README

🧠 Local ChatGPT using Llama 3.1 with Personal Memory

This Streamlit application implements a fully local ChatGPT-like experience using Llama 3.1, featuring personalized memory storage for each user. All components, including the language model, embeddings, and vector store, run locally without requiring external API keys.

Features

• Fully local implementation with no external API dependencies
• Powered by Llama 3.1 via Ollama
• Personal memory space for each user
• Local embedding generation using Nomic Embed
• Vector storage with Qdrant

How to get Started?

1. Clone the GitHub repository

git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd awesome-llm-apps/llm_apps_with_memory_tutorials/local_chatgpt_with_memory

2. Install the required dependencies:

cd awesome-llm-apps/rag_tutorials/local_rag_agent
pip install -r requirements.txt

3. Install and start Qdrant vector database locally

docker pull qdrant/qdrant
docker run -p 6333:6333 qdrant/qdrant

4. Install Ollama and pull Llama 3.1

ollama pull llama3.1

5. Run the Streamlit App

streamlit run local_chatgpt_memory.py