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108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
"""Unit tests for ReAct."""
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from typing import Any, List, Mapping, Optional, Union
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from pydantic import BaseModel
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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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from langchain.schema import AgentAction
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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, BaseModel):
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"""Fake LLM for testing that outputs elements of a list."""
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responses: List[str]
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i: int = -1
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "fake_list"
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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(responses=outputs)
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tools = [
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Tool(name="Search", func=lambda x: x, description="foo"),
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Tool(name="Lookup", func=lambda x: x, description="bar"),
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]
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agent = ReActDocstoreAgent.from_llm_and_tools(fake_llm, tools)
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output = agent.plan([], input="")
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expected_output = AgentAction("Search", "foo", outputs[0])
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assert output == expected_output
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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(responses=outputs)
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tools = [
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Tool(name="Search", func=lambda x: x, description="foo"),
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Tool(name="Lookup", func=lambda x: x, description="bar"),
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]
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agent = ReActDocstoreAgent.from_llm_and_tools(fake_llm, tools)
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output = agent.plan([], input="")
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expected_output = AgentAction("Search", "foo", "foo\nAction 1: Search[foo]")
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assert output == expected_output
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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=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=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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