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

Clone the repository and install dependencies:
```
pip install -r requirements.txt
```

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