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Co-authored-by: jacoblee93 <jacoblee93@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
38 lines
1.5 KiB
Plaintext
38 lines
1.5 KiB
Plaintext
```python
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from langchain.agents import AgentType, initialize_agent, load_tools
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from langchain.llms import OpenAI
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# The language model we're going to use to control the agent.
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llm = OpenAI(temperature=0)
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# The tools we'll give the Agent access to. Note that the 'llm-math' tool uses an LLM, so we need to pass that in.
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tools = load_tools(["serpapi", "llm-math"], llm=llm)
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# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
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agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
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# Let's test it out!
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agent.run("What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?")
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```
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```pycon
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> Entering new AgentExecutor chain...
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Thought: I need to find the temperature first, then use the calculator to raise it to the .023 power.
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Action: Search
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Action Input: "High temperature in SF yesterday"
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Observation: San Francisco Temperature Yesterday. Maximum temperature yesterday: 57 °F (at 1:56 pm) Minimum temperature yesterday: 49 °F (at 1:56 am) Average temperature ...
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Thought: I now have the temperature, so I can use the calculator to raise it to the .023 power.
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Action: Calculator
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Action Input: 57^.023
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Observation: Answer: 1.0974509573251117
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Thought: I now know the final answer
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Final Answer: The high temperature in SF yesterday in Fahrenheit raised to the .023 power is 1.0974509573251117.
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> Finished chain.
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```
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```pycon
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The high temperature in SF yesterday in Fahrenheit raised to the .023 power is 1.0974509573251117.
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```
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