Update third.py

third.py

@@ -11,12 +11,22 @@ from langchain.agents.tools import Tool
 from bs4 import BeautifulSoup
 import asyncio
 from datetime import timedelta
+from pydantic import BaseModel
+from langchain.schema.output_parser import PydanticOutputParser
 
 # APIキーと検索エンジンID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")
 
+# Pydanticのモデルを定義
+class LLMOutput(BaseModel):
+    action: str
+    action_input: str
+
+# PydanticOutputParserのインスタンスを作成
+output_parser = PydanticOutputParser(LLMOutput)
+
 # 追加: 実行された指示を追跡するリスト
 executed_instructions = []
 
@@ -71,10 +81,14 @@ async def main(editable_output2, keyword_id):
     # Convert the purpose into an instruction in the form of a question.
     instruction = f"Can you research {purpose} and include specific details such as names, ages, careers, product names, service names, store names, locations, times, and any relevant numerical data or statistics in your response?"
 
-    # Run the instruction
+    # Run the instruction with a clear expectation of the output format
     if instruction not in executed_instructions:
-        output_text = agent.run(instruction)
+        raw_output = agent.run(instruction)
         executed_instructions.append(instruction)
+        
+        # Parse the LLM output using the PydanticOutputParser
+        parsed_output = output_parser.parse(raw_output)
+        output_text = parsed_output.action_input
         research_results.append(output_text)
     else:
         output_text = "This instruction has already been executed."