third.py

# -*- coding: utf-8 -*-

import os
import openai
import json
import requests
from langchain.chat_models import ChatOpenAI
from langchain_experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_planner
from langchain.llms import OpenAI
from langchain.agents.tools import Tool
from bs4 import BeautifulSoup
import asyncio
from datetime import timedelta

# APIキーの設定
openai.api_key = os.getenv("OPENAI_API_KEY")
TAVILY_API_KEY = os.getenv('TAVILY_API_KEY')

# Tavily APIのツールを定義
class TavilySearchTool:
    @staticmethod
    def search(query):
        response = requests.post('https://api.tavily.com/search', headers={
            'Content-Type': 'application/json'
        }, json={
            'api_key': TAVILY_API_KEY,
            'query': query,
            'max_results': 10,
            'include_answers': True
        })
        if response.status_code == 200:
            return response.json()['results']
        else:
            raise Exception("Failed to fetch data from Tavily API")

# 実行された指示を追跡するリスト
executed_instructions = []
# 調査結果を保存するリスト
research_results = []

async def main(editable_output2, keyword_id):
    tavily_search_tool = Tool(
        name="TavilySearch",
        func=TavilySearchTool.search,
        description="Search tool using Tavily API"
    )

    tools = [tavily_search_tool]

    # PlannerとExecutorの定義
    model_name = "gpt-3.5-turbo-1106"
    llm = ChatOpenAI(model_name=model_name, temperature=0, max_tokens=1000)
    planner = load_chat_planner(llm)
    executor = load_agent_executor(llm, tools, verbose=True)

    agent = PlanAndExecute(planner=planner, executor=executor, verbose=True)

    # HTML解析
    soup = BeautifulSoup(editable_output2, 'html.parser')
    h1_text = soup.find('h1').get_text()
    h2_texts = [h2.get_text() for h2 in soup.find_all('h2')]
    h3_texts = [h3.get_text() for h3 in soup.find_all('h3')]

    purpose = f"about {h1_text}, focusing particularly on {' and '.join(h2_texts)} and {' and '.join(h3_texts)}, to investigate the latest information and details"

    # 特定情報の指定
    if "人物" in h1_text or any("人物" in h2 for h2 in h2_texts) or any("人物" in h3 for h3 in h3_texts):
        purpose += " including the person's name and career"
    elif "商品" in h1_text or any("商品" in h2 for h2 in h2_texts) or any("商品" in h3 for h3 in h3_texts):
        purpose += " including the brand name, product name, and price"
    elif "イベント" in h1_text or any("イベント" in h2 for h2 in h2_texts) or any("イベント" in h3 for h3 in h3_texts):
        purpose += " including the event's content, schedule, and venue"

    instruction = f"Can you research {purpose} and include specific details in your response? Please provide the information in Japanese."

    if instruction not in executed_instructions:
        raw_output = agent.run(instruction)
        executed_instructions.append(instruction)
        response_content = raw_output
        research_results.append(response_content)
    else:
        index = executed_instructions.index(instruction)
        response_content = research_results[index]

    system_message = {
        "role": "system",
        "content": "あなたはプロのライターです。すべての回答を日本語でお願いします。"
    }

    research_summary = "\n".join(research_results)
    instructions = []

    instructions.append(f"""
    <h1>{h1_text}</h1> 
    "{h1_text}"に関する導入文を日本語で作成してください。直接的なコピーまたは近いフレーズを避けて、オリジナルな内容にしてください。""")

    sentences = research_summary.split('。')

    for idx, h2_text in enumerate(h2_texts):
        h3_for_this_h2 = [h3 for h3 in h3_texts if h3.startswith(f"{idx+1}-")]
        instructions.append(f"""
            <h2>{h2_text}</h2>
            "{h2_text}"に関する導入文を日本語で作成してください。この導入文は、以下の小見出しの内容を考慮してください：{"、".join(h3_for_this_h2)}。直接的なコピーまたは近いフレーズを避けて、オリジナルな内容にしてください。""")
        for h3 in h3_for_this_h2:
            related_sentences = [sentence for sentence in sentences if h3 in sentence]
            if related_sentences:
                content_for_h3 = "。".join(related_sentences) + "。"
                instructions.append(f"""
                    <h3>{h3}</h3>
                    "{h3}"に関する詳細な内容として、以下の情報を日本語で記述してください：{content_for_h3} ここでも、オリジナルな内容を心がけてください。""")
            else:
                instructions.append(f"""
                    <h3>{h3}</h3>
                    "{h3}"に関する詳細な内容を日本語で記述してください。オリジナルな内容を心がけてください。""")

    user_message = {
        "role": "user",
        "content": "\n".join(instructions)
    }

    response = openai.ChatCompletion.create(
        model="gpt-4-0125-preview",
        messages=[system_message, user_message],
        temperature=0.7,
    )
    result = response.choices[0]["message"]["content"]

    with open('output3.txt', 'w', encoding='utf-8') as f:
        f.write(result)

    print(result)