import streamlit as st
    import os
    import tempfile
    from pathlib import Path
    from pydantic import BaseModel, Field
    import streamlit as st
    from langchain.chat_models import ChatOpenAI
    from langchain.agents import Tool
    from langchain.embeddings.openai import OpenAIEmbeddings
    from langchain.text_splitter import CharacterTextSplitter
    from langchain.vectorstores import FAISS
    from langchain.document_loaders import PyPDFLoader
    from langchain.chains import RetrievalQA
    from langchain.agents import initialize_agent
    import openai
os.environ["OPENAI_API_KEY"] = ""
os.environ['OPENAI_API_TYPE'] = 'azure'
os.environ['OPENAI_API_VERSION'] = '2023-03-15-preview'
os.environ['OPENAI_API_BASE'] = "https://summarization"

#API settings for embedding
openai.api_type = "azure"
openai.api_base = "https://summarization"
openai.api_version = '2023-03-15-'
openai.api_key = ""
    
    
    class DocumentInput(BaseModel):
        question: str = Field()
    # Create a temporary directory in the script's folder
    script_dir = Path(__file__).resolve().parent
    temp_dir = os.path.join(script_dir, "tempDir")
    
    
    def main():
        st.title("PDF Document Comparison")
    
        # Create a form to upload PDF files and enter a question
        st.write("Upload the first PDF file:")
        pdf1 = st.file_uploader("Choose a PDF file", type=["pdf"], key="pdf1")
    
        st.write("Upload the second PDF file:")
        pdf2 = st.file_uploader("Choose a PDF file", type=["pdf"], key="pdf2")
    
        question = st.text_input("Enter your question")
        submit_button = st.button("Compare PDFs")
    
        if submit_button:
            if pdf1 and pdf2:
                if not os.path.exists(temp_dir):
                    os.makedirs(temp_dir)
                else:
                    # Clear the previous contents of the "tempDir" folder
                    for file in os.listdir(temp_dir):
                        file_path = os.path.join(temp_dir, file)
                        try:
                            if os.path.isfile(file_path):
                                os.unlink(file_path)
                        except Exception as e:
                            print(f"Error deleting file: {e}")
    
                # Save the PDF files to the "tempDir" directory
                pdf1_path = os.path.join(temp_dir, pdf1.name)
                with open(pdf1_path, 'wb') as f:
                    f.write(pdf1.getbuffer())
    
                pdf2_path = os.path.join(temp_dir, pdf2.name)
                with open(pdf2_path, 'wb') as f:
                    f.write(pdf2.getbuffer())
    
    
    
                llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo",engine="gpt-35-turbo")
    
                tools = []
                files = [
    
                    {
                        "name": pdf1.name,
                        "path": pdf1_path,
                    },
    
                    {
                        "name": pdf2.name,
                        "path": pdf2_path,
                    },
                ]
    
                for file in files:
                    loader = PyPDFLoader(file["path"])
                    pages = loader.load_and_split()
                    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
                    docs = text_splitter.split_documents(pages)
                    embeddings = OpenAIEmbeddings()
                    retriever = FAISS.from_documents(docs, embeddings).as_retriever()
    
                    # Wrap retrievers in a Tool
                    tools.append(
                        Tool(
                            args_schema=DocumentInput,
                            name=file["name"],
                            description=f"useful when you want to answer questions about {file['name']}",
                            func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever),
                        )
                    )
                agent = initialize_agent(
                    tools=tools,
                    llm=llm,
                    verbose=True,
                )
    
                st.write(agent({"input": question}))
                # Now you have both PDFs saved in the "tempDir" folder
                # You can perform your PDF comparison here
    
    
    if __name__ == "__main__":
        main()

我得到以下错误：pydantic.v1.error_wrappers.ValidationError：1 validation error for Tool args_schema subclass of BaseModel expected（type=type_error.subclass; expected_class=BaseModel）我遵循langchain文档中的示例：https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit