forked from Archives/langchain
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
114 lines
3.9 KiB
Python
114 lines
3.9 KiB
Python
"""Unit tests for ReAct."""
|
|
|
|
from typing import List, Optional, Tuple
|
|
|
|
import pytest
|
|
|
|
from langchain.chains.llm import LLMChain
|
|
from langchain.chains.react.base import ReActChain, predict_until_observation
|
|
from langchain.docstore.base import Docstore
|
|
from langchain.docstore.document import Document
|
|
from langchain.llms.base import LLM, CompletionOutput
|
|
from langchain.prompt import Prompt
|
|
|
|
_PAGE_CONTENT = """This is a page about LangChain.
|
|
|
|
It is a really cool framework.
|
|
|
|
What isn't there to love about langchain?
|
|
|
|
Made in 2022."""
|
|
|
|
_FAKE_PROMPT = Prompt(input_variables=["input"], template="{input}")
|
|
|
|
|
|
class FakeListLLM(LLM):
|
|
"""Fake LLM for testing that outputs elements of a list."""
|
|
|
|
def __init__(self, responses: List[str]):
|
|
"""Initialize with list of responses."""
|
|
self.responses = responses
|
|
self.i = -1
|
|
|
|
def generate(self, prompt: str, stop: Optional[List[str]] = None) -> List[CompletionOutput]:
|
|
"""Increment counter, and then return response in that index."""
|
|
self.i += 1
|
|
return [CompletionOutput(text=self.responses[self.i])]
|
|
|
|
|
|
class FakeDocstore(Docstore):
|
|
"""Fake docstore for testing purposes."""
|
|
|
|
def search(self, search: str) -> Tuple[str, Optional[Document]]:
|
|
"""Return the fake document."""
|
|
document = Document(page_content=_PAGE_CONTENT)
|
|
return document.summary, document
|
|
|
|
|
|
def test_predict_until_observation_normal() -> None:
|
|
"""Test predict_until_observation when observation is made normally."""
|
|
outputs = ["foo\nAction 1: search[foo]"]
|
|
fake_llm = FakeListLLM(outputs)
|
|
fake_llm_chain = LLMChain(llm=fake_llm, prompt=_FAKE_PROMPT)
|
|
ret_text, action, directive = predict_until_observation(fake_llm_chain, "", 1)
|
|
assert ret_text == outputs[0]
|
|
assert action == "search"
|
|
assert directive == "foo"
|
|
|
|
|
|
def test_predict_until_observation_repeat() -> None:
|
|
"""Test when no action is generated initially."""
|
|
outputs = ["foo", " search[foo]"]
|
|
fake_llm = FakeListLLM(outputs)
|
|
fake_llm_chain = LLMChain(llm=fake_llm, prompt=_FAKE_PROMPT)
|
|
ret_text, action, directive = predict_until_observation(fake_llm_chain, "", 1)
|
|
assert ret_text == "foo\nAction 1: search[foo]"
|
|
assert action == "search"
|
|
assert directive == "foo"
|
|
|
|
|
|
def test_predict_until_observation_error() -> None:
|
|
"""Test handling of generation of text that cannot be parsed."""
|
|
outputs = ["foo\nAction 1: foo"]
|
|
fake_llm = FakeListLLM(outputs)
|
|
fake_llm_chain = LLMChain(llm=fake_llm, prompt=_FAKE_PROMPT)
|
|
with pytest.raises(ValueError):
|
|
predict_until_observation(fake_llm_chain, "", 1)
|
|
|
|
|
|
def test_react_chain() -> None:
|
|
"""Test react chain."""
|
|
responses = [
|
|
"I should probably search\nAction 1: Search[langchain]",
|
|
"I should probably lookup\nAction 2: Lookup[made]",
|
|
"Ah okay now I know the answer\nAction 3: Finish[2022]",
|
|
]
|
|
fake_llm = FakeListLLM(responses)
|
|
react_chain = ReActChain(llm=fake_llm, docstore=FakeDocstore())
|
|
inputs = {"question": "when was langchain made"}
|
|
output = react_chain(inputs)
|
|
assert output["answer"] == "2022"
|
|
expected_full_output = (
|
|
"when was langchain made\n"
|
|
"Thought 1:I should probably search\n"
|
|
"Action 1: Search[langchain]\n"
|
|
"Observation 1: This is a page about LangChain.\n"
|
|
"Thought 2:I should probably lookup\n"
|
|
"Action 2: Lookup[made]\n"
|
|
"Observation 2: (Result 1/1) Made in 2022.\n"
|
|
"Thought 3:Ah okay now I know the answer\n"
|
|
"Action 3: Finish[2022]"
|
|
)
|
|
assert output["full_logic"] == expected_full_output
|
|
|
|
|
|
def test_react_chain_bad_action() -> None:
|
|
"""Test react chain when bad action given."""
|
|
responses = [
|
|
"I should probably search\nAction 1: BadAction[langchain]",
|
|
]
|
|
fake_llm = FakeListLLM(responses)
|
|
react_chain = ReActChain(llm=fake_llm, docstore=FakeDocstore())
|
|
with pytest.raises(ValueError):
|
|
react_chain.run("when was langchain made")
|