"""
Pattern 168: Constitutional Chain
Description:
The Constitutional Chain pattern ensures AI outputs align with predefined principles,
values, and rules through iterative self-critique and revision. The agent generates
an initial response, critiques it against constitutional principles, and refines it
until it meets all requirements.
Components:
1. Constitution: Set of principles and rules
2. Generator: Creates initial responses
3. Critic: Evaluates responses against constitution
4. Reviser: Improves responses based on critique
5. Compliance Checker: Validates final alignment
6. Iteration Manager: Controls refinement loops
Use Cases:
- Ethical AI systems
- Content moderation
- Policy-compliant generation
- Bias reduction
- Safety-critical applications
- Regulated industries
Benefits:
- Value alignment
- Explicit principles
- Iterative improvement
- Audit trail
- Consistent behavior
- Safety guarantees
Trade-offs:
- Multiple LLM calls
- Increased latency
- May be overly cautious
- Requires good constitution
- Complexity overhead
LangChain Implementation:
Implements iterative critique-revision loops with LangChain. Uses structured
prompts for critique and revision stages with constitutional principles.
"""
import os
from typing import List, Dict, Any, Optional
from dataclasses import dataclass, field
from enum import Enum
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
load_dotenv()
class PrincipleType(Enum):
"""Types of constitutional principles"""
ETHICAL = "ethical"
SAFETY = "safety"
ACCURACY = "accuracy"
BIAS = "bias"
TONE = "tone"
LEGAL = "legal"
PRIVACY = "privacy"
class ComplianceLevel(Enum):
"""Compliance assessment levels"""
COMPLIANT = "compliant"
MINOR_ISSUES = "minor_issues"
MAJOR_VIOLATIONS = "major_violations"
CRITICAL = "critical"
@dataclass
class ConstitutionalPrinciple:
"""Represents a principle in the constitution"""
name: str
description: str
principle_type: PrincipleType
priority: int = 5 # 1-10, higher is more important
examples_good: List[str] = field(default_factory=list)
examples_bad: List[str] = field(default_factory=list)
@dataclass
class CritiqueResult:
"""Result of critiquing a response"""
compliance_level: ComplianceLevel
violations: List[str]
suggestions: List[str]
score: float # 0-1 scale
critique_text: str
@dataclass
class ConstitutionalResponse:
"""Final response with compliance information"""
response: str
iterations: int
final_compliance: ComplianceLevel
improvements_made: List[str]
critique_history: List[CritiqueResult]
class Constitution:
"""Defines a set of principles for AI behavior"""
def __init__(self, name: str):
self.name = name
self.principles: List[ConstitutionalPrinciple] = []
def add_principle(self, principle: ConstitutionalPrinciple):
"""Add a principle to the constitution"""
self.principles.append(principle)
def get_principles_text(self) -> str:
"""Get formatted text of all principles"""
lines = [f"Constitution: {self.name}", "=" * 60, ""]
for i, principle in enumerate(sorted(self.principles,
key=lambda p: p.priority,
reverse=True), 1):
lines.append(f"{i}. {principle.name} (Priority: {principle.priority})")
lines.append(f" Type: {principle.principle_type.value}")
lines.append(f" {principle.description}")
if principle.examples_good:
lines.append(f" Good: {principle.examples_good[0]}")
if principle.examples_bad:
lines.append(f" Bad: {principle.examples_bad[0]}")
lines.append("")
return "\n".join(lines)
class ConstitutionalChain:
"""Implements constitutional AI pattern with critique-revision loops"""
def __init__(self, constitution: Constitution, max_iterations: int = 3):
"""
Initialize constitutional chain
Args:
constitution: The constitution to follow
max_iterations: Maximum number of revision iterations
"""
self.constitution = constitution
self.max_iterations = max_iterations
self.llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)
# Prompts
self.generation_prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful AI assistant."),
("user", "{query}")
])
self.critique_prompt = ChatPromptTemplate.from_messages([
("system", """You are a constitutional AI critic. Evaluate the response
against the following principles:
{principles}
Identify any violations and provide specific suggestions for improvement."""),
("user", """Query: {query}
Response to evaluate:
{response}
Provide:
1. Compliance level: compliant, minor_issues, major_violations, or critical
2. List of violations (if any)
3. Specific suggestions for improvement
4. Overall score (0-1)
Format your response as:
COMPLIANCE: [level]
VIOLATIONS: [list violations or "none"]
SUGGESTIONS: [list suggestions]
SCORE: [0-1 score]
CRITIQUE: [detailed explanation]""")
])
self.revision_prompt = ChatPromptTemplate.from_messages([
("system", """You are an AI assistant that revises responses to align with
constitutional principles. Improve the response based on the critique while
maintaining helpfulness and relevance."""),
("user", """Original query: {query}
Previous response:
{response}
Critique:
{critique}
Revise the response to address all issues while staying helpful and relevant.""")
])
self.generation_chain = self.generation_prompt | self.llm | StrOutputParser()
self.critique_chain = self.critique_prompt | self.llm | StrOutputParser()
self.revision_chain = self.revision_prompt | self.llm | StrOutputParser()
def generate_constitutional_response(self, query: str) -> ConstitutionalResponse:
"""Generate a response that complies with constitutional principles"""
# Generate initial response
response = self.generation_chain.invoke({"query": query})
critique_history = []
improvements = []
iterations = 0
principles_text = self.constitution.get_principles_text()
for iteration in range(self.max_iterations):
iterations += 1
# Critique current response
critique = self._critique_response(query, response, principles_text)
critique_history.append(critique)
# Check if compliant
if critique.compliance_level == ComplianceLevel.COMPLIANT:
break
if critique.compliance_level == ComplianceLevel.CRITICAL:
# Critical issues require immediate attention
improvements.append(f"Iteration {iteration + 1}: Fixed critical issues")
elif critique.compliance_level == ComplianceLevel.MAJOR_VIOLATIONS:
improvements.append(f"Iteration {iteration + 1}: Fixed major violations")
else:
improvements.append(f"Iteration {iteration + 1}: Addressed minor issues")
# Revise response
response = self._revise_response(query, response, critique.critique_text)
# Final critique
final_critique = critique_history[-1]
return ConstitutionalResponse(
response=response,
iterations=iterations,
final_compliance=final_critique.compliance_level,
improvements_made=improvements,
critique_history=critique_history
)
def _critique_response(self, query: str, response: str,
principles: str) -> CritiqueResult:
"""Critique a response against constitutional principles"""
critique_text = self.critique_chain.invoke({
"query": query,
"response": response,
"principles": principles
})
# Parse critique
compliance_level = ComplianceLevel.COMPLIANT
violations = []
suggestions = []
score = 1.0
for line in critique_text.split('\n'):
line = line.strip()
if line.startswith('COMPLIANCE:'):
level_str = line.split(':', 1)[1].strip().lower()
if 'critical' in level_str:
compliance_level = ComplianceLevel.CRITICAL
elif 'major' in level_str:
compliance_level = ComplianceLevel.MAJOR_VIOLATIONS
elif 'minor' in level_str:
compliance_level = ComplianceLevel.MINOR_ISSUES
else:
compliance_level = ComplianceLevel.COMPLIANT
elif line.startswith('VIOLATIONS:'):
viol_text = line.split(':', 1)[1].strip()
if viol_text.lower() != 'none':
violations = [v.strip() for v in viol_text.split(',')]
elif line.startswith('SUGGESTIONS:'):
sugg_text = line.split(':', 1)[1].strip()
suggestions = [s.strip() for s in sugg_text.split(',')]
elif line.startswith('SCORE:'):
try:
score = float(line.split(':', 1)[1].strip())
except:
score = 0.8
return CritiqueResult(
compliance_level=compliance_level,
violations=violations,
suggestions=suggestions,
score=score,
critique_text=critique_text
)
def _revise_response(self, query: str, response: str, critique: str) -> str:
"""Revise response based on critique"""
revised = self.revision_chain.invoke({
"query": query,
"response": response,
"critique": critique
})
return revised
def demonstrate_constitutional_chain():
"""Demonstrate constitutional chain pattern"""
print("=" * 80)
print("CONSTITUTIONAL CHAIN PATTERN DEMONSTRATION")
print("=" * 80)
# Example 1: Basic constitutional AI
print("\n" + "=" * 80)
print("Example 1: Basic Constitutional AI")
print("=" * 80)
# Create a simple constitution
basic_constitution = Constitution("Basic Safety Constitution")
basic_constitution.add_principle(ConstitutionalPrinciple(
name="Helpful and Harmless",
description="Responses must be helpful while avoiding harmful content",
principle_type=PrincipleType.ETHICAL,
priority=10,
examples_good=["I can help you learn programming"],
examples_bad=["I can help you hack systems"]
))
basic_constitution.add_principle(ConstitutionalPrinciple(
name="No Misinformation",
description="Avoid spreading false or misleading information",
principle_type=PrincipleType.ACCURACY,
priority=9,
examples_good=["According to research..."],
examples_bad=["I think this is true but not sure..."]
))
basic_constitution.add_principle(ConstitutionalPrinciple(
name="Respectful Tone",
description="Maintain respectful and professional communication",
principle_type=PrincipleType.TONE,
priority=7,
examples_good=["Let me help you understand this"],
examples_bad=["That's a stupid question"]
))
print("\n" + basic_constitution.get_principles_text())
# Create chain
chain = ConstitutionalChain(basic_constitution, max_iterations=3)
# Test query
query = "Tell me how to get rich quick with cryptocurrency"
print("\nQuery:", query)
print("\nGenerating constitutional response...")
print("-" * 60)
result = chain.generate_constitutional_response(query)
print(f"\nIterations required: {result.iterations}")
print(f"Final compliance: {result.final_compliance.value}")
if result.improvements_made:
print("\nImprovements made:")
for improvement in result.improvements_made:
print(f" - {improvement}")
print(f"\nFinal response:")
print(result.response)
print("\n" + "-" * 60)
print("Critique History:")
for i, critique in enumerate(result.critique_history, 1):
print(f"\nIteration {i}:")
print(f" Compliance: {critique.compliance_level.value}")
print(f" Score: {critique.score:.2f}")
if critique.violations:
print(f" Violations: {', '.join(critique.violations[:2])}")
if critique.suggestions:
print(f" Suggestions: {', '.join(critique.suggestions[:2])}")
# Example 2: Content moderation constitution
print("\n" + "=" * 80)
print("Example 2: Content Moderation")
print("=" * 80)
moderation_constitution = Constitution("Content Moderation")
moderation_constitution.add_principle(ConstitutionalPrinciple(
name="No Hate Speech",
description="Responses must not contain discriminatory or hateful content",
principle_type=PrincipleType.ETHICAL,
priority=10
))
moderation_constitution.add_principle(ConstitutionalPrinciple(
name="Age Appropriate",
description="Content should be suitable for general audiences",
principle_type=PrincipleType.SAFETY,
priority=9
))
moderation_constitution.add_principle(ConstitutionalPrinciple(
name="No Personal Attacks",
description="Avoid personal attacks or aggressive language",
principle_type=PrincipleType.TONE,
priority=8
))
mod_chain = ConstitutionalChain(moderation_constitution, max_iterations=2)
queries = [
"What do you think about people who disagree with me?",
"How do I respond to criticism online?"
]
for query in queries:
print(f"\nQuery: {query}")
result = mod_chain.generate_constitutional_response(query)
print(f"Compliance: {result.final_compliance.value}")
print(f"Response: {result.response[:120]}...")
# Example 3: Professional communication constitution
print("\n" + "=" * 80)
print("Example 3: Professional Communication")
print("=" * 80)
prof_constitution = Constitution("Professional Communication")
prof_constitution.add_principle(ConstitutionalPrinciple(
name="Clarity",
description="Responses must be clear and unambiguous",
principle_type=PrincipleType.TONE,
priority=8
))
prof_constitution.add_principle(ConstitutionalPrinciple(
name="Evidence-Based",
description="Claims should be supported by evidence or clearly marked as opinions",
principle_type=PrincipleType.ACCURACY,
priority=9
))
prof_constitution.add_principle(ConstitutionalPrinciple(
name="Conciseness",
description="Responses should be concise while complete",
principle_type=PrincipleType.TONE,
priority=6
))
prof_chain = ConstitutionalChain(prof_constitution, max_iterations=3)
query = "Explain quantum computing"
print(f"\nQuery: {query}")
print("\nGenerating professional response...")
result = prof_chain.generate_constitutional_response(query)
print(f"\nIterations: {result.iterations}")
print(f"Final compliance: {result.final_compliance.value}")
print(f"\nResponse:\n{result.response}")
# Example 4: Comparing with and without constitutional chain
print("\n" + "=" * 80)
print("Example 4: Comparison - With vs Without Constitution")
print("=" * 80)
query = "Should I invest all my money in a single stock?"
# Without constitution
print(f"\nQuery: {query}")
print("\n1. Without Constitutional Chain:")
basic_llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)
basic_chain = ChatPromptTemplate.from_messages([
("user", "{query}")
]) | basic_llm | StrOutputParser()
basic_response = basic_chain.invoke({"query": query})
print(f" {basic_response[:200]}...")
# With constitution
print("\n2. With Constitutional Chain:")
safety_constitution = Constitution("Financial Safety")
safety_constitution.add_principle(ConstitutionalPrinciple(
name="Risk Disclosure",
description="Always disclose risks in financial advice",
principle_type=PrincipleType.ETHICAL,
priority=10
))
safety_constitution.add_principle(ConstitutionalPrinciple(
name="No Guarantees",
description="Avoid making guarantees about financial outcomes",
principle_type=PrincipleType.ACCURACY,
priority=9
))
safety_chain = ConstitutionalChain(safety_constitution, max_iterations=2)
result = safety_chain.generate_constitutional_response(query)
print(f" {result.response[:200]}...")
print(f"\n Iterations: {result.iterations}")
print(f" Compliance: {result.final_compliance.value}")
# Summary
print("\n" + "=" * 80)
print("SUMMARY")
print("=" * 80)
print("""
The Constitutional Chain pattern enables:
✓ Value-aligned AI responses
✓ Iterative self-critique and improvement
✓ Explicit principle enforcement
✓ Audit trail of improvements
✓ Consistent ethical behavior
✓ Safety and compliance guarantees
✓ Transparent reasoning process
This pattern is valuable for:
- Production AI systems requiring safety
- Content moderation platforms
- Regulated industries (finance, healthcare)
- Ethical AI development
- Bias mitigation
- Policy-compliant generation
- High-stakes decision support
""")
if __name__ == "__main__":
demonstrate_constitutional_chain()
Agentic Design Pattern: Constitutional Chain
Pattern 168: Constitutional Chain – Line-by-Line Explanation
Overview
This document provides a detailed line-by-line explanation of the Constitutional Chain pattern implementation.
Pattern Description
Pattern 168: Constitutional Chain
Description: The Constitutional Chain pattern ensures AI outputs align with predefined principles, values, and rules through iterative self-critique and revision. The agent generates an initial response, critiques it against constitutional principles, and refines it until it meets all requirements.
Components: 1. Constitution: Set of principles and rules 2. Generator: Creates initial responses 3. Critic: Evaluates responses against constitution 4. Reviser: Improves responses based on critique 5. Compliance Checker: Validates final alignment 6. Iteration Manager: Controls refinement loops
Use Cases: – Ethical AI systems – Content moderation – Policy-compliant generation – Bias reduction – Safety-critical applications – Regulated industries
Benefits: – Value alignment – Explicit principles – Iterative improvement – Audit trail – Consistent behavior – Safety guarantees
Trade-offs: – Multiple LLM calls – Increased latency – May be overly cautious – Requires good constitution – Complexity overhead
LangChain Implementation: Implements iterative critique-revision loops with LangChain. Uses structured prompts for critique and revision stages with constitutional principles.
File Statistics
- Total Lines: 532
- Classes: 7
- Functions: 1
- Constants: 0
Module Documentation
"""
Pattern 168: Constitutional Chain
Description:
The Constitutional Chain pattern ensures AI outputs align with predefined principles,
values, and rules through iterative self-critique and revision. The agent generates
an initial response, critiques it against constitutional principles, and refines it
until it meets all requirements.
Components:
1. Constitution: Set of principles and rules
2. Generator: Creates initial responses
3. Critic: Evaluates responses against constitution
4. Reviser: Improves responses based on critique
5. Compliance Checker: Validates final alignment
6. Iteration Manager: Controls refinement loops
Use Cases:
- Ethical AI systems
- Content moderation
- Policy-compliant generation
- Bias reduction
- Safety-critical applications
- Regulated industries
Benefits:
- Value alignment
- Explicit principles
- Iterative improvement
- Audit trail
- Consistent behavior
- Safety guarantees
Trade-offs:
- Multiple LLM calls
- Increased latency
- May be overly cautious
- Requires good constitution
- Complexity overhead
LangChain Implementation:
Implements iterative critique-revision loops with LangChain. Uses structured
prompts for critique and revision stages with constitutional principles.
"""
Purpose: This module implements the Constitutional Chain pattern, providing a structured approach to agent design and implementation.
Import Statements
Standard Library Imports
import dataclasses.dataclass
import dataclasses.field
import enum.Enum
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import typing.Any
import typing.Dict
import typing.List
import typing.Optional
Purpose: Core Python functionality:
– dataclasses.dataclass: Simplified class definitions with @dataclass
– dataclasses.field: Simplified class definitions with @dataclass
– enum.Enum: Enumeration support
– langchain_core.output_parsers.StrOutputParser: Regular expression operations
– langchain_core.prompts.ChatPromptTemplate: Regular expression operations
– os: Environment variable access and system operations
– typing.Any: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Dict: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.List: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional)
LangChain Imports
from langchain_openai.ChatOpenAI
Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows
Other Imports
from dotenv.load_dotenv
Purpose: Additional dependencies for specialized functionality.
Classes
Class: PrincipleType
Lines 59-67
Inherits from: Enum
Purpose:
Types of constitutional principles
Class: ComplianceLevel
Lines 70-75
Inherits from: Enum
Purpose:
Compliance assessment levels
Class: ConstitutionalPrinciple
Lines 79-86
Purpose:
Represents a principle in the constitution
Class: CritiqueResult
Lines 90-96
Purpose:
Result of critiquing a response
Class: ConstitutionalResponse
Lines 100-106
Purpose:
Final response with compliance information
Class: Constitution
Lines 109-136
Purpose:
Defines a set of principles for AI behavior
Methods:
__init__(self, name)
Lines 112-114
Purpose: Initialize the class instance with required parameters.
Parameters:
– name: Name
add_principle(self, principle)
Lines 116-118
Purpose:
Add a principle to the constitution
Parameters:
– principle: Principle
get_principles_text(self)
Lines 120-136
Purpose:
Get formatted text of all principles
Parameters:
Returns: str
Class: ConstitutionalChain
Lines 139-304
Purpose:
Implements constitutional AI pattern with critique-revision loops
Methods:
__init__(self, constitution, max_iterations)
Lines 142-203
Purpose:
Initialize constitutional chain
Args:
constitution: The constitution to follow
max_iterations: Maximum number of revision iterations
Parameters:
– constitution: Constitution
– max_iterations: Max iterations
generate_constitutional_response(self, query)
Lines 205-247
Purpose:
Generate a response that complies with constitutional principles
Parameters:
– query: Input query or question
Returns: ConstitutionalResponse
_critique_response(self, query, response, principles)
Lines 249-295
Purpose:
Critique a response against constitutional principles
Parameters:
– query: Input query or question
– response: Response
– principles: Principles
Returns: CritiqueResult
_revise_response(self, query, response, critique)
Lines 297-304
Purpose:
Revise response based on critique
Parameters:
– query: Input query or question
– response: Response
– critique: Critique
Returns: str
Functions
demonstrate_constitutional_chain()
Lines 307-527
Purpose:
Demonstrate constitutional chain pattern
Key Concepts Summary
Pattern Overview
The Constitutional Chain pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.
Core Components
- Agent Class: Main implementation of the pattern logic
- Prompt Engineering: Carefully crafted prompts to guide LLM behavior
- Tool Integration: Optional tools for extended functionality
- Memory Management: State handling across interactions
- Output Parsing: Structured response processing
When to Use
✅ Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability
❌ Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)
Best Practices Demonstrated
1. Environment Configuration
- ✅ Use
.envfiles for API keys - ✅ Never hardcode credentials
- ✅ Validate environment variables on startup
2. Error Handling
- ✅ Graceful degradation on failures
- ✅ Informative error messages
- ✅ Logging for debugging
3. Prompt Engineering
- ✅ Clear, specific instructions
- ✅ Structured output formats
- ✅ Few-shot examples when beneficial
4. Code Organization
- ✅ Separation of concerns (class-based design)
- ✅ Type hints for clarity
- ✅ Docstrings for documentation
5. Performance Optimization
- ✅ Appropriate temperature settings
- ✅ Token budget management
- ✅ Caching where applicable
LangChain Components Used
- ChatOpenAI: OpenAI model interface
- Chains: Sequential component composition
Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development
Execution Flow
1. Initialize agent with configuration
↓
2. Load environment and dependencies
↓
3. Prepare prompt with input data
↓
4. Invoke LLM through LangChain
↓
5. Parse and validate response
↓
6. Return formatted result
Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation
Performance Considerations
Computational Cost
- LLM Calls: Primary cost factor (based on tokens)
- Latency: Depends on model and input size
- Memory: Minimal for most patterns
Optimization Strategies
- Prompt Optimization: Minimize tokens while maintaining clarity
- Caching: Cache repeated queries/responses
- Model Selection: Use smaller models when appropriate
- Batch Processing: Group similar requests
- Streaming: For long responses, stream tokens
Cost Estimates (Approximate)
- GPT-3.5-turbo: ~$0.002 per request
- GPT-4: ~$0.03 per request
- GPT-4-turbo: ~$0.01 per request
Actual costs vary based on input/output token counts
Common Pitfalls to Avoid
1. Prompt Design Issues
❌ Vague or ambiguous instructions
✅ Clear, specific, testable prompts
2. Error Handling
❌ Ignoring API errors or rate limits
✅ Implement retry logic and graceful degradation
3. Token Management
❌ Exceeding model token limits
✅ Truncate inputs, chunk processing
4. Security
❌ Exposing API keys in code
✅ Use environment variables
5. Testing
❌ Only testing happy paths
✅ Test edge cases, failures, malformed inputs
6. Cost Management
❌ Unmonitored API usage
✅ Implement budgets, alerts, and monitoring
Further Improvements
Production Enhancements
- Monitoring: Add logging, metrics, tracing
- Validation: Input/output schema validation
- Rate Limiting: Implement request throttling
- Caching: Redis or in-memory cache for responses
- Async Support: Non-blocking operations for scalability
Feature Extensions
- Multi-Model Support: Switch between providers
- Fallback Models: Graceful degradation strategy
- Custom Tools: Domain-specific function calling
- Advanced Memory: Vector stores for long-term memory
- Evaluation: Automated testing and benchmarking
Code Quality
- Testing: Unit tests, integration tests
- Documentation: API docs, usage examples
- Type Safety: Stricter type hints
- Configuration: Externalized config files
Related Patterns
- Pattern 167
- Pattern 169
Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…
Summary
The Constitutional Chain pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:
✅ Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns
🎯 Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability
📚 Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/
File: 168_constitutional_chain.py
Total Lines: 532
Pattern Category: AI Agent Design Pattern #168
Generated from actual implementation code
For educational and reference purposes