构建企业代理系统：核心组件设计与优化

from typing import protocol, dictfrom jinja2 import templateclass prompttemplate(protocol):    def render(self, **kwargs) -> str:        passclass 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, listfrom datetime import datetimeclass memoryentry:    def __init__(self, content: any, importance: float):        self.content = content        self.importance = importance        self.timestamp = datetime.now()        self.access_count = 0class 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, tupleimport numpy as npfrom sklearn.metrics.pairwise import cosine_similarityclass 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, optionalfrom dataclasses import dataclassimport uuid@dataclassclass 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 loggingfrom opentelemetry import tracefrom prometheus_client import histogramreasoning_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, abstractmethodfrom typing import generic, typevart = typevar('t')class component(abc, generic[t]):    @abstractmethod    def process(self, input_data: t) -> t:        passclass 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 dataclassfrom typing import dictimport time@dataclassclass performancemetrics:    latency: float    memory_usage: float    token_count: int    success_rate: floatclass 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