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test_vertexai.py
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test_vertexai.py
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"""Test Vertex AI API wrapper.
In order to run this test, you need to install VertexAI SDK:
pip install google-cloud-aiplatform>=1.36.0
Your end-user credentials would be used to make the calls (make sure you've run
`gcloud auth login` first).
"""
import os
from typing import Optional
import pytest
from langchain_core.outputs import LLMResult
from langchain_community.llms import VertexAI, VertexAIModelGarden
model_names_to_test = ["text-bison@001", "gemini-pro"]
model_names_to_test_with_default = [None] + model_names_to_test
@pytest.mark.parametrize(
"model_name",
model_names_to_test_with_default,
)
def test_vertex_initialization(model_name: str) -> None:
llm = VertexAI(model_name=model_name) if model_name else VertexAI()
assert llm._llm_type == "vertexai"
try:
assert llm.model_name == llm.client._model_id
except AttributeError:
assert llm.model_name == llm.client._model_name.split("/")[-1]
@pytest.mark.parametrize(
"model_name",
model_names_to_test_with_default,
)
def test_vertex_call(model_name: str) -> None:
llm = (
VertexAI(model_name=model_name, temperature=0)
if model_name
else VertexAI(temperature=0.0)
)
output = llm.invoke("Say foo:")
assert isinstance(output, str)
@pytest.mark.scheduled
def test_vertex_generate() -> None:
llm = VertexAI(temperature=0.3, n=2, model_name="text-bison@001")
output = llm.generate(["Say foo:"])
assert isinstance(output, LLMResult)
assert len(output.generations) == 1
assert len(output.generations[0]) == 2
@pytest.mark.scheduled
def test_vertex_generate_code() -> None:
llm = VertexAI(temperature=0.3, n=2, model_name="code-bison@001")
output = llm.generate(["generate a python method that says foo:"])
assert isinstance(output, LLMResult)
assert len(output.generations) == 1
assert len(output.generations[0]) == 2
@pytest.mark.scheduled
async def test_vertex_agenerate() -> None:
llm = VertexAI(temperature=0)
output = await llm.agenerate(["Please say foo:"])
assert isinstance(output, LLMResult)
@pytest.mark.scheduled
@pytest.mark.parametrize(
"model_name",
model_names_to_test_with_default,
)
def test_vertex_stream(model_name: str) -> None:
llm = (
VertexAI(temperature=0, model_name=model_name)
if model_name
else VertexAI(temperature=0)
)
outputs = list(llm.stream("Please say foo:"))
assert isinstance(outputs[0], str)
async def test_vertex_consistency() -> None:
llm = VertexAI(temperature=0)
output = llm.generate(["Please say foo:"])
streaming_output = llm.generate(["Please say foo:"], stream=True)
async_output = await llm.agenerate(["Please say foo:"])
assert output.generations[0][0].text == streaming_output.generations[0][0].text
assert output.generations[0][0].text == async_output.generations[0][0].text
@pytest.mark.parametrize(
"endpoint_os_variable_name,result_arg",
[("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
)
def test_model_garden(
endpoint_os_variable_name: str, result_arg: Optional[str]
) -> None:
"""In order to run this test, you should provide endpoint names.
Example:
export FALCON_ENDPOINT_ID=...
export LLAMA_ENDPOINT_ID=...
export PROJECT=...
"""
endpoint_id = os.environ[endpoint_os_variable_name]
project = os.environ["PROJECT"]
location = "europe-west4"
llm = VertexAIModelGarden(
endpoint_id=endpoint_id,
project=project,
result_arg=result_arg,
location=location,
)
output = llm.invoke("What is the meaning of life?")
assert isinstance(output, str)
assert llm._llm_type == "vertexai_model_garden"
@pytest.mark.parametrize(
"endpoint_os_variable_name,result_arg",
[("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
)
def test_model_garden_generate(
endpoint_os_variable_name: str, result_arg: Optional[str]
) -> None:
"""In order to run this test, you should provide endpoint names.
Example:
export FALCON_ENDPOINT_ID=...
export LLAMA_ENDPOINT_ID=...
export PROJECT=...
"""
endpoint_id = os.environ[endpoint_os_variable_name]
project = os.environ["PROJECT"]
location = "europe-west4"
llm = VertexAIModelGarden(
endpoint_id=endpoint_id,
project=project,
result_arg=result_arg,
location=location,
)
output = llm.generate(["What is the meaning of life?", "How much is 2+2"])
assert isinstance(output, LLMResult)
assert len(output.generations) == 2
@pytest.mark.asyncio
@pytest.mark.parametrize(
"endpoint_os_variable_name,result_arg",
[("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
)
async def test_model_garden_agenerate(
endpoint_os_variable_name: str, result_arg: Optional[str]
) -> None:
endpoint_id = os.environ[endpoint_os_variable_name]
project = os.environ["PROJECT"]
location = "europe-west4"
llm = VertexAIModelGarden(
endpoint_id=endpoint_id,
project=project,
result_arg=result_arg,
location=location,
)
output = await llm.agenerate(["What is the meaning of life?", "How much is 2+2"])
assert isinstance(output, LLMResult)
assert len(output.generations) == 2
@pytest.mark.parametrize(
"model_name",
model_names_to_test,
)
def test_vertex_call_count_tokens(model_name: str) -> None:
llm = VertexAI(model_name=model_name)
output = llm.get_num_tokens("How are you?")
assert output == 4