建構具有成本效益的多模型系統：GPT- GPT- 實施指南-Python教學-PHP中文網

建構具有成本效益的多模型系統：GPT- GPT- 實施指南

Barbara Streisand

發布： 2024-11-20 04:56:01

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Building a Cost-Effective Multi-Model System: GPT- GPT- Implementation Guide

長話短說

了解如何有效結合GPT-4和GPT-3.5的優點
掌握多模型系統的成本最佳化策略
基於LangChain的實用實作方案
詳細的效能指標與成本比較

為什麼要進行多模式協作？

在實際商業場景中，我們經常面臨以下挑戰：

GPT-4 性能出色，但成本高昂（約 0.03 美元/1K 代幣）
GPT-3.5 具有成本效益，但在某些任務中表現不佳（約 0.002 美元/1K 代幣）
不同的任務需要不同的模型表現水準

理想的解決方案是根據任務複雜度動態選擇合適的模型，確保效能的同時控製成本。

系統架構設計

核心組件

任務分析器：評估任務複雜度
路由中間件：模型選擇策略
成本控制器：預算管理與成本追蹤
績效監控：回應品質評估

工作流程

接收用戶輸入
任務複雜度評估
模型選擇決定
執行與監控
結果品質驗證

具體實施

1. 基本環境設定

from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.prompts import ChatPromptTemplate
from langchain.callbacks import get_openai_callback
from typing import Dict, List, Optional
import json

# Initialize models
class ModelPool:
    def __init__(self):
        self.gpt4 = ChatOpenAI(
            model_name="gpt-4",
            temperature=0.7,
            max_tokens=1000
        )
        self.gpt35 = ChatOpenAI(
            model_name="gpt-3.5-turbo",
            temperature=0.7,
            max_tokens=1000
        )

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2. 任務複雜度分析器

class ComplexityAnalyzer:
    def __init__(self):
        self.complexity_prompt = ChatPromptTemplate.from_template(
            "Analyze the complexity of the following task, return a score from 1-10:\n{task}"
        )
        self.analyzer_chain = LLMChain(
            llm=ChatOpenAI(model_name="gpt-3.5-turbo"),
            prompt=self.complexity_prompt
        )

    async def analyze(self, task: str) -> int:
        result = await self.analyzer_chain.arun(task=task)
        return int(result.strip())

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3、智慧路由中介軟體

class ModelRouter:
    def __init__(self, complexity_threshold: int = 7):
        self.complexity_threshold = complexity_threshold
        self.model_pool = ModelPool()
        self.analyzer = ComplexityAnalyzer()

    async def route(self, task: str) -> ChatOpenAI:
        complexity = await self.analyzer.analyze(task)
        if complexity >= self.complexity_threshold:
            return self.model_pool.gpt4
        return self.model_pool.gpt35

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4. 成本控制員

class CostController:
    def __init__(self, budget_limit: float):
        self.budget_limit = budget_limit
        self.total_cost = 0.0

    def track_cost(self, callback_data):
        cost = callback_data.total_cost
        self.total_cost += cost
        if self.total_cost > self.budget_limit:
            raise Exception("Budget exceeded")
        return cost

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5. 完整的系統實施

class MultiModelSystem:
    def __init__(self, budget_limit: float = 10.0):
        self.router = ModelRouter()
        self.cost_controller = CostController(budget_limit)

    async def process(self, task: str) -> Dict:
        model = await self.router.route(task)

        with get_openai_callback() as cb:
            response = await model.agenerate([[task]])
            cost = self.cost_controller.track_cost(cb)

        return {
            "result": response.generations[0][0].text,
            "model": model.model_name,
            "cost": cost
        }

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實際應用範例

讓我們透過一個客戶服務範例來示範該系統：

async def customer_service_demo():
    system = MultiModelSystem(budget_limit=1.0)

    # Simple query - should route to GPT-3.5
    simple_query = "What are your business hours?"
    simple_result = await system.process(simple_query)

    # Complex query - should route to GPT-4
    complex_query = """
    I'd like to understand your return policy. Specifically:
    1. If the product has quality issues but has been used for a while
    2. If it's a limited item but the packaging has been opened
    3. If it's a cross-border purchase
    How should these situations be handled? What costs are involved?
    """
    complex_result = await system.process(complex_query)

    return simple_result, complex_result

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