code: """Chat agent with question answering
  """
  from dotenv import load_dotenv
  from langchain.cache import InMemoryCache
  import langchain
  import os
  from dataclasses import dataclass
  
  from langchain.chains import LLMChain, LLMRequestsChain
  from langchain import Wikipedia, OpenAI
  from langchain.agents.react.base import DocstoreExplorer
  from langchain.agents import Tool, AgentExecutor, get_all_tool_names, load_tools, initialize_agent
  from langchain.prompts import PromptTemplate
  from langchain.chains.conversation.memory import ConversationBufferMemory
  from langchain.agents.conversational.base import ConversationalAgent
  from datetime import datetime
  
  # Load the environment variables
  load_dotenv()
  
  langchain.llm_cache = InMemoryCache()
  news_api_key = os.getenv("NEWS_API_KEY")
  
  
  @dataclass
  class ChatAgent:
      agent_executor: AgentExecutor = None
  
      def _get_docstore_agent(self):
          docstore = DocstoreExplorer(Wikipedia())
          docstore_tools = [
              Tool(
                  name="Search",
                  func=docstore.search
              ),
              Tool(
                  name="Lookup",
                  func=docstore.lookup
              )
          ]
          docstore_llm = OpenAI(temperature=0, model_name="text-davinci-003")
          docstore_agent = initialize_agent(
              docstore_tools, docstore_llm, agent="react-docstore", verbose=True)
          return docstore_agent
  
      def _get_requests_llm_tool(self):
  
          template = """
          Extracted: {requests_result}"""
  
          PROMPT = PromptTemplate(
              input_variables=["requests_result"],
              template=template,
          )
  
          def lambda_func(input):
              out = chain = LLMRequestsChain(llm_chain=LLMChain(
                  llm=OpenAI(temperature=0),
                  prompt=PROMPT)).run(input)
              return out.strip()
          return lambda_func
  
      def __init__(self, *, conversation_chain: LLMChain = None, history_array):
          date = datetime.today().strftime('%A %d, %B, %Y, %I:%M%p')
          print("DATETIME:", date)
  
          # set up a Wikipedia docstore agent
          docstore_agent = self._get_docstore_agent()
  
          tool_names = get_all_tool_names()
  
          tool_names.remove("pal-math")
          tool_names.remove("requests")  # let's use the llm_requests instead
          # let's use the llm_requests instead
          tool_names.remove("google-search")
          tool_names.remove("pal-colored-objects")
          tool_names.remove("python_repl")
          tool_names.remove("terminal")
          tool_names.remove("serpapi"),
          tool_names.remove("tmdb-api")
  
          requests_tool = self._get_requests_llm_tool()
  
          print("ALL TOOLS:", tool_names)
          tools = load_tools(tool_names,
                             llm=OpenAI(temperature=0,
                                        model_name="text-davinci-003"),
                             news_api_key=news_api_key,
                             )
  
          # Tweak some of the tool descriptions
          for tool in tools:
              if tool.name == "Search":
                  tool.description = "Use this tool exclusively for questions relating to current events, or when you can't find an answer using any of the other tools."
              if tool.name == "Calculator":
                  tool.description = "Use this to solve numeric math questions and do arithmetic. Don't use it for general or abstract math questions."
  
          tools = tools + [
              Tool(
                  name="WikipediaSearch",
                  description="Useful for answering a wide range of factual, scientific, academic, political and historical questions.",
                  func=docstore_agent.run
              ),
              Tool(
                  name="Requests",
                  func=requests_tool,
                  description="A portal to the internet. Use this when you need to get specific content from a site. Input should be a specific url, and the output will be all the text on that page."
              )
          ]
          ai_prefix = "FWROG-E"
          human_prefix = "Bence"
  
          prefix = os.getenv("PROMPT_PREFIX")
  
          suffix = f"""
          The person's name that you are interacting with is {human_prefix}. Please be entertaining and respectful towards him.
          The current date is {date}. Questions that refer to a specific date or time period will be interpreted relative to this date.
  After you answer the question, you MUST to determine which langauge your answer is written in, and append the language code to the end of the Final Answer, within parentheses, like this (en-US).
  Begin!
  Previous conversation history:
  {{chat_history}}
  New input: {{input}}
  {{agent_scratchpad}}
  """
  
          memory = ConversationBufferMemory(memory_key="chat_history")
          for item in history_array:
              memory.save_context(
                  {f"{ai_prefix}": item["prompt"]}, {f"{human_prefix}": item["response"]})
  
          llm = OpenAI(temperature=.5, max_tokens=384,
                       model_name="text-davinci-003")
          llm_chain = LLMChain(
              llm=llm,
              prompt=ConversationalAgent.create_prompt(
                  tools,
                  prefix=prefix,
                  ai_prefix=ai_prefix,
                  human_prefix=human_prefix,
                  suffix=suffix
              ),
          )
  
          agent_obj = ConversationalAgent(
              llm_chain=llm_chain, ai_prefix=ai_prefix)
  
          self.agent_executor = AgentExecutor.from_agent_and_tools(
              agent=agent_obj,
              tools=tools,
              verbose=True,
              max_iterations=5,
              memory=memory)
  
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