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Jarvison

Assistant AI Local

wilsto/JarvisOne00

Molt Pulse

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README.md
<!-- markdownlint-disable MD029 -->

JarvisOne: Your Modular Personal AI Assistant

JarvisOne is a modular and scalable conversational AI assistant that integrates with external tools, notably for file searching. Designed to be flexible and customizable, JarvisOne allows you to interact with different LLMs and manage your files effectively.

Key Features ✨

  • Modular Architecture: Built for easy integration of new features and external tools.
  • File Search: Powerful file searching capability via integration with Everything.
  • Multiple LLMs: Supports various Language Models, including Gemini 2.0, ChatGPT-4, Ollama and Anthropic.
  • Workspace Support: Multiple workspaces each with unique system prompt.
  • RAG Integration: Enhanced context-aware responses using document retrieval and embeddings.
  • Intuitive Chat Interface: User-friendly chat interface built with Streamlit.
  • Flexible Configuration: Easy configuration of LLM providers, external tools, and system prompts for different use cases.

Features

  • 🤖 Multiple LLM Support (Gemini 2.0, ChatGPT-4, Ollama, Anthropic)
  • 🎯 Focused System Prompts
  • 🗂️ Workspace Management & 🎭 Specialized Roles:
    • 👨‍💻 Dev: Code Assistant & Project Management
    • 👔 Work: Professional Communication & Analysis
    • 🎓 Coaching: Personal Development & Learning
    • 🏠 Personal: Daily Tasks & Organization
  • 🔍 RAG-Enhanced Contextual Understanding
  • 📝 Document Processing:
    • Text Files (TXT, JSON, MD)
    • Office Documents (PDF, DOCX, XLSX, PPTX)
    • E-Books (EPUB)

Installation 🚀

  1. Clone the repository:

    git clone https://github.com/your-username/JarvisOne.git
cd JarvisOne

  • Install dependencies:

    pip install -r requirements.txt

  • Configure your environment:

    • Copy .env.example to .env.
    • Add your API keys and paths to external tools to the .env file as needed.
    • Make sure to setup your config/config.yaml to your specifications.
    • Configure the vector database path in config.yaml for RAG functionality.

  • Install required external tools:

    • For file searching: Download and install Everything.
    • Ensure that external tools are correctly configured and running.

    • Usage 💬

    1. Launch the application:

      streamlit run src/main.py

    2. In the interface:

      • Select your preferred LLM provider and workspace from the sidebar.
      • Use the chat input to interact with JarvisOne.
      • Access external tool functionalities via natural language in the chat.
      • Process documents for enhanced context-aware responses using RAG.

    RAG Features 🔍

    • Document Processing: Automatically processes and indexes documents for context-aware responses.
    • Workspace Isolation: Each workspace maintains its own document collection.
    • Importance Levels: Documents can be classified by importance (High/Medium/Low).
    • Asynchronous Processing: Non-blocking document processing for optimal performance.

    Usage Examples 💡

    • "Find markdown files modified today" (using Everything).
    • "Search for Word documents containing 'report'" (using Everything).
    • "Explain the prompt system"
    • "Process documents for RAG"

    Contribution Guidelines 🤝

    Contributions are welcome! Here’s how you can contribute:

    1. Fork the project.
    2. Create a new branch: git checkout -b feature/YourFeature.
    3. Commit your changes: git commit -m 'Add YourFeature'.
    4. Push to the branch: git push origin feature/YourFeature.
    5. Open a pull request.

    Ecosystem Role

    Standard MoltPulse indexed agent.