mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-09 17:18:31 +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()
```
125 lines
3.6 KiB
Python
125 lines
3.6 KiB
Python
"""Integration tests for the langchain tracer module."""
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import asyncio
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import os
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from aiohttp import ClientSession
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from langchain_community.callbacks import wandb_tracing_enabled
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from langchain_community.llms import OpenAI
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questions = [
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(
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"Who won the US Open men's final in 2019? "
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"What is his age raised to the 0.334 power?"
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),
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(
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"Who is Olivia Wilde's boyfriend? "
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"What is his current age raised to the 0.23 power?"
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),
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(
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"Who won the most recent formula 1 grand prix? "
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"What is their age raised to the 0.23 power?"
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),
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(
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"Who won the US Open women's final in 2019? "
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"What is her age raised to the 0.34 power?"
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),
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("Who is Beyonce's husband? " "What is his age raised to the 0.19 power?"),
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]
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def test_tracing_sequential() -> None:
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from langchain.agents import AgentType, initialize_agent, load_tools
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os.environ["LANGCHAIN_WANDB_TRACING"] = "true"
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os.environ["WANDB_PROJECT"] = "langchain-tracing"
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for q in questions[:3]:
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llm = OpenAI(temperature=0)
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tools = load_tools(
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["llm-math", "serpapi"],
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llm=llm,
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)
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agent = initialize_agent(
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tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)
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agent.run(q)
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def test_tracing_session_env_var() -> None:
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from langchain.agents import AgentType, initialize_agent, load_tools
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os.environ["LANGCHAIN_WANDB_TRACING"] = "true"
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llm = OpenAI(temperature=0)
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tools = load_tools(
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["llm-math", "serpapi"],
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llm=llm,
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)
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agent = initialize_agent(
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tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)
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agent.run(questions[0])
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async def test_tracing_concurrent() -> None:
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from langchain.agents import AgentType, initialize_agent, load_tools
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os.environ["LANGCHAIN_WANDB_TRACING"] = "true"
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aiosession = ClientSession()
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llm = OpenAI(temperature=0)
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async_tools = load_tools(
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["llm-math", "serpapi"],
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llm=llm,
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aiosession=aiosession,
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)
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agent = initialize_agent(
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async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)
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tasks = [agent.arun(q) for q in questions[:3]]
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await asyncio.gather(*tasks)
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await aiosession.close()
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def test_tracing_context_manager() -> None:
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from langchain.agents import AgentType, initialize_agent, load_tools
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llm = OpenAI(temperature=0)
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tools = load_tools(
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["llm-math", "serpapi"],
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llm=llm,
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)
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agent = initialize_agent(
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tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)
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if "LANGCHAIN_WANDB_TRACING" in os.environ:
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del os.environ["LANGCHAIN_WANDB_TRACING"]
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with wandb_tracing_enabled():
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agent.run(questions[0]) # this should be traced
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agent.run(questions[0]) # this should not be traced
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async def test_tracing_context_manager_async() -> None:
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from langchain.agents import AgentType, initialize_agent, load_tools
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llm = OpenAI(temperature=0)
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async_tools = load_tools(
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["llm-math", "serpapi"],
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llm=llm,
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)
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agent = initialize_agent(
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async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)
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if "LANGCHAIN_WANDB_TRACING" in os.environ:
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del os.environ["LANGCHAIN_TRACING"]
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# start a background task
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task = asyncio.create_task(agent.arun(questions[0])) # this should not be traced
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with wandb_tracing_enabled():
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tasks = [agent.arun(q) for q in questions[1:4]] # these should be traced
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await asyncio.gather(*tasks)
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await task
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