PHP AND LLMs
PHP AND LLMs
Practical Integrations using Laravel
About the Book
This book is a practical guide for PHP developers navigating the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond the hype, it delves into real-world applications and solutions, providing step-by-step instructions on how to integrate LLMs into your existing projects.
From mastering prompts to building reusable tools and working with various LLMs, this book equips you with the skills needed to leverage the power of Ai in everyday development tasks.
Whether you’re looking to automate complex workflows, enhance content verification, or simply stay ahead in a changing industry, this book offers the insights and hands-on examples to get you there. This isn’t just another “Hello World” guide—it’s a deep dive into the future of PHP development, focusing on real solutions that drive real results.
Sample book here https://bit.ly/php_llms_sample
See all the code at https://github.com/alnutile/php-llms
About the Author
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Table of Contents
- Introduction - Embracing the Reality of LLMs in Development
- Code Examples
- Book Structure
- Thanks
- Follow Me
- Why Use LLMs
- What You’ll Learn
- Big Picture View
- It is your pair coding colleague
- It is there to do the grunt work for you
- It is there to help you try new ways to solve problems for your customers and yourself.
- It Can Empower Your Users
- Why Not LLMs?
- Wrapping it up
- What You’ve Learned
- Local LLMs: Streamlining Your Development Workflow
- Big Picture
- What You Will Learn
- Setting up your local Computer
- Ollama
- HTTP Client with Tests
- Using the Chat Method
- Update Client for Chat
- Real Use Case - News Summary
- What You Learned
- Prompting Overview
- What You’ll Learn
- Big Picture View
- The term “Prompt Engineering” is not as stupid as I first thought!
- Keep it simple
- System Prompt
- First Shot vs Multi-Shot
- Tools
- Place Holders
- Putting it all together
- What You’ve Learned
- Links
- Tools Overview - Enhancing LLM Capabilities
- The Tool Flow In Detail
- Claude and Tools
- Tool Response
- How can we test this?
- In Summary
- Agnostic Tools
- What you will learn:
- Intro
- Curl and JSON
- Claude
curl
- OpenAI
curl
- Agnostic Tools Prism
- Stand-Alone Classes
- When to Attach Tools
- What Next?
- Building the LLM Agnostic Driver
- Summary of What You Will Learn
- Summary of What You Will Learn
- Step One: The Facade
- Step Two: Mock Client and Data Objects
- Step Three: OllamaClient
- Step Four: Claude, Groq, OpenAI
- Extra Features
- What You’ve Learned
- Links
- RAG and Vector
- What You Will Learn About Building a RAG System
- Why RAG?
- What’ll we do with RAG?
- Getting Your Application Database Set up
- Data and Ingestion
- Document Table
- Chunk Table
- Ingestion and Embedding
- Embedding
- Our First RAG Question
- Pull in all the Files and Computer Considerations
- Adding Images
- Adding Voice
- Unstructured vs Structured
- Beyond RAG
- What You Learned
- Parsing Opt-Out Text
- Summary of What You Will Learn
- The Goal
- What You Have Learned
- - Web Scraping and Building an Events Database
- Summary of What You Will Learn
- Big Picture View
- Orchestration
- Deep Dive
- What You’ve Learned
- [COMPLETE]Agentic Workflows - A Practical Approach
- What You’ll Learn
- Intro
- Let’s Take It One Agent at a Time
- Back on the chain nth times or fail
- Web Search Tool (Recipe)
- What You Will Learn
- The Prompt
- Summary
- Marketing Assistant and Other Automations
- What you will learn in this chapter
- The Model Structures and the Reasoning
- Starting the Project
- Scheduler
- Automations aka Agents
- Email to Google Sheets
- Step Three: Automation with Feedback Flow
- Hand Written Notes to Task List (Recipe)
- What You Will Learn
- The Prompt
- The Tools
- What You’ve Learned
- SiteMap - Feed to RAG (Recipe)
- What You’ll Learn
- The Process
- Chat
- Size Considerations
- Standards Checking Tool
- What You Will Learn
- Intro
- Process Overview
- Step one: Breaking up the PDF Page
- Review Report Page Job
- Finalize ReportJob
- Limitations
- Summary
- Final Thoughts
- Resources
- Deeplearning.ai
- HuggingFace
- Troubleshooting Tips and Tricks
- Saving JSON Messages and Payload
- Alphabetically Sorted Terminology
- API (Application Programming Interface)
- Agnostic Tools
- Assistant
- Automation
- Batch Jobs
- Batch Processing
- Chat
- CI (Continuous Integration)
- Completion
- CompletionResponse
- Context Window
- Cron jobs
- Data Transfer Object (DTO)
- Driver Pattern
- Embedding
- Facade Pattern
- Fine-tuning
- First-Shot Prompting
- Fixture
- FunctionResponse
- GIS (Geographic Information System)
- Groq
- Hallucination
- HTTP Facade
- HTTP Pooling
- Inference
- LangChain
- Laravel
- Laravel Dependency Injection
- LLM (Large Language Model)
- LLM Agnostic Driver
- LLM Router
- Machine Learning
- Message Thread
- Mixture of Agents
- Mock
- Mock Driver
- Multi-shot Prompting
- Multi-shot Strategy
- NativePHP
- Ollama
- Orchestrate Class
- Placeholder
- Prism
- Process Facade
- Prompt
- Prompt Engineering
- Prompt Injection
- RAG (Retrieval-Augmented Generation)
- Real-Time Facades
- Round-Robin
- Round-Robin
- Scheduling Prompt
- Sentiment Analysis
- System Prompt
- Task Scheduling
- Temperature
- Templatizer
- Throttling
- TLDR
- Token
- Tool (in LLM context)
- Tool Schema
- LLM (Large Language Model)
- Tool Chaining
- Tool Calling
- Tool Driven Design (TDD)
- Tool
- Tool Invocation
- User Prompt
- Introduction - Embracing the Reality of LLMs in Development
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