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