mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-04 22:58:42 +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()
```
67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
"""Test `Kinetica` chat models"""
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import logging
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from typing import Any
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from langchain_core.messages import AIMessage
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from langchain_community.chat_models.kinetica import ChatKinetica, KineticaUtil
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LOG = logging.getLogger(__name__)
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class TestChatKinetica:
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test_ctx_json = """
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{
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"payload":{
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"context":[
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{
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"table":"demo.test_profiles",
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"columns":[
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"username VARCHAR (32) NOT NULL",
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"name VARCHAR (32) NOT NULL",
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"sex VARCHAR (1) NOT NULL",
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"address VARCHAR (64) NOT NULL",
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"mail VARCHAR (32) NOT NULL",
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"birthdate TIMESTAMP NOT NULL"
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],
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"description":"Contains user profiles.",
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"rules":[
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]
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},
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{
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"samples":{
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"How many male users are there?":
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"select count(1) as num_users from demo.test_profiles where sex = ''M'';"
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}
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}
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]
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}
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}
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"""
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def test_convert_messages(self, monkeypatch: Any) -> None:
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"""Test convert messages from context."""
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def patch_kdbc() -> None:
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return None
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monkeypatch.setattr(KineticaUtil, "create_kdbc", patch_kdbc)
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def patch_execute_sql(*args: Any, **kwargs: Any) -> dict:
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return dict(Prompt=self.test_ctx_json)
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monkeypatch.setattr(ChatKinetica, "_execute_sql", patch_execute_sql)
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kinetica_llm = ChatKinetica() # type: ignore[call-arg]
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test_messages = kinetica_llm.load_messages_from_context("test")
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LOG.info(f"test_messages: {test_messages}")
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ai_message = test_messages[-1]
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assert isinstance(ai_message, AIMessage)
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assert (
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ai_message.content
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== "select count(1) as num_users from demo.test_profiles where sex = 'M';"
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)
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