Generative AI and Large Language Model (LLM) in PHP
summary
Shows a working PHP pipeline that takes a PDF, splits it into 56 chunks, generates embeddings via OpenAI, indexes them into OpenSearch/Elasticsearch, and answers queries with a retrieval‑augmented generation flow using the LLPhant project and local examples including GPT4All; demo returns the extracted source chunks and the generated answer. Also cites scale numbers (GPT‑4 ~1.76 trillion parameters, large GPU fleet and multimillion dollar training costs) to justify when to use local models versus cloud APIs.
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chapters (11)
- 0:00Introduction to generative AI
- 2:30Deep learning and neural networks
- 5:00Mathematical foundations of AI
- 10:05Large language models explained
- 14:47GPT architecture and scale
- 16:40Prompt engineering and parameters
- 22:11Advanced prompting strategies
- 26:44Implementing AI in PHP
- 31:09Local AI models with Ollama
- 34:54Retrieval Augmented Generation
- 43:00Live RAG implementation demo
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