引言
構建企業級 AI Agent 系統需要仔細考慮元件設計、系統架構和工程實踐。本文將探討構建穩健可擴充套件的 Agent 系統的關鍵元件和最佳實踐。
1. Prompt 模板工程
1.1 模板設計模式
from typing import Protocol, Dict
from jinja2 import Template
class PromptTemplate(Protocol):
def render(self, **kwargs) -> str:
pass
class JinjaPromptTemplate:
def __init__(self, template_string: str):
self.template = Template(template_string)
def render(self, **kwargs) -> str:
return self.template.render(**kwargs)
class PromptLibrary:
def __init__(self):
self.templates: Dict[str, PromptTemplate] = {}
def register_template(self, name: str, template: PromptTemplate):
self.templates[name] = template
def get_template(self, name: str) -> PromptTemplate:
return self.templates[name]
1.2 版本控制與測試
class PromptVersion:
def __init__(self, version: str, template: str, metadata: dict):
self.version = version
self.template = template
self.metadata = metadata
self.test_cases = []
def add_test_case(self, inputs: dict, expected_output: str):
self.test_cases.append((inputs, expected_output))
def validate(self) -> bool:
template = JinjaPromptTemplate(self.template)
for inputs, expected in self.test_cases:
result = template.render(**inputs)
if not self._validate_output(result, expected):
return False
return True
2. 分層記憶系統
2.1 記憶架構
from typing import Any, List
from datetime import datetime
class MemoryEntry:
def __init__(self, content: Any, importance: float):
self.content = content
self.importance = importance
self.timestamp = datetime.now()
self.access_count = 0
class MemoryLayer:
def __init__(self, capacity: int):
self.capacity = capacity
self.memories: List[MemoryEntry] = []
def add(self, entry: MemoryEntry):
if len(self.memories) >= self.capacity:
self._evict()
self.memories.append(entry)
def _evict(self):
# 實現記憶淘汰策略
self.memories.sort(key=lambda x: x.importance * x.access_count)
self.memories.pop(0)
class HierarchicalMemory:
def __init__(self):
self.working_memory = MemoryLayer(capacity=5)
self.short_term = MemoryLayer(capacity=50)
self.long_term = MemoryLayer(capacity=1000)
def store(self, content: Any, importance: float):
entry = MemoryEntry(content, importance)
if importance > 0.8:
self.working_memory.add(entry)
elif importance > 0.5:
self.short_term.add(entry)
else:
self.long_term.add(entry)
2.2 記憶檢索與索引
from typing import List, Tuple
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
class MemoryIndex:
def __init__(self, embedding_model):
self.embedding_model = embedding_model
self.embeddings = []
self.memories = []
def add(self, memory: MemoryEntry):
embedding = self.embedding_model.embed(memory.content)
self.embeddings.append(embedding)
self.memories.append(memory)
def search(self, query: str, k: int = 5) -> List[Tuple[MemoryEntry, float]]:
query_embedding = self.embedding_model.embed(query)
similarities = cosine_similarity(
[query_embedding],
self.embeddings
)[0]
top_k_indices = np.argsort(similarities)[-k:]
return [
(self.memories[i], similarities[i])
for i in top_k_indices
]
3. 可觀測推理鏈路
3.1 鏈路結構
from typing import List, Optional
from dataclasses import dataclass
import uuid
@dataclass
class ThoughtNode:
content: str
confidence: float
supporting_evidence: List[str]
class ReasoningChain:
def __init__(self):
self.chain_id = str(uuid.uuid4())
self.nodes: List[ThoughtNode] = []
self.metadata = {}
def add_thought(self, thought: ThoughtNode):
self.nodes.append(thought)
def get_path(self) -> List[str]:
return [node.content for node in self.nodes]
def get_confidence(self) -> float:
if not self.nodes:
return 0.0
return sum(node.confidence for node in self.nodes) / len(self.nodes)
4. 元件解耦與複用
4.1 模組化設計
from abc import ABC, abstractmethod
from typing import Any, Dict
class Component(ABC):
@abstractmethod
def process(self, input_data: Any) -> Any:
pass
class ComponentRegistry:
def __init__(self):
self._components: Dict[str, Component] = {}
def register(self, name: str, component: Component):
self._components[name] = component
def get(self, name: str) -> Component:
return self._components[name]
class Pipeline:
def __init__(self, registry: ComponentRegistry):
self.registry = registry
self.steps = []
def add_step(self, component_name: str):
self.steps.append(component_name)
def execute(self, input_data: Any) -> Any:
result = input_data
for step in self.steps:
component = self.registry.get(step)
result = component.process(result)
return result
5. 效能監控與調優
5.1 效能指標收集
from dataclasses import dataclass
from datetime import datetime
import time
@dataclass
class PerformanceMetrics:
latency: float
memory_usage: float
token_count: int
timestamp: datetime
class PerformanceMonitor:
def __init__(self):
self.metrics_history = []
def record_operation(self, func):
def wrapper(*args, **kwargs):
start_time = time.time()
result = func(*args, **kwargs)
end_time = time.time()
metrics = PerformanceMetrics(
latency=end_time - start_time,
memory_usage=self._get_memory_usage(),
token_count=self._count_tokens(result),
timestamp=datetime.now()
)
self.metrics_history.append(metrics)
return result
return wrapper
def get_average_latency(self) -> float:
if not self.metrics_history:
return 0.0
return sum(m.latency for m in self.metrics_history) / len(self.metrics_history)
5.2 自適應最佳化
class AdaptiveOptimizer:
def __init__(self, performance_monitor: PerformanceMonitor):
self.monitor = performance_monitor
self.thresholds = {
'latency': 1.0, # 秒
'memory': 1024, # MB
'tokens': 2000 # 令牌數
}
def optimize(self, component: Component) -> Component:
metrics = self.monitor.metrics_history[-1]
if metrics.latency > self.thresholds['latency']:
component = self._optimize_latency(component)
if metrics.memory_usage > self.thresholds['memory']:
component = self._optimize_memory(component)
if metrics.token_count > self.thresholds['tokens']:
component = self._optimize_tokens(component)
return component
總結
本文詳細介紹了企業級 Agent 系統的核心元件設計和最佳化策略，包括：
- 靈活的 Prompt 模板系統
- 高效的分層記憶架構
- 可追蹤的推理鏈路
- 模組化的元件設計
- 完善的效能監控體系
這些元件和最佳實踐為構建可靠、可擴充套件的企業級 Agent 系統提供了堅實的基礎。在實際應用中，可以根據具體需求對這些元件進行定製和擴充套件。