{"id":205085,"date":"2025-05-29T10:28:54","date_gmt":"2025-05-29T02:28:54","guid":{"rendered":"https:\/\/server.hk\/cnblog\/205085\/"},"modified":"2025-05-29T10:28:54","modified_gmt":"2025-05-29T02:28:54","slug":"%e6%9e%84%e5%bb%ba%e4%bc%81%e4%b8%9a%e4%bb%a3%e7%90%86%e7%b3%bb%e7%bb%9f%ef%bc%9a%e6%a0%b8%e5%bf%83%e7%bb%84%e4%bb%b6%e8%ae%be%e8%ae%a1%e4%b8%8e%e4%bc%98%e5%8c%96","status":"publish","type":"post","link":"https:\/\/server.hk\/cnblog\/205085\/","title":{"rendered":"\u6784\u5efa\u4f01\u4e1a\u4ee3\u7406\u7cfb\u7edf\uff1a\u6838\u5fc3\u7ec4\u4ef6\u8bbe\u8ba1\u4e0e\u4f18\u5316"},"content":{"rendered":"

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from typing import protocol, dict\nfrom jinja2 import template\n\nclass prompttemplate(protocol):\n    def render(self, **kwargs) -> str:\n        pass\n\nclass jinjaprompttemplate:\n    def __init__(self, template_string: str):\n        self.template = template(template_string)\n\n    def render(self, **kwargs) -> str:\n        return self.template.render(**kwargs)\n\nclass promptlibrary:\n    def __init__(self):\n        self.templates: dict[str, prompttemplate] = {}\n\n    def register_template(self, name: str, template: prompttemplate):\n        self.templates[name] = template\n\n    def get_template(self, name: str) -> prompttemplate:\n        return self.templates[name]\n<\/pre>\n
class promptversion:\n    def __init__(self, version: str, template: str, metadata: dict):\n        self.version = version\n        self.template = template\n        self.metadata = metadata\n        self.test_cases = []\n\n    def add_test_case(self, inputs: dict, expected_output: str):\n        self.test_cases.append((inputs, expected_output))\n\n    def validate(self) -> bool:\n        template = jinjaprompttemplate(self.template)\n        for inputs, expected in self.test_cases:\n            result = template.render(**inputs)\n            if not self._validate_output(result, expected):\n                return false\n        return true\n<\/pre>\n
from typing import any, list\nfrom datetime import datetime\n\nclass memoryentry:\n    def __init__(self, content: any, importance: float):\n        self.content = content\n        self.importance = importance\n        self.timestamp = datetime.now()\n        self.access_count = 0\n\nclass memorylayer:\n    def __init__(self, capacity: int):\n        self.capacity = capacity\n        self.memories: list[memoryentry] = []\n\n    def add(self, entry: memoryentry):\n        if len(self.memories) >= self.capacity:\n            self._evict()\n        self.memories.append(entry)\n\n    def _evict(self):\n        # implement memory eviction strategy\n        self.memories.sort(key=lambda x: x.importance * x.access_count)\n        self.memories.pop(0)\n\nclass hierarchicalmemory:\n    def __init__(self):\n        self.working_memory = memorylayer(capacity=5)\n        self.short_term = memorylayer(capacity=50)\n        self.long_term = memorylayer(capacity=1000)\n\n    def store(self, content: any, importance: float):\n        entry = memoryentry(content, importance)\n\n        if importance > 0.8:\n            self.working_memory.add(entry)\n        elif importance > 0.5:\n            self.short_term.add(entry)\n        else:\n            self.long_term.add(entry)\n<\/pre>\n
from typing import list, tuple\nimport numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity\n\nclass memoryindex:\n    def __init__(self, embedding_model):\n        self.embedding_model = embedding_model\n        self.embeddings = []\n        self.memories = []\n\n    def add(self, memory: memoryentry):\n        embedding = self.embedding_model.embed(memory.content)\n        self.embeddings.append(embedding)\n        self.memories.append(memory)\n\n    def search(self, query: str, k: int = 5) -> list[tuple[memoryentry, float]]:\n        query_embedding = self.embedding_model.embed(query)\n        similarities = cosine_similarity(\n            [query_embedding], \n            self.embeddings\n        )[0]\n\n        top_k_indices = np.argsort(similarities)[-k:]\n\n        return [\n            (self.memories[i], similarities[i]) \n            for i in top_k_indices\n        ]\n<\/pre>\n
from typing import list, optional\nfrom dataclasses import dataclass\nimport uuid\n\n@dataclass\nclass thoughtnode:\n    content: str\n    confidence: float\n    supporting_evidence: list[str]\n\nclass reasoningchain:\n    def __init__(self):\n        self.chain_id = str(uuid.uuid4())\n        self.nodes: list[thoughtnode] = []\n        self.metadata = {}\n\n    def add_thought(self, thought: thoughtnode):\n        self.nodes.append(thought)\n\n    def get_path(self) -> list[str]:\n        return [node.content for node in self.nodes]\n\n    def get_confidence(self) -> float:\n        if not self.nodes:\n            return 0.0\n        return sum(n.confidence for n in self.nodes) \/ len(self.nodes)\n<\/pre>\n
import logging\nfrom opentelemetry import trace\nfrom prometheus_client import histogram\n\nreasoning_time = histogram(\n    'reasoning_chain_duration_seconds',\n    'time spent in reasoning chain'\n)\n\nclass chainmonitor:\n    def __init__(self):\n        self.tracer = trace.get_tracer(__name__)\n\n    def monitor_chain(self, chain: reasoningchain):\n        with self.tracer.start_as_current_span(\"reasoning_chain\") as span:\n            span.set_attribute(\"chain_id\", chain.chain_id)\n\n            with reasoning_time.time():\n                for node in chain.nodes:\n                    with self.tracer.start_span(\"thought\") as thought_span:\n                        thought_span.set_attribute(\n                            \"confidence\", \n                            node.confidence\n                        )\n                        logging.info(\n                            f\"thought: {node.content} \"\n                            f\"(confidence: {node.confidence})\"\n                        )\n<\/pre>\n
from abc import abc, abstractmethod\nfrom typing import generic, typevar\n\nt = typevar('t')\n\nclass component(abc, generic[t]):\n    @abstractmethod\n    def process(self, input_data: t) -> t:\n        pass\n\nclass pipeline:\n    def __init__(self):\n        self.components: list[component] = []\n\n    def add_component(self, component: component):\n        self.components.append(component)\n\n    def process(self, input_data: any) -> any:\n        result = input_data\n        for component in self.components:\n            result = component.process(result)\n        return result\n<\/pre>\n
class componentregistry:\n    _instance = none\n\n    def __new__(cls):\n        if cls._instance is none:\n            cls._instance = super().__new__(cls)\n            cls._instance.components = {}\n        return cls._instance\n\n    def register(self, name: str, component: component):\n        self.components[name] = component\n\n    def get(self, name: str) -> optional[component]:\n        return self.components.get(name)\n\n    def create_pipeline(self, component_names: list[str]) -> pipeline:\n        pipeline = pipeline()\n        for name in component_names:\n            component = self.get(name)\n            if component:\n                pipeline.add_component(component)\n        return pipeline\n<\/pre>\n
from dataclasses import dataclass\nfrom typing import dict\nimport time\n\n@dataclass\nclass performancemetrics:\n    latency: float\n    memory_usage: float\n    token_count: int\n    success_rate: float\n\nclass performancemonitor:\n    def __init__(self):\n        self.metrics: dict[str, list[performancemetrics]] = {}\n\n    def record_operation(\n        self,\n        operation_name: str,\n        metrics: performancemetrics\n    ):\n        if operation_name not in self.metrics:\n            self.metrics[operation_name] = []\n        self.metrics[operation_name].append(metrics)\n\n    def get_average_metrics(\n        self,\n        operation_name: str\n    ) -> optional[performancemetrics]:\n        if operation_name not in self.metrics:\n            return none\n\n        metrics_list = self.metrics[operation_name]\n        return performancemetrics(\n            latency=sum(m.latency for m in metrics_list) \/ len(metrics_list),\n            memory_usage=sum(m.memory_usage for m in metrics_list) \/ len(metrics_list),\n            token_count=sum(m.token_count for m in metrics_list) \/ len(metrics_list),\n            success_rate=sum(m.success_rate for m in metrics_list) \/ len(metrics_list)\n        )\n<\/pre>\n
class PerformanceOptimizer:\n    def __init__(self, monitor: PerformanceMonitor):\n        self.monitor = monitor\n        self.thresholds = {\n            'latency': 1.0,  # seconds\n            'memory_usage': 512,  # MB\n            'token_count': 1000,\n            'success_rate': 0.95\n        }\n\n    def analyze_performance(self, operation_name: str) -> List[str]:\n        metrics = self.monitor.get_average_metrics(operation_name)\n        if not metrics:\n            return []\n\n        recommendations = []\n\n        if metrics.latency > self.thresholds['latency']:\n            recommendations.append(\n                \"Consider implementing caching or parallel processing\"\n            )\n\n        if metrics.memory_usage > self.thresholds['memory_usage']:\n            recommendations.append(\n                \"Optimize memory usage through batch processing\"\n            )\n\n        if metrics.token_count > self.thresholds['token_count']:\n            recommendations.append(\n                \"Implement prompt optimization to reduce token usage\"\n            )\n\n        if metrics.success_rate < self.thresholds['success_rate']:\n            recommendations.append(\n                \"Review error handling and implement retry mechanisms\"\n            )\n\n        return recommendations\n<\/pre>\n
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