621 lines
20 KiB
Markdown
621 lines
20 KiB
Markdown
# System Architecture Document
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## Virtual Board Member AI System
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**Document Version**: 1.0
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**Date**: August 2025
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**Classification**: Confidential
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---
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## 1. Executive Summary
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This document defines the complete system architecture for the Virtual Board Member AI system, incorporating microservices architecture, event-driven design patterns, and enterprise-grade security controls. The architecture supports both local development and cloud-scale production deployment.
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## 2. High-Level Architecture
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### 2.1 System Overview
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ CLIENT LAYER │
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├─────────────────┬───────────────────┬──────────────────────────┤
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│ Web Portal │ Mobile Apps │ API Clients │
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└────────┬────────┴────────┬──────────┴────────┬─────────────────┘
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│ │ │
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▼ ▼ ▼
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┌─────────────────────────────────────────────────────────────────┐
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│ API GATEWAY (Kong/AWS API GW) │
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│ • Rate Limiting • Authentication • Request Routing │
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└────────┬─────────────────────────────────────┬──────────────────┘
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│ │
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▼ ▼
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┌──────────────────────────────┬─────────────────────────────────┐
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│ SECURITY LAYER │ ORCHESTRATION LAYER │
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├──────────────────────────────┼─────────────────────────────────┤
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│ • OAuth 2.0/OIDC │ • LangChain Controller │
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│ • JWT Validation │ • Workflow Engine (Airflow) │
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│ • RBAC │ • Model Router │
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└──────────────┬───────────────┴───────────┬─────────────────────┘
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│ │
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▼ ▼
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┌──────────────────────────────────────────────────────────────┐
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│ MICROSERVICES LAYER │
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├────────────────┬────────────────┬───────────────┬─────────────┤
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│ LLM Service │ RAG Service │ Doc Processor │ Analytics │
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│ • OpenRouter │ • Qdrant │ • PDF/XLSX │ • Metrics │
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│ • Fallback │ • Embedding │ • OCR │ • Insights │
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└────────┬───────┴────────┬───────┴───────┬──────┴──────┬──────┘
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│ │ │ │
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▼ ▼ ▼ ▼
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┌──────────────────────────────────────────────────────────────┐
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│ DATA LAYER │
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├─────────────┬──────────────┬──────────────┬─────────────────┤
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│ Vector DB │ Document │ Cache │ Message Queue │
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│ (Qdrant) │ Store (S3) │ (Redis) │ (Kafka/SQS) │
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└─────────────┴──────────────┴──────────────┴─────────────────┘
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```
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### 2.2 Component Responsibilities
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| Component | Primary Responsibility | Technology Stack |
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|-----------|----------------------|------------------|
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| API Gateway | Request routing, rate limiting, authentication | Kong, AWS API Gateway |
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| LLM Service | Model orchestration, prompt management | LangChain, OpenRouter |
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| RAG Service | Document retrieval, context management | Qdrant, LangChain |
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| Document Processor | File parsing, OCR, extraction | Python libs, Tesseract |
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| Analytics Service | Usage tracking, insights generation | PostgreSQL, Grafana |
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| Vector Database | Semantic search, document storage | Qdrant |
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| Cache Layer | Response caching, session management | Redis |
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| Message Queue | Async processing, event streaming | Kafka/AWS SQS |
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## 3. Detailed Component Architecture
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### 3.1 LLM Orchestration Service
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```python
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class LLMOrchestrationArchitecture:
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"""
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Core orchestration service managing multi-model routing and execution
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"""
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components = {
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"model_router": {
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"responsibility": "Route requests to optimal models",
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"implementation": "Strategy pattern with cost/quality optimization",
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"models": {
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"extraction": "gpt-4o-mini",
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"analysis": "claude-3.5-sonnet",
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"synthesis": "gpt-4-turbo",
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"vision": "gpt-4-vision"
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}
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},
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"prompt_manager": {
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"responsibility": "Manage and version prompt templates",
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"storage": "PostgreSQL with version control",
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"caching": "Redis with 1-hour TTL"
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},
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"chain_executor": {
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"responsibility": "Execute multi-step reasoning chains",
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"framework": "LangChain with custom extensions",
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"patterns": ["MapReduce", "Sequential", "Parallel"]
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},
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"memory_manager": {
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"responsibility": "Maintain conversation context",
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"types": {
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"short_term": "Redis (24-hour TTL)",
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"long_term": "PostgreSQL",
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"semantic": "Qdrant vectors"
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}
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}
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}
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```
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### 3.2 Document Processing Pipeline
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```yaml
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pipeline:
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stages:
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- ingestion:
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supported_formats: [pdf, xlsx, csv, pptx, txt]
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max_file_size: 100MB
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concurrent_processing: 10
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- extraction:
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pdf:
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primary: pdfplumber
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fallback: PyPDF2
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ocr: tesseract-ocr
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excel:
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library: openpyxl
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preserve: [formulas, formatting, charts]
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powerpoint:
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library: python-pptx
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image_extraction: gpt-4-vision
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- transformation:
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chunking:
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strategy: semantic
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size: 1000-1500 tokens
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overlap: 200 tokens
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metadata:
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extraction: automatic
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enrichment: business_context
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- indexing:
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embedding_model: voyage-3-large
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batch_size: 100
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parallel_workers: 4
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```
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### 3.3 Vector Database Architecture
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```python
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class VectorDatabaseSchema:
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"""
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Qdrant collection schema for board documents
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"""
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collection_config = {
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"name": "board_documents",
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"vector_size": 1024,
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"distance": "Cosine",
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"optimizers_config": {
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"indexing_threshold": 20000,
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"memmap_threshold": 50000,
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"default_segment_number": 4
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},
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"payload_schema": {
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"document_id": "keyword",
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"document_type": "keyword", # report|presentation|minutes
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"department": "keyword", # finance|hr|legal|operations
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"date_created": "datetime",
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"reporting_period": "keyword",
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"confidentiality": "keyword", # public|internal|confidential
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"stakeholders": "keyword[]",
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"key_topics": "text[]",
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"content": "text",
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"chunk_index": "integer",
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"total_chunks": "integer"
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}
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}
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```
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## 4. Data Flow Architecture
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### 4.1 Document Ingestion Flow
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```
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User Upload → API Gateway → Document Processor
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↓
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Validation & Security Scan
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↓
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Format-Specific Parser
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↓
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Content Extraction
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↓
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┌──────────┴──────────┐
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↓ ↓
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Raw Storage (S3) Text Processing
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↓
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Chunking Strategy
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↓
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Embedding Generation
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↓
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Vector Database
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↓
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Indexing Complete
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```
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### 4.2 Query Processing Flow
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```
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User Query → API Gateway → Authentication
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↓
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Query Processor
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↓
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Intent Classification
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↓
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┌─────────────┼─────────────┐
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↓ ↓ ↓
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RAG Pipeline Direct LLM Analytics
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↓ ↓ ↓
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Vector Search Model Router SQL Query
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↓ ↓ ↓
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Context Build Prompt Build Data Fetch
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↓ ↓ ↓
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└─────────────┼─────────────┘
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↓
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Response Synthesis
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↓
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Output Validation
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↓
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Client Response
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```
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## 5. Security Architecture
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### 5.1 Security Layers
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```yaml
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security_architecture:
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perimeter_security:
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- waf: AWS WAF / Cloudflare
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- ddos_protection: Cloudflare / AWS Shield
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- api_gateway: Rate limiting, API key validation
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authentication:
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- protocol: OAuth 2.0 / OIDC
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- provider: Auth0 / AWS Cognito
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- mfa: Required for admin access
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authorization:
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- model: RBAC with attribute-based extensions
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- roles:
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- board_member: Full access to all features
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- executive: Department-specific access
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- analyst: Read-only access
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- admin: System configuration
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data_protection:
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encryption_at_rest:
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- algorithm: AES-256-GCM
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- key_management: AWS KMS / HashiCorp Vault
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encryption_in_transit:
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- protocol: TLS 1.3
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- certificate: EV SSL
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llm_security:
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- prompt_injection_prevention: Input validation
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- output_filtering: PII detection and masking
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- audit_logging: All queries and responses
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- rate_limiting: Per-user and per-endpoint
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```
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### 5.2 Zero-Trust Architecture
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```python
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class ZeroTrustImplementation:
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"""
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Zero-trust security model implementation
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"""
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principles = {
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"never_trust": "All requests validated regardless of source",
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"always_verify": "Continuous authentication and authorization",
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"least_privilege": "Minimal access rights by default",
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"assume_breach": "Design assumes compromise has occurred"
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}
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implementation = {
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"micro_segmentation": {
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"network": "Service mesh with Istio",
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"services": "Individual service authentication",
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"data": "Field-level encryption where needed"
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},
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"continuous_validation": {
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"token_refresh": "15-minute intervals",
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"behavior_analysis": "Anomaly detection on usage patterns",
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"device_trust": "Device fingerprinting and validation"
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}
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}
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```
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## 6. Scalability Architecture
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### 6.1 Horizontal Scaling Strategy
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```yaml
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scaling_configuration:
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kubernetes:
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autoscaling:
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- type: HorizontalPodAutoscaler
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metrics:
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- cpu: 70%
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- memory: 80%
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- custom: requests_per_second > 100
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services:
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llm_service:
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min_replicas: 2
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max_replicas: 20
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target_cpu: 70%
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rag_service:
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min_replicas: 3
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max_replicas: 15
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target_cpu: 60%
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document_processor:
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min_replicas: 2
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max_replicas: 10
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scaling_policy: job_queue_length
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database:
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qdrant:
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sharding: 4 shards
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replication: 3 replicas per shard
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distribution: Consistent hashing
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redis:
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clustering: Redis Cluster mode
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nodes: 6 (3 masters, 3 replicas)
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```
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### 6.2 Performance Optimization
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```python
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class PerformanceOptimization:
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"""
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System-wide performance optimization strategies
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"""
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caching_strategy = {
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"l1_cache": {
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"type": "Application memory",
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"ttl": "5 minutes",
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"size": "1GB per instance"
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},
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"l2_cache": {
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"type": "Redis",
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"ttl": "1 hour",
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"size": "10GB cluster"
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},
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"l3_cache": {
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"type": "CDN (CloudFront)",
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"ttl": "24 hours",
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"content": "Static assets, common reports"
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}
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}
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database_optimization = {
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"connection_pooling": {
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"min_connections": 10,
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"max_connections": 100,
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"timeout": 30
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},
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"query_optimization": {
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"indexes": "Automated index recommendation",
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"partitioning": "Time-based for logs",
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"materialized_views": "Common aggregations"
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}
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}
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llm_optimization = {
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"batching": "Group similar requests",
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"caching": "Semantic similarity matching",
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"model_routing": "Cost-optimized selection",
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"token_optimization": "Prompt compression"
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}
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```
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## 7. Deployment Architecture
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### 7.1 Environment Strategy
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```yaml
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environments:
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development:
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infrastructure: Docker Compose
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database: Chroma (local)
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llm: OpenRouter sandbox
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data: Synthetic test data
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staging:
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infrastructure: Kubernetes (single node)
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database: Qdrant Cloud (dev tier)
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llm: OpenRouter with rate limits
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data: Anonymized production sample
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production:
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infrastructure: EKS/GKE/AKS
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database: Qdrant Cloud (production)
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llm: OpenRouter production
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data: Full production access
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backup: Real-time replication
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```
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### 7.2 CI/CD Pipeline
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```yaml
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pipeline:
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source_control:
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platform: GitHub/GitLab
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branching: GitFlow
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protection: Main branch protected
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continuous_integration:
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- trigger: Pull request
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- steps:
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- lint: Black, isort, mypy
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- test: pytest with 80% coverage
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- security: Bandit, safety
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- build: Docker multi-stage
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continuous_deployment:
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- staging:
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trigger: Merge to develop
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approval: Automatic
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rollback: Automatic on failure
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- production:
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trigger: Merge to main
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approval: Manual (2 approvers)
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strategy: Blue-green deployment
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rollback: One-click rollback
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```
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## 8. Monitoring & Observability
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### 8.1 Monitoring Stack
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```yaml
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monitoring:
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metrics:
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collection: Prometheus
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storage: VictoriaMetrics
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visualization: Grafana
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logging:
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aggregation: Fluentd
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storage: Elasticsearch
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analysis: Kibana
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tracing:
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instrumentation: OpenTelemetry
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backend: Jaeger
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sampling: 1% in production
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alerting:
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manager: AlertManager
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channels: [email, slack, pagerduty]
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escalation: 3-tier support model
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```
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### 8.2 Key Performance Indicators
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```python
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class SystemKPIs:
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"""
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Critical metrics for system health monitoring
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"""
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availability = {
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"uptime_target": "99.9%",
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"measurement": "Synthetic monitoring",
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"alert_threshold": "99.5%"
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}
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performance = {
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"response_time_p50": "< 2 seconds",
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"response_time_p95": "< 5 seconds",
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"response_time_p99": "< 10 seconds",
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"throughput": "> 100 requests/second"
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}
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business_metrics = {
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"daily_active_users": "Track unique users",
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"query_success_rate": "> 95%",
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"document_processing_rate": "> 500/hour",
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"cost_per_query": "< $0.10"
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}
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ai_metrics = {
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"model_accuracy": "> 90%",
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"hallucination_rate": "< 2%",
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"context_relevance": "> 85%",
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"user_satisfaction": "> 4.5/5"
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}
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```
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## 9. Disaster Recovery
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### 9.1 Backup Strategy
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```yaml
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backup_strategy:
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data_classification:
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critical:
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- vector_database
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- document_store
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- configuration
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important:
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- logs
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- metrics
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- cache
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backup_schedule:
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critical:
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frequency: Real-time replication
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retention: 90 days
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location: Multi-region
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important:
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frequency: Daily
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retention: 30 days
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location: Single region
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recovery_objectives:
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rto: 4 hours # Recovery Time Objective
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rpo: 1 hour # Recovery Point Objective
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```
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### 9.2 Failure Scenarios
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```python
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class FailureScenarios:
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"""
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Documented failure scenarios and recovery procedures
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"""
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scenarios = {
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"llm_service_failure": {
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"detection": "Health check failure",
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"immediate_action": "Fallback to secondary model",
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"recovery": "Auto-restart with exponential backoff",
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"escalation": "Page on-call after 3 failures"
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},
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"database_failure": {
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"detection": "Connection timeout",
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"immediate_action": "Serve from cache",
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"recovery": "Automatic failover to replica",
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"escalation": "Immediate page to DBA"
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},
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"data_corruption": {
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"detection": "Checksum validation",
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"immediate_action": "Isolate affected data",
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"recovery": "Restore from last known good backup",
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"escalation": "Executive notification"
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}
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}
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```
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## 10. Integration Architecture
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### 10.1 External System Integrations
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```yaml
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integrations:
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document_sources:
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sharepoint:
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protocol: REST API
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auth: OAuth 2.0
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sync: Incremental every 15 minutes
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google_drive:
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protocol: REST API
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auth: OAuth 2.0
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sync: Real-time via webhooks
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email:
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protocol: IMAP/Exchange
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auth: OAuth 2.0
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sync: Every 5 minutes
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identity_providers:
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primary: Active Directory
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protocol: SAML 2.0
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attributes: [email, department, role]
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notification_systems:
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email: SMTP with TLS
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slack: Webhook API
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teams: Graph API
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```
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### 10.2 API Specifications
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```python
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class APISpecification:
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"""
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RESTful API design following OpenAPI 3.0
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"""
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endpoints = {
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"/api/v1/documents": {
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"POST": "Upload document",
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"GET": "List documents",
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"DELETE": "Remove document"
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},
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"/api/v1/query": {
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"POST": "Submit |