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 conversational_completion(self, df, args=None, pred_args=None):
      pred_args = pred_args if pred_args else {}
  
      # langchain tool setup
      model_kwargs = self._get_chat_model_params(args, pred_args)
      llm = self._create_chat_model(args, pred_args)
      max_tokens = pred_args.get(
          "max_tokens", args.get("max_tokens", self.default_max_tokens)
      )
      tools = setup_tools(
          llm,
          model_kwargs,
          pred_args,
          args["executor"],
          self.default_agent_tools,
          get_api_key("openai", args, self.engine_storage),
      )
  
      memory = ConversationSummaryBufferMemory(
          llm=llm, max_token_limit=max_tokens, memory_key="chat_history"
      )
  
      # fill memory
  
      # system prompt
      prompt = args["prompt_template"]
      if (
          "prompt_template" in pred_args
          and pred_args["prompt_template"] is not None
      ):
          prompt = pred_args["prompt_template"]
      if "context" in pred_args:
          prompt += (
              "\n\n"
              + "Useful information:\n"
              + pred_args["context"]
              + "\n"
          )
      memory.chat_memory.messages.insert(
          0, SystemMessage(content=prompt)
      )
  
      # user - assistant conversation. get all except the last message
      for row in df[:-1].to_dict("records"):
          question = row[args["user_column"]]
          answer = row[args["assistant_column"]]
  
          if question:
              memory.chat_memory.add_user_message(question)
          if answer:
              memory.chat_memory.add_ai_message(answer)
  
      # use last message as prompt, remove other questions
      df.iloc[:-1, df.columns.get_loc(args["user_column"])] = ""
  
      agent_name = AgentType.CONVERSATIONAL_REACT_DESCRIPTION
      agent = initialize_agent(
          tools,
          llm,
          memory=memory,
          agent=agent_name,
          max_iterations=pred_args.get(
              "max_iterations",
              args.get("max_iterations", _DEFAULT_MAX_ITERATIONS),
          ),
          verbose=pred_args.get("verbose", args.get("verbose", False)),
          handle_parsing_errors=False,
      )
  
      # setup model description
      description = {
          "allowed_tools": [
              agent.agent.allowed_tools
          ],  # packed as list to avoid additional rows
          "agent_type": agent_name,
          "max_iterations": agent.max_iterations,
          "memory_type": memory.__class__.__name__,
      }
  
      description = {**description, **model_kwargs}
      description.pop("openai_api_key", None)
      self.model_storage.json_set("description", description)
  
      return agent, df
  
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 8 for calling the LangChainHandler obj's underlying member method code with example usage: obj.conversational_completion(df, args, pred_args) and returns: agent,  df