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https://github.com/hwchase17/langchain
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c3044b1bf0
- confirm creation - confirm functionality with a simple dimension check. The test now is calling OpenAI API directly, but learning from @vowelparrot that we’re caching the requests, so that it’s not that expensive. I also found we’re calling OpenAI api in other integration tests. Please lmk if there is any concern of real external API calls. I can alternatively make a fake LLM for this test. Thanks
31 lines
925 B
Python
31 lines
925 B
Python
import re
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import numpy as np
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import pytest
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from pandas import DataFrame
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from langchain.agents import create_pandas_dataframe_agent
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from langchain.agents.agent import AgentExecutor
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from langchain.llms import OpenAI
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@pytest.fixture(scope="module")
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def df() -> DataFrame:
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random_data = np.random.rand(4, 4)
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df = DataFrame(random_data, columns=["name", "age", "food", "sport"])
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return df
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def test_pandas_agent_creation(df: DataFrame) -> None:
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agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df)
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assert isinstance(agent, AgentExecutor)
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def test_data_reading(df: DataFrame) -> None:
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agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df)
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assert isinstance(agent, AgentExecutor)
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response = agent.run("how many rows in df? Give me a number.")
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result = re.search(rf".*({df.shape[0]}).*", response)
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assert result is not None
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assert result.group(1) is not None
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