MAIA

MAIA (Memory-Augmented Intelligent Assistant) implements prompt chaining with enhanced long-term recall capabilities for LLM-powered intelligent assistants.

Requirements

• Python 3.9
• CUDA 11.7
• PyTorch
• Transformers
• Additional dependencies listed in environment.yml or requirements.txt

Installation

Install dependencies using either conda:

conda env create --file environment.yml

Or pip:

pip install -r requirements.txt

Configuration

Create a .env file with the following environment variables:

SSL_CERT_PATH={Path to SSL fullchain}
SSL_KEY_PATH={Path to SSL privkey}
OPENAI_API_KEY={OpenAI API Key}
PALM_API_KEY={PaLM API Key}
GOOGLE_TTS_API_KEY={Google Cloud Text-to-Speech API Key}

Implementation

MAIA's core framework is implemented in the MAIAPrompter class (conversation/prompter.py). Key features include:

• Context extraction using pretrained transformer attention
• Short-term and long-term memory management
• Multi-step reasoning with dynamic prompt generation
• DPR-based memory retrieval

Usage

Web Interface

Launch the Gradio-based web interface for voice and text interactions:

python run_gradio.py [--server_name SERVER_NAME] [--server_port PORT] [--share]

Optional arguments:

• --server_name: Specify server name (default: 0.0.0.0)
• --server_port: Specify port number
• --share: Enable Gradio's share feature

Console Interface

Start text-based interactions in console:

python app.py

Architecture

MAIA consists of four main components:

1. Context Extraction: Pretrained transformer attention mechanism
2. Memory Module: DPR-based retrieval with STM/LTM
3. Prompt Generation: Dynamic multi-step reasoning chain
4. Response Generation: Template-based response synthesis

License

MIT License - see LICENSE file for details