Contextual AI Assistant Framework: Research & Proposal

(Updated with Mem0 Integration)

1. Introduction

The proposed system aims to create a contextual AI assistant that can understand user activities across applications by continuously ingesting screen data and responding to complex queries that traditional RAG systems struggle with. This document outlines a comprehensive approach using Mem0's hybrid memory architecture.

2. System Architecture

2.1 High-Level Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│                 │    │                 │    │                 │
│  Client Device  │───►│  Ingestion API  │───►│ Processing Layer│
│                 │    │                 │    │                 │
└─────────────────┘    └─────────────────┘    └────────┬────────┘
                                                       │
                                                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│                 │    │                 │    │                 │
│   Query API     │◄───│     LLM Layer   │◄───│   Mem0 Memory   │
│                 │    │                 │    │     System      │
└─────────────────┘    └─────────────────┘    └─────────────────┘

2.2 Core Components

1. Ingestion API (/ingest)
   • Receives screen data every 5 seconds
   • Performs initial parsing and classification
2. Processing Layer
   • Entity extraction
   • Relationship identification
   • Temporal sequence tracking
   • Application context detection
3. Mem0 Memory System
   • Hybrid storage architecture combining:
     • Vector database for semantic search
     • Key-value store for direct lookups
     • Graph database for relationship tracking
   • Multi-level memory management
4. LLM Layer
   • Context-aware query processing
   • Memory augmentation
   • Response generation
5. Query API (/chat_completion)
   • Handles user questions
   • Retrieves relevant memories
   • Returns formatted responses

3. Technical Approach

3.1 Mem0 Integration

Based on research, Mem0 provides an ideal foundation for our system as it:

1. Combines Multiple Storage Types: Integrates vector, key-value, and graph databases in a unified memory layer
2. Supports Multi-Level Memory: Manages user, session, and agent memory with adaptive personalization
3. Provides Self-Improving Memory: Continuously learns from user interactions
4. Offers Simple APIs: Streamlines memory management with straightforward methods