mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-21 15:07:35 +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()
```
334 lines
11 KiB
Python
334 lines
11 KiB
Python
"""Test ChatOpenAI wrapper."""
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from typing import Any, Optional
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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, SystemMessage
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from langchain_core.outputs import (
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ChatGeneration,
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ChatResult,
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LLMResult,
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)
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.pydantic_v1 import BaseModel, Field
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from langchain_community.chat_models.openai import ChatOpenAI
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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_chat_openai() -> None:
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"""Test ChatOpenAI wrapper."""
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chat = ChatOpenAI(
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temperature=0.7,
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base_url=None,
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organization=None,
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openai_proxy=None,
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timeout=10.0,
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max_retries=3,
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http_client=None,
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n=1,
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max_tokens=10,
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default_headers=None,
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default_query=None,
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)
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message = HumanMessage(content="Hello")
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response = chat.invoke([message])
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assert isinstance(response, BaseMessage)
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assert isinstance(response.content, str)
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def test_chat_openai_model() -> None:
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"""Test ChatOpenAI wrapper handles model_name."""
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chat = ChatOpenAI(model="foo")
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assert chat.model_name == "foo"
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chat = ChatOpenAI(model_name="bar") # type: ignore[call-arg]
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assert chat.model_name == "bar"
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def test_chat_openai_system_message() -> None:
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"""Test ChatOpenAI wrapper with system message."""
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chat = ChatOpenAI(max_tokens=10)
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system_message = SystemMessage(content="You are to chat with the user.")
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human_message = HumanMessage(content="Hello")
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response = chat.invoke([system_message, human_message])
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assert isinstance(response, BaseMessage)
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assert isinstance(response.content, str)
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@pytest.mark.scheduled
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def test_chat_openai_generate() -> None:
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"""Test ChatOpenAI wrapper with generate."""
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chat = ChatOpenAI(max_tokens=10, n=2)
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message = HumanMessage(content="Hello")
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response = chat.generate([[message], [message]])
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assert isinstance(response, LLMResult)
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assert len(response.generations) == 2
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assert response.llm_output
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for generations in response.generations:
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assert len(generations) == 2
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for generation in generations:
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assert isinstance(generation, ChatGeneration)
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assert isinstance(generation.text, str)
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assert generation.text == generation.message.content
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@pytest.mark.scheduled
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def test_chat_openai_multiple_completions() -> None:
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"""Test ChatOpenAI wrapper with multiple completions."""
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chat = ChatOpenAI(max_tokens=10, n=5)
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message = HumanMessage(content="Hello")
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response = chat._generate([message])
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assert isinstance(response, ChatResult)
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assert len(response.generations) == 5
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for generation in response.generations:
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assert isinstance(generation.message, BaseMessage)
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assert isinstance(generation.message.content, str)
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@pytest.mark.scheduled
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def test_chat_openai_streaming() -> 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 = ChatOpenAI(
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max_tokens=10,
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streaming=True,
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temperature=0,
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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="Hello")
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response = chat.invoke([message])
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assert callback_handler.llm_streams > 0
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assert isinstance(response, BaseMessage)
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@pytest.mark.scheduled
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def test_chat_openai_streaming_generation_info() -> None:
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"""Test that generation info is preserved when streaming."""
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class _FakeCallback(FakeCallbackHandler):
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saved_things: dict = {}
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def on_llm_end(
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self,
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*args: Any,
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**kwargs: Any,
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) -> Any:
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# Save the generation
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self.saved_things["generation"] = args[0]
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callback = _FakeCallback()
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callback_manager = CallbackManager([callback])
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chat = ChatOpenAI(
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max_tokens=2,
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temperature=0,
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callback_manager=callback_manager,
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)
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list(chat.stream("hi"))
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generation = callback.saved_things["generation"]
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# `Hello!` is two tokens, assert that that is what is returned
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assert generation.generations[0][0].text == "Hello!"
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def test_chat_openai_llm_output_contains_model_name() -> None:
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"""Test llm_output contains model_name."""
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chat = ChatOpenAI(max_tokens=10)
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message = HumanMessage(content="Hello")
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llm_result = chat.generate([[message]])
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assert llm_result.llm_output is not None
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assert llm_result.llm_output["model_name"] == chat.model_name
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def test_chat_openai_streaming_llm_output_contains_model_name() -> None:
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"""Test llm_output contains model_name."""
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chat = ChatOpenAI(max_tokens=10, streaming=True)
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message = HumanMessage(content="Hello")
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llm_result = chat.generate([[message]])
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assert llm_result.llm_output is not None
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assert llm_result.llm_output["model_name"] == chat.model_name
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def test_chat_openai_invalid_streaming_params() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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with pytest.raises(ValueError):
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ChatOpenAI(
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max_tokens=10,
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streaming=True,
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temperature=0,
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n=5,
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)
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@pytest.mark.scheduled
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async def test_async_chat_openai() -> None:
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"""Test async generation."""
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chat = ChatOpenAI(max_tokens=10, n=2)
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message = HumanMessage(content="Hello")
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response = await chat.agenerate([[message], [message]])
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assert isinstance(response, LLMResult)
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assert len(response.generations) == 2
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assert response.llm_output
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for generations in response.generations:
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assert len(generations) == 2
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for generation in generations:
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assert isinstance(generation, ChatGeneration)
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assert isinstance(generation.text, str)
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assert generation.text == generation.message.content
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@pytest.mark.scheduled
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async def test_async_chat_openai_streaming() -> 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 = ChatOpenAI(
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max_tokens=10,
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streaming=True,
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temperature=0,
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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="Hello")
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response = await chat.agenerate([[message], [message]])
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assert callback_handler.llm_streams > 0
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assert isinstance(response, LLMResult)
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assert len(response.generations) == 2
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for generations in response.generations:
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assert len(generations) == 1
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for generation in generations:
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assert isinstance(generation, ChatGeneration)
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assert isinstance(generation.text, str)
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assert generation.text == generation.message.content
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@pytest.mark.scheduled
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async def test_async_chat_openai_bind_functions() -> None:
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"""Test ChatOpenAI wrapper with multiple completions."""
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class Person(BaseModel):
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"""Identifying information about a person."""
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name: str = Field(..., title="Name", description="The person's name")
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age: int = Field(..., title="Age", description="The person's age")
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fav_food: Optional[str] = Field(
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default=None, title="Fav Food", description="The person's favorite food"
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)
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chat = ChatOpenAI(
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max_tokens=30,
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n=1,
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streaming=True,
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).bind_functions(functions=[Person], function_call="Person")
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", "Use the provided Person function"),
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("user", "{input}"),
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]
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)
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chain = prompt | chat
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message = HumanMessage(content="Sally is 13 years old")
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response = await chain.abatch([{"input": message}])
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assert isinstance(response, list)
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assert len(response) == 1
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for generation in response:
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assert isinstance(generation, AIMessage)
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def test_chat_openai_extra_kwargs() -> None:
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"""Test extra kwargs to chat openai."""
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# Check that foo is saved in extra_kwargs.
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llm = ChatOpenAI(foo=3, max_tokens=10) # type: ignore[call-arg]
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assert llm.max_tokens == 10
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assert llm.model_kwargs == {"foo": 3}
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# Test that if extra_kwargs are provided, they are added to it.
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llm = ChatOpenAI(foo=3, model_kwargs={"bar": 2}) # type: ignore[call-arg]
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assert llm.model_kwargs == {"foo": 3, "bar": 2}
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# Test that if provided twice it errors
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with pytest.raises(ValueError):
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ChatOpenAI(foo=3, model_kwargs={"foo": 2}) # type: ignore[call-arg]
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# Test that if explicit param is specified in kwargs it errors
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with pytest.raises(ValueError):
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ChatOpenAI(model_kwargs={"temperature": 0.2})
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# Test that "model" cannot be specified in kwargs
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with pytest.raises(ValueError):
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ChatOpenAI(model_kwargs={"model": "gpt-3.5-turbo-instruct"})
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@pytest.mark.scheduled
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def test_openai_streaming() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = ChatOpenAI(max_tokens=10)
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for token in llm.stream("I'm Pickle Rick"):
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assert isinstance(token.content, str)
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@pytest.mark.scheduled
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async def test_openai_astream() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = ChatOpenAI(max_tokens=10)
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async for token in llm.astream("I'm Pickle Rick"):
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assert isinstance(token.content, str)
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@pytest.mark.scheduled
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async def test_openai_abatch() -> None:
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"""Test streaming tokens from ChatOpenAI."""
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llm = ChatOpenAI(max_tokens=10)
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result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
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for token in result:
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assert isinstance(token.content, str)
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@pytest.mark.scheduled
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async def test_openai_abatch_tags() -> None:
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"""Test batch tokens from ChatOpenAI."""
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llm = ChatOpenAI(max_tokens=10)
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result = await llm.abatch(
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["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
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)
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for token in result:
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assert isinstance(token.content, str)
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@pytest.mark.scheduled
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def test_openai_batch() -> None:
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"""Test batch tokens from ChatOpenAI."""
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llm = ChatOpenAI(max_tokens=10)
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result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
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for token in result:
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assert isinstance(token.content, str)
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@pytest.mark.scheduled
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async def test_openai_ainvoke() -> None:
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"""Test invoke tokens from ChatOpenAI."""
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llm = ChatOpenAI(max_tokens=10)
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result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
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assert isinstance(result.content, str)
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@pytest.mark.scheduled
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def test_openai_invoke() -> None:
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"""Test invoke tokens from ChatOpenAI."""
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llm = ChatOpenAI(max_tokens=10)
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result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
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assert isinstance(result.content, str)
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