Generative AI Full Course – Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More

Introduction

This tutorial provides a comprehensive guide to the concepts and tools related to Generative AI, as explored in the full course hosted by freeCodeCamp. It covers essential frameworks such as OpenAI, LangChain, and Pinecone, and provides insights into various projects, including building a medical chatbot and understanding vector databases. Whether you're a beginner or looking to enhance your skills, this guide will help you navigate the world of Generative AI.

Step 1: Understanding Generative AI

• Familiarize yourself with the basic concepts of Generative AI.
• Explore how AI models can generate text and visual material.
• Recognize the significance of generative models in various applications.

Step 2: Introduction to OpenAI and the OpenAI API

• Learn about OpenAI and its suite of tools.
• Set up an account on the OpenAI platform.
• Access the OpenAI API by following these steps:
  1. Sign up for API access.
  2. Retrieve your API key from the OpenAI dashboard.
  3. Test the API using a simple command in your programming environment.

Step 3: Exploring LangChain

• Understand the purpose of LangChain in building applications.
• Install LangChain with the following command:

  pip install langchain
  

• Experiment with basic LangChain functionalities to manage interactions with language models.

Step 4: Integrating Hugging Face API with LangChain

• Learn about Hugging Face and its role in NLP.
• Set up the Hugging Face API:
  1. Create an account on Hugging Face.
  2. Generate an access token.
• Integrate Hugging Face with LangChain to leverage pre-trained models.

Step 5: Implementing Memory in LangChain

• Discover how memory can enhance the capabilities of LangChain applications.
• Implement memory features in your project to maintain context over interactions.

Step 6: Building an LLM Generative AI Project

• Combine OpenAI and LangChain to create a Generative AI project.
• Follow these steps:
  1. Define the project scope and requirements.
  2. Write code to integrate OpenAI's API with LangChain.
  3. Test and refine your project.

Step 7: Deploying Your LLM Project

• Learn the deployment process to make your project accessible.
• Choose a hosting platform (e.g., Heroku, AWS).
• Follow the platform-specific instructions to deploy your application.

Step 8: Introduction to Vector Databases

• Understand the role of vector databases in AI applications.
• Recognize the advantages of using vector databases for storing and retrieving data.

Step 9: Mastering Pinecone Vector Database

• Set up a Pinecone account and create a new project.
• Integrate Pinecone with your application:
  1. Install the Pinecone client:

     pip install pinecone-client
     

  2. Use the client to create vectors and perform queries.

Step 10: Mastering ChromaDB Vector Databases

• Explore the features of ChromaDB as an alternative vector database.
• Set up ChromaDB and integrate it into your projects similarly to Pinecone.

Step 11: Introducing Meta Llama 2

• Get acquainted with the Meta Llama 2 model and its capabilities.
• Analyze its applications in Generative AI and how to implement it in your projects.

Step 12: Building an End-to-End Medical Chatbot

• Understand the requirements for creating a medical chatbot.
• Develop the chatbot in two parts:
  1. Part 1: Design the chatbot's architecture and implement core functionalities.
  2. Part 2: Enhance the chatbot with additional features like user interaction and data handling.

Step 13: Utilizing Google Gemini Pro

• Learn about the Google Gemini Pro LLM and its unique features.
• Develop an end-to-end Generative AI project using Google Gemini Pro.

Conclusion

This tutorial has outlined the essential steps to explore and implement Generative AI concepts, tools, and projects. You now have a foundational understanding of frameworks like OpenAI, LangChain, and various vector databases. As a next step, consider diving deeper into specific projects or applications that interest you, and continue to experiment with the tools provided in this course. Happy coding!