api/tests/integration_tests/model_runtime/chatglm/test_llm.py

import os
from collections.abc import Generator

import pytest

from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
from core.model_runtime.entities.message_entities import (
    AssistantPromptMessage,
    PromptMessageTool,
    SystemPromptMessage,
    UserPromptMessage,
)
from core.model_runtime.entities.model_entities import AIModelEntity
from core.model_runtime.errors.validate import CredentialsValidateFailedError
from core.model_runtime.model_providers.chatglm.llm.llm import ChatGLMLargeLanguageModel
from tests.integration_tests.model_runtime.__mock.openai import setup_openai_mock


def test_predefined_models():
    model = ChatGLMLargeLanguageModel()
    model_schemas = model.predefined_models()
    assert len(model_schemas) >= 1
    assert isinstance(model_schemas[0], AIModelEntity)


@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
def test_validate_credentials_for_chat_model(setup_openai_mock):
    model = ChatGLMLargeLanguageModel()

    with pytest.raises(CredentialsValidateFailedError):
        model.validate_credentials(model="chatglm2-6b", credentials={"api_base": "invalid_key"})

    model.validate_credentials(model="chatglm2-6b", credentials={"api_base": os.environ.get("CHATGLM_API_BASE")})


@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
def test_invoke_model(setup_openai_mock):
    model = ChatGLMLargeLanguageModel()

    response = model.invoke(
        model="chatglm2-6b",
        credentials={"api_base": os.environ.get("CHATGLM_API_BASE")},
        prompt_messages=[
            SystemPromptMessage(
                content="You are a helpful AI assistant.",
            ),
            UserPromptMessage(content="Hello World!"),
        ],
        model_parameters={
            "temperature": 0.7,
            "top_p": 1.0,
        },
        stop=["you"],
        user="abc-123",
        stream=False,
    )

    assert isinstance(response, LLMResult)
    assert len(response.message.content) > 0
    assert response.usage.total_tokens > 0


@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
def test_invoke_stream_model(setup_openai_mock):
    model = ChatGLMLargeLanguageModel()

    response = model.invoke(
        model="chatglm2-6b",
        credentials={"api_base": os.environ.get("CHATGLM_API_BASE")},
        prompt_messages=[
            SystemPromptMessage(
                content="You are a helpful AI assistant.",
            ),
            UserPromptMessage(content="Hello World!"),
        ],
        model_parameters={
            "temperature": 0.7,
            "top_p": 1.0,
        },
        stop=["you"],
        stream=True,
        user="abc-123",
    )

    assert isinstance(response, Generator)
    for chunk in response:
        assert isinstance(chunk, LLMResultChunk)
        assert isinstance(chunk.delta, LLMResultChunkDelta)
        assert isinstance(chunk.delta.message, AssistantPromptMessage)
        assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True


@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
def test_invoke_stream_model_with_functions(setup_openai_mock):
    model = ChatGLMLargeLanguageModel()

    response = model.invoke(
        model="chatglm3-6b",
        credentials={"api_base": os.environ.get("CHATGLM_API_BASE")},
        prompt_messages=[
            SystemPromptMessage(
                content="你是一个天气机器人，你不知道今天的天气怎么样，你需要通过调用一个函数来获取天气信息。"
            ),
            UserPromptMessage(content="波士顿天气如何？"),
        ],
        model_parameters={
            "temperature": 0,
            "top_p": 1.0,
        },
        stop=["you"],
        user="abc-123",
        stream=True,
        tools=[
            PromptMessageTool(
                name="get_current_weather",
                description="Get the current weather in a given location",
                parameters={
                    "type": "object",
                    "properties": {
                        "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"},
                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                    },
                    "required": ["location"],
                },
            )
        ],
    )

    assert isinstance(response, Generator)

    call: LLMResultChunk = None
    chunks = []

    for chunk in response:
        chunks.append(chunk)
        assert isinstance(chunk, LLMResultChunk)
        assert isinstance(chunk.delta, LLMResultChunkDelta)
        assert isinstance(chunk.delta.message, AssistantPromptMessage)
        assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True

        if chunk.delta.message.tool_calls and len(chunk.delta.message.tool_calls) > 0:
            call = chunk
            break

    assert call is not None
    assert call.delta.message.tool_calls[0].function.name == "get_current_weather"


@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
def test_invoke_model_with_functions(setup_openai_mock):
    model = ChatGLMLargeLanguageModel()

    response = model.invoke(
        model="chatglm3-6b",
        credentials={"api_base": os.environ.get("CHATGLM_API_BASE")},
        prompt_messages=[UserPromptMessage(content="What is the weather like in San Francisco?")],
        model_parameters={
            "temperature": 0.7,
            "top_p": 1.0,
        },
        stop=["you"],
        user="abc-123",
        stream=False,
        tools=[
            PromptMessageTool(
                name="get_current_weather",
                description="Get the current weather in a given location",
                parameters={
                    "type": "object",
                    "properties": {
                        "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"},
                        "unit": {"type": "string", "enum": ["c", "f"]},
                    },
                    "required": ["location"],
                },
            )
        ],
    )

    assert isinstance(response, LLMResult)
    assert len(response.message.content) > 0
    assert response.usage.total_tokens > 0
    assert response.message.tool_calls[0].function.name == "get_current_weather"


def test_get_num_tokens():
    model = ChatGLMLargeLanguageModel()

    num_tokens = model.get_num_tokens(
        model="chatglm2-6b",
        credentials={"api_base": os.environ.get("CHATGLM_API_BASE")},
        prompt_messages=[
            SystemPromptMessage(
                content="You are a helpful AI assistant.",
            ),
            UserPromptMessage(content="Hello World!"),
        ],
        tools=[
            PromptMessageTool(
                name="get_current_weather",
                description="Get the current weather in a given location",
                parameters={
                    "type": "object",
                    "properties": {
                        "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"},
                        "unit": {"type": "string", "enum": ["c", "f"]},
                    },
                    "required": ["location"],
                },
            )
        ],
    )

    assert isinstance(num_tokens, int)
    assert num_tokens == 77

    num_tokens = model.get_num_tokens(
        model="chatglm2-6b",
        credentials={"api_base": os.environ.get("CHATGLM_API_BASE")},
        prompt_messages=[
            SystemPromptMessage(
                content="You are a helpful AI assistant.",
            ),
            UserPromptMessage(content="Hello World!"),
        ],
    )

    assert isinstance(num_tokens, int)
    assert num_tokens == 21