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51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
"""Unit tests for agents."""
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from typing import Any, List, Mapping, Optional
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from pydantic import BaseModel
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from langchain.agents import Tool, initialize_agent
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from langchain.llms.base import LLM
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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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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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print(self.i)
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print(self.responses)
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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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@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 test_agent_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: {bad_action_name}\nAction Input: misalignment",
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"Oh well\nAction: Final Answer\nAction Input: curses foiled again",
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]
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fake_llm = FakeListLLM(responses=responses)
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tools = [
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Tool("Search", lambda x: x, "Useful for searching"),
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Tool("Lookup", lambda x: x, "Useful for looking up things in a table"),
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]
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agent = initialize_agent(
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tools, fake_llm, agent="zero-shot-react-description", verbose=True
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)
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output = agent.run("when was langchain made")
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assert output == "curses foiled again"
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