mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-05 14:43:08 +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()
```
183 lines
5.5 KiB
Python
183 lines
5.5 KiB
Python
"""Test Vertex AI API wrapper.
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In order to run this test, you need to install VertexAI SDK:
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pip install google-cloud-aiplatform>=1.36.0
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Your end-user credentials would be used to make the calls (make sure you've run
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`gcloud auth login` first).
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"""
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import os
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from typing import Optional
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import pytest
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from langchain_core.outputs import LLMResult
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from langchain_community.llms import VertexAI, VertexAIModelGarden
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model_names_to_test = ["text-bison@001", "gemini-pro"]
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model_names_to_test_with_default = [None] + model_names_to_test
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@pytest.mark.parametrize(
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"model_name",
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model_names_to_test_with_default,
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)
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def test_vertex_initialization(model_name: str) -> None:
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llm = VertexAI(model_name=model_name) if model_name else VertexAI()
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assert llm._llm_type == "vertexai"
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try:
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assert llm.model_name == llm.client._model_id
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except AttributeError:
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assert llm.model_name == llm.client._model_name.split("/")[-1]
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@pytest.mark.parametrize(
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"model_name",
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model_names_to_test_with_default,
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)
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def test_vertex_call(model_name: str) -> None:
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llm = (
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VertexAI(model_name=model_name, temperature=0)
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if model_name
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else VertexAI(temperature=0.0)
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)
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output = llm.invoke("Say foo:")
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assert isinstance(output, str)
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@pytest.mark.scheduled
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def test_vertex_generate() -> None:
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llm = VertexAI(temperature=0.3, n=2, model_name="text-bison@001")
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output = llm.generate(["Say foo:"])
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assert isinstance(output, LLMResult)
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assert len(output.generations) == 1
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assert len(output.generations[0]) == 2
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@pytest.mark.scheduled
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def test_vertex_generate_code() -> None:
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llm = VertexAI(temperature=0.3, n=2, model_name="code-bison@001")
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output = llm.generate(["generate a python method that says foo:"])
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assert isinstance(output, LLMResult)
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assert len(output.generations) == 1
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assert len(output.generations[0]) == 2
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@pytest.mark.scheduled
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async def test_vertex_agenerate() -> None:
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llm = VertexAI(temperature=0)
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output = await llm.agenerate(["Please say foo:"])
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assert isinstance(output, LLMResult)
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@pytest.mark.scheduled
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@pytest.mark.parametrize(
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"model_name",
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model_names_to_test_with_default,
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)
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def test_vertex_stream(model_name: str) -> None:
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llm = (
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VertexAI(temperature=0, model_name=model_name)
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if model_name
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else VertexAI(temperature=0)
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)
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outputs = list(llm.stream("Please say foo:"))
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assert isinstance(outputs[0], str)
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async def test_vertex_consistency() -> None:
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llm = VertexAI(temperature=0)
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output = llm.generate(["Please say foo:"])
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streaming_output = llm.generate(["Please say foo:"], stream=True)
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async_output = await llm.agenerate(["Please say foo:"])
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assert output.generations[0][0].text == streaming_output.generations[0][0].text
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assert output.generations[0][0].text == async_output.generations[0][0].text
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@pytest.mark.parametrize(
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"endpoint_os_variable_name,result_arg",
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[("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
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)
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def test_model_garden(
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endpoint_os_variable_name: str, result_arg: Optional[str]
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) -> None:
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"""In order to run this test, you should provide endpoint names.
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Example:
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export FALCON_ENDPOINT_ID=...
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export LLAMA_ENDPOINT_ID=...
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export PROJECT=...
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"""
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endpoint_id = os.environ[endpoint_os_variable_name]
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project = os.environ["PROJECT"]
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location = "europe-west4"
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llm = VertexAIModelGarden(
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endpoint_id=endpoint_id,
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project=project,
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result_arg=result_arg,
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location=location,
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)
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output = llm.invoke("What is the meaning of life?")
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assert isinstance(output, str)
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assert llm._llm_type == "vertexai_model_garden"
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@pytest.mark.parametrize(
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"endpoint_os_variable_name,result_arg",
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[("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
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)
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def test_model_garden_generate(
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endpoint_os_variable_name: str, result_arg: Optional[str]
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) -> None:
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"""In order to run this test, you should provide endpoint names.
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Example:
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export FALCON_ENDPOINT_ID=...
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export LLAMA_ENDPOINT_ID=...
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export PROJECT=...
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"""
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endpoint_id = os.environ[endpoint_os_variable_name]
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project = os.environ["PROJECT"]
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location = "europe-west4"
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llm = VertexAIModelGarden(
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endpoint_id=endpoint_id,
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project=project,
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result_arg=result_arg,
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location=location,
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)
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output = llm.generate(["What is the meaning of life?", "How much is 2+2"])
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assert isinstance(output, LLMResult)
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assert len(output.generations) == 2
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"endpoint_os_variable_name,result_arg",
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[("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
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)
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async def test_model_garden_agenerate(
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endpoint_os_variable_name: str, result_arg: Optional[str]
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) -> None:
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endpoint_id = os.environ[endpoint_os_variable_name]
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project = os.environ["PROJECT"]
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location = "europe-west4"
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llm = VertexAIModelGarden(
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endpoint_id=endpoint_id,
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project=project,
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result_arg=result_arg,
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location=location,
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)
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output = await llm.agenerate(["What is the meaning of life?", "How much is 2+2"])
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assert isinstance(output, LLMResult)
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assert len(output.generations) == 2
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@pytest.mark.parametrize(
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"model_name",
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model_names_to_test,
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)
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def test_vertex_call_count_tokens(model_name: str) -> None:
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llm = VertexAI(model_name=model_name)
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output = llm.get_num_tokens("How are you?")
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assert output == 4
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