memory_key="chat_history", return_messages=True, output_key="output"
)
llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
    verbose=True,
    memory=memory,
    return_intermediate_steps=True, # This is needed for the evaluation later
)
Testing the Agent#
Now let’s try our agent out on some example queries.
query_one = "How many ping pong balls would it take to fill the entire Empire State Building?"
test_outputs_one = agent({"input": query_one}, return_only_outputs=False)
> Entering new AgentExecutor chain...
{
    "action": "Search the Web (SerpAPI)",
    "action_input": "How many ping pong balls would it take to fill the entire Empire State Building?"
}
Observation: 12.8 billion. The volume of the Empire State Building Googles in at around 37 million ft³. A golf ball comes in at about 2.5 in³.
Thought:{
    "action": "Final Answer",
    "action_input": "It would take approximately 12.8 billion ping pong balls to fill the entire Empire State Building."
}
> Finished chain.
This looks good! Let’s try it out on another query.
query_two = "If you laid the Eiffel Tower end to end, how many would you need cover the US from coast to coast?"
test_outputs_two = agent({"input": query_two}, return_only_outputs=False)
> Entering new AgentExecutor chain...
{
    "action": "Calculator",
rtdocs_stable/api.python.langchain.com/en/stable/use_cases/evaluation/generic_agent_evaluation.html