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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:
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.
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