🏗️ LLM App Architecture

🎓 Page Overview

Trang này cung cấp kiến thức nền tảng về kiến trúc LLM applications, từ components cơ bản đến orchestration patterns cho production-ready systems.

Level: Foundation Solves: Thiết kế architecture cho LLM-powered applications với proper separation of concerns và scalability

🎯 Core Concepts

Tầng Kiến Trúc LLM Application

Component Responsibilities

Component	Responsibility	Key Considerations
LLM Orchestrator	Coordinate request flow, manage context	Retry logic, timeout handling, fallback chains
Model Router	Select optimal model per request	Cost, latency, capability matching
Semantic Cache	Cache similar queries	Similarity threshold, cache invalidation
Embedding Service	Convert text to vectors	Batch processing, model versioning
Vector DB	Store và retrieve embeddings	Index strategy, hybrid search support
Reranker	Re-score retrieved documents	Latency budget, accuracy trade-offs

🔄 Data Flow Patterns

Request-Response Flow

Streaming vs Batch Patterns

Pattern	Use Case	Implementation
Streaming	Interactive chat, real-time	SSE/WebSocket, token-by-token
Batch	Document processing, offline	Queue-based, async workers
Hybrid	Initial answer + enhancement	Stream first response, batch enrichment

🧠 Orchestration Patterns

1. Simple Chain Pattern

User Query → Retrieval → LLM → Response

• Use when: Single-turn Q&A, simple RAG
• Pros: Low latency, easy to debug
• Cons: Limited reasoning capability

2. Agent Pattern

User Query → Planning → [Tool Use Loop] → Response

• Use when: Complex tasks, multi-step reasoning
• Pros: Flexible, can handle diverse tasks
• Cons: Higher latency, harder to control

3. Multi-Agent Pattern

User Query → Router → [Specialist Agent 1..N] → Aggregator → Response

• Use when: Domain-specific expertise required
• Pros: Better quality per domain
• Cons: Complex orchestration, coordination overhead

4. Workflow Pattern

User Query → [Predefined Steps 1..N] → Response

• Use when: Structured processes, compliance requirements
• Pros: Predictable, auditable
• Cons: Less flexible

⚙️ Context Management

Context Window Strategy

Strategy	Description	Trade-offs
Sliding Window	Keep last N tokens	Simple, may lose important context
Summarization	Compress old context	Preserves key info, adds latency
Hierarchical	Multiple summary levels	Best retention, highest complexity
Selective	Keep relevant sections only	Efficient, requires relevance scoring

Memory Architecture

🔧 Production Considerations

Reliability Patterns

Pattern	Implementation	Purpose
Circuit Breaker	Track failure rate, open circuit on threshold	Prevent cascade failures
Retry with Backoff	Exponential backoff with jitter	Handle transient failures
Fallback Chain	Primary → Secondary → Tertiary model	Ensure availability
Request Hedging	Send to multiple providers, use first response	Reduce tail latency

Latency Optimization

1. Parallel Retrieval: Fetch from multiple sources concurrently
2. Speculative Execution: Pre-generate likely follow-ups
3. Edge Caching: Cache embeddings and common responses at edge
4. Model Quantization: Use quantized models for low-latency paths

Security Architecture

📋 Architecture Checklist

Design Phase

• [ ] Define latency SLO (p50, p95, p99)
• [ ] Identify fallback model chain
• [ ] Design context management strategy
• [ ] Plan caching layer (semantic + exact match)
• [ ] Define security boundaries

Implementation Phase

• [ ] Implement circuit breaker for LLM calls
• [ ] Set up observability (traces, metrics, logs)
• [ ] Configure rate limiting per user/tenant
• [ ] Implement request/response logging (PII-aware)
• [ ] Set up async processing for batch operations

Production Readiness

• [ ] Load test with realistic traffic patterns
• [ ] Test fallback chain end-to-end
• [ ] Verify cache hit rates meet targets
• [ ] Validate security controls (injection, DLP)
• [ ] Document runbook for common failures

🔗 Cross-References

• 📎 Deep Learning Architectures - Transformer foundations
• 📎 System Design Fundamentals - Distributed system patterns
• 📎 Kubernetes - Container orchestration for LLM workloads
• 📎 AWS Compute - Cloud deployment options

📚 Further Reading

• "Building LLM Applications for Production" - Chip Huyen
• "Patterns for LLM-based Applications" - Anthropic Cookbook
• "Production-Ready LLM Applications" - LangChain Documentation