mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
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
|
|
|
|
import numpy as np
|
|
import pytest
|
|
from pandas import DataFrame
|
|
|
|
from langchain.agents import create_pandas_dataframe_agent
|
|
from langchain.agents.agent import AgentExecutor
|
|
from langchain.llms import OpenAI
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def df() -> DataFrame:
|
|
random_data = np.random.rand(4, 4)
|
|
df = DataFrame(random_data, columns=["name", "age", "food", "sport"])
|
|
return df
|
|
|
|
|
|
def test_pandas_agent_creation(df: DataFrame) -> None:
|
|
agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df)
|
|
assert isinstance(agent, AgentExecutor)
|
|
|
|
|
|
def test_data_reading(df: DataFrame) -> None:
|
|
agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df)
|
|
assert isinstance(agent, AgentExecutor)
|
|
response = agent.run("how many rows in df? Give me a number.")
|
|
result = re.search(rf".*({df.shape[0]}).*", response)
|
|
assert result is not None
|
|
assert result.group(1) is not None
|