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 run_agent(self, df, agent, args, pred_args):
      # TODO abstract prompt templating into a common utility method, this is also used in vanilla OpenAI
      if "prompt_template" in pred_args:
          base_template = pred_args[
              "prompt_template"
          ]  # override with predict-time template if available
      elif "prompt_template" in args:
          base_template = args[
              "prompt_template"
          ]  # use create-time template if not
      else:
          base_template = "{{question}}"  # default template otherwise
  
      input_variables = []
      matches = list(re.finditer("{{(.*?)}}", base_template))
  
      for m in matches:
          input_variables.append(m[0].replace("{", "").replace("}", ""))
  
      empty_prompt_ids = np.where(
          df[input_variables].isna().all(axis=1).values
      )[0]
  
      base_template = base_template.replace("{{", "{").replace(
          "}}", "}"
      )
      prompts = []
  
      for i, row in df.iterrows():
          if i not in empty_prompt_ids:
              prompt = PromptTemplate(
                  input_variables=input_variables,
                  template=base_template,
              )
              kwargs = {}
              for col in input_variables:
                  kwargs[col] = (
                      row[col] if row[col] is not None else ""
                  )  # add empty quote if data is missing
              prompts.append(prompt.format(**kwargs))
          elif row.get(args["user_column"]):
              # just add prompt
              prompts.append(row[args["user_column"]])
  
      def _run_agent_with_prompt(agent, prompt):
          # TODO: ensure that agent completion plus prompt match the maximum allowed by the user
          if not prompt:
              return ""
          try:
              return agent.run(prompt)
          except ValueError as e:
              # Handle parsing errors ourselves instead of using handle_parsing_errors=True in initialize_agent.
              response = str(e)
              if not response.startswith(_PARSING_ERROR_PREFIX):
                  return f"agent failed with error:\n{str(e)}..."
              else:
                  # By far the most common error is a Langchain parsing error. Some OpenAI models
                  # always output a response formatted correctly. Anthropic, and others, sometimes just output
                  # the answer as text (when not using tools), even when prompted to output in a specific format.
                  # As a somewhat dirty workaround, we accept the output formatted incorrectly and use it as a response.
                  #
                  # Ideally, in the future, we would write a parser that is more robust and flexible than the one Langchain uses.
                  # Response is wrapped in ``
                  logger.info(f"Agent failure, salvaging response...")
                  response_output = response.split("`")
                  if len(response_output) >= 2:
                      response = response_output[-2]
                  return response
          except Exception as e:
              return f"agent failed with error:\n{str(e)}..."
  
      completions = []
      # max_workers defaults to number of processors on the machine multiplied by 5.
      # https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ThreadPoolExecutor
      max_workers = args.get("max_workers", None)
      agent_timeout_seconds = args.get(
          "timeout", _DEFAULT_AGENT_TIMEOUT_SECONDS
      )
      executor = ContextThreadPoolExecutor(max_workers=max_workers)
      futures = [
          executor.submit(_run_agent_with_prompt, agent, prompt)
          for prompt in prompts
      ]
      try:
          for future in as_completed(
              futures, timeout=agent_timeout_seconds
          ):
              completions.append(future.result())
      except TimeoutError:
          completions.append(
              "I'm sorry! I couldn't come up with a response in time. Please try again."
          )
      # Can't use ThreadPoolExecutor as context manager since we need wait=False.
      executor.shutdown(wait=False)
  
      # add null completion for empty prompts
      for i in sorted(empty_prompt_ids):
          completions.insert(i, None)
  
      pred_df = pd.DataFrame(completions, columns=[args["target"]])
  
      return pred_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 10 for calling the LangChainHandler obj's underlying member method code with example usage: obj.run_agent(df, agent, args, pred_args) and returns: pred_df, str, agent, str, str, str, str, str, str, response