Welcome to the GenAI Tutorial repository! In this repository, you'll find tutorials on how to use OpenAI's Generative Artificial Intelligence Language Model (GenAI LLM) to generate text.

This repository contains the following self-contained tutorials:

1. OpenAI Chat: Explore how to create a chatbot using GenAI LLM for various conversational tasks.
2. Resume Chat: Dive into building a chatbot that holds chat memory to be able to resume chats based on user input.
3. Chroma QA Chat: Learn how to build a question-answering chatbot using GenAI LLM and documents stored in a Chroma vector DB.
4. RAG Chat: Understand how to use Retrieval-Augmented Generation (RAG) with GenAI LLM for enhanced text generation.

Before you begin, make sure you have the following installed:

• Python 3
• OpenAI Python Library (openai)
• Literal API key (literalai)

To use the GenAI LLM, you'll need an OpenAI API key. If you don't have one, you can sign up for the API access here.

To use the GenAI LLM, you'll need an LiteralAI API key. If you don't have one, you can sign up for the API access here.

Clone this repository to your local machine:

git clone https://github.com/your-username/genai-tutorial.git
cd genai-tutorial

1. User can easily interact with OpenAI.
2. User can enter questions.
3. Question is passed to OpenAI.

1. User initiates a chat with the chatbot.
2. User can enter questions or statements.
3. The chatbot processes the input and stores it in the chat memory.
4. The chatbot generates a response based on the user's input and the chat memory.
5. If the user resumes the chat after a break, the chatbot retrieves the chat memory and continues the conversation based on the previous context.

1. User initiates a chat with the Chroma QA chatbot.
2. User can ask questions related to a specific topic or domain.
3. The chatbot processes the question and retrieves relevant information from its knowledge base.
4. The chatbot uses the GenAI LLM to generate a coherent and accurate answer based on the retrieved information.
5. The chatbot presents the answer to the user.
6. The user can continue to ask more questions, and the chatbot will continue to provide answers based on the GenAI LLM and its knowledge base.

1. User initiates a chat with the RAG chatbot.
2. User can ask questions or make statements.
3. The chatbot processes the input and uses the Retrieval-Augmented Generation (RAG) technique to retrieve relevant information from its knowledge base.
4. The chatbot uses the GenAI LLM to generate a coherent and accurate response based on the retrieved information and the RAG technique.
5. The chatbot presents the response to the user.
6. The user can continue to interact with the chatbot, and the chatbot will continue to provide responses using the RAG technique and the GenAI LLM.

For more information and advanced usage of GenAI LLM, refer to the following resources:

• OpenAI Documentation
• OpenAI API Reference
• OpenAI GitHub Repository

If you encounter any issues or have suggestions for improvement, feel free to open an issue or submit a pull request in this repository.

Happy generating! 🎉

Feel free to use this updated version in your repository! Let me know if there's anything else you need.