mirror of
https://github.com/hwchase17/langchain
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134 lines
4.4 KiB
Python
134 lines
4.4 KiB
Python
"""Test MRKL functionality."""
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from typing import Tuple
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import pytest
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from langchain.agents.mrkl.base import ZeroShotAgent
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from langchain.agents.mrkl.output_parser import MRKLOutputParser
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from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
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from langchain.agents.tools import Tool
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from langchain.prompts import PromptTemplate
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from langchain.schema import AgentAction
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from tests.unit_tests.llms.fake_llm import FakeLLM
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def get_action_and_input(text: str) -> Tuple[str, str]:
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output = MRKLOutputParser().parse(text)
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if isinstance(output, AgentAction):
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return output.tool, str(output.tool_input)
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else:
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return "Final Answer", output.return_values["output"]
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def test_get_action_and_input() -> None:
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"""Test getting an action from text."""
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llm_output = (
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"Thought: I need to search for NBA\n" "Action: Search\n" "Action Input: NBA"
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)
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action, action_input = get_action_and_input(llm_output)
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assert action == "Search"
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assert action_input == "NBA"
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def test_get_action_and_input_whitespace() -> None:
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"""Test getting an action from text."""
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llm_output = "Thought: I need to search for NBA\nAction: Search \nAction Input: NBA"
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action, action_input = get_action_and_input(llm_output)
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assert action == "Search"
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assert action_input == "NBA"
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def test_get_action_and_input_newline() -> None:
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"""Test getting an action from text where Action Input is a code snippet."""
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llm_output = (
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"Now I need to write a unittest for the function.\n\n"
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"Action: Python\nAction Input:\n```\nimport unittest\n\nunittest.main()\n```"
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)
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action, action_input = get_action_and_input(llm_output)
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assert action == "Python"
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assert action_input == "```\nimport unittest\n\nunittest.main()\n```"
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def test_get_final_answer() -> None:
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"""Test getting final answer."""
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llm_output = (
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"Thought: I need to search for NBA\n"
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"Action: Search\n"
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"Action Input: NBA\n"
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"Observation: founded in 1994\n"
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"Thought: I can now answer the question\n"
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"Final Answer: 1994"
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)
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action, action_input = get_action_and_input(llm_output)
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assert action == "Final Answer"
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assert action_input == "1994"
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def test_get_final_answer_new_line() -> None:
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"""Test getting final answer."""
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llm_output = (
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"Thought: I need to search for NBA\n"
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"Action: Search\n"
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"Action Input: NBA\n"
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"Observation: founded in 1994\n"
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"Thought: I can now answer the question\n"
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"Final Answer:\n1994"
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)
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action, action_input = get_action_and_input(llm_output)
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assert action == "Final Answer"
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assert action_input == "1994"
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def test_get_final_answer_multiline() -> None:
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"""Test getting final answer that is multiline."""
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llm_output = (
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"Thought: I need to search for NBA\n"
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"Action: Search\n"
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"Action Input: NBA\n"
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"Observation: founded in 1994 and 1993\n"
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"Thought: I can now answer the question\n"
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"Final Answer: 1994\n1993"
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)
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action, action_input = get_action_and_input(llm_output)
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assert action == "Final Answer"
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assert action_input == "1994\n1993"
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def test_bad_action_input_line() -> None:
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"""Test handling when no action input found."""
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llm_output = "Thought: I need to search for NBA\n" "Action: Search\n" "Thought: NBA"
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with pytest.raises(ValueError):
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get_action_and_input(llm_output)
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def test_bad_action_line() -> None:
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"""Test handling when no action input found."""
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llm_output = (
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"Thought: I need to search for NBA\n" "Thought: Search\n" "Action Input: NBA"
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)
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with pytest.raises(ValueError):
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get_action_and_input(llm_output)
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def test_from_chains() -> None:
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"""Test initializing from chains."""
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chain_configs = [
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Tool(name="foo", func=lambda x: "foo", description="foobar1"),
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Tool(name="bar", func=lambda x: "bar", description="foobar2"),
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]
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agent = ZeroShotAgent.from_llm_and_tools(FakeLLM(), chain_configs)
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expected_tools_prompt = "foo: foobar1\nbar: foobar2"
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expected_tool_names = "foo, bar"
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expected_template = "\n\n".join(
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[
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PREFIX,
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expected_tools_prompt,
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FORMAT_INSTRUCTIONS.format(tool_names=expected_tool_names),
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SUFFIX,
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]
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)
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prompt = agent.llm_chain.prompt
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assert isinstance(prompt, PromptTemplate)
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assert prompt.template == expected_template
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