When building complex LLM applications, single model calls often fail to meet business requirements. This article will detail how to build a reliable LLM chain architecture, including basic design patterns, prompt engineering, and error handling mechanisms.
Why Chain Architecture?
Before diving into technical details, let's understand why we need chain architecture:
-
Limitations of Single Model Calls
- Single input/output format
- Lack of context management
- Limited error handling capabilities
-
Challenges in Complex Business Scenarios
- Multi-step processing requirements
- Data cleaning and transformation
- Result validation and quality control
-
Advantages of Chain Architecture
- Modular design for easy maintenance
- Flexible extensibility
- Unified error handling
- Reusable components
Basic Chain Architecture Design
1. Core Components
from typing import Any, Dict, Optional
from abc import ABC, abstractmethod
class BaseProcessor(ABC):
@abstractmethod
def process(self, data: Any) -> Any:
pass
class BaseChain:
def __init__(self):
self.preprocessor: Optional[BaseProcessor] = None
self.prompt_manager: Optional[PromptManager] = None
self.llm: Optional[BaseLLM] = None
self.postprocessor: Optional[BaseProcessor] = None
self.error_handler: Optional[ErrorHandler] = None
def process(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
try:
# 1. Preprocessing
processed_input = self._preprocess(input_data)
# 2. Generate prompt
prompt = self._generate_prompt(processed_input)
# 3. LLM call
response = self._call_llm(prompt)
# 4. Postprocessing
result = self._postprocess(response)
return result
except Exception as e:
return self.error_handler.handle(e)
2. Component Decoupling Design
class PreProcessor(BaseProcessor):
def process(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""Data preprocessing logic"""
# 1. Data cleaning
cleaned_data = self._clean_data(data)
# 2. Format conversion
formatted_data = self._format_data(cleaned_data)
# 3. Validation
self._validate_data(formatted_data)
return formatted_data
class PostProcessor(BaseProcessor):
def process(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""Result post-processing logic"""
# 1. Result parsing
parsed_result = self._parse_result(data)
# 2. Format output
formatted_result = self._format_output(parsed_result)
# 3. Quality check
self._quality_check(formatted_result)
return formatted_result
Prompt Engineering Fundamentals
1. Prompt Template Management
class PromptTemplate:
def __init__(self, template: str, input_variables: List[str]):
self.template = template
self.input_variables = input_variables
class PromptManager:
def __init__(self):
self.templates: Dict[str, PromptTemplate] = {}
self.version_control = VersionControl()
def register_template(self, name: str, template: str,
input_variables: List[str]) -> None:
"""Register prompt template"""
self.templates[name] = PromptTemplate(
template=template,
input_variables=input_variables
)
def generate_prompt(self, template_name: str, **kwargs) -> str:
"""Generate prompt"""
template = self.templates.get(template_name)
if not template:
raise ValueError(f"Template {template_name} not found")
# Validate required parameters
self._validate_inputs(template, kwargs)
# Generate prompt
return template.template.format(**kwargs)
2. Prompt Optimization Strategies
class PromptOptimizer:
def __init__(self):
self.few_shots: List[Dict[str, str]] = []
self.context: Dict[str, Any] = {}
def add_few_shot(self, example: Dict[str, str]) -> None:
"""Add few-shot example"""
self.few_shots.append(example)
def set_context(self, context: Dict[str, Any]) -> None:
"""Set context information"""
self.context.update(context)
def optimize_prompt(self, base_prompt: str) -> str:
"""Optimize prompt"""
# 1. Add role setting
prompt = self._add_role_setting(base_prompt)
# 2. Inject context
prompt = self._inject_context(prompt)
# 3. Add few-shot examples
prompt = self._add_few_shots(prompt)
return prompt
Error Handling Mechanism
1. Error Handling Infrastructure
class LLMChainError(Exception):
"""Base chain error"""
pass
class ErrorHandler:
def __init__(self):
self.retry_strategy = RetryStrategy()
self.fallback_handler = FallbackHandler()
self.monitor = Monitor()
def handle(self, error: Exception) -> Dict[str, Any]:
"""Unified error handling"""
try:
# 1. Log error
self.monitor.log_error(error)
# 2. Check if retryable
if self.is_retryable(error):
return self.retry_strategy.retry()
# 3. Fallback handling
return self.fallback_handler.handle(error)
finally:
# 4. Error notification
self.monitor.notify(error)
2. Retry Strategy Implementation
class RetryStrategy:
def __init__(self, max_retries: int = 3,
base_delay: float = 1.0):
self.max_retries = max_retries
self.base_delay = base_delay
self.current_retry = 0
def retry(self) -> bool:
"""Implement exponential backoff retry"""
if self.current_retry >= self.max_retries:
return False
delay = self.base_delay * (2 ** self.current_retry)
time.sleep(delay)
self.current_retry += 1
return True
Practical Case: Intelligent Q&A System
Let's look at how to apply these concepts through a practical Q&A system:
class QAChain(BaseChain):
def __init__(self):
super().__init__()
self.setup_components()
def setup_components(self):
# 1. Set up preprocessor
self.preprocessor = QAPreProcessor()
# 2. Configure prompt manager
self.prompt_manager = self._setup_prompt_manager()
# 3. Configure LLM
self.llm = self._setup_llm()
# 4. Set up postprocessor
self.postprocessor = QAPostProcessor()
# 5. Configure error handling
self.error_handler = QAErrorHandler()
def _setup_prompt_manager(self):
manager = PromptManager()
manager.register_template(
"qa_template",
"""
As an intelligent Q&A assistant, please answer the following question:
Question: {question}
Requirements:
1. Answer should be concise and clear
2. If uncertain, please explicitly state so
3. If more information is needed, specify what information is required
""",
["question"]
)
return manager
Best Practice Recommendations
-
Architecture Design Principles
- Maintain low coupling between modules
- Implement testable components
- Ensure proper logging and monitoring
-
Common Pitfall Prevention
- Avoid hardcoding prompts
- Pay attention to error propagation chains
- Prevent retry storms
-
Performance Optimization Tips
- Use caching appropriately
- Implement request batching
- Control concurrency levels
Summary
This article introduces the core components and best practices for building LLM chain applications. Through proper architecture design, prompt management, and error handling, we can build more reliable and maintainable LLM applications.
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