RAG Chatbot with Python(Environment Setup)

#python #streamlit #groq #llama

Environment Setup for Building a RAG Chatbot with Python, Streamlit, Groq, LLaMA, FAISS, and VS Code

This guide provides step-by-step instructions to set up a development environment for building a Retrieval-Augmented Generation (RAG) chatbot using Python, Streamlit, Groq with LLaMA, FAISS, and Visual Studio Code (VS Code). The instructions are tailored for beginners and assume you are starting from scratch on a Windows, macOS, or Linux system.

Prerequisites

Before you begin, ensure you have the following:

A computer with Windows, macOS, or Linux.
An internet connection to download tools and libraries.
A free Groq API key for accessing LLaMA models.
A GitHub account

Step 1: Install Python

Streamlit and other required libraries need Python 3.7 or higher. Follow these steps to install Python:

1.Download Python:

Visit the official Python website and download the latest version (e.g., Python 3.10 or higher).

Choose the installer for your operating system (Windows, macOS, or Linux).

2.Install Python:

Windows: Run the installer. Check the box to "Add Python to PATH" during installation.

macOS/Linux: Use the installer or a package manager like Homebrew (brew install python) on macOS or apt/yum on Linux.

Follow the prompts to complete the installation.

python --version

You should see the installed Python version (e.g., Python 3.10.12). If not, ensure Python is added to your system's PATH.

Step 2: Install Visual Studio Code

VS Code is a lightweight, powerful IDE for Python development with excellent Streamlit support.

1.Download VS Code:

Go to the VS Code website and download the installer for your operating system.

2.Install VS Code:

Run the installer and follow the prompts to install.

Open VS Code after installation to confirm it works.

3.Install Python Extension:

In VS Code, go to the Extensions view.

Search for "Python" and install the official Python extension by Microsoft.

This extension provides syntax highlighting, debugging, and environment management for Python.

Step 3: Set Up a virtual environment with uv

A virtual environment isolates project dependencies to avoid conflicts.

uv init chatbot(You can choose a different name to use in place of the chatbot)

Step 4: Install Required Libraries

With the virtual environment active, install the necessary Python libraries using pip.

uv add streamlit groq faiss-cpu sentence-transformers PyPDF2

Step 5: Configure the Groq API Key

To use LLaMA models via Groq, you need an API key.

1.Obtain a Groq API Key:

Copy the key and keep it secure.

2.Set Up a .env
Create a .env file in your root directory

In your .env file, have GROQ_API_KEY=your-groq-api-key
NB: Leave no whitespaces in your .env file else API keys might not work as expected.

Add .env to your .gitignore file to avoid exposing the key if using version control.

Step 8: Next Steps

With your environment set up, you can start building the RAG chatbot:

Document Ingestion: Use pypdf to load documents and sentence-transformers to create embeddings.

Vector Store: Use FAISS to store and search document embeddings.

LLM Integration: Use with Groq to query LLaMA models for generating responses.

Streamlit UI: Build an interactive chat interface with Streamlit.

Deployment: Deploy your app to Streamlit Community Cloud by following the

Additional Resources

Streamlit Documentation for building web apps.
FAISS Documentation for vector store setup.
Groq Documentation for LLaMA model access.
VS Code Python Tutorial for IDE setup.

This setup provides a foundation for building a RAG chatbot. You can now proceed to implement document ingestion, vector search, and LLM-powered responses, all within an interactive Streamlit interface in the next session. Happy coding!