Contextual AI Assistant Project: Complete End-to-End Proposal

1. Research

State of the Art in Contextual AI

Recent advancements in contextual AI systems have moved beyond traditional RAG (Retrieval-Augmented Generation) to incorporate structured knowledge representation:

1. GraphRAG: Developed by Microsoft Research, this approach represents information as a knowledge graph, enabling complex relational queries. It shows 15-30% improvement over traditional RAG for questions requiring understanding of relationships between entities.
2. Memory Mechanisms: Systems like Grafiti and Mem0 implement sophisticated memory architectures that distinguish between different types of memory (working, episodic, semantic) to better model human-like recall.
3. Temporal Understanding: Recent research emphasizes the importance of temporal relationships in contextual AI, tracking how information and relationships evolve over time.
4. Multi-modal Processing: Advanced systems now integrate text, visual, and structured data into unified knowledge representations.

Key Research Insights for Our System

1. Knowledge graphs outperform vector databases for relational queries
2. Application-specific parsing yields better entity extraction
3. Temporal information is crucial for contextual understanding
4. Hybrid retrieval (combining graph and vector search) provides best results

2. System Design Proposal

High-Level Architecture

┌─────────────────┐     ┌───────────────────┐     ┌─────────────────┐
│ Data Ingestion  │────▶│ Knowledge Building │────▶│ Query Processing│
└─────────────────┘     └───────────────────┘     └─────────────────┘
        │                        │                         │
        ▼                        ▼                         ▼
┌─────────────────┐     ┌───────────────────┐     ┌─────────────────┐
│ App-Specific    │     │ Neo4j Knowledge   │     │ FastAPI Service │
│ Parsers         │     │ Graph             │     │                 │
└─────────────────┘     └───────────────────┘     └─────────────────┘

Detailed Component Design

1. Data Ingestion Pipeline

@app.post("/ingest")
async def ingest_data(screen_data: ScreenData):
    # 1. Identify application type
    app_type = application_detector.identify(screen_data)

    # 2. Apply app-specific parsing
    parser = parser_factory.get_parser(app_type)
    parsed_data = parser.parse(screen_data)

    # 3. Extract entities and relationships
    entities, relationships = entity_extractor.extract(parsed_data, app_type)

    # 4. Update knowledge graph
    graph_builder.update(entities, relationships)

    return {"status": "success"}

2. Knowledge Graph Schema