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https://github.com/ezimuel/php-llm-examples
PHP examples about the usage of GenAI and LLM
https://github.com/ezimuel/php-llm-examples
genai llama3 llm ollama openai-api php
Last synced: about 1 month ago
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PHP examples about the usage of GenAI and LLM
- Host: GitHub
- URL: https://github.com/ezimuel/php-llm-examples
- Owner: ezimuel
- License: mit
- Created: 2024-05-22T08:55:06.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-08-14T17:03:45.000Z (3 months ago)
- Last Synced: 2024-10-10T05:38:52.327Z (about 1 month ago)
- Topics: genai, llama3, llm, ollama, openai-api, php
- Language: PHP
- Homepage:
- Size: 1010 KB
- Stars: 5
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Examples of using LLM in PHP
In this repository, I collected some PHP examples about the usage
of Generative AI and Large Language Model (LLM) in PHP.
For the PHP code, I used [LLPhant](https://github.com/theodo-group/LLPhant) and [openai-php/client](https://github.com/openai-php/client) projects.
For the LLM models I used [OpenAI](https://openai.com/) and [Llama 3](https://llama.meta.com/llama3/).
For semantic search, I used [Elasticsearch](https://github.com/elastic/elasticsearch)
as vector database.
## Configure the environment
To execute the examples you need to set some environment variables:
```bash
export OPENAI_API_KEY=xxx
export ELASTIC_URL=https://yyy
export ELASTIC_API_KEY=zzz
```
If you want to run Llama 3 locally you can install [Ollama](https://ollama.com/)
running the following command (in this case you don't need `OPENAI_API_KEY`):
```bash
ollama pull llama3
```
This will install Llama3 and the model will be available through HTTP API at
`http://localhost:11434/api/`.
If you want to interact with LLama 3 using a chat interface, you can execute
the following command:
```bash
ollama run llama3
```
## Examples
For OpenAI API example usage look at the following scripts:
- [openai_chat](src/openai_chat.php), a simple chat use case;
- [openai_image](src/openai_image.php), generate an image using `dall-e-3` model;
- [openai_speech](src/openai_speech.php), text-to-speech example using `tts-1` model;
- [openai_moderation](src/openai_moderation.php), moderation using `text-moderation-latest` model;
- [openai_function](src/openai_function.php), [function calling](https://platform.openai.com/docs/guides/function-calling) example;
For LLPhant examples you can see the following scripts:
- [llphant_chat](src/llphant_chat.php), a simple chat use case;
- [llphant_tool](src/llphant_tool.php), the function calling tool in LLPhant;
The Retrieval-Augmented Generation examples are in the [src/rag](src/rag/) folder.
I divided the folderusing different embedding models: [ELSER](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-elser.html),
[Llama3](https://llama.meta.com/llama3/) using ollama and GPT-3.5-turbo by [OpenAI](https://openai.com/).
For the RAG examples I used a simple PDF document that contains the [AI act](data/AI_act.pdf)
regulation proposed by the European Union in July 2023.
This document is not part of the knowledge of `GPT-3.5-turbo` that is fixed to 2022.
In the examples we store the document in the vector database (Elasticsearch) using the
`embedding.php` script, than we use the `qa.php` to ask for the question "What is the AI act?".
Using the RAG architecture we can expand the knowledge of the LLM, without fine-tuning the model,
providing also the sources (chunks) used to answer the question.
## Copyright
Copyright (C) 2024 by Enrico Zimuel