Model Runtime (#1858)
Co-authored-by: StyleZhang <jasonapring2015@outlook.com> Co-authored-by: Garfield Dai <dai.hai@foxmail.com> Co-authored-by: chenhe <guchenhe@gmail.com> Co-authored-by: jyong <jyong@dify.ai> Co-authored-by: Joel <iamjoel007@gmail.com> Co-authored-by: Yeuoly <admin@srmxy.cn>
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287
api/tests/integration_tests/model_runtime/chatglm/test_llm.py
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287
api/tests/integration_tests/model_runtime/chatglm/test_llm.py
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import os
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import pytest
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from typing import Generator
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from core.model_runtime.entities.message_entities import AssistantPromptMessage, TextPromptMessageContent, UserPromptMessage, \
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SystemPromptMessage, PromptMessageTool
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from core.model_runtime.entities.model_entities import AIModelEntity
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from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunkDelta, \
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LLMResultChunk
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from core.model_runtime.errors.validate import CredentialsValidateFailedError
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from core.model_runtime.model_providers.chatglm.llm.llm import ChatGLMLargeLanguageModel
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from tests.integration_tests.model_runtime.__mock.openai import setup_openai_mock
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def test_predefined_models():
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model = ChatGLMLargeLanguageModel()
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model_schemas = model.predefined_models()
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assert len(model_schemas) >= 1
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assert isinstance(model_schemas[0], AIModelEntity)
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@pytest.mark.parametrize('setup_openai_mock', [['chat']], indirect=True)
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def test_validate_credentials_for_chat_model(setup_openai_mock):
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model = ChatGLMLargeLanguageModel()
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with pytest.raises(CredentialsValidateFailedError):
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model.validate_credentials(
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model='chatglm2-6b',
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credentials={
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'api_base': 'invalid_key'
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}
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)
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model.validate_credentials(
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model='chatglm2-6b',
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credentials={
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'api_base': os.environ.get('CHATGLM_API_BASE')
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}
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)
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@pytest.mark.parametrize('setup_openai_mock', [['chat']], indirect=True)
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def test_invoke_model(setup_openai_mock):
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model = ChatGLMLargeLanguageModel()
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response = model.invoke(
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model='chatglm2-6b',
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credentials={
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'api_base': os.environ.get('CHATGLM_API_BASE')
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},
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prompt_messages=[
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SystemPromptMessage(
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content='You are a helpful AI assistant.',
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),
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UserPromptMessage(
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content='Hello World!'
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)
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],
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model_parameters={
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'temperature': 0.7,
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'top_p': 1.0,
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},
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stop=['you'],
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user="abc-123",
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stream=False
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)
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assert isinstance(response, LLMResult)
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assert len(response.message.content) > 0
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assert response.usage.total_tokens > 0
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@pytest.mark.parametrize('setup_openai_mock', [['chat']], indirect=True)
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def test_invoke_stream_model(setup_openai_mock):
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model = ChatGLMLargeLanguageModel()
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response = model.invoke(
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model='chatglm2-6b',
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credentials={
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'api_base': os.environ.get('CHATGLM_API_BASE')
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},
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prompt_messages=[
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SystemPromptMessage(
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content='You are a helpful AI assistant.',
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),
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UserPromptMessage(
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content='Hello World!'
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)
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],
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model_parameters={
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'temperature': 0.7,
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'top_p': 1.0,
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},
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stop=['you'],
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stream=True,
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user="abc-123"
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)
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assert isinstance(response, Generator)
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for chunk in response:
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assert isinstance(chunk, LLMResultChunk)
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assert isinstance(chunk.delta, LLMResultChunkDelta)
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assert isinstance(chunk.delta.message, AssistantPromptMessage)
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assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True
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@pytest.mark.parametrize('setup_openai_mock', [['chat']], indirect=True)
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def test_invoke_stream_model_with_functions(setup_openai_mock):
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model = ChatGLMLargeLanguageModel()
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response = model.invoke(
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model='chatglm3-6b',
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credentials={
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'api_base': os.environ.get('CHATGLM_API_BASE')
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},
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prompt_messages=[
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SystemPromptMessage(
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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。'
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),
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UserPromptMessage(
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content='波士顿天气如何？'
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)
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],
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model_parameters={
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'temperature': 0,
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'top_p': 1.0,
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},
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stop=['you'],
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user='abc-123',
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stream=True,
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tools=[
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PromptMessageTool(
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name='get_current_weather',
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description='Get the current weather in a given location',
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parameters={
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state e.g. San Francisco, CA"
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},
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"unit": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"]
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}
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},
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"required": [
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"location"
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]
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}
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)
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]
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)
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assert isinstance(response, Generator)
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call: LLMResultChunk = None
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chunks = []
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for chunk in response:
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chunks.append(chunk)
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assert isinstance(chunk, LLMResultChunk)
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assert isinstance(chunk.delta, LLMResultChunkDelta)
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assert isinstance(chunk.delta.message, AssistantPromptMessage)
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assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True
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if chunk.delta.message.tool_calls and len(chunk.delta.message.tool_calls) > 0:
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call = chunk
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break
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assert call is not None
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assert call.delta.message.tool_calls[0].function.name == 'get_current_weather'
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@pytest.mark.parametrize('setup_openai_mock', [['chat']], indirect=True)
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def test_invoke_model_with_functions(setup_openai_mock):
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model = ChatGLMLargeLanguageModel()
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response = model.invoke(
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model='chatglm3-6b',
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credentials={
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'api_base': os.environ.get('CHATGLM_API_BASE')
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},
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prompt_messages=[
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UserPromptMessage(
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content='What is the weather like in San Francisco?'
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)
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],
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model_parameters={
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'temperature': 0.7,
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'top_p': 1.0,
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},
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stop=['you'],
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user='abc-123',
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stream=False,
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tools=[
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PromptMessageTool(
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name='get_current_weather',
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description='Get the current weather in a given location',
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parameters={
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state e.g. San Francisco, CA"
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},
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"unit": {
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"type": "string",
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"enum": [
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"c",
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"f"
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]
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}
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},
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"required": [
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"location"
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]
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}
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)
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]
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)
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assert isinstance(response, LLMResult)
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assert len(response.message.content) > 0
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assert response.usage.total_tokens > 0
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assert response.message.tool_calls[0].function.name == 'get_current_weather'
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def test_get_num_tokens():
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model = ChatGLMLargeLanguageModel()
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num_tokens = model.get_num_tokens(
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model='chatglm2-6b',
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credentials={
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'api_base': os.environ.get('CHATGLM_API_BASE')
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},
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prompt_messages=[
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SystemPromptMessage(
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content='You are a helpful AI assistant.',
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),
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UserPromptMessage(
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content='Hello World!'
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)
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],
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tools=[
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PromptMessageTool(
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name='get_current_weather',
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description='Get the current weather in a given location',
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parameters={
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state e.g. San Francisco, CA"
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},
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"unit": {
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"type": "string",
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"enum": [
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"c",
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"f"
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]
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}
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},
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"required": [
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"location"
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]
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}
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)
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]
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)
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assert isinstance(num_tokens, int)
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assert num_tokens == 77
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num_tokens = model.get_num_tokens(
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model='chatglm2-6b',
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credentials={
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'api_base': os.environ.get('CHATGLM_API_BASE')
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},
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prompt_messages=[
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SystemPromptMessage(
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content='You are a helpful AI assistant.',
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),
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UserPromptMessage(
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content='Hello World!'
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)
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],
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)
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assert isinstance(num_tokens, int)
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assert num_tokens == 21
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@@ -0,0 +1,25 @@
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import os
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import pytest
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from core.model_runtime.errors.validate import CredentialsValidateFailedError
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from core.model_runtime.model_providers.chatglm.chatglm import ChatGLMProvider
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from tests.integration_tests.model_runtime.__mock.openai import setup_openai_mock
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@pytest.mark.parametrize('setup_openai_mock', [['chat']], indirect=True)
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def test_validate_provider_credentials(setup_openai_mock):
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provider = ChatGLMProvider()
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with pytest.raises(CredentialsValidateFailedError):
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provider.validate_provider_credentials(
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credentials={
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'api_base': 'hahahaha'
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}
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)
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provider.validate_provider_credentials(
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credentials={
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'api_base': os.environ.get('CHATGLM_API_BASE')
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}
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)
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