.3: Factual, deterministic outputs (code, analysis)
• 0.4-0.7: Balanced creativity and accuracy
• 0.8-1.0: Creative writing, brainstorming

3. Prompt Engineering

4. Error Handling

5. Performance Optimization

🧪 Testing

Run the test suite:

Example test for your AI service:

📝 Changelog, Contributing, and Security

• Changelog
• Contributing

🎖️ Credits

• Toni Soriano

📄 License

MIT License