构建企业级人工智能代理需要仔细考虑组件设计、系统架构和工程实践。本文探讨了构建健壮且可扩展的代理系统的关键组件和最佳实践。
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]
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
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):
# Implement memory eviction strategy
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)
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
]
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(n.confidence for n in self.nodes) / len(self.nodes)
import logging
from opentelemetry import trace
from prometheus_client import Histogram
reasoning_time = Histogram(
'reasoning_chain_duration_seconds',
'Time spent in reasoning chain'
)
class ChainMonitor:
def __init__(self):
self.tracer = trace.get_tracer(__name__)
def monitor_chain(self, chain: ReasoningChain):
with self.tracer.start_as_current_span("reasoning_chain") as span:
span.set_attribute("chain_id", chain.chain_id)
with reasoning_time.time():
for node in chain.nodes:
with self.tracer.start_span("thought") as thought_span:
thought_span.set_attribute(
"confidence",
node.confidence
)
logging.info(
f"Thought: {node.content} "
f"(confidence: {node.confidence})"
)
from abc import ABC, abstractmethod
from typing import Generic, TypeVar
T = TypeVar('T')
class Component(ABC, Generic[T]):
@abstractmethod
def process(self, input_data: T) -> T:
pass
class Pipeline:
def __init__(self):
self.components: List[Component] = []
def add_component(self, component: Component):
self.components.append(component)
def process(self, input_data: Any) -> Any:
result = input_data
for component in self.components:
result = component.process(result)
return result
class ComponentRegistry:
_instance = None
def __new__(cls):
if cls._instance is None:
cls._instance = super().__new__(cls)
cls._instance.components = {}
return cls._instance
def register(self, name: str, component: Component):
self.components[name] = component
def get(self, name: str) -> Optional[Component]:
return self.components.get(name)
def create_pipeline(self, component_names: List[str]) -> Pipeline:
pipeline = Pipeline()
for name in component_names:
component = self.get(name)
if component:
pipeline.add_component(component)
return pipeline
from dataclasses import dataclass
from typing import Dict
import time
@dataclass
class PerformanceMetrics:
latency: float
memory_usage: float
token_count: int
success_rate: float
class PerformanceMonitor:
def __init__(self):
self.metrics: Dict[str, List[PerformanceMetrics]] = {}
def record_operation(
self,
operation_name: str,
metrics: PerformanceMetrics
):
if operation_name not in self.metrics:
self.metrics[operation_name] = []
self.metrics[operation_name].append(metrics)
def get_average_metrics(
self,
operation_name: str
) -> Optional[PerformanceMetrics]:
if operation_name not in self.metrics:
return None
metrics_list = self.metrics[operation_name]
return PerformanceMetrics(
latency=sum(m.latency for m in metrics_list) / len(metrics_list),
memory_usage=sum(m.memory_usage for m in metrics_list) / len(metrics_list),
token_count=sum(m.token_count for m in metrics_list) / len(metrics_list),
success_rate=sum(m.success_rate for m in metrics_list) / len(metrics_list)
)
class PerformanceOptimizer:
def __init__(self, monitor: PerformanceMonitor):
self.monitor = monitor
self.thresholds = {
'latency': 1.0, # seconds
'memory_usage': 512, # MB
'token_count': 1000,
'success_rate': 0.95
}
def analyze_performance(self, operation_name: str) -> List[str]:
metrics = self.monitor.get_average_metrics(operation_name)
if not metrics:
return []
recommendations = []
if metrics.latency > self.thresholds['latency']:
recommendations.append(
"Consider implementing caching or parallel processing"
)
if metrics.memory_usage > self.thresholds['memory_usage']:
recommendations.append(
"Optimize memory usage through batch processing"
)
if metrics.token_count > self.thresholds['token_count']:
recommendations.append(
"Implement prompt optimization to reduce token usage"
)
if metrics.success_rate < self.thresholds['success_rate']:
recommendations.append(
"Review error handling and implement retry mechanisms"
)
return recommendations
构建企业级Agent系统需要仔细注意：
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