Chain of Responsibility Pattern
The Chain of Responsibility pattern is perfectly suited for LLM applications where requests need to be processed through a series of handlers, each with specific capabilities and decision-making authority.
Why Chain of Responsibility for LLM?
LLM applications often require:
Sequential processing: Requests flow through multiple AI agents or processing stages
Conditional handling: Different handlers for different types of queries or complexity levels
Flexible routing: Dynamic decision-making about which agent handles what
Fallback mechanisms: Graceful degradation when primary handlers fail
Key LLM Use Cases
1. Multi-Agent Request Routing
The most common application - routing user queries to specialized AI agents:
class AgentChain:
def __init__(self):
self.first_handler = None
def add_handler(self, handler):
if not self.first_handler:
self.first_handler = handler
else:
current = self.first_handler
while current.next_handler:
current = current.next_handler
current.next_handler = handler
def handle_request(self, query):
if self.first_handler:
return self.first_handler.handle(query)
return None
# Specialized agents
math_agent = MathAgent() # Handles math queries
code_agent = CodeAgent() # Handles coding questions
general_agent = GeneralAgent() # Handles everything else
chain = AgentChain()
chain.add_handler(math_agent)
chain.add_handler(code_agent)
chain.add_handler(general_agent)Benefits:
Clear separation of agent responsibilities
Easy to add/remove specialized agents
Automatic fallback to general agent
Request routing based on content analysis
2. Complexity-Based Processing Pipeline
Processing requests based on complexity levels:
class ComplexityChain:
def handle(self, query):
complexity = self.analyze_complexity(query)
if complexity == "simple":
return self.simple_llm.process(query) # Fast, lightweight model
elif complexity == "medium":
return self.medium_llm.process(query) # Balanced model
else:
return self.advanced_llm.process(query) # Most capable model
# Usage
simple_handler = SimpleQueryHandler()
complex_handler = ComplexQueryHandler()
research_handler = ResearchQueryHandler()Benefits:
Cost optimization (use cheaper models for simple queries)
Performance optimization (faster responses for simple questions)
Quality assurance (complex queries get the best models)
Resource management
3. RAG Document Processing Chain
Processing documents through different retrieval and augmentation stages:
class RAGChain:
def process_query(self, query, documents):
# Stage 1: Quick keyword search
if self.keyword_handler.can_handle(query):
results = self.keyword_handler.search(query, documents)
if results.confidence > 0.8:
return results
# Stage 2: Semantic search with embeddings
if self.semantic_handler.can_handle(query):
results = self.semantic_handler.search(query, documents)
if results.confidence > 0.7:
return results
# Stage 3: Full LLM processing with context
return self.llm_handler.process_with_full_context(query, documents)Benefits:
Performance optimization for different query types
Progressive enhancement of search quality
Fallback mechanisms for edge cases
Adaptive resource usage
4. Content Moderation Pipeline
Processing content through multiple safety and quality checks:
class ModerationChain:
def moderate_content(self, content):
# Stage 1: Basic content filter
if self.basic_filter.is_inappropriate(content):
return {"status": "blocked", "reason": "basic_filter"}
# Stage 2: AI-powered toxicity detection
toxicity_score = self.toxicity_detector.analyze(content)
if toxicity_score > 0.7:
return {"status": "flagged", "reason": "toxicity"}
# Stage 3: Context-aware moderation
if self.context_moderator.needs_review(content):
return {"status": "review", "reason": "context_sensitive"}
return {"status": "approved", "reason": "passed_all_checks"}Benefits:
Layered security approach
Performance optimization (quick filters first)
Detailed rejection reasoning
Scalable moderation architecture
5. Error Handling and Recovery Chain
Managing failures and providing alternative responses:
class ErrorRecoveryChain:
def process_with_recovery(self, query):
try:
# Try primary AI service
return self.primary_ai.process(query)
except AIServiceError:
try:
# Fallback to secondary service
return self.secondary_ai.process(query)
except AIServiceError:
# Final fallback to cached responses
return self.cache_handler.get_similar_response(query)Benefits:
High availability and reliability
Graceful degradation of service quality
Multiple backup strategies
User experience continuity
Implementation Advantages
1. Modularity
Each handler has a single responsibility
Easy to test individual components
Clean separation of concerns
Independent development of handlers
2. Flexibility
Runtime chain configuration
Dynamic handler addition/removal
Conditional processing paths
Context-aware routing decisions
3. Scalability
Easy to add new specialized agents
Horizontal scaling of handler types
Load balancing across handlers
Performance monitoring per handler
4. Maintainability
Clear request flow visualization
Easy debugging of processing steps
Isolated error handling per stage
Configuration-driven chain setup
Real-World Impact
The Chain of Responsibility pattern in LLM applications provides:
Cost Efficiency: Route simple queries to cheaper models, complex ones to premium models
Performance Optimization: Fast responses through appropriate handler selection
Quality Assurance: Specialized handlers for different domains and complexities
Reliability: Multiple fallback options and error recovery mechanisms
This pattern is essential for production LLM systems where intelligent request routing, cost optimization, and reliable service delivery are critical requirements.
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