mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
"""Test Google GenerativeAI API wrapper.
|
|
|
|
Note: This test must be run with the GOOGLE_API_KEY environment variable set to a
|
|
valid API key.
|
|
"""
|
|
|
|
import pytest
|
|
from langchain_core.outputs import LLMResult
|
|
|
|
from langchain_google_genai.llms import GoogleGenerativeAI
|
|
|
|
model_names = ["models/text-bison-001", "gemini-pro"]
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model_name",
|
|
model_names,
|
|
)
|
|
def test_google_generativeai_call(model_name: str) -> None:
|
|
"""Test valid call to Google GenerativeAI text API."""
|
|
if model_name:
|
|
llm = GoogleGenerativeAI(max_output_tokens=10, model=model_name)
|
|
else:
|
|
llm = GoogleGenerativeAI(max_output_tokens=10)
|
|
output = llm("Say foo:")
|
|
assert isinstance(output, str)
|
|
assert llm._llm_type == "google_palm"
|
|
if model_name and "gemini" in model_name:
|
|
assert llm.client.model_name == "models/gemini-pro"
|
|
else:
|
|
assert llm.model == "models/text-bison-001"
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model_name",
|
|
model_names,
|
|
)
|
|
def test_google_generativeai_generate(model_name: str) -> None:
|
|
n = 1 if model_name == "gemini-pro" else 2
|
|
llm = GoogleGenerativeAI(temperature=0.3, n=n, model=model_name)
|
|
output = llm.generate(["Say foo:"])
|
|
assert isinstance(output, LLMResult)
|
|
assert len(output.generations) == 1
|
|
assert len(output.generations[0]) == n
|
|
|
|
|
|
def test_google_generativeai_get_num_tokens() -> None:
|
|
llm = GoogleGenerativeAI(model="models/text-bison-001")
|
|
output = llm.get_num_tokens("How are you?")
|
|
assert output == 4
|
|
|
|
|
|
async def test_google_generativeai_agenerate() -> None:
|
|
llm = GoogleGenerativeAI(temperature=0, model="gemini-pro")
|
|
output = await llm.agenerate(["Please say foo:"])
|
|
assert isinstance(output, LLMResult)
|
|
|
|
|
|
def test_generativeai_stream() -> None:
|
|
llm = GoogleGenerativeAI(temperature=0, model="gemini-pro")
|
|
outputs = list(llm.stream("Please say foo:"))
|
|
assert isinstance(outputs[0], str)
|