different perspectives of the people living in the city, and what they think of the creature. In the end, the filmmaker's findings lead them to some surprising and heartwarming conclusions about the creature's existence and the importance it holds in the lives of the people in Paris."}]Scenario 3: Using an Agent with Tools‚ÄãFinally, as a more advanced workflow, you can create an agent that uses some tools. So that ArgillaCallbackHandler will keep track of the input and the output, but not about the intermediate steps/thoughts, so that given a prompt we log the original prompt and the final response to that given prompt.Note that for this scenario we'll be using Google Search API (Serp API) so you will need to both install google-search-results as pip install google-search-results, and to set the Serp API Key as os.environ["SERPAPI_API_KEY"] = "..." (you can find it at https://serpapi.com/dashboard), otherwise the example below won't work.from langchain.agents import AgentType, initialize_agent, load_toolsfrom langchain.callbacks import ArgillaCallbackHandler, StdOutCallbackHandlerfrom langchain.llms import OpenAIargilla_callback = ArgillaCallbackHandler(    dataset_name="langchain-dataset",    api_url=os.environ["ARGILLA_API_URL"],    api_key=os.environ["ARGILLA_API_KEY"],)callbacks = [StdOutCallbackHandler(), argilla_callback]llm = OpenAI(temperature=0.9, callbacks=callbacks)tools = load_tools(["serpapi"], llm=llm, callbacks=callbacks)agent = initialize_agent(    tools,    llm,    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,    callbacks=callbacks,)agent.run("Who was the first president of the United States of America?")            > Entering new AgentExecutor chain...     I need to answer a historical question    Action: Search    Action Input: "who was the first president of the United States of America"     Observation: George Washington    Thought: George Washington was the first president    Final Answer: George Washington was the first president of the
Argilla - Open-source data platform for LLMs
Argilla - Open-source data platform for LLMs ->: different perspectives of the people living in the city, and what they think of the creature. In the end, the filmmaker's findings lead them to some surprising and heartwarming conclusions about the creature's existence and the importance it holds in the lives of the people in Paris."}]Scenario 3: Using an Agent with Tools‚ÄãFinally, as a more advanced workflow, you can create an agent that uses some tools. So that ArgillaCallbackHandler will keep track of the input and the output, but not about the intermediate steps/thoughts, so that given a prompt we log the original prompt and the final response to that given prompt.Note that for this scenario we'll be using Google Search API (Serp API) so you will need to both install google-search-results as pip install google-search-results, and to set the Serp API Key as os.environ["SERPAPI_API_KEY"] = "..." (you can find it at https://serpapi.com/dashboard), otherwise the example below won't work.from langchain.agents import AgentType, initialize_agent, load_toolsfrom langchain.callbacks import ArgillaCallbackHandler, StdOutCallbackHandlerfrom langchain.llms import OpenAIargilla_callback = ArgillaCallbackHandler(    dataset_name="langchain-dataset",    api_url=os.environ["ARGILLA_API_URL"],    api_key=os.environ["ARGILLA_API_KEY"],)callbacks = [StdOutCallbackHandler(), argilla_callback]llm = OpenAI(temperature=0.9, callbacks=callbacks)tools = load_tools(["serpapi"], llm=llm, callbacks=callbacks)agent = initialize_agent(    tools,    llm,    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,    callbacks=callbacks,)agent.run("Who was the first president of the United States of America?")            > Entering new AgentExecutor chain...     I need to answer a historical question    Action: Search    Action Input: "who was the first president of the United States of America"     Observation: George Washington    Thought: George Washington was the first president    Final Answer: George Washington was the first president of the