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bc7e56e8df
Supporting asyncio in langchain primitives allows for users to run them concurrently and creates more seamless integration with asyncio-supported frameworks (FastAPI, etc.) Summary of changes: **LLM** * Add `agenerate` and `_agenerate` * Implement in OpenAI by leveraging `client.Completions.acreate` **Chain** * Add `arun`, `acall`, `_acall` * Implement them in `LLMChain` and `LLMMathChain` for now **Agent** * Refactor and leverage async chain and llm methods * Add ability for `Tools` to contain async coroutine * Implement async SerpaPI `arun` Create demo notebook. Open questions: * Should all the async stuff go in separate classes? I've seen both patterns (keeping the same class and having async and sync methods vs. having class separation)
86 lines
2.5 KiB
Python
86 lines
2.5 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.llms.loading import load_llm
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from langchain.llms.openai import OpenAI
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from langchain.schema import LLMResult
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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_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 OpenAPI 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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@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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