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Restaurant Recommendation System

Architecture documentation for a Restaurant Recommendation System.

Status

🚧 Under Development


v2 Architecture — Knowledge Graph + Graph-RAG + GNN + AI Agent

The v2 architecture is built on FalkorDB (Knowledge Graph), CocoIndex, PyTorch Geometric (GNN), and GPT-4o (AI Agent). It replaces traditional collaborative/content-based filtering with a graph-aware, hallucination-free chatbot experience.

Architecture Docs

Document Description
System Overview 3-flow architecture diagram, component layout, latency analysis
Tech Stack FalkorDB, CocoIndex, PyG, GPT-4o, LangChain — why each was chosen
Data Flow Sequence diagrams for all 3 system flows
Graph-RAG Pipeline Deep dive into the Graph-RAG retrieval and prompt design

Module Docs

Document Description
01 — Data Layer PostgreSQL schemas, CocoIndex CDC pipeline, data quality
02 — Knowledge Graph CRKG design, FalkorDB config, Cypher query patterns
03 — GNN Engine GCN architecture, multi-task loss, training pipeline
04 — AI Agent AI Waiter chatbot, intent classification, LangChain integration
05 — Serving API FastAPI endpoints, caching strategy, WebSocket support
06 — Observability Metrics, ML monitoring, alerting, Grafana dashboards

Schema Docs

Document Description
CRKG Graph Schema Complete node/edge schema for the Knowledge Graph
Recipe Catalog Recipe JSON schema, nutrition facts, NDS formula
User Profile User profile JSON schema, segments, health priority score
Nutrition Tags Full tag taxonomy, thresholds, LLM extraction process

API Docs

Document Description
API Specification All endpoints, request/response examples, WebSocket protocol, error codes

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