Crafting Conversational AI: Unleashing RAG-Powered Python Chat Bots with Gemini, Ollama, and Streamlit!

Welcome to Python chat bots powered by RAG (Retrieval Augmented Generation)! 🐍 In this project, we harness the capabilities of Gemini, Ollama, and Streamlit to create an intelligent and entertaining chat bot.

Discover the art of crafting intelligent Python chat bots with RAG-powered technology using Gemini, Ollama, and Streamlit. Explore advanced features like LangChain integration for enhanced retrieval and generation, and follow a step-by-step guide to contribute via GitHub. Unleash the potential of conversational AI with this comprehensive and engaging approach.

Key Features:

• Gemini Brilliance: Explore the cutting-edge capabilities of Gemini in enhancing the bot's retrieval and generation mechanisms.

• Ollama Charm: Experience the conversational charm brought to the bot by Ollama, making interactions smoother and more engaging.

• Sleek Streamlit Interface: Navigate through a sleek and user-friendly interface powered by Streamlit, providing a seamless user experience.

Creating a GitHub Pull Request: A Step-by-Step Guide

To get a pull request from the GitHub repository at https://github.com/giriallu/gemini-RAG-chatbot, follow these steps:

1. Fork the Repository: Go to the repository page and click on the "Fork" button at the top right corner to create a copy of the repository under your GitHub account.

2. Clone the Forked Repository: Clone the forked repository to your local machine using the command:

    git clone https://github.com/your-username/gemini-RAG-chatbot.git
   

   Replace your-username with your GitHub username.

3. Create a New Branch: Navigate to the cloned repository directory and create a new branch for your changes:

    git checkout -b your-branch-name
   

4. Make Changes: Implement the changes or additions you want to propose.

5. Commit Changes: After making changes, commit them with a descriptive message:

    git add .
    git commit -m "Description of changes"
   

6. Push Changes: Push your changes to your forked repository on GitHub:

    git push origin your-branch-name
   

7. Create a Pull Request: Go to the original repository on GitHub, and you should see a prompt to create a pull request from your branch. Click on "Compare & pull request" and provide a description of your changes.

8. Submit the Pull Request: Review your changes and submit the pull request for the repository maintainers to review.

Secret Sauce – LangChain Integration:

Uncover the magic behind the scenes with LangChain integration, adding a unique layer of functionality to elevate your chat bot.

1. Install Dependencies:

    pip install -r requirements.txt
   

   OR

    pip3 install -r requirements.txt
   

2. Run the Code: to webscrape the url and download docs into docs folder

python3  download_docs.py

3. update GOOGLE_API_KEY in .env file

4. ** update persistant directory in code for chroma db (RAG_Working) persist_directory:"XXX"

5. Run the Code:

streamlit run RAG_Working.py

5 steps about this project

1. Website Scraping: This program scrapes websites and stores the data into the file system using Beautiful Soup.

2. Document Loading: In the Retrieval Augmented Generation (RAG) framework, a language model retrieves contextual documents from an external dataset as part of its execution. PyPDF loaders are used to load PDFs, and TextLoader is used for text.

3. Document Splitting: Document splitting is required to divide documents into smaller chunks. This occurs after loading data into a standardized document format but before it goes into the vector store. The input text is split based on a defined chunk size with some overlap. Chunk size is a length function to measure the size of the chunk, often in characters or tokens.

4. Vector Store and Embeddings: After splitting the document into small chunks, these chunks are put into an index for easy retrieval when answering questions. Embeddings and vector stores are used for this purpose. Embeddings create a numerical representation of the text, allowing semantically similar content to have similar vectors in embedding space. This enables comparison of embeddings to find similar texts.

5. Retrieval and Question Answering: Retrieval is the centerpiece of the RAG flow, addressing the challenge of question-answering over documents. Advanced retrieval mechanisms in LangChain, such as Self-query and Contextual Compression, are discussed. Retrieval is crucial at query time to find the most relevant splits. The retrieved documents and the original question are then passed to a language model to answer the question.

In conclusion, crafting a conversational AI using RAG-powered Python chat bots with Gemini, Ollama, and Streamlit offers a dynamic and engaging user experience. By integrating advanced technologies like LangChain, the project not only enhances the bot's retrieval and generation capabilities but also ensures smooth and interactive conversations. The step-by-step guide for creating a GitHub pull request and the detailed explanation of the project's components provide a comprehensive understanding for developers. This innovative approach to chat bot development showcases the potential of combining cutting-edge tools and frameworks to create intelligent and responsive AI systems.