text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsAmadeus ToolkitAzure Cognitive Services ToolkitCSV AgentDocument ComparisonGitHubGmail ToolkitJiraJSON AgentMultion ToolkitOffice365 ToolkitOpenAPI agentsNatural Language APIsPandas Dataframe AgentPlayWright Browser ToolkitPowerBI Dataset AgentPython AgentSpark Dataframe AgentSpark SQL AgentSQL Database AgentVectorstore AgentXorbits AgentToolsVector storesGrouped by providerIntegrationsAgent toolkitsDocument ComparisonOn this pageDocument ComparisonThis notebook shows how to use an agent to compare two documents.The high level idea is we will create a question-answering chain for each document, and then use that from pydantic import BaseModel, Fieldfrom langchain.chat_models import ChatOpenAIfrom langchain.agents import Toolfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import FAISSfrom langchain.document_loaders import PyPDFLoaderfrom langchain.chains import RetrievalQAclass DocumentInput(BaseModel):    question: str = Field()llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")tools = []files = [    # https://abc.xyz/investor/static/pdf/2023Q1_alphabet_earnings_release.pdf    {        "name": "alphabet-earnings",        "path": "/Users/harrisonchase/Downloads/2023Q1_alphabet_earnings_release.pdf",    },    #