LuBot Architecture
Enterprise AI Self-Learning Analytics Platform - 100% NVIDIA Powered
AI Models - 100% NVIDIA Stack
NVIDIA Nemotron Nano
8B
Tier 1 - Fast Analytics
Simple queries, 50ms response
Simple queries, 50ms response
NVIDIA Nemotron Ultra
253B
Tier 2 - PhD Analysis
Complex stats, correlations
Complex stats, correlations
NVIDIA NV-EMBEDQA-E5
v5
Embeddings Model
Intent classification, memory
Intent classification, memory
NVIDIA Nemotron-3-Nano
30B
1M context, on-premise GPU
Enterprise Server Self-Hosted
Groq (Fallback)
<1%
Emergency Fallback Only
🧬 Self-Learning Architecture
LuBot: A Self-Learning AI Agent
LuBot combines proven engineering patterns into a unique self-learning analytics platform. Through 22 nightly batch workers, 34 database tables tracking user behavior, and adaptive learning systems, LuBot becomes smarter over time - learning your preferences, optimizing response routes, and personalizing insights based on your interaction patterns. Every query makes LuBot better at serving you.
🔄 Request Flow Architecture
User Query
→
Web Router
(Tier 0)
(Tier 0)
→
Intent Classifier
(4-Tier)
(4-Tier)
→
NVIDIA LLM Router
→
Tool Execution
→
Grounded Pipeline
→
Response
🎯 4-Tier Intent Routing System
Tier 0 - Deterministic Detection
0ms - Regex
PhD Analysis
Correlation
Concentration
Anomaly
Web Search Fast-Path
Tier 1 - Core Intents (80% of queries)
0ms - Pattern Match
GREETING
IDENTITY
CAPABILITIES
MEMORY_RECALL
MEMORY_UPDATE
DATA_MODE_SWITCH
DATA_QUERY
WEB_SEARCH
DOCUMENT_QA
PREDICTION
DOCUMENT_GENERATION
DATA_LIBRARY
GENERAL
Tier 2 - NVIDIA Embeddings (15% of queries)
5ms - Semantic
ADVICE_REQUEST
FOLLOWUP
CLARIFICATION
DEEP_DIVE
COMPARISON
Tier 3 - NVIDIA LLM Fallback (5% of queries)
100ms - LLM
Ambiguous Queries
Complex Multi-Intent
Edge Cases
⚡ 3-Tier Response System (Smart Model Routing)
Routes queries to the optimal NVIDIA model based on complexity.
Tier 0 - DIRECT (No LLM Needed)
0ms - Template
Criteria: Simple aggregation (COUNT, SUM, AVG) with 1 row result
user_generic_count
user_generic_total
user_generic_avg
user_generic_min
user_generic_max
Example: "How many employees?" → "You have 320 employees"
Tier 1 - ENHANCED (Nemotron Nano 8B)
50ms - 50K tok/s
Criteria: Medium complexity, GROUP BY with 2-10 rows, basic analysis
Tables with insights
Grouped summaries
Top N queries
Basic comparisons
Example: "Revenue by department" → Table + "Sales is highest at $2M"
Tier 2 - FULL PhD (Nemotron Ultra 253B)
500ms - 253B params
Criteria: Statistical analysis OR >10 rows requiring deep insights
correlation
concentration
simpsons_paradox
outliers
trend
comparison
Example: "Correlation between sales and marketing?" → "Pearson r=0.95, p<0.001 - strong positive"
🔄 Routing Flow
Query → ResponseTierRouter.get_response_tier() → Tier 0 | Tier 1 | Tier 2 → LLMRouter → NVIDIA Model
🛠️ AI Tools & Capabilities (28)
📊 Data & Query Tools
1. SQL Query Engine
Safe SQL execution with injection protection, schema-aware queries
2. SQL Template Matcher
Pattern matching for SQL generation, 30+ templates
3. Schema Extractor
Auto-detect database schema, column mapping
4. Data Analyzer
Metric summaries, trend detection, dimension analysis
📈 Visualization Tools
5. Chart Generator
15+ Plotly chart types: bar, line, pie, scatter, heatmap, etc.
6. Chart Preference Parser
Learn user preferences, personalized defaults
7. Report Generator
PDF, Excel, Word export with charts and insights
8. Report Intelligence
Executive summaries, traffic insights, recommendations
🔬 PhD-Level Intelligence (7 Analyzers)
9. Correlation Analysis
Pearson/Spearman correlation, statistical significance
10. Concentration Analysis
Pareto analysis, Gini coefficient, HHI index
11. Simpson's Paradox
Mix shift detection, segment-level reversal analysis
12. Anomaly Detection
Statistical outliers, Z-score, IQR methods
13. Drivers Analysis
Sensitivity analysis, impact attribution
14. Scenario Analysis
What-if projections, growth scenarios
15. Timeseries Analysis
7 sub-analyzers: trend, seasonality, volatility, momentum
16. Prediction Tools
Prophet forecasting, trend analysis, future projections
🧠 AI Processing Pipeline
17. Grounded Pipeline
20-stage response generation with fact extraction
18. Code Interpreter
Python execution, 20+ analysis templates
19. LLM Router
Intelligent model routing, Nano vs Ultra selection
20. Response Tier Router
Complexity detection, tier assignment
🌐 External Integration Tools
21. Web Search
Real-time web queries with source citations
22. Document RAG
PDF/Word processing, FAISS vector search, Q&A
23. Image Analysis
Vision model integration, multi-modal analysis
24. NVIDIA Embeddings
NV-EMBEDQA-E5-v5 for semantic search
💾 Memory & Storage Tools
25. Memory System
Cross-session memory, conversation indexing, FAISS
26. User Context Builder
Personalization, topic ranking, dimension ordering
27. B2 Cold Storage
Hot/cold pattern: PostgreSQL + Backblaze B2
28. Template Learning
Adaptive learning, route weights, preferences
🗄️ Database Schema (34 Tables)
🏛️ Core Foundation
users
chat_sessions
↓
📦 User Data & Storage
user_uploads
user_uploaded_metrics
user_uploaded_rows
user_uploaded_documents
user_document_chunks
user_document_metadata
user_text_extractions
↓
🧠 Memory & Context
conversation_memory
conversation_embedding_log
generated_output_context
user_conversation_insights
↓
📚 Learning & Preferences
user_preferences
user_chart_preferences
user_route_weights
user_web_search_preferences
user_rag_preferences
user_prediction_preferences
user_report_preferences
preference_events
parameter_learning
template_learning_data
↓
📊 Analytics & Tracking
click_logs
daily_click_summary
user_events
interaction_log
message_feedback
chart_data
↓
👥 Profiles & Segmentation
user_data_profiles
user_daily_summary
user_visitor_segments
user_visitor_segmentation_data
completion_queue
⏰ Nightly Batch Workers (22 Jobs)
12:00 AM
Click Aggregator ETL
2:00 AM
Route Weights Learning
3:00 AM
Chart Preferences
4:00 AM
Few-Shot RAG Learning
5:00 AM
User Profile Builder
6:00 AM
Data Profile Baselines
6:30 AM
Multi-Tenant Profiles
7:00 AM
GDPR Data Cleanup
8:00 AM
Memory Extraction
8:30 AM
Conversation Embeddings
9:00 AM
Semantic Clustering
9:30 AM
Web Search Prefs
10:00 AM
RAG Preferences
10:30 AM
Prediction Prefs
11:00 AM
Report Preferences
11:30 AM
K-Means Segmentation
🔌 API Endpoints (40+)
GET/health
GET/health/memory
POST/v1/agent/run
GET/v1/agent/stream
POST/v1/agent/switch-model
GET/v1/agent/current-model
POST/v1/agent/reset-memory
GET/v1/agent/memory-status
POST/api/upload-document
POST/api/upload-data/{user_id}
DELETE/api/users/{id}/files/{name}
GET/api/users/{id}/storage
GET/api/users/{id}/schema
POST/api/users/{id}/data-mode
GET/api/chat-sessions
GET/api/memories
POST/api/interaction
POST/api/feedback
POST/api/analyze-image
GET/api/analytics/topics
POST/api/track-click
GET/api/segments/chart/{id}
GET/api/chart/{chart_id}
GET/api/download/{file_id}
🌐 External Services
NVIDIA API
Primary LLM & Embeddings
Nemotron Nano 8B, Ultra 253B
NV-EMBEDQA-E5-v5
Nemotron Nano 8B, Ultra 253B
NV-EMBEDQA-E5-v5
Groq API
Fallback LLM (<1%)
Emergency backup only
Emergency backup only
Neon PostgreSQL
Primary Database
34 tables, 230MB+
Real-time OLTP
34 tables, 230MB+
Real-time OLTP
Backblaze B2
Cold Storage
Raw file storage
Cost-effective archive
Raw file storage
Cost-effective archive