LLM OpenAI Notebook

This repository contains Jupyter notebooks to explore and utilize OpenAI's Large Language Models (LLMs) for various applications, including chatbots, retrieval-augmented generation, text generation, prompt engineering, and vector embedding. These notebooks provide a comprehensive toolkit for working with OpenAI models in diverse contexts.

Repository Structure

• OPENAI-CHAT.ipynb: Demonstrates the setup of a chatbot using OpenAI models, focusing on conversational interactions and response generation.
• OPENAI-RAG.ipynb: Implements Retrieval-Augmented Generation (RAG), combining retrieval of relevant data with OpenAI model responses for context-aware answers.
• OPENAI-TEXTGEN.ipynb: Focuses on text generation using OpenAI models, suitable for creative writing, content creation, and informative text outputs.
• OPENAI_PROMPTING.ipynb: Provides methods and techniques for effective prompt engineering, demonstrating how to optimize prompts to guide model behavior.
• OPENAI_REFERENCE_RAG.ipynb: An advanced notebook on Retrieval-Augmented Generation that includes reference material integration for highly accurate responses.
• OPENAI_VECTOR_EMB.ipynb: Explores vector embeddings with OpenAI models, showcasing how to use embeddings for similarity search, clustering, and other applications in natural language processing.

Getting Started

Prerequisites

To run these notebooks, you will need:

• Python 3.8+
• Jupyter Notebook
• Dependencies listed in requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/simonpierreboucher/llm_openai_notebook.git
   cd llm_openai_notebook

2. Install the dependencies:

   pip install -r requirements.txt

Running the Notebooks

1. Start Jupyter Notebook: Open Jupyter by navigating to the repository folder and running:

   jupyter notebook

2. Select a Notebook: Open any of the notebooks to explore functionalities such as chat, RAG, or text generation.
3. Follow Instructions: Each notebook contains instructions and steps for interacting with OpenAI models in the respective application.

Use Cases

• Chatbot Development: With OPENAI-CHAT.ipynb and OPENAI_PROMPTING.ipynb, you can create and optimize a conversational agent.
• Information Retrieval: Use OPENAI-RAG.ipynb and OPENAI_REFERENCE_RAG.ipynb for applications that require accurate, source-grounded responses.
• Content Creation: OPENAI-TEXTGEN.ipynb provides tools for generating creative or informational content.
• Embedding and Similarity Search: OPENAI_VECTOR_EMB.ipynb is ideal for NLP tasks involving similarity matching, clustering, and more.

Contributing

We welcome contributions! Feel free to submit issues or pull requests to enhance the functionality, add features, or fix bugs.

License

This repository is licensed under the MIT License.