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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docstr: Dispatch is performed depending on the underlying model type. Currently, only the default text completion
  is supported.
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code: def predict(self, df, args=None):
      """
      Dispatch is performed depending on the underlying model type. Currently, only the default text completion
      is supported.
      """
      executor = args[
          "executor"
      ]  # used as tool in custom tool for the agent to have mindsdb-wide access
      pred_args = args["predict_params"] if args else {}
      args = self.model_storage.json_get("args")
      args["executor"] = executor
  
      df = df.reset_index(drop=True)
  
      if args.get("mode") != "conversational":
          if (
              "prompt_template" not in args
              and "prompt_template" not in pred_args
          ):
              raise Exception(
                  f"This model expects a prompt template, please provide one."
              )
  
      if "stops" in pred_args:
          self.stops = pred_args["stops"]
  
      # TODO: offload creation to the `create` method instead for faster inference?
  
      modal_dispatch = {
          "default": "default_completion",
          "sql_agent": "sql_agent_completion",
      }
  
      agent_creation_method = modal_dispatch.get(
          args.get("modal_dispatch", "default"), "default_completion"
      )
  
      if args.get("mode") == "conversational":
          agent_creation_method = "conversational_completion"
  
      # input dataframe can be modified
      agent, df = getattr(self, agent_creation_method)(
          df, args, pred_args
      )
      return self.run_agent(df, agent, args, pred_args)
  
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 5 for calling the LangChainHandler obj's underlying member method code with example usage: obj.predict(df, args) and returns: self