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119 lines
3.9 KiB
Python
119 lines
3.9 KiB
Python
"""Test Vertex AI API wrapper.
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In order to run this test, you need to install VertexAI SDK (that is is the private
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preview) and be whitelisted to list the models themselves:
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In order to run this test, you need to install VertexAI SDK
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pip install google-cloud-aiplatform>=1.25.0
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Your end-user credentials would be used to make the calls (make sure you've run
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`gcloud auth login` first).
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"""
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from unittest.mock import Mock, patch
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import pytest
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from langchain.chat_models import ChatVertexAI
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from langchain.chat_models.vertexai import _MessagePair, _parse_chat_history
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from langchain.schema import (
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AIMessage,
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HumanMessage,
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SystemMessage,
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)
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def test_vertexai_single_call() -> None:
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model = ChatVertexAI()
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message = HumanMessage(content="Hello")
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response = model([message])
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assert isinstance(response, AIMessage)
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assert isinstance(response.content, str)
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assert model._llm_type == "vertexai"
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assert model.model_name == model.client._model_id
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def test_vertexai_single_call_with_context() -> None:
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model = ChatVertexAI()
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raw_context = (
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"My name is Ned. You are my personal assistant. My favorite movies "
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"are Lord of the Rings and Hobbit."
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)
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question = (
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"Hello, could you recommend a good movie for me to watch this evening, please?"
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)
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context = SystemMessage(content=raw_context)
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message = HumanMessage(content=question)
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response = model([context, message])
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assert isinstance(response, AIMessage)
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assert isinstance(response.content, str)
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def test_parse_chat_history_correct() -> None:
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text_context = (
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"My name is Ned. You are my personal assistant. My "
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"favorite movies are Lord of the Rings and Hobbit."
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)
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context = SystemMessage(content=text_context)
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text_question = (
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"Hello, could you recommend a good movie for me to watch this evening, please?"
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)
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question = HumanMessage(content=text_question)
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text_answer = (
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"Sure, You might enjoy The Lord of the Rings: The Fellowship of the Ring "
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"(2001): This is the first movie in the Lord of the Rings trilogy."
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)
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answer = AIMessage(content=text_answer)
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history = _parse_chat_history([context, question, answer, question, answer])
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assert history.system_message == context
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assert len(history.history) == 2
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assert history.history[0] == _MessagePair(question=question, answer=answer)
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def test_parse_chat_history_wrong_sequence() -> None:
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text_question = (
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"Hello, could you recommend a good movie for me to watch this evening, please?"
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)
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question = HumanMessage(content=text_question)
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with pytest.raises(ValueError) as exc_info:
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_ = _parse_chat_history([question, question])
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assert (
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str(exc_info.value)
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== "A human message should follow a bot one, got human, human."
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)
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def test_vertexai_single_call_failes_no_message() -> None:
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chat = ChatVertexAI()
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with pytest.raises(ValueError) as exc_info:
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_ = chat([])
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assert (
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str(exc_info.value)
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== "You should provide at least one message to start the chat!"
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)
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def test_vertexai_args_passed() -> None:
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response_text = "Goodbye"
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user_prompt = "Hello"
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prompt_params = {
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"max_output_tokens": 1,
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"temperature": 10000.0,
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"top_k": 10,
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"top_p": 0.5,
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}
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# Mock the library to ensure the args are passed correctly
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with patch(
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"vertexai.language_models._language_models.ChatSession.send_message"
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) as send_message:
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mock_response = Mock(text=response_text)
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send_message.return_value = mock_response
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model = ChatVertexAI(**prompt_params)
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message = HumanMessage(content=user_prompt)
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response = model([message])
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assert response.content == response_text
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send_message.assert_called_once_with(
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user_prompt,
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**prompt_params,
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)
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