readme.md

metadata

title: DART-LLM_Task_Decomposer
app_file: gradio_llm_interface.py
sdk: gradio
sdk_version: 5.38.0

QA_LLM_Module

QA_LLM_Module is a core component of the DART-LLM (Dependency-Aware Multi-Robot Task Decomposition and Execution) system. It provides an intelligent interface for parsing natural language instructions into structured, dependency-aware task sequences for multi-robot systems. The module supports multiple LLM providers including OpenAI GPT, Anthropic Claude, and LLaMA models, enabling sophisticated task decomposition with explicit handling of dependencies between subtasks.

Features

• Multiple LLM Support: Compatible with OpenAI GPT-4, GPT-3.5-turbo, Anthropic Claude, and LLaMA models
• Dependency-Aware Task Decomposition: Breaks down complex tasks into subtasks with explicit dependencies
• Structured Output Format: Generates standardized JSON output for consistent task parsing
• Real-time Processing: Supports real-time task decomposition and execution

Installation

Installation

You can choose one of the following ways to set up the module:

Option 1: Install Dependencies Directly (Recommended for Linux/Mac)

1. Clone the repository and install dependencies:

   pip install -r requirements.txt
   

2. Configure API keys in environment variables:

   export OPENAI_API_KEY="your_openai_key"
   export ANTHROPIC_API_KEY="your_anthropic_key" 
   export GROQ_API_KEY="your_groq_key"
   

Option 2: Use Docker (Recommended for Windows)

For Windows users, it is recommended to use the pre-configured Docker environment to avoid compatibility issues.

1. Clone the Docker repository:

   git clone https://github.com/wyd0817/DART_LLM_Docker.git
   

2. Follow the instructions in the DART_LLM_Docker repository to build and run the container.

3. Configure API keys in environment variables:

   export OPENAI_API_KEY="your_openai_key"
   export ANTHROPIC_API_KEY="your_anthropic_key" 
   export GROQ_API_KEY="your_groq_key"
   

Usage

Example Usage:

Run the main interface with Gradio:

gradio main.py

Output Format:

The module processes natural language instructions into structured JSON output following this format:

{
    "instruction_function": {
        "name": "<breakdown function 1>",
        "dependencies": ["<dep 1>", "<dep 2>", "...", "<dep n>"]
    },
    "object_keywords": ["<key 1>", "<key 2>", "...", "<key n>"],
    ...
}

Configuration

The module can be configured through:

• config.py: Model settings and API configurations
• prompts/: Directory containing prompt templates
• llm_request_handler.py: Core logic for handling LLM requests