Local AI - Assistant

Guide: Building an Offline AI Assistant with Mistral and ChromaDB

Github: https://github.com/streamlinecoreinitiative/LocalAIAssistant

Demo video: Coming soon

Introduction

This guide provides a step-by-step approach to setting up an offline AI assistant using Mistral 7B for text-based interactions and ChromaDB for local memory storage. This solution is designed for low-resource environments where internet access is limited or unavailable.

Why Offline AI?

An offline AI assistant offers several advantages:

• Independence from Internet Access – Can operate in remote areas without requiring a connection.

• Data Privacy – No cloud storage or third-party tracking.

• Cost-Effective – Eliminates the need for expensive online API subscriptions.

• Low Latency – Responses are generated locally without delay.

• Scalability – Can be deployed across multiple machines in different locations.

Minimum System Requirements

The hardware requirements depend on the model size and the expected workload.

Basic Setup (For Small-Scale Use)

• CPU: Intel i5 / Ryzen 5 (Quad-core)

• RAM: 8GB (16GB recommended for better performance)

• Storage: 20GB free (SSD recommended for speed)

• OS: Windows 10/11 or Ubuntu 22.04+ / Debian-based Linux

Optimized Performance (For Faster Responses & Multi-User Support)

• CPU: Intel i7 / Ryzen 7+

• RAM: 16GB – 32GB

• Storage: 50GB free (for expanding memory and logs)

• Optional GPU: NVIDIA GTX 1650+ (for faster AI inference using CUDA)

Step 1: Installing Mistral 7B Locally

Mistral 7B is an efficient open-source AI model. Ollama provides an easy way to install and run Mistral on both Windows and Linux.

Windows Installation

1. Download and install Ollama:

• https://ollama.com/download

2. Open Command Prompt (cmd) and run:

ollama pull mistral

This command downloads and prepares Mistral 7B for offline use.

ollama run mistral

Linux Installation (Ubuntu/Debian)

1. Open a terminal and install necessary dependencies:

sudo apt update && sudo apt install curl -y

2. Install Ollama using the following command:

curl -fsSL https://ollama.com/install.sh | sh

3. Restart the terminal and download Mistral:

ollama pull mistral

4. Run a test to confirm proper installation:

ollama run mistral

Step 2: Setting Up ChromaDB for Memory

ChromaDB enables the AI to store and retrieve previous interactions.

Installation (Windows & Linux)

1. Install Python (if not installed):

• Windows: Download from https://www.python.org/downloads/

• Linux (Ubuntu): Run the following command in terminal:

sudo apt install python3 python3-pip -y

2. Install ChromaDB via pip:

pip install chromadb

3. Create a Python script (memory.py) to manage conversation memory:

Code in Github: https://github.com/streamlinecoreinitiative/LocalAIAssistant/blob/main/memory.py

Step 3: Building the AI Chatbot

Now that Mistral and ChromaDB are installed, the following script will integrate them to create an offline chatbot.

Create offline_ai.py

Code in Github:https://github.com/streamlinecoreinitiative/LocalAIAssistant/blob/main/AIChatbot_with_Memory.py

Step 4: Use Cases

This offline AI chatbot can be applied to various sectors:

1. Education

• Acts as a tutor for students in remote areas.

• Provides automated learning on subjects like math, science, and language.

2. Business Support

• Assists small businesses by managing FAQs and customer interactions.

• Helps in basic inventory tracking (integrated with other tools).

3. Community Assistance

• Can be used in health centers to answer common medical questions.

• Serves as a legal aid bot for basic legal advice.

4. Accessibility

• Assists people with visual impairments by responding via text-to-speech (optional).

• Provides language translation for multilingual communities.

Potential Improvements

• Voice Support: Implement speech recognition and text-to-speech for accessibility.

• Multi-Language AI: Expand to local dialects and multiple languages.

• Offline Web Interface: Use Flask or Tkinter to create a simple graphical user interface.