JARVIS

An open-source, multi-phase project to build a modular, "Jarvis-like" AI assistant. This system is designed to be scalable and eventually integrate advanced reasoning, persistent memory, full speech interaction, vision, and OS-level automation.

Core Vision

The goal is to create a powerful, general-purpose assistant, not as a single monolithic application, but as a system of interconnected, intelligent modules. This modularity allows for focused development and easy scalability.

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Project Roadmap

The project is broken down into several distinct phases. We are currently in Phase 1.

✅ Phase 1: The Core (Brain, Speech & Memory) (Current Focus)

Goal: Build a fully interactive, voice-enabled assistant with a persistent memory.

This foundational phase integrates the core intelligence with the primary user interface (voice) and memory systems.

• Brain: Using a Mistral LLM (via API) for reasoning, generation, and decision-making.
• Speech-to-Text (STT): An integrated module to transcribe user voice commands into text for the Brain.
• Text-to-Speech (TTS): A module to give the assistant a voice, converting the Brain's text responses back into audio.
• Memory System:
  • Short-Term Memory: A conversation buffer for contextual follow-ups.
  • Long-Term Memory: A ChromaDB vector database to store and recall facts, user preferences, and long-term information.
• Orchestration: Using LangChain to create a unified pipeline (STT -> Brain -> Memory -> TTS).

➡️ Phase 2: Vision (Image & Video)

Goal: Allow the assistant to see and understand the visual world.

• Multimodal Models: Integrate a model (e.g., LLaVA) to process and reason about images and videos.
• Integration: Connect the vision module to the Phase 1 Brain, allowing for questions like, "What do you see in this picture?"

➡️ Phase 3: OS Implementation & Automation

Goal: Enable the assistant to take action and perform tasks on the user's behalf.

• Tooling: Develop a "control" module with tools for OS-level automation (e.g., file management, opening applications, web browsing).
• Agentic Behavior: Evolve the Brain into a true agent that can use these tools to fulfill complex, multi-step requests (e.g., "Find the report from last week, summarize it, and email it to my boss").

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🛠️ Technology Stack (Phase 1)

• Language: Python
• Core LLM: Mistral (via API)
• Orchestration: LangChain
• Vector Database: ChromaDB
• API Server: FastAPI (or Flask)
• Speech-to-Text: [e.g., Whisper, Google Speech-to-Text]
• Text-to-Speech: [e.g., gTTS, ElevenLabs, pyttsx3]

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🏁 Getting Started

This project is in active development.

1. Clone the repository:

   git clone [https://github.com/](https://github.com/)[YOUR_USERNAME]/[YOUR_REPO].git
   cd [YOUR_REPO]

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables: Create a .env file and add your API keys and configuration (e.g., MISTRAL_API_KEY).

4. Run the application: (Instructions to run your main app.py or main.py, e.g., using uvicorn)

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