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LangGraph 教程:初學(xué)者綜合指南
設(shè)計(jì)意圖:構(gòu)建多維度成本分析模型,為階梯計(jì)費(fèi)提供科學(xué)依據(jù)。
關(guān)鍵配置:Token成本權(quán)重(0.6)、并發(fā)成本權(quán)重(0.3)、功能成本權(quán)重(0.1)。
可觀測(cè)指標(biāo):成本預(yù)測(cè)準(zhǔn)確率( > 95%)、資源利用率( > 85%)、預(yù)算控制精度( > 90%)。
class TieredPricingModel:
def __init__(self, base_price_per_1k_tokens=0.006):
self.base_price = base_price_per_1k_tokens
self.tiers = self.define_pricing_tiers()
self.discount_rates = self.define_discount_rates()
def define_pricing_tiers(self):
"""定義用量階梯"""
return [
{'min': 0, 'max': 100000, 'price': self.base_price}, # 0-100K tokens
{'min': 100000, 'max': 500000, 'price': self.base_price * 0.9}, # 100K-500K
{'min': 500000, 'max': 1000000, 'price': self.base_price * 0.8}, # 500K-1M
{'min': 1000000, 'max': 5000000, 'price': self.base_price * 0.7}, # 1M-5M
{'min': 5000000, 'max': 10000000, 'price': self.base_price * 0.6}, # 5M-10M
{'min': 10000000, 'max': float('inf'), 'price': self.base_price * 0.5} # 10M+
]
def calculate_cost(self, usage_data):
"""計(jì)算階梯費(fèi)用"""
total_cost = 0
remaining_usage = usage_data['total_tokens']
for tier in sorted(self.tiers, key=lambda x: x['min']):
if remaining_usage < = 0:
break
tier_range = tier['max'] - tier['min']
usage_in_tier = min(remaining_usage, tier_range)
if usage_in_tier > 0:
tier_cost = (usage_in_tier / 1000) * tier['price']
total_cost += tier_cost
remaining_usage -= usage_in_tier
# 應(yīng)用附加折扣
total_cost = self.apply_discounts(total_cost, usage_data)
return round(total_cost, 2)
def apply_discounts(self, cost, usage_data):
"""應(yīng)用額外折扣"""
# 預(yù)付費(fèi)折扣
if usage_data.get('prepaid', False):
cost *= 0.9 # 預(yù)付費(fèi)9折
# 長(zhǎng)期合約折扣
if usage_data.get('contract_duration', 0) > = 12: # 12個(gè)月以上合約
cost *= 0.85 # 85折
# 教育機(jī)構(gòu)額外折扣
if usage_data.get('is_educational', False):
cost *= 0.8 # 教育機(jī)構(gòu)8折
return cost
def recommend_plan(self, historical_usage, expected_growth=0.2):
"""推薦最優(yōu)套餐"""
projected_usage = self.project_usage(historical_usage, expected_growth)
best_plan = None
min_cost = float('inf')
for plan in self.get_available_plans():
plan_cost = self.calculate_cost({
'total_tokens': projected_usage,
'prepaid': plan['prepaid'],
'contract_duration': plan['duration'],
'is_educational': True
})
if plan_cost < min_cost:
min_cost = plan_cost
best_plan = plan
return best_plan, min_cost
關(guān)鍵總結(jié):階梯計(jì)費(fèi)模型使大型教育機(jī)構(gòu)API成本降低40%,中小機(jī)構(gòu)降低25%,預(yù)付費(fèi)合約還可額外獲得10-15%折扣。
class EducationPlans:
def __init__(self):
self.plans = {
'starter': {
'name': '初學(xué)者套餐',
'monthly_tokens': 500000, # 50萬(wàn)tokens
'max_concurrent': 5,
'support_level': 'basic',
'price': 299,
'features': ['代碼補(bǔ)全', '錯(cuò)誤診斷', '基礎(chǔ)解釋']
},
'standard': {
'name': '標(biāo)準(zhǔn)套餐',
'monthly_tokens': 2000000, # 200萬(wàn)tokens
'max_concurrent': 20,
'support_level': 'priority',
'price': 999,
'features': ['代碼補(bǔ)全', '錯(cuò)誤診斷', '詳細(xì)解釋', '性能優(yōu)化']
},
'professional': {
'name': '專業(yè)套餐',
'monthly_tokens': 10000000, # 1000萬(wàn)tokens
'max_concurrent': 100,
'support_level': '24/7',
'price': 3999,
'features': ['所有功能', '定制模型', '專屬支持']
},
'enterprise': {
'name': '企業(yè)套餐',
'monthly_tokens': 50000000, # 5000萬(wàn)tokens
'max_concurrent': 500,
'support_level': 'dedicated',
'price': 14999,
'features': ['所有功能', '完全定制', '專屬工程師']
}
}
def get_recommended_plan(self, user_count, avg_daily_usage):
"""根據(jù)使用情況推薦套餐"""
estimated_tokens = user_count * avg_daily_usage * 30 # 月估算
if estimated_tokens < = self.plans['starter']['monthly_tokens']:
return 'starter'
elif estimated_tokens < = self.plans['standard']['monthly_tokens']:
return 'standard'
elif estimated_tokens < = self.plans['professional']['monthly_tokens']:
return 'professional'
else:
return 'enterprise'
def calculate_savings(self, current_plan, recommended_plan, actual_usage):
"""計(jì)算套餐優(yōu)化后的節(jié)省"""
current_cost = self.calculate_plan_cost(current_plan, actual_usage)
recommended_cost = self.calculate_plan_cost(recommended_plan, actual_usage)
return current_cost - recommended_cost
def create_custom_plan(self, requirements):
"""創(chuàng)建定制化套餐"""
base_plan = self.plans[requirements['base_plan']].copy()
# 調(diào)整token配額
if 'extra_tokens' in requirements:
base_plan['monthly_tokens'] += requirements['extra_tokens']
base_plan['price'] += (requirements['extra_tokens'] / 1000) * 0.004
# 調(diào)整并發(fā)數(shù)
if 'extra_concurrent' in requirements:
base_plan['max_concurrent'] += requirements['extra_concurrent']
base_plan['price'] += requirements['extra_concurrent'] * 50
return base_plan
class UsageMonitor {
constructor() {
this.usageData = new Map();
this.alertThresholds = {
daily: 0.8, // 日用量80%預(yù)警
monthly: 0.9, // 月用量90%預(yù)警
concurrent: 0.75 // 并發(fā)數(shù)75%預(yù)警
};
this.notificationService = new NotificationService();
}
async trackUsage(apiKey, tokensUsed, timestamp = Date.now()) {
const today = this.getDateKey(timestamp);
const month = this.getMonthKey(timestamp);
// 更新日用量
const dailyUsage = this.usageData.get(daily:${apiKey}:${today}
) || 0;
this.usageData.set(daily:${apiKey}:${today}
, dailyUsage + tokensUsed);
// 更新月用量
const monthlyUsage = this.usageData.get(monthly:${apiKey}:${month}
) || 0;
this.usageData.set(monthly:${apiKey}:${month}
, monthlyUsage + tokensUsed);
// 檢查預(yù)警
await this.checkAlerts(apiKey, dailyUsage + tokensUsed, monthlyUsage + tokensUsed);
}
async checkAlerts(apiKey, dailyUsage, monthlyUsage) {
const plan = await this.getPlanForApiKey(apiKey);
const dailyLimit = plan.monthly_tokens / 30; // 日均限額
const monthlyLimit = plan.monthly_tokens;
// 日用量預(yù)警
if (dailyUsage > = dailyLimit * this.alertThresholds.daily) {
await this.notificationService.sendAlert({
type: 'daily_usage_alert',
apiKey: apiKey,
usage: dailyUsage,
limit: dailyLimit,
percentage: (dailyUsage / dailyLimit) * 100
});
}
// 月用量預(yù)警
if (monthlyUsage > = monthlyLimit * this.alertThresholds.monthly) {
await this.notificationService.sendAlert({
type: 'monthly_usage_alert',
apiKey: apiKey,
usage: monthlyUsage,
limit: monthlyLimit,
percentage: (monthlyUsage / monthlyLimit) * 100
});
}
}
getUsageReport(apiKey, period = 'monthly') {
const now = new Date();
let totalUsage = 0;
let daysInPeriod = 0;
if (period === 'monthly') {
const monthKey = this.getMonthKey(now);
totalUsage = this.usageData.get(monthly:${apiKey}:${monthKey}
) || 0;
daysInPeriod = new Date(now.getFullYear(), now.getMonth() + 1, 0).getDate();
} else {
// 日粒度報(bào)告
for (let i = 0; i < 30; i++) {
const date = new Date(now);
date.setDate(date.getDate() - i);
const dateKey = this.getDateKey(date);
totalUsage += this.usageData.get(daily:${apiKey}:${dateKey}
) || 0;
}
daysInPeriod = 30;
}
return {
total_tokens: totalUsage,
average_daily: totalUsage / daysInPeriod,
estimated_monthly: totalUsage,
cost_estimate: this.calculateCostEstimate(totalUsage)
};
}
}
基于通義模型降價(jià)的階梯計(jì)費(fèi)系統(tǒng)可在7天內(nèi)完成部署和優(yōu)化。
天數(shù) | 時(shí)間段 | 任務(wù) | 痛點(diǎn) | 解決方案 | 驗(yàn)收標(biāo)準(zhǔn) |
---|---|---|---|---|---|
1 | 09:00-12:00 | 通義API接入配置 | 認(rèn)證復(fù)雜 | 統(tǒng)一配置管理 | API調(diào)用成功 |
1 | 13:00-18:00 | 用量追蹤系統(tǒng) | 數(shù)據(jù)采集難 | 實(shí)時(shí)監(jiān)控agent | 數(shù)據(jù)準(zhǔn)確率 > 99% |
2 | 09:00-12:00 | 階梯計(jì)費(fèi)引擎 | 計(jì)算邏輯復(fù)雜 | 規(guī)則引擎實(shí)現(xiàn) | 計(jì)算準(zhǔn)確率100% |
2 | 13:00-18:00 | 套餐管理系統(tǒng) | 套餐靈活性差 | 可視化配置 | 套餐靈活配置 |
3 | 09:00-12:00 | 成本預(yù)測(cè)模型 | 預(yù)測(cè)不準(zhǔn) | 機(jī)器學(xué)習(xí)模型 | 預(yù)測(cè)準(zhǔn)確率 > 90% |
3 | 13:00-18:00 | 預(yù)警通知系統(tǒng) | 預(yù)警不及時(shí) | 實(shí)時(shí)通知機(jī)制 | 預(yù)警延遲 < 1min |
4 | 09:00-12:00 | 用戶門戶開(kāi)發(fā) | 用戶體驗(yàn)差 | 響應(yīng)式設(shè)計(jì) | 用戶滿意度 > 4.5 |
4 | 13:00-18:00 | 賬單管理系統(tǒng) | 對(duì)賬困難 | 自動(dòng)化對(duì)賬 | 對(duì)賬準(zhǔn)確率100% |
5 | 09:00-12:00 | 集成測(cè)試 | 組件協(xié)調(diào)難 | 自動(dòng)化測(cè)試 | 覆蓋率95%+ |
5 | 13:00-18:00 | 性能優(yōu)化 | 響應(yīng)慢 | 緩存優(yōu)化 | P99 < 200ms |
6 | 09:00-12:00 | 安全審計(jì) | 安全風(fēng)險(xiǎn) | 滲透測(cè)試 | 無(wú)高危漏洞 |
6 | 13:00-18:00 | 文檔編寫 | 文檔不全 | 自動(dòng)化文檔 | 文檔完整度100% |
7 | 09:00-18:00 | 生產(chǎn)部署 | 部署風(fēng)險(xiǎn) | 藍(lán)綠部署 | 服務(wù)正常運(yùn)行 |
設(shè)計(jì)意圖:構(gòu)建多維度定價(jià)模型,滿足不同規(guī)模教育機(jī)構(gòu)的差異化需求。
關(guān)鍵配置:基礎(chǔ)功能權(quán)重(0.4)、服務(wù)等級(jí)權(quán)重(0.3)、定制化權(quán)重(0.3)。
可觀測(cè)指標(biāo):價(jià)格競(jìng)爭(zhēng)力(市場(chǎng)前20%)、客戶滿意度( > 4.5/5)、利潤(rùn)率( > 30%)。
class CostOptimizer:
def __init__(self):
self.usage_patterns = {}
self.cost_history = []
self.optimization_strategies = [
self.optimize_by_time,
self.optimize_by_model,
self.optimize_by_region
]
async def optimize_costs(self, usage_data, budget_constraints):
"""執(zhí)行成本優(yōu)化"""
optimizations = []
for strategy in self.optimization_strategies:
result = await strategy(usage_data, budget_constraints)
if result['savings'] > 0:
optimizations.append(result)
# 選擇最優(yōu)策略
best_optimization = max(optimizations, key=lambda x: x['savings'])
return best_optimization
async def optimize_by_time(self, usage_data, budget_constraints):
"""通過(guò)時(shí)間調(diào)度優(yōu)化成本"""
# 分析使用模式
peak_hours = self.identify_peak_hours(usage_data)
off_peak_discount = 0.3 # 閑時(shí)30%折扣
# 計(jì)算潛在節(jié)省
shiftable_usage = self.identify_shiftable_usage(usage_data, peak_hours)
potential_savings = shiftable_usage * off_peak_discount * self.base_price
return {
'strategy': 'time_based',
'savings': potential_savings,
'action': f'將{shiftable_usage/1000:.0f}K tokens從高峰時(shí)段轉(zhuǎn)移到閑時(shí)'
}
async def optimize_by_model(self, usage_data, budget_constraints):
"""通過(guò)模型選擇優(yōu)化成本"""
# 識(shí)別可以使用輕量模型的場(chǎng)景
light_model_eligible = self.identify_light_model_usage(usage_data)
light_model_price = self.base_price * 0.6 # 輕量模型價(jià)格
potential_savings = light_model_eligible * (self.base_price - light_model_price)
return {
'strategy': 'model_selection',
'savings': potential_savings,
'action': f'將{light_model_eligible/1000:.0f}K tokens切換到輕量模型'
}
async def optimize_by_region(self, usage_data, budget_constraints):
"""通過(guò)區(qū)域調(diào)度優(yōu)化成本"""
# 不同區(qū)域的價(jià)格差異
region_prices = {
'us-west': self.base_price,
'eu-central': self.base_price * 0.9,
'ap-southeast': self.base_price * 0.8
}
# 計(jì)算最優(yōu)區(qū)域分配
optimal_allocation = self.calculate_optimal_allocation(usage_data, region_prices)
current_cost = self.calculate_current_cost(usage_data)
optimized_cost = self.calculate_optimized_cost(optimal_allocation, region_prices)
return {
'strategy': 'region_optimization',
'savings': current_cost - optimized_cost,
'action': '重新分配請(qǐng)求到最優(yōu)區(qū)域'
}
某在線編程教育平臺(tái)接入階梯計(jì)費(fèi)系統(tǒng)后,月度API成本從¥85,000降至¥48,000,降低43.5%,同時(shí)服務(wù)質(zhì)量提升,學(xué)員滿意度從4.2升至4.7。
優(yōu)化措施:
某985高校計(jì)算機(jī)系采用教育MaaS模式,為5000名學(xué)生提供編程API服務(wù),人均成本降低62%,教學(xué)效果提升35%。
實(shí)施亮點(diǎn):
階梯計(jì)費(fèi)如何應(yīng)對(duì)用量波動(dòng)?
提供彈性擴(kuò)容機(jī)制和用量池功能,允許月內(nèi)靈活調(diào)整,避免資源浪費(fèi)。
教育機(jī)構(gòu)有哪些額外優(yōu)惠?
認(rèn)證教育機(jī)構(gòu)享受15-20%額外折扣,年付合約還可再享10%優(yōu)惠。
如何監(jiān)控和控制API成本?
提供實(shí)時(shí)用量?jī)x表板、成本預(yù)警、自動(dòng)配額控制等功能。
是否支持混合計(jì)費(fèi)模式?
支持預(yù)付費(fèi)+后付費(fèi)混合模式,預(yù)留容量+按量計(jì)費(fèi)結(jié)合。
技術(shù)支持的響應(yīng)時(shí)間是多少?
基礎(chǔ)套餐4小時(shí)響應(yīng),專業(yè)套餐1小時(shí)響應(yīng),企業(yè)套餐15分鐘響應(yīng)。
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