Welcome to the RAG with Gemini tutorial! In this project, we'll guide you through the process of creating a RAG (Retrieval-Augmented Generation) application using the Gemini-Pro model. The best part? You can achieve all this with the free version of the API.
This tutorial is divided into two main sections, each designed to enhance your understanding and skills in working with Gemini.
In the initial section, we will delve into a comprehensive notebook demonstrating the utilization of ChromaDB as a vector database. By following along, you'll learn how to:
- Extract data from JSON or PDF files.
- Store the vector representation of data in ChromaDB.
- Utilize the embedding model to embed data chunks.
- Efficiently split large texts into manageable chunks using LangChain.
The second part is all about interaction. We'll guide you on building a visually appealing interface that facilitates the interaction with the Gemini model. Throughout this section, you'll gain insights into:
- Creating a user-friendly interface using Chainlit.
By the end of this tutorial, you'll have a solid understanding of the following:
- Data extraction from various sources.
- Vector representation and storage using ChromaDB.
- Efficient text handling with LangChain.
- Interface development with Chainlit.
To get started with the tutorial, make sure you have the following Python libraries installed:
- pandas
- PyPDF2
- chromadb
- chainlit
- python-dotenv
- google-generativeai==0.3.2
Installed them directly from the requirements.txt file by running the following command
pip install -r requirements.txtBefore you dive into the tutorial, obtain your API key from Google Maker Suite.
Follow the step-by-step instructions in the provided notebook to create your own RAG application with Gemini. If you have any questions or need assistance, don't hesitate to reach out by leaving a comment on YouTube.
Happy coding!