mirror of
https://github.com/hwchase17/langchain.git
synced 2025-12-23 16:06:15 +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()
```
122 lines
4.4 KiB
Python
122 lines
4.4 KiB
Python
"""Test SmartLLM."""
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from langchain_community.chat_models import FakeListChatModel
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from langchain_community.llms import FakeListLLM
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from langchain_core.prompts.prompt import PromptTemplate
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from langchain_experimental.smart_llm import SmartLLMChain
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def test_ideation() -> None:
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# test that correct responses are returned
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responses = ["Idea 1", "Idea 2", "Idea 3"]
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llm = FakeListLLM(responses=responses)
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prompt = PromptTemplate(
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input_variables=["product"],
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template="What is a good name for a company that makes {product}?",
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)
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chain = SmartLLMChain(llm=llm, prompt=prompt)
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prompt_value, _ = chain.prep_prompts({"product": "socks"})
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chain.history.question = prompt_value.to_string()
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results = chain._ideate()
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assert results == responses
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# test that correct number of responses are returned
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for i in range(1, 5):
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responses = [f"Idea {j+1}" for j in range(i)]
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llm = FakeListLLM(responses=responses)
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chain = SmartLLMChain(llm=llm, prompt=prompt, n_ideas=i)
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prompt_value, _ = chain.prep_prompts({"product": "socks"})
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chain.history.question = prompt_value.to_string()
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results = chain._ideate()
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assert len(results) == i
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def test_critique() -> None:
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response = "Test Critique"
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llm = FakeListLLM(responses=[response])
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prompt = PromptTemplate(
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input_variables=["product"],
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template="What is a good name for a company that makes {product}?",
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)
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chain = SmartLLMChain(llm=llm, prompt=prompt, n_ideas=2)
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prompt_value, _ = chain.prep_prompts({"product": "socks"})
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chain.history.question = prompt_value.to_string()
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chain.history.ideas = ["Test Idea 1", "Test Idea 2"]
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result = chain._critique()
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assert result == response
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def test_resolver() -> None:
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response = "Test resolution"
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llm = FakeListLLM(responses=[response])
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prompt = PromptTemplate(
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input_variables=["product"],
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template="What is a good name for a company that makes {product}?",
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)
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chain = SmartLLMChain(llm=llm, prompt=prompt, n_ideas=2)
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prompt_value, _ = chain.prep_prompts({"product": "socks"})
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chain.history.question = prompt_value.to_string()
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chain.history.ideas = ["Test Idea 1", "Test Idea 2"]
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chain.history.critique = "Test Critique"
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result = chain._resolve()
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assert result == response
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def test_all_steps() -> None:
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joke = "Why did the chicken cross the Mobius strip?"
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response = "Resolution response"
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ideation_llm = FakeListLLM(responses=["Ideation response" for _ in range(20)])
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critique_llm = FakeListLLM(responses=["Critique response" for _ in range(20)])
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resolver_llm = FakeListLLM(responses=[response for _ in range(20)])
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prompt = PromptTemplate(
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input_variables=["joke"],
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template="Explain this joke to me: {joke}?",
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)
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chain = SmartLLMChain(
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ideation_llm=ideation_llm,
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critique_llm=critique_llm,
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resolver_llm=resolver_llm,
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prompt=prompt,
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)
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result = chain(joke)
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assert result["joke"] == joke
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assert result["resolution"] == response
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def test_intermediate_output() -> None:
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joke = "Why did the chicken cross the Mobius strip?"
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llm = FakeListLLM(responses=[f"Response {i+1}" for i in range(5)])
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prompt = PromptTemplate(
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input_variables=["joke"],
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template="Explain this joke to me: {joke}?",
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)
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chain = SmartLLMChain(llm=llm, prompt=prompt, return_intermediate_steps=True)
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result = chain(joke)
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assert result["joke"] == joke
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assert result["ideas"] == [f"Response {i+1}" for i in range(3)]
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assert result["critique"] == "Response 4"
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assert result["resolution"] == "Response 5"
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def test_all_steps_with_chat_model() -> None:
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joke = "Why did the chicken cross the Mobius strip?"
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response = "Resolution response"
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ideation_llm = FakeListChatModel(responses=["Ideation response" for _ in range(20)])
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critique_llm = FakeListChatModel(responses=["Critique response" for _ in range(20)])
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resolver_llm = FakeListChatModel(responses=[response for _ in range(20)])
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prompt = PromptTemplate(
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input_variables=["joke"],
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template="Explain this joke to me: {joke}?",
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)
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chain = SmartLLMChain(
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ideation_llm=ideation_llm,
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critique_llm=critique_llm,
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resolver_llm=resolver_llm,
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prompt=prompt,
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)
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result = chain(joke)
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assert result["joke"] == joke
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assert result["resolution"] == response
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