mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-05 07:08:03 +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()
```
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
"""Test Anthropic Chat API wrapper."""
|
|
|
|
import os
|
|
from typing import List
|
|
|
|
import pytest
|
|
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
|
|
|
|
from langchain_community.chat_models import ChatAnthropic
|
|
from langchain_community.chat_models.anthropic import (
|
|
convert_messages_to_prompt_anthropic,
|
|
)
|
|
|
|
os.environ["ANTHROPIC_API_KEY"] = "foo"
|
|
|
|
|
|
@pytest.mark.requires("anthropic")
|
|
def test_anthropic_model_name_param() -> None:
|
|
llm = ChatAnthropic(model_name="foo")
|
|
assert llm.model == "foo"
|
|
|
|
|
|
@pytest.mark.requires("anthropic")
|
|
def test_anthropic_model_param() -> None:
|
|
llm = ChatAnthropic(model="foo") # type: ignore[call-arg]
|
|
assert llm.model == "foo"
|
|
|
|
|
|
@pytest.mark.requires("anthropic")
|
|
def test_anthropic_model_kwargs() -> None:
|
|
llm = ChatAnthropic(model_kwargs={"foo": "bar"})
|
|
assert llm.model_kwargs == {"foo": "bar"}
|
|
|
|
|
|
@pytest.mark.requires("anthropic")
|
|
def test_anthropic_invalid_model_kwargs() -> None:
|
|
with pytest.raises(ValueError):
|
|
ChatAnthropic(model_kwargs={"max_tokens_to_sample": 5})
|
|
|
|
|
|
@pytest.mark.requires("anthropic")
|
|
def test_anthropic_incorrect_field() -> None:
|
|
with pytest.warns(match="not default parameter"):
|
|
llm = ChatAnthropic(foo="bar") # type: ignore[call-arg]
|
|
assert llm.model_kwargs == {"foo": "bar"}
|
|
|
|
|
|
@pytest.mark.requires("anthropic")
|
|
def test_anthropic_initialization() -> None:
|
|
"""Test anthropic initialization."""
|
|
# Verify that chat anthropic can be initialized using a secret key provided
|
|
# as a parameter rather than an environment variable.
|
|
ChatAnthropic(model="test", anthropic_api_key="test") # type: ignore[arg-type, call-arg]
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("messages", "expected"),
|
|
[
|
|
([HumanMessage(content="Hello")], "\n\nHuman: Hello\n\nAssistant:"),
|
|
(
|
|
[HumanMessage(content="Hello"), AIMessage(content="Answer:")],
|
|
"\n\nHuman: Hello\n\nAssistant: Answer:",
|
|
),
|
|
(
|
|
[
|
|
SystemMessage(content="You're an assistant"),
|
|
HumanMessage(content="Hello"),
|
|
AIMessage(content="Answer:"),
|
|
],
|
|
"You're an assistant\n\nHuman: Hello\n\nAssistant: Answer:",
|
|
),
|
|
],
|
|
)
|
|
def test_formatting(messages: List[BaseMessage], expected: str) -> None:
|
|
result = convert_messages_to_prompt_anthropic(messages)
|
|
assert result == expected
|