mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
68666d6a22
Not yet tested, but very simple change, assumption is that we're cool with just producing a generic output when tool is not found
104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
"""Unit tests for ReAct."""
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from typing import Any, List, Mapping, Optional, Union
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from langchain.agents.react.base import ReActChain, ReActDocstoreAgent
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from langchain.agents.tools import Tool
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from langchain.docstore.base import Docstore
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from langchain.docstore.document import Document
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from langchain.llms.base import LLM
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from langchain.prompts.prompt import PromptTemplate
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_PAGE_CONTENT = """This is a page about LangChain.
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It is a really cool framework.
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What isn't there to love about langchain?
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Made in 2022."""
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_FAKE_PROMPT = PromptTemplate(input_variables=["input"], template="{input}")
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class FakeListLLM(LLM):
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"""Fake LLM for testing that outputs elements of a list."""
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def __init__(self, responses: List[str]):
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"""Initialize with list of responses."""
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self.responses = responses
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self.i = -1
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def __call__(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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"""Increment counter, and then return response in that index."""
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self.i += 1
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return self.responses[self.i]
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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return {}
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class FakeDocstore(Docstore):
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"""Fake docstore for testing purposes."""
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def search(self, search: str) -> Union[str, Document]:
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"""Return the fake document."""
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document = Document(page_content=_PAGE_CONTENT)
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return document
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def test_predict_until_observation_normal() -> None:
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"""Test predict_until_observation when observation is made normally."""
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outputs = ["foo\nAction 1: Search[foo]"]
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fake_llm = FakeListLLM(outputs)
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tools = [
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Tool("Search", lambda x: x),
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Tool("Lookup", lambda x: x),
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]
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agent = ReActDocstoreAgent.from_llm_and_tools(fake_llm, tools)
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output = agent.get_action("")
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assert output.log == outputs[0]
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assert output.tool == "Search"
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assert output.tool_input == "foo"
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def test_predict_until_observation_repeat() -> None:
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"""Test when no action is generated initially."""
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outputs = ["foo", " Search[foo]"]
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fake_llm = FakeListLLM(outputs)
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tools = [
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Tool("Search", lambda x: x),
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Tool("Lookup", lambda x: x),
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]
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agent = ReActDocstoreAgent.from_llm_and_tools(fake_llm, tools)
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output = agent.get_action("")
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assert output.log == "foo\nAction 1: Search[foo]"
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assert output.tool == "Search"
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assert output.tool_input == "foo"
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def test_react_chain() -> None:
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"""Test react chain."""
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responses = [
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"I should probably search\nAction 1: Search[langchain]",
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"I should probably lookup\nAction 2: Lookup[made]",
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"Ah okay now I know the answer\nAction 3: Finish[2022]",
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]
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fake_llm = FakeListLLM(responses)
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react_chain = ReActChain(llm=fake_llm, docstore=FakeDocstore())
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output = react_chain.run("when was langchain made")
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assert output == "2022"
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def test_react_chain_bad_action() -> None:
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"""Test react chain when bad action given."""
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bad_action_name = "BadAction"
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responses = [
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f"I'm turning evil\nAction 1: {bad_action_name}[langchain]",
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"Oh well\nAction 2: Finish[curses foiled again]",
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]
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fake_llm = FakeListLLM(responses)
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react_chain = ReActChain(llm=fake_llm, docstore=FakeDocstore())
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output = react_chain.run("when was langchain made")
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assert output == "curses foiled again"
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