mirror of
https://github.com/hwchase17/langchain.git
synced 2025-08-18 09:01:03 +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()
```
110 lines
4.0 KiB
Python
110 lines
4.0 KiB
Python
"""Test AnthropicFunctions"""
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import unittest
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from langchain_community.chat_models.anthropic import ChatAnthropic
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from langchain_community.chat_models.bedrock import BedrockChat
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from langchain_experimental.llms.anthropic_functions import AnthropicFunctions
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class TestAnthropicFunctions(unittest.TestCase):
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"""
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Test AnthropicFunctions with default llm (ChatAnthropic) as well as a passed-in llm
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"""
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def test_default_chat_anthropic(self) -> None:
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base_model = AnthropicFunctions(model="claude-2") # type: ignore[call-arg]
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self.assertIsInstance(base_model.model, ChatAnthropic)
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# bind functions
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model = base_model.bind(
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functions=[
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{
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, "
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"e.g. San Francisco, CA",
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},
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"unit": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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},
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},
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"required": ["location"],
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},
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}
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],
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function_call={"name": "get_current_weather"},
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)
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res = model.invoke("What's the weather in San Francisco?")
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function_call = res.additional_kwargs.get("function_call")
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assert function_call
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self.assertEqual(function_call.get("name"), "get_current_weather")
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self.assertEqual(
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function_call.get("arguments"),
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'{"location": "San Francisco, CA", "unit": "fahrenheit"}',
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)
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def test_bedrock_chat_anthropic(self) -> None:
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"""
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const chatBedrock = new ChatBedrock({
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region: process.env.BEDROCK_AWS_REGION ?? "us-east-1",
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model: "anthropic.claude-v2",
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temperature: 0.1,
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credentials: {
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secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!,
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accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!,
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},
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});"""
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llm = BedrockChat( # type: ignore[call-arg]
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model_id="anthropic.claude-v2",
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model_kwargs={"temperature": 0.1},
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region_name="us-east-1",
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)
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base_model = AnthropicFunctions(llm=llm)
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assert isinstance(base_model.model, BedrockChat)
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# bind functions
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model = base_model.bind(
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functions=[
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{
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, "
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"e.g. San Francisco, CA",
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},
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"unit": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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},
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},
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"required": ["location"],
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},
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}
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],
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function_call={"name": "get_current_weather"},
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)
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res = model.invoke("What's the weather in San Francisco?")
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function_call = res.additional_kwargs.get("function_call")
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assert function_call
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self.assertEqual(function_call.get("name"), "get_current_weather")
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self.assertEqual(
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function_call.get("arguments"),
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'{"location": "San Francisco, CA", "unit": "fahrenheit"}',
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)
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