mirror of
https://github.com/hwchase17/langchain.git
synced 2025-08-16 08:06:14 +00:00
a0c2281540
```python
"""python scripts/update_mypy_ruff.py"""
import glob
import tomllib
from pathlib import Path
import toml
import subprocess
import re
ROOT_DIR = Path(__file__).parents[1]
def main():
for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True):
print(path)
with open(path, "rb") as f:
pyproject = tomllib.load(f)
try:
pyproject["tool"]["poetry"]["group"]["typing"]["dependencies"]["mypy"] = (
"^1.10"
)
pyproject["tool"]["poetry"]["group"]["lint"]["dependencies"]["ruff"] = (
"^0.5"
)
except KeyError:
continue
with open(path, "w") as f:
toml.dump(pyproject, f)
cwd = "/".join(path.split("/")[:-1])
completed = subprocess.run(
"poetry lock --no-update; poetry install --with typing; poetry run mypy . --no-color",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
logs = completed.stdout.split("\n")
to_ignore = {}
for l in logs:
if re.match("^(.*)\:(\d+)\: error:.*\[(.*)\]", l):
path, line_no, error_type = re.match(
"^(.*)\:(\d+)\: error:.*\[(.*)\]", l
).groups()
if (path, line_no) in to_ignore:
to_ignore[(path, line_no)].append(error_type)
else:
to_ignore[(path, line_no)] = [error_type]
print(len(to_ignore))
for (error_path, line_no), error_types in to_ignore.items():
all_errors = ", ".join(error_types)
full_path = f"{cwd}/{error_path}"
try:
with open(full_path, "r") as f:
file_lines = f.readlines()
except FileNotFoundError:
continue
file_lines[int(line_no) - 1] = (
file_lines[int(line_no) - 1][:-1] + f" # type: ignore[{all_errors}]\n"
)
with open(full_path, "w") as f:
f.write("".join(file_lines))
subprocess.run(
"poetry run ruff format .; poetry run ruff --select I --fix .",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
if __name__ == "__main__":
main()
```
110 lines
3.2 KiB
Python
110 lines
3.2 KiB
Python
from unittest.mock import Mock
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import pytest
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from langchain_core.documents import Document
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from langchain_ai21 import AI21ContextualAnswers
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from langchain_ai21.contextual_answers import ContextualAnswerInput
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from tests.unit_tests.conftest import DUMMY_API_KEY
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@pytest.mark.parametrize(
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ids=[
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"when_no_context__should_raise_exception",
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"when_no_question__should_raise_exception",
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"when_question_is_an_empty_string__should_raise_exception",
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"when_context_is_an_empty_string__should_raise_exception",
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"when_context_is_an_empty_list",
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],
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argnames="input",
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argvalues=[
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({"question": "What is the capital of France?"}),
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({"context": "Paris is the capital of France"}),
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({"question": "", "context": "Paris is the capital of France"}),
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({"context": "", "question": "some question?"}),
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({"context": [], "question": "What is the capital of France?"}),
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],
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)
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def test_invoke__on_bad_input(
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input: ContextualAnswerInput,
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mock_client_with_contextual_answers: Mock,
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) -> None:
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tsm = AI21ContextualAnswers(
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api_key=DUMMY_API_KEY, # type: ignore[arg-type]
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client=mock_client_with_contextual_answers, # type: ignore[arg-type]
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)
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with pytest.raises(ValueError) as error:
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tsm.invoke(input)
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assert (
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error.value.args[0]
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== f"Input must contain a 'context' and 'question' fields. Got {input}"
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)
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@pytest.mark.parametrize(
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ids=[
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"when_context_is_not_str_or_list_of_docs_or_str",
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],
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argnames="input",
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argvalues=[
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({"context": 1242, "question": "What is the capital of France?"}),
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],
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)
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def test_invoke__on_context_bad_input(
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input: ContextualAnswerInput, mock_client_with_contextual_answers: Mock
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) -> None:
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tsm = AI21ContextualAnswers(
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api_key=DUMMY_API_KEY, # type: ignore[arg-type]
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client=mock_client_with_contextual_answers,
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)
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with pytest.raises(ValueError) as error:
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tsm.invoke(input)
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assert (
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error.value.args[0] == f"Expected input to be a list of strings or Documents."
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f" Received {type(input)}"
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)
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@pytest.mark.parametrize(
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ids=[
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"when_context_is_a_list_of_strings",
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"when_context_is_a_list_of_documents",
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"when_context_is_a_string",
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],
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argnames="input",
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argvalues=[
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(
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{
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"context": ["Paris is the capital of france"],
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"question": "What is the capital of France?",
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}
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),
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(
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{
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"context": [Document(page_content="Paris is the capital of france")],
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"question": "What is the capital of France?",
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}
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),
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(
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{
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"context": "Paris is the capital of france",
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"question": "What is the capital of France?",
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}
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),
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],
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)
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def test_invoke__on_good_input(
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input: ContextualAnswerInput, mock_client_with_contextual_answers: Mock
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) -> None:
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tsm = AI21ContextualAnswers(
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api_key=DUMMY_API_KEY, # type: ignore[arg-type]
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client=mock_client_with_contextual_answers,
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)
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response = tsm.invoke(input)
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assert isinstance(response, str)
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