高质量场景架构选型
1. 复杂规划执行架构
当准确性是关键要求时，采用Plan-Execute-Evaluate架构：
class HighQualityAgent:
def __init__(self):
self.planner = TaskPlanner()
self.executor = ToolExecutor()
self.evaluator = QualityEvaluator()
self.critic = SelfCritic()
async def process(self, query: str) -> str:
# 详细规划
plan = await self.planner.create_plan(query)
# 分步执行
results = []
for step in plan.steps:
result = await self.executor.execute(step)
results.append(result)
# 实时质量检查
quality_score = await self.evaluator.evaluate(result)
if quality_score < 0.8:
# 触发自我修正
result = await self.critic.improve(result)
# 最终质量评估
final_result = self._combine_results(results)
return await self.evaluator.final_check(final_result)2. 多模型集成架构
通过多个模型的集成提高准确性：
class MultiModelEnsembleAgent:
def __init__(self):
self.models = {
"reasoning": GPT4Model(), # 逻辑推理
"creative": ClaudeModel(), # 创造性思考
"factual": GeminiModel() # 事实性检查
}
self.voter = ResultVoter()
async def process(self, query: str) -> str:
# 并行调用多个模型
tasks = {
name: model.generate(query)
for name, model in self.models.items()
}
results = await asyncio.gather(*tasks.values())
# 集成投票机制
final_result = self.voter.ensemble(query, list(results))
return final_result高吞吐量场景架构选型
1. 微服务架构设计
# docker-compose.yml
version: '3.8'
services:
gateway:
image: agent-gateway:latest
ports:
- "8080:8080"
environment:
- RATE_LIMIT=1000/s
planner-service:
image: planner-service:latest
replicas: 3
environment:
- WORKER_ID=${WORKER_ID}
executor-service:
image: executor-service:latest
replicas: 5
environment:
- MAX_CONCURRENT=10
evaluator-service:
image: evaluator-service:latest
replicas: 2
environment:
- BATCH_SIZE=50
redis:
image: redis:alpine
command: redis-server --maxmemory 2gb --maxmemory-policy allkeys-lru
message-queue:
image: rabbitmq:management
environment:
- RABBITMQ_DEFAULT_USER=admin
- RABBITMQ_DEFAULT_PASS=password2. 异步队列处理架构
class HighThroughputAgent:
def __init__(self):
self.queue = asyncio.Queue(maxsize=1000)
self.semaphore = asyncio.Semaphore(50) # 并发控制
self.worker_pool = [
asyncio.create_task(self._worker(i))
for i in range(10)
]
async def submit_task(self, query: str) -> str:
# 异步提交任务
future = asyncio.Future()
await self.queue.put((query, future))
return await future
async def _worker(self, worker_id: int):
while True:
try:
query, future = await self.queue.get()
async with self.semaphore:
result = await self._process_query(query)
future.set_result(result)
self.queue.task_done()
except Exception as e:
future.set_exception(e)成本敏感场景架构选型
1. 智能缓存策略
class CostOptimizedAgent:
def __init__(self):
self.cache_hierarchy = {
"memory": MemoryCache(), # 零成本
"redis": RedisCache(), # 低成本
"database": DatabaseCache() # 存储成本
}
self.usage_tracker = UsageTracker()
async def process(self, query: str) -> str:
# 多级缓存检查
for level, cache in self.cache_hierarchy.items():
cached = await cache.get(query)
if cached is not None:
await self.usage_tracker.record_cache_hit(level)
return cached
await self.usage_tracker.record_cache_miss()
response = await self._call_llm(query)
await self.cache_hierarchy["redis"].set(query, response, ttl=3600)
return response工程实现要点
LLM Agent架构设计模式与核心组件分析
工程实现要点
工程实现是将架构设计落地为可运行系统的关键环节。本节将从模型选择、规划实现、记忆管理、工具适配等核心维度，详细阐述LLM Agent系统的工程化实现要点。
1. LLM选择与Prompt工程
1.1 模型能力评估与选择
不同模型在指令遵循、工具调用、思维链推理等方面存在显著差异，需要根据业务场景进行针对性选择。
class ModelSelector:
def __init__(self):
self.model_capabilities = {
"gpt-4": {
"instruction_following": 0.95,
"tool_calling": 0.90,
"reasoning": 0.95,
"cost_per_token": 0.03,
"latency_ms": 800
},
"claude-3": {
"instruction_following": 0.92,
"tool_calling": 0.85,
"reasoning": 0.93,
"cost_per_token": 0.025,
"latency_ms": 650
},
"llama-2-70b": {
"instruction_following": 0.80,
"tool_calling": 0.70,
"reasoning": 0.85,
"cost_per_token": 0.015,
"latency_ms": 1200
}
}
def select_model(self, requirements):
"""根据需求选择合适的模型"""
scores = {}
for model, capabilities in self.model_capabilities.items():
score = 0
for req, weight in requirements.items():
if req in capabilities:
score += capabilities[req] * weight
scores[model] = score
return max(scores, key=scores.get)1.2 Prompt工程最佳实践
Prompt设计需要考虑结构化模板、动态参数注入和安全防护。
from jinja2 import Template
from typing import Dict, Any, List
class PromptTemplate:
def __init__(self, template_str: str):
self.template = Template(template_str)
self.variables = set()
def render(self, **kwargs) -> str:
"""渲染模板并验证变量"""
# 变量验证
missing = self.variables - set(kwargs.keys())
if missing:
raise ValueError(f"Missing variables: {missing}")
return self.template.render(**kwargs)
class AgentPromptManager:
def __init__(self):
self.templates = {}
self._initialize_templates()
def _initialize_templates(self):
"""初始化标准模板"""
self.templates["system_prompt"] = """
你是一个专业的{{agent_type}}，负责{{role_description}}。
核心职责：
{% for responsibility in responsibilities %}
- {{responsibility}}
{% endfor %}
工作流程：
{{workflow_description}}
约束条件：
{% for constraint in constraints %}
- {{constraint}}
{% endfor %}
记住：始终保持准确性、透明度和用户利益优先。
"""
self.templates["tool_calling_prompt"] = """
根据用户请求：{{user_query}}
可用工具：
{% for tool in available_tools %}
- {{tool.name}}: {{tool.description}}
参数: {{tool.parameters}}
{% endfor %}
请按以下格式响应：
{
"reasoning": "你的思考过程",
"action": "选择调用的工具名",
"parameters": {
"param1": "参数值",
"param2": "参数值"
},
"confidence": 0.8
}
"""
def get_prompt(self, template_name: str, **context) -> str:
"""获取渲染后的Prompt"""
if template_name not in self.templates:
raise ValueError(f"Unknown template: {template_name}")
template = PromptTemplate(self.templates[template_name])
return template.render(**context)
# 使用示例
prompt_manager = AgentPromptManager()
system_prompt = prompt_manager.get_prompt(
"system_prompt",
agent_type="数据分析助手",
role_description="帮助用户分析数据并生成洞察",
responsibilities=[
"理解数据结构和业务含义",
"执行统计分析和可视化",
"解释分析结果和提供建议"
],
workflow_description="先理解需求，再分析数据，最后提供结论",
constraints=[
"不泄露敏感信息",
"基于实际数据进行分析",
"明确标注假设条件"
]
)1.3 Prompt安全与防护
import re
from typing import List, Tuple
class PromptSecurityFilter:
def __init__(self):
self.dangerous_patterns = [
r"(?i)(ignore.*previous.*instructions|forget.*previous.*instructions)",
r"(?i)(reveal.*system.*prompt|show.*prompt)",
r"(?i)(jailbreak|bypass|override)",
r"(?i)(malicious|harmful|dangerous).*instructions"
]
self.safe_completion_phrases = [
"我无法执行此请求",
"该请求不符合安全准则",
"我无法提供此类信息"
]
def validate_prompt(self, prompt: str) -> Tuple[bool, List[str]]:
"""验证Prompt安全性"""
warnings = []
# 检查危险模式
for pattern in self.dangerous_patterns:
if re.search(pattern, prompt):
warnings.append(f"检测到潜在危险模式: {pattern}")
# 检查长度（防止prompt injection）
if len(prompt) > 8000:
warnings.append("Prompt过长，可能存在注入风险")
# 检查特殊字符
special_chars = ['<', '>', '{', '}', '[', ']', '(', ')']
if sum(1 for char in special_chars if char in prompt) > 20:
warnings.append("特殊字符过多，建议简化")
return len(warnings) == 0, warnings
def sanitize_prompt(self, prompt: str) -> str:
"""净化Prompt"""
# 移除明显的注入尝试
injection_patterns = [
r"(?i)(system.*prompt|ignore.*above)",
r"(?i)(pretend.*you.*are.*system)",
r"(?i)(disregard.*previous.*instructions)"
]
sanitized = prompt
for pattern in injection_patterns:
sanitized = re.sub(pattern, "", sanitized)
return sanitized.strip()2. 规划（Planning）实现
2.1 任务分解策略
任务分解是复杂Agent系统的核心，需要考虑依赖关系、并行性和执行效率。
from abc import ABC, abstractmethod
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
from enum import Enum
class TaskStatus(Enum):
PENDING = "pending"
RUNNING = "running"
COMPLETED = "completed"
FAILED = "failed"
SKIPPED = "skipped"
@dataclass
class Task:
id: str
name: str
description: str
dependencies: List[str]
function: callable
parameters: Dict[str, Any]
status: TaskStatus = TaskStatus.PENDING
result: Any = None
error: Optional[str] = None
estimated_duration: int = 60 # 秒
class TaskPlanner:
def __init__(self):
self.tasks: Dict[str, Task] = {}
self.execution_graph: Dict[str, List[str]] = {}
def add_task(self, task: Task):
"""添加任务到计划"""
self.tasks[task.id] = task
if task.id not in self.execution_graph:
self.execution_graph[task.id] = []
for dep in task.dependencies:
if dep not in self.execution_graph:
self.execution_graph[dep] = []
self.execution_graph[dep].append(task.id)
def get_execution_order(self) -> List[str]:
"""获取拓扑排序的执行顺序"""
in_degree = {task_id: 0 for task_id in self.tasks}
for task_id in self.tasks:
for dep in self.tasks[task_id].dependencies:
in_degree[task_id] += 1
queue = [task_id for task_id in in_degree if in_degree[task_id] == 0]
result = []
while queue:
current = queue.pop(0)
result.append(current)
for next_task in self.execution_graph.get(current, []):
in_degree[next_task] -= 1
if in_degree[next_task] == 0:
queue.append(next_task)
return result