class_docstr: This is a MindsDB integration for the LangChain library, which provides a unified interface for interacting with
  various large language models (LLMs).
  
  Currently, this integration supports exposing OpenAI and Anthrophic's LLMs with normal text completion support. They are then
  wrapped in a zero shot react description agent that offers a few third party tools out of the box, with support
  for additional ones if an API key is provided. Ongoing memory is also provided.
  
  Full tool support list:
      - wikipedia
      - python_repl
      - serper.dev search
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code: def describe(self, attribute: Optional[str] = None) -> pd.DataFrame:
      info = self.model_storage.json_get("description")
  
      if attribute == "info":
          if info is None:
              # we do this due to the huge amount of params that can be changed
              #  at prediction time to customize behavior.
              # for them, we report the last observed value
              raise Exception(
                  "This model needs to be used before it can be described."
              )
  
          description = pd.DataFrame(info)
          return description
      else:
          tables = ["info"]
          return pd.DataFrame(tables, columns=["tables"])
  
imports: import re
  import os
  from typing import Optional, Dict
  from concurrent.futures import as_completed, TimeoutError
  import numpy
  import pandas
  from langchain.schema import SystemMessage
  from langchain.agents import AgentType
  from langchain.llms import OpenAI
  from langchain.chat_models import ChatAnthropic, ChatOpenAI
  from langchain.agents import initialize_agent, create_sql_agent
  from langchain.prompts import PromptTemplate
  from langchain.agents.agent_toolkits import SQLDatabaseToolkit
  from langchain.chains.conversation.memory import ConversationSummaryBufferMemory
  from mindsdb.integrations.handlers.openai_handler.constants import CHAT_MODELS
  from mindsdb.integrations.handlers.langchain_handler.mindsdb_database_agent import MindsDBSQL
  from mindsdb.integrations.handlers.langchain_handler.tools import setup_tools
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.integrations.utilities.handler_utils import get_api_key
  from mindsdb.utilities import log
  from mindsdb.utilities.context_executor import ContextThreadPoolExecutor
label_desc: Use image node_id 11 for calling the LangChainHandler obj's underlying member method code with example usage: obj.describe(attribute) and returns: description, pd