mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-19 22:11:41 +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()
```
127 lines
3.9 KiB
Python
127 lines
3.9 KiB
Python
"""Test ChatDeepInfra wrapper."""
|
|
|
|
from typing import List
|
|
|
|
from langchain_core.messages import BaseMessage, HumanMessage
|
|
from langchain_core.messages.ai import AIMessage
|
|
from langchain_core.messages.tool import ToolMessage
|
|
from langchain_core.outputs import ChatGeneration, LLMResult
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
from langchain_core.runnables.base import RunnableBinding
|
|
|
|
from langchain_community.chat_models.deepinfra import ChatDeepInfra
|
|
from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
|
|
|
|
|
|
class GenerateMovieName(BaseModel):
|
|
"Get a movie name from a description"
|
|
|
|
description: str
|
|
|
|
|
|
def test_chat_deepinfra() -> None:
|
|
"""Test valid call to DeepInfra."""
|
|
chat = ChatDeepInfra(
|
|
max_tokens=10,
|
|
)
|
|
response = chat.invoke([HumanMessage(content="Hello")])
|
|
assert isinstance(response, BaseMessage)
|
|
assert isinstance(response.content, str)
|
|
|
|
|
|
def test_chat_deepinfra_streaming() -> None:
|
|
callback_handler = FakeCallbackHandler()
|
|
chat = ChatDeepInfra(
|
|
callbacks=[callback_handler],
|
|
streaming=True,
|
|
max_tokens=10,
|
|
)
|
|
response = chat.invoke([HumanMessage(content="Hello")])
|
|
assert callback_handler.llm_streams > 0
|
|
assert isinstance(response, BaseMessage)
|
|
|
|
|
|
async def test_async_chat_deepinfra() -> None:
|
|
"""Test async generation."""
|
|
chat = ChatDeepInfra(
|
|
max_tokens=10,
|
|
)
|
|
message = HumanMessage(content="Hello")
|
|
response = await chat.agenerate([[message]])
|
|
assert isinstance(response, LLMResult)
|
|
assert len(response.generations) == 1
|
|
assert len(response.generations[0]) == 1
|
|
generation = response.generations[0][0]
|
|
assert isinstance(generation, ChatGeneration)
|
|
assert isinstance(generation.text, str)
|
|
assert generation.text == generation.message.content
|
|
|
|
|
|
async def test_async_chat_deepinfra_streaming() -> None:
|
|
callback_handler = FakeCallbackHandler()
|
|
chat = ChatDeepInfra(
|
|
# model="meta-llama/Llama-2-7b-chat-hf",
|
|
callbacks=[callback_handler],
|
|
max_tokens=10,
|
|
streaming=True,
|
|
timeout=5,
|
|
)
|
|
message = HumanMessage(content="Hello")
|
|
response = await chat.agenerate([[message]])
|
|
assert callback_handler.llm_streams > 0
|
|
assert isinstance(response, LLMResult)
|
|
assert len(response.generations) == 1
|
|
assert len(response.generations[0]) == 1
|
|
generation = response.generations[0][0]
|
|
assert isinstance(generation, ChatGeneration)
|
|
assert isinstance(generation.text, str)
|
|
assert generation.text == generation.message.content
|
|
|
|
|
|
def test_chat_deepinfra_bind_tools() -> None:
|
|
class Foo(BaseModel):
|
|
pass
|
|
|
|
chat = ChatDeepInfra(
|
|
max_tokens=10,
|
|
)
|
|
tools = [Foo]
|
|
chat_with_tools = chat.bind_tools(tools)
|
|
assert isinstance(chat_with_tools, RunnableBinding)
|
|
chat_tools = chat_with_tools.tools
|
|
assert chat_tools
|
|
assert chat_tools == {
|
|
"tools": [
|
|
{
|
|
"function": {
|
|
"description": "",
|
|
"name": "Foo",
|
|
"parameters": {"properties": {}, "type": "object"},
|
|
},
|
|
"type": "function",
|
|
}
|
|
]
|
|
}
|
|
|
|
|
|
def test_tool_use() -> None:
|
|
llm = ChatDeepInfra(model="meta-llama/Meta-Llama-3-70B-Instruct", temperature=0)
|
|
llm_with_tool = llm.bind_tools(tools=[GenerateMovieName], tool_choice=True)
|
|
msgs: List = [
|
|
HumanMessage(content="It should be a movie explaining humanity in 2133.")
|
|
]
|
|
ai_msg = llm_with_tool.invoke(msgs)
|
|
|
|
assert isinstance(ai_msg, AIMessage)
|
|
assert isinstance(ai_msg.tool_calls, list)
|
|
assert len(ai_msg.tool_calls) == 1
|
|
tool_call = ai_msg.tool_calls[0]
|
|
assert "args" in tool_call
|
|
|
|
tool_msg = ToolMessage(
|
|
content="Year 2133",
|
|
tool_call_id=ai_msg.additional_kwargs["tool_calls"][0]["id"],
|
|
)
|
|
msgs.extend([ai_msg, tool_msg])
|
|
llm_with_tool.invoke(msgs)
|