mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-19 14:01:50 +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()
```
139 lines
3.9 KiB
Python
139 lines
3.9 KiB
Python
"""Test Fireworks AI API Wrapper."""
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from typing import Generator
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import pytest
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from langchain_core.outputs import LLMResult
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from langchain_community.llms.fireworks import Fireworks
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@pytest.fixture
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def llm() -> Fireworks:
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return Fireworks(model_kwargs={"temperature": 0, "max_tokens": 512})
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@pytest.mark.scheduled
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def test_fireworks_call(llm: Fireworks) -> None:
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"""Test valid call to fireworks."""
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output = llm.invoke("How is the weather in New York today?")
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assert isinstance(output, str)
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@pytest.mark.scheduled
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def test_fireworks_model_param() -> None:
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"""Tests model parameters for Fireworks"""
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llm = Fireworks(model="foo")
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assert llm.model == "foo"
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@pytest.mark.scheduled
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def test_fireworks_invoke(llm: Fireworks) -> None:
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"""Tests completion with invoke"""
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output = llm.invoke("How is the weather in New York today?", stop=[","])
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assert isinstance(output, str)
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assert output[-1] == ","
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@pytest.mark.scheduled
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async def test_fireworks_ainvoke(llm: Fireworks) -> None:
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"""Tests completion with invoke"""
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output = await llm.ainvoke("How is the weather in New York today?", stop=[","])
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assert isinstance(output, str)
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assert output[-1] == ","
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@pytest.mark.scheduled
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def test_fireworks_batch(llm: Fireworks) -> None:
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"""Tests completion with invoke"""
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llm = Fireworks()
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output = llm.batch(
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[
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"How is the weather in New York today?",
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"How is the weather in New York today?",
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],
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stop=[","],
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)
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for token in output:
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assert isinstance(token, str)
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assert token[-1] == ","
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@pytest.mark.scheduled
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async def test_fireworks_abatch(llm: Fireworks) -> None:
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"""Tests completion with invoke"""
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output = await llm.abatch(
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[
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"How is the weather in New York today?",
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"How is the weather in New York today?",
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],
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stop=[","],
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)
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for token in output:
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assert isinstance(token, str)
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assert token[-1] == ","
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@pytest.mark.scheduled
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def test_fireworks_multiple_prompts(
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llm: Fireworks,
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) -> None:
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"""Test completion with multiple prompts."""
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output = llm.generate(["How is the weather in New York today?", "I'm pickle rick"])
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assert isinstance(output, LLMResult)
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assert isinstance(output.generations, list)
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assert len(output.generations) == 2
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@pytest.mark.scheduled
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def test_fireworks_streaming(llm: Fireworks) -> None:
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"""Test stream completion."""
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generator = llm.stream("Who's the best quarterback in the NFL?")
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assert isinstance(generator, Generator)
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for token in generator:
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assert isinstance(token, str)
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@pytest.mark.scheduled
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def test_fireworks_streaming_stop_words(llm: Fireworks) -> None:
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"""Test stream completion with stop words."""
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generator = llm.stream("Who's the best quarterback in the NFL?", stop=[","])
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assert isinstance(generator, Generator)
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last_token = ""
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for token in generator:
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last_token = token
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assert isinstance(token, str)
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assert last_token[-1] == ","
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@pytest.mark.scheduled
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async def test_fireworks_streaming_async(llm: Fireworks) -> None:
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"""Test stream completion."""
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last_token = ""
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async for token in llm.astream(
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"Who's the best quarterback in the NFL?", stop=[","]
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):
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last_token = token
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assert isinstance(token, str)
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assert last_token[-1] == ","
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@pytest.mark.scheduled
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async def test_fireworks_async_agenerate(llm: Fireworks) -> None:
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"""Test async."""
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output = await llm.agenerate(["What is the best city to live in California?"])
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assert isinstance(output, LLMResult)
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@pytest.mark.scheduled
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async def test_fireworks_multiple_prompts_async_agenerate(llm: Fireworks) -> None:
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output = await llm.agenerate(
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["How is the weather in New York today?", "I'm pickle rick"]
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)
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assert isinstance(output, LLMResult)
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assert isinstance(output.generations, list)
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assert len(output.generations) == 2
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