Update third.py

third.py

@@ -3,78 +3,76 @@
 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.utilities import GoogleSearchAPIWrapper
 from langchain.agents.tools import Tool
 from bs4 import BeautifulSoup
 import asyncio
 from datetime import timedelta
 
-# APIキーと検索エンジンIDの設定
+# APIキーの設定
 openai.api_key = os.getenv("OPENAI_API_KEY")
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-CUSTOM_SEARCH_ENGINE_ID = os.getenv("CUSTOM_SEARCH_ENGINE_ID")
+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):
-    search = GoogleSearchAPIWrapper(google_cse_id=CUSTOM_SEARCH_ENGINE_ID, google_api_key=GOOGLE_API_KEY)
+    tavily_search_tool = Tool(
+        name="TavilySearch",
+        func=TavilySearchTool.search,
+        description="Search tool using Tavily API"
+    )
 
-    tools = [
-        Tool(
-            name = "Search",
-            func=search.run,
-            description="useful for when you need to answer questions about current events"
-        ),
-    ]
+    tools = [tavily_search_tool]
 
-    # 「Planner」,「Executor」, および Agentの定義
+    # 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)
 
-    # ここでexecutorの内容を出力して確認
-    print("Executor:", executor)
-
     agent = PlanAndExecute(planner=planner, executor=executor, verbose=True)
 
-    # editable_output2 is the text of the article structure (h1, h2, h3...).
+    # HTML解析
     soup = BeautifulSoup(editable_output2, 'html.parser')
-
-    # Use the text of the h1 tag as the purpose.
     h1_text = soup.find('h1').get_text()
-
-    # Use the text of the h2 tags as part of the purpose.
     h2_texts = [h2.get_text() for h2 in soup.find_all('h2')]
-
-    # Use the text of the h3 tags as part of the purpose.
     h3_texts = [h3.get_text() for h3 in soup.find_all('h3')]
 
-    # Generate the purpose.
     purpose = f"about {h1_text}, focusing particularly on {' and '.join(h2_texts)} and {' and '.join(h3_texts)}, to investigate the latest information and details"
 
-    # Specify the type of information you want to research.
-    if "人物" in h1_text or any("人物" in h2_text for h2_text in h2_texts) or any("人物" in h3_text for h3_text in h3_texts):
-        # If the topic is about a person, specify that you want to research their name and career.
+    # 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_text for h2_text in h2_texts) or any("商品" in h3_text for h3_text in h3_texts):
-        # If the topic is about a product, specify that you want to research the brand name, product name, and price.
+    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_text for h2_text in h2_texts) or any("イベント" in h3_text for h3_text in h3_texts):
-        # If the topic is about an event, specify that you want to research the event's content, schedule, and venue.
+    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"
 
-    # Convert the purpose into an instruction in the form of a question.
     instruction = f"Can you research {purpose} and include specific details in your response? Please provide the information in Japanese."
 
-    # Run the instruction with a clear expectation of the output format
     if instruction not in executed_instructions:
         raw_output = agent.run(instruction)
         executed_instructions.append(instruction)
@@ -84,41 +82,33 @@ async def main(editable_output2, keyword_id):
         index = executed_instructions.index(instruction)
         response_content = research_results[index]
 
-    # Prepare the system message
     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。"
     }
 
-    # Prepare the user message
     research_summary = "\n".join(research_results)
     instructions = []
 
-    # 記事タイトルに関する指示
     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。""")
 
-    # research_summaryを文単位に分割
     sentences = research_summary.split('。')
 
-    # 各見出しと小見出しに関する指示
     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:
-            # 関連する文を基に、<h3>の内容に関する記事本文を生成
             related_sentences = [sentence for sentence in sentences if h3 in sentence]
             if related_sentences:
                 content_for_h3 = "。".join(related_sentences) + "。"
-                # <h3>タグを使用した指示に修正
                 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:
-                # <h3>タグを使用した指示に修正
                 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。""")
@@ -128,7 +118,6 @@ async def main(editable_output2, keyword_id):
         "content": "\n".join(instructions)
     }
 
-    # Generate a new text using the ChatCompletion API
     response = openai.ChatCompletion.create(
         model="gpt-4-0125-preview",
         messages=[system_message, user_message],
@@ -136,10 +125,7 @@ async def main(editable_output2, keyword_id):
     )
     result = response.choices[0]["message"]["content"]
 
-    # Save the generated message to output3.txt
     with open('output3.txt', 'w', encoding='utf-8') as f:
         f.write(result)
 
-    # Print the generated message
-    print(result)
-    
+    print(result)