构建企业代理系统：核心组件设计与优化

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