mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-18 21:41:24 +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()
```
90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
"""Test Anthropic API wrapper."""
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from typing import List
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import pytest
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from langchain_core.callbacks import CallbackManager
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from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
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from langchain_core.outputs import ChatGeneration, LLMResult
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from langchain_community.chat_models.anthropic import (
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ChatAnthropic,
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)
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from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
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@pytest.mark.scheduled
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def test_anthropic_call() -> None:
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"""Test valid call to anthropic."""
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chat = ChatAnthropic(model="test") # type: ignore[call-arg]
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message = HumanMessage(content="Hello")
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response = chat.invoke([message])
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assert isinstance(response, AIMessage)
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assert isinstance(response.content, str)
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@pytest.mark.scheduled
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def test_anthropic_generate() -> None:
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"""Test generate method of anthropic."""
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chat = ChatAnthropic(model="test") # type: ignore[call-arg]
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chat_messages: List[List[BaseMessage]] = [
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[HumanMessage(content="How many toes do dogs have?")]
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]
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messages_copy = [messages.copy() for messages in chat_messages]
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result: LLMResult = chat.generate(chat_messages)
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assert isinstance(result, LLMResult)
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for response in result.generations[0]:
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assert isinstance(response, ChatGeneration)
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assert isinstance(response.text, str)
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assert response.text == response.message.content
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assert chat_messages == messages_copy
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@pytest.mark.scheduled
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def test_anthropic_streaming() -> None:
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"""Test streaming tokens from anthropic."""
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chat = ChatAnthropic(model="test", streaming=True) # type: ignore[call-arg]
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message = HumanMessage(content="Hello")
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response = chat.invoke([message])
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assert isinstance(response, AIMessage)
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assert isinstance(response.content, str)
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@pytest.mark.scheduled
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def test_anthropic_streaming_callback() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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chat = ChatAnthropic( # type: ignore[call-arg]
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model="test",
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streaming=True,
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callback_manager=callback_manager,
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verbose=True,
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)
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message = HumanMessage(content="Write me a sentence with 10 words.")
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chat.invoke([message])
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assert callback_handler.llm_streams > 1
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@pytest.mark.scheduled
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async def test_anthropic_async_streaming_callback() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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chat = ChatAnthropic( # type: ignore[call-arg]
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model="test",
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streaming=True,
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callback_manager=callback_manager,
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verbose=True,
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)
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chat_messages: List[BaseMessage] = [
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HumanMessage(content="How many toes do dogs have?")
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]
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result: LLMResult = await chat.agenerate([chat_messages])
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assert callback_handler.llm_streams > 1
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assert isinstance(result, LLMResult)
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for response in result.generations[0]:
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assert isinstance(response, ChatGeneration)
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assert isinstance(response.text, str)
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assert response.text == response.message.content
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