Conversational RAG Application with Ollama and Llama 3.2

Implement RAG using Llama 3.2.ipynb notebook implements a Conversational Retrieval-Augmented Generation (RAG) application using Ollama and the Llama 3.2 model. The application allows for efficient document loading, splitting, embedding, and conversation management. Below are detailed descriptions of the key functions and installation instructions for Ollama. The Streamlit app file: app.py

Installation Instructions for Ollama and Llama 3.2

Follow these steps to install and run Ollama locally:

1. Download Ollama
   Visit the following link to download Ollama:
   Download Ollama

2. Extract the Zip File
   Once downloaded, extract the zip file and locate the Ollama executable.

3. Run Ollama
   After installation, the Ollama icon will appear in your taskbar. This indicates that the Ollama server is up and running, listening on HTTP port 11434 of your localhost. You can confirm the server status by visiting the following URL:
   http://localhost:11434/
   You should see a message indicating that Ollama is running.

4. Open Command Prompt
   Launch a Windows terminal (Command Prompt) and execute the following command to run the Llama-3 model locally:

   ollama run llama3.2

5. Import Ollama in Your Code
   In your Python script, import the Ollama class and create an instance for the Llama-3 model:

   from langchain_community.llms import Ollama
   llm = Ollama(model="llama3.2")