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from langchain.agents import load_tools | |
from langchain.agents import initialize_agent | |
from langchain.agents import AgentType | |
from langchain.llms import OpenAI | |
from langchain.callbacks import get_openai_callback | |
# First, let's load the language model we're going to use to control the agent. | |
llm = OpenAI(temperature=0) | |
# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in. | |
tools = load_tools(["serpapi", "llm-math"], llm=llm) | |
# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use. | |
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True) | |
# Now let's test it out! | |
agent.run("昨天北京气温如何?") | |
with get_openai_callback() as cb: | |
response = agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?") | |
print(f"Total Tokens: {cb.total_tokens}") | |
print(f"Prompt Tokens: {cb.prompt_tokens}") | |
print(f"Completion Tokens: {cb.completion_tokens}") | |
print(f"Total Cost (USD): ${cb.total_cost}") |