reflexion pour maths

agent.py

@@ -6,8 +6,16 @@ from langchain.chat_models import init_chat_model
 import os
 from langchain_openai import ChatOpenAI
 from langgraph.prebuilt import create_react_agent
+from langchain_experimental.agents import create_pandas_dataframe_agent
 from openai import OpenAI
 import re
+from pydantic import BaseModel
+from typing import List, Dict
+import pandas as pd
+
+class ToolInput(BaseModel):
+    data: List[Dict]
+    question: str
 
 api_open_ai_agent_key=os.environ["OPENAI_API_KEY"]
 client = OpenAI(api_key=api_open_ai_agent_key)
@@ -123,6 +131,27 @@ def divide(a: float, b: float):
     """Divide two numbers."""
     return a / b
 
+
+@tool
+def open_xlsx_doc(path: str) -> pd.DataFrame:
+    """ Open a pandas DataFrame from a xlsx file path"""
+    df = pd.read_excel(path)
+    return df.to_dict(orient="records")
+@tool
+def create_agent_and_answer(input: ToolInput) -> str:
+    """ From a dataframe, can anwser any question
+    The input should be like : input_data = ToolInput(
+    question="Quel est l'âge moyen ?",
+    data= 'ouput of the open_xlsx_doc tool'
+    """
+    df = pd.DataFrame(input.data)
+    question = input.question
+    agent_excel = create_pandas_dataframe_agent(llm_4o, df, verbose=True, allow_dangerous_code=True)
+    text = agent_excel.run(question)
+
+    return text
+
+
 research_agent = create_react_agent(
     model=llm_4o,
     tools=[wiki_search, arvix_search],

requirements.txt

@@ -7,4 +7,7 @@ langgraph-supervisor
 langchain-openai
 wikipedia
 arxiv
-pymupdf
+pymupdf
+langchain-experimental
+tabulate
+pandas