AI Examples

A collection of AI implementation examples and tools for various use cases, from flight simulation assistants to local LLM interactions.

Overview

This repository contains practical examples of AI integration in different contexts, demonstrating how to use various AI technologies, including:

• Voice assistants using OpenAI GPT and VOSK
• Local LLM interactions with Ollama models
• Document retrieval systems using embedding and vector search
• AI-powered terminal command generation and interpretation
• Semantic search implementations
• Flight Simulator (MSFS) AI assistant

Examples Included

FlightGPT (MSFS Assistant)

A voice-controlled AI assistant for Microsoft Flight Simulator that provides real-time flight data through natural language conversations.

Local LLM Interactions

Tools for interacting with locally hosted language models via Ollama:

• Command generation from natural language descriptions
• Error explanation and troubleshooting
• Prompt-based interactions with local models

RAG (Retrieval Augmented Generation)

Examples of document retrieval systems that:

• Load documents from directories
• Create embeddings and vector stores
• Support history-aware retrieval chains
• Implement question-answering capabilities

Semantic Search

Implementation of semantic search using embeddings to find relevant content based on meaning rather than exact match.

Requirements

Core dependencies:

• Python 3.8+
• OpenAI API (for some examples)
• Ollama (for local LLM examples)
• VOSK (for speech recognition)
• Additional libraries in reqs.txt

Installation

1. Clone this repository:

   git clone https://github.com/yourusername/ai-examples.git
   cd ai-examples

2. Install required packages:

   pip install -r reqs.txt

3. For speech recognition, download a VOSK model from https://alphacephei.com/vosk/models and place it in the vosk folder

4. For examples using OpenAI, set up your API key:

   export OPENAI_API_KEY=your_api_key
   export OPENAI_ORG=your_organization_id
   export OPENAI_PROJ=your_project_id

5. For local LLM examples, ensure Ollama is installed and running

Usage Examples

FlightGPT (MSFS Assistant)

python app.py

Semantic Search

python semantic_search.py

Local LLM Command Generation

python home/bin/ollama_prompt.py run "list all Python files in the current directory"

Local LLM Prompt

python home/bin/ollama_prompt.py prompt "Explain how vector embeddings work"

Local LLM Error Explanation

python home/bin/ollama_prompt.py error "pip install tensorflow" "ERROR: Could not find a version that satisfies the requirement tensorflow"

Project Structure

• app.py: FlightGPT application entry point
• semantic_search.py: Example of semantic search implementation
• lib/: Core libraries and modules
  • modules/: Basic functionality modules
    • ai.py: AI interaction wrapper for OpenAI
    • audio.py: Voice recognition with VOSK
    • msfs.py: Microsoft Flight Simulator interface
    • speech.py: Text-to-speech utilities
  • mcp/: Model Context Protocol implementation
    • actions.py: Terminal command generation and execution
    • transport.py: MCP message handling
  • chat/: Chat implementations
    • openai.py: OpenAI-based chat
    • history_ollama.py: Chat with history using Ollama
  • storage/: Data storage and retrieval
    • docs.py: Document storage and RAG implementation
  • backend/: Backend services
    • run.py: Command execution API
• home/bin/: Utility scripts
  • ollama_prompt.py: CLI for interacting with Ollama

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.