mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
5f839beab9
This is technically a breaking change because it'll switch out default models from `text-davinci-003` to `gpt-3.5-turbo-instruct`, but OpenAI is shutting off those endpoints on 1/4 anyways. Feels less disruptive to switch out the default instead.
295 lines
8.7 KiB
Python
295 lines
8.7 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_core.callbacks import CallbackManager
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from langchain_core.outputs import LLMResult
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from langchain_community.chat_models.openai import ChatOpenAI
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from langchain_community.llms.loading import load_llm
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from langchain_community.llms.openai import OpenAI
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from tests.unit_tests.callbacks.fake_callback_handler import (
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FakeCallbackHandler,
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)
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@pytest.mark.scheduled
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def test_openai_call() -> None:
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"""Test valid call to openai."""
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llm = OpenAI()
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output = llm("Say something nice:")
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assert isinstance(output, str)
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def test_openai_model_param() -> None:
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llm = OpenAI(model="foo")
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assert llm.model_name == "foo"
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llm = OpenAI(model_name="foo")
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assert llm.model_name == "foo"
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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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# Test that if explicit param is specified in kwargs it errors
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with pytest.raises(ValueError):
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OpenAI(model_kwargs={"temperature": 0.2})
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# Test that "model" cannot be specified in kwargs
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with pytest.raises(ValueError):
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OpenAI(model_kwargs={"model": "gpt-3.5-turbo-instruct"})
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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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@pytest.mark.scheduled
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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, str)
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@pytest.mark.scheduled
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async def test_openai_astream() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = OpenAI(max_tokens=10)
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async for token in llm.astream("I'm Pickle Rick"):
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assert isinstance(token, str)
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@pytest.mark.scheduled
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async def test_openai_abatch() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = OpenAI(max_tokens=10)
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result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
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for token in result:
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assert isinstance(token, str)
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async def test_openai_abatch_tags() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = OpenAI(max_tokens=10)
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result = await llm.abatch(
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["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
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)
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for token in result:
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assert isinstance(token, str)
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@pytest.mark.scheduled
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def test_openai_batch() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = OpenAI(max_tokens=10)
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result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
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for token in result:
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assert isinstance(token, str)
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@pytest.mark.scheduled
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async def test_openai_ainvoke() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = OpenAI(max_tokens=10)
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result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
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assert isinstance(result, str)
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@pytest.mark.scheduled
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def test_openai_invoke() -> None:
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"""Test streaming tokens from OpenAI."""
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llm = OpenAI(max_tokens=10)
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result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
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assert isinstance(result, str)
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@pytest.mark.scheduled
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def test_openai_multiple_prompts() -> None:
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"""Test completion with multiple prompts."""
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llm = OpenAI(max_tokens=10)
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output = llm.generate(["I'm Pickle Rick", "I'm Pickle Rick"])
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assert isinstance(output, LLMResult)
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assert isinstance(output.generations, list)
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assert len(output.generations) == 2
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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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@pytest.mark.scheduled
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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.scheduled
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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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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_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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_EXPECTED_NUM_TOKENS = {
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"ada": 17,
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"babbage": 17,
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"curie": 17,
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"davinci": 17,
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"gpt-4": 12,
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"gpt-4-32k": 12,
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"gpt-3.5-turbo": 12,
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}
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_MODELS = models = [
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"ada",
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"babbage",
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"curie",
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"davinci",
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]
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_CHAT_MODELS = [
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"gpt-4",
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"gpt-4-32k",
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"gpt-3.5-turbo",
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]
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@pytest.mark.parametrize("model", _MODELS)
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def test_openai_get_num_tokens(model: str) -> None:
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"""Test get_tokens."""
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llm = OpenAI(model=model)
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assert llm.get_num_tokens("表情符号是\n🦜🔗") == _EXPECTED_NUM_TOKENS[model]
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@pytest.mark.parametrize("model", _CHAT_MODELS)
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def test_chat_openai_get_num_tokens(model: str) -> None:
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"""Test get_tokens."""
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llm = ChatOpenAI(model=model)
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assert llm.get_num_tokens("表情符号是\n🦜🔗") == _EXPECTED_NUM_TOKENS[model]
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@pytest.fixture
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def mock_completion() -> dict:
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return {
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"id": "cmpl-3evkmQda5Hu7fcZavknQda3SQ",
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"object": "text_completion",
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"created": 1689989000,
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"model": "gpt-3.5-turbo-instruct",
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"choices": [
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{"text": "Bar Baz", "index": 0, "logprobs": None, "finish_reason": "length"}
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],
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"usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3},
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}
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