forked from Archives/langchain
225 lines
7.0 KiB
Python
225 lines
7.0 KiB
Python
"""Test OpenAI API wrapper."""
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from pathlib import Path
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from typing import Generator
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import pytest
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from langchain.callbacks.base import CallbackManager
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from langchain.llms.loading import load_llm
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from langchain.llms.openai import OpenAI, OpenAIChat
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from langchain.schema import LLMResult
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from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
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def test_openai_call() -> None:
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"""Test valid call to openai."""
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llm = OpenAI(max_tokens=10)
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output = llm("Say foo:")
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assert isinstance(output, str)
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def test_openai_extra_kwargs() -> None:
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"""Test extra kwargs to openai."""
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# Check that foo is saved in extra_kwargs.
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llm = OpenAI(foo=3, max_tokens=10)
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assert llm.max_tokens == 10
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assert llm.model_kwargs == {"foo": 3}
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# Test that if extra_kwargs are provided, they are added to it.
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llm = OpenAI(foo=3, model_kwargs={"bar": 2})
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assert llm.model_kwargs == {"foo": 3, "bar": 2}
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# Test that if provided twice it errors
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with pytest.raises(ValueError):
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OpenAI(foo=3, model_kwargs={"foo": 2})
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def test_openai_llm_output_contains_model_name() -> None:
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"""Test llm_output contains model_name."""
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llm = OpenAI(max_tokens=10)
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llm_result = llm.generate(["Hello, how are you?"])
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assert llm_result.llm_output is not None
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assert llm_result.llm_output["model_name"] == llm.model_name
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def test_openai_stop_valid() -> None:
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"""Test openai stop logic on valid configuration."""
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query = "write an ordered list of five items"
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first_llm = OpenAI(stop="3", temperature=0)
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first_output = first_llm(query)
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second_llm = OpenAI(temperature=0)
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second_output = second_llm(query, stop=["3"])
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# Because it stops on new lines, shouldn't return anything
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assert first_output == second_output
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def test_openai_stop_error() -> None:
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"""Test openai stop logic on bad configuration."""
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llm = OpenAI(stop="3", temperature=0)
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with pytest.raises(ValueError):
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llm("write an ordered list of five items", stop=["\n"])
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def test_saving_loading_llm(tmp_path: Path) -> None:
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"""Test saving/loading an OpenAI LLM."""
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llm = OpenAI(max_tokens=10)
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llm.save(file_path=tmp_path / "openai.yaml")
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loaded_llm = load_llm(tmp_path / "openai.yaml")
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assert loaded_llm == llm
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def test_openai_streaming() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = OpenAI(max_tokens=10)
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generator = llm.stream("I'm Pickle Rick")
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assert isinstance(generator, Generator)
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for token in generator:
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assert isinstance(token["choices"][0]["text"], str)
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def test_openai_streaming_error() -> None:
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"""Test error handling in stream."""
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llm = OpenAI(best_of=2)
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with pytest.raises(ValueError):
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llm.stream("I'm Pickle Rick")
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def test_openai_streaming_best_of_error() -> None:
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"""Test validation for streaming fails if best_of is not 1."""
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with pytest.raises(ValueError):
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OpenAI(best_of=2, streaming=True)
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def test_openai_streaming_n_error() -> None:
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"""Test validation for streaming fails if n is not 1."""
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with pytest.raises(ValueError):
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OpenAI(n=2, streaming=True)
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def test_openai_streaming_multiple_prompts_error() -> None:
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"""Test validation for streaming fails if multiple prompts are given."""
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with pytest.raises(ValueError):
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OpenAI(streaming=True).generate(["I'm Pickle Rick", "I'm Pickle Rick"])
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def test_openai_streaming_call() -> None:
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"""Test valid call to openai."""
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llm = OpenAI(max_tokens=10, streaming=True)
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output = llm("Say foo:")
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assert isinstance(output, str)
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def test_openai_streaming_callback() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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llm = OpenAI(
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max_tokens=10,
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streaming=True,
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temperature=0,
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callback_manager=callback_manager,
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verbose=True,
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)
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llm("Write me a sentence with 100 words.")
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assert callback_handler.llm_streams == 10
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@pytest.mark.asyncio
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async def test_openai_async_generate() -> None:
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"""Test async generation."""
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llm = OpenAI(max_tokens=10)
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output = await llm.agenerate(["Hello, how are you?"])
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assert isinstance(output, LLMResult)
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@pytest.mark.asyncio
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async def test_openai_async_streaming_callback() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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llm = OpenAI(
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max_tokens=10,
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streaming=True,
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temperature=0,
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callback_manager=callback_manager,
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verbose=True,
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)
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result = await llm.agenerate(["Write me a sentence with 100 words."])
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assert callback_handler.llm_streams == 10
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assert isinstance(result, LLMResult)
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def test_openai_chat_wrong_class() -> None:
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"""Test OpenAIChat with wrong class still works."""
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llm = OpenAI(model_name="gpt-3.5-turbo")
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output = llm("Say foo:")
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assert isinstance(output, str)
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def test_openai_chat() -> None:
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"""Test OpenAIChat."""
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llm = OpenAIChat(max_tokens=10)
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output = llm("Say foo:")
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assert isinstance(output, str)
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def test_openai_chat_streaming() -> None:
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"""Test OpenAIChat with streaming option."""
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llm = OpenAIChat(max_tokens=10, streaming=True)
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output = llm("Say foo:")
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assert isinstance(output, str)
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def test_openai_chat_streaming_callback() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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llm = OpenAIChat(
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max_tokens=10,
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streaming=True,
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temperature=0,
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callback_manager=callback_manager,
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verbose=True,
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)
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llm("Write me a sentence with 100 words.")
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assert callback_handler.llm_streams != 0
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@pytest.mark.asyncio
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async def test_openai_chat_async_generate() -> None:
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"""Test async chat."""
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llm = OpenAIChat(max_tokens=10)
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output = await llm.agenerate(["Hello, how are you?"])
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assert isinstance(output, LLMResult)
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@pytest.mark.asyncio
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async def test_openai_chat_async_streaming_callback() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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llm = OpenAIChat(
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max_tokens=10,
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streaming=True,
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temperature=0,
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callback_manager=callback_manager,
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verbose=True,
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)
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result = await llm.agenerate(["Write me a sentence with 100 words."])
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assert callback_handler.llm_streams != 0
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assert isinstance(result, LLMResult)
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def test_openai_modelname_to_contextsize_valid() -> None:
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"""Test model name to context size on a valid model."""
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assert OpenAI().modelname_to_contextsize("davinci") == 2049
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def test_openai_modelname_to_contextsize_invalid() -> None:
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"""Test model name to context size on an invalid model."""
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with pytest.raises(ValueError):
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OpenAI().modelname_to_contextsize("foobar")
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