"""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) def test_generativeai_get_num_tokens_gemini() -> None: llm = GoogleGenerativeAI(temperature=0, model="gemini-pro") output = llm.get_num_tokens("How are you?") assert output == 4