mirror of
https://github.com/hwchase17/langchain.git
synced 2025-04-29 04:16:02 +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()
```
176 lines
6.1 KiB
Python
176 lines
6.1 KiB
Python
"""Evaluate ChatKonko Interface."""
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from typing import Any
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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 BaseMessage, HumanMessage, SystemMessage
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from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult
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from langchain_community.chat_models.konko import ChatKonko
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from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
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def test_konko_chat_test() -> None:
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"""Evaluate basic ChatKonko functionality."""
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chat_instance = ChatKonko(max_tokens=10)
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msg = HumanMessage(content="Hi")
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chat_response = chat_instance.invoke([msg])
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assert isinstance(chat_response, BaseMessage)
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assert isinstance(chat_response.content, str)
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def test_konko_chat_test_openai() -> None:
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"""Evaluate basic ChatKonko functionality."""
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chat_instance = ChatKonko(max_tokens=10, model="meta-llama/llama-2-70b-chat")
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msg = HumanMessage(content="Hi")
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chat_response = chat_instance.invoke([msg])
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assert isinstance(chat_response, BaseMessage)
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assert isinstance(chat_response.content, str)
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def test_konko_model_test() -> None:
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"""Check how ChatKonko manages model_name."""
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chat_instance = ChatKonko(model="alpha")
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assert chat_instance.model == "alpha"
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chat_instance = ChatKonko(model="beta")
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assert chat_instance.model == "beta"
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def test_konko_available_model_test() -> None:
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"""Check how ChatKonko manages model_name."""
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chat_instance = ChatKonko(max_tokens=10, n=2)
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res = chat_instance.get_available_models()
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assert isinstance(res, set)
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def test_konko_system_msg_test() -> None:
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"""Evaluate ChatKonko's handling of system messages."""
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chat_instance = ChatKonko(max_tokens=10)
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sys_msg = SystemMessage(content="Initiate user chat.")
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user_msg = HumanMessage(content="Hi there")
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chat_response = chat_instance.invoke([sys_msg, user_msg])
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assert isinstance(chat_response, BaseMessage)
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assert isinstance(chat_response.content, str)
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def test_konko_generation_test() -> None:
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"""Check ChatKonko's generation ability."""
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chat_instance = ChatKonko(max_tokens=10, n=2)
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msg = HumanMessage(content="Hi")
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gen_response = chat_instance.generate([[msg], [msg]])
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assert isinstance(gen_response, LLMResult)
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assert len(gen_response.generations) == 2
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for gen_list in gen_response.generations:
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assert len(gen_list) == 2
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for gen in gen_list:
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assert isinstance(gen, ChatGeneration)
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assert isinstance(gen.text, str)
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assert gen.text == gen.message.content
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def test_konko_multiple_outputs_test() -> None:
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"""Test multiple completions with ChatKonko."""
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chat_instance = ChatKonko(max_tokens=10, n=5)
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msg = HumanMessage(content="Hi")
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gen_response = chat_instance._generate([msg])
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assert isinstance(gen_response, ChatResult)
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assert len(gen_response.generations) == 5
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for gen in gen_response.generations:
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assert isinstance(gen.message, BaseMessage)
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assert isinstance(gen.message.content, str)
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def test_konko_streaming_callback_test() -> None:
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"""Evaluate streaming's token callback functionality."""
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callback_instance = FakeCallbackHandler()
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callback_mgr = CallbackManager([callback_instance])
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chat_instance = ChatKonko(
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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_mgr,
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verbose=True,
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)
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msg = HumanMessage(content="Hi")
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chat_response = chat_instance.invoke([msg])
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assert callback_instance.llm_streams > 0
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assert isinstance(chat_response, BaseMessage)
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def test_konko_streaming_info_test() -> None:
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"""Ensure generation details are retained during streaming."""
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class TestCallback(FakeCallbackHandler):
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data_store: dict = {}
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def on_llm_end(self, *args: Any, **kwargs: Any) -> Any:
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self.data_store["generation"] = args[0]
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callback_instance = TestCallback()
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callback_mgr = CallbackManager([callback_instance])
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chat_instance = ChatKonko(
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max_tokens=2,
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temperature=0,
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callback_manager=callback_mgr,
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)
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list(chat_instance.stream("hey"))
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gen_data = callback_instance.data_store["generation"]
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assert gen_data.generations[0][0].text == " Hey"
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def test_konko_llm_model_name_test() -> None:
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"""Check if llm_output has model info."""
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chat_instance = ChatKonko(max_tokens=10)
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msg = HumanMessage(content="Hi")
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llm_data = chat_instance.generate([[msg]])
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assert llm_data.llm_output is not None
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assert llm_data.llm_output["model_name"] == chat_instance.model
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def test_konko_streaming_model_name_test() -> None:
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"""Check model info during streaming."""
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chat_instance = ChatKonko(max_tokens=10, streaming=True)
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msg = HumanMessage(content="Hi")
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llm_data = chat_instance.generate([[msg]])
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assert llm_data.llm_output is not None
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assert llm_data.llm_output["model_name"] == chat_instance.model
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def test_konko_streaming_param_validation_test() -> None:
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"""Ensure correct token callback during streaming."""
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with pytest.raises(ValueError):
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ChatKonko(
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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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def test_konko_additional_args_test() -> None:
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"""Evaluate extra arguments for ChatKonko."""
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chat_instance = ChatKonko(extra=3, max_tokens=10) # type: ignore[call-arg]
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assert chat_instance.max_tokens == 10
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assert chat_instance.model_kwargs == {"extra": 3}
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chat_instance = ChatKonko(extra=3, model_kwargs={"addition": 2}) # type: ignore[call-arg]
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assert chat_instance.model_kwargs == {"extra": 3, "addition": 2}
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with pytest.raises(ValueError):
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ChatKonko(extra=3, model_kwargs={"extra": 2}) # type: ignore[call-arg]
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with pytest.raises(ValueError):
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ChatKonko(model_kwargs={"temperature": 0.2})
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with pytest.raises(ValueError):
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ChatKonko(model_kwargs={"model": "text-davinci-003"})
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def test_konko_token_streaming_test() -> None:
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"""Check token streaming for ChatKonko."""
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chat_instance = ChatKonko(max_tokens=10)
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for token in chat_instance.stream("Just a test"):
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assert isinstance(token.content, str)
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