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163 lines
4.9 KiB
Python
163 lines
4.9 KiB
Python
import os
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from collections.abc import Generator
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import pytest
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from core.model_runtime.entities.llm_entities import (
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LLMResult,
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LLMResultChunk,
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LLMResultChunkDelta,
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)
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from core.model_runtime.entities.message_entities import (
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AssistantPromptMessage,
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PromptMessageTool,
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SystemPromptMessage,
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UserPromptMessage,
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)
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from core.model_runtime.errors.validate import CredentialsValidateFailedError
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from core.model_runtime.model_providers.gpustack.llm.llm import GPUStackLanguageModel
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def test_validate_credentials_for_chat_model():
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model = GPUStackLanguageModel()
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with pytest.raises(CredentialsValidateFailedError):
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model.validate_credentials(
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model="llama-3.2-1b-instruct",
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credentials={
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"endpoint_url": "invalid_url",
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"api_key": "invalid_api_key",
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"mode": "chat",
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},
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)
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model.validate_credentials(
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model="llama-3.2-1b-instruct",
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credentials={
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"endpoint_url": os.environ.get("GPUSTACK_SERVER_URL"),
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"api_key": os.environ.get("GPUSTACK_API_KEY"),
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"mode": "chat",
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},
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)
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def test_invoke_completion_model():
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model = GPUStackLanguageModel()
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response = model.invoke(
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model="llama-3.2-1b-instruct",
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credentials={
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"endpoint_url": os.environ.get("GPUSTACK_SERVER_URL"),
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"api_key": os.environ.get("GPUSTACK_API_KEY"),
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"mode": "completion",
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},
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prompt_messages=[UserPromptMessage(content="ping")],
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model_parameters={"temperature": 0.7, "top_p": 1.0, "max_tokens": 10},
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stop=[],
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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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def test_invoke_chat_model():
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model = GPUStackLanguageModel()
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response = model.invoke(
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model="llama-3.2-1b-instruct",
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credentials={
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"endpoint_url": os.environ.get("GPUSTACK_SERVER_URL"),
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"api_key": os.environ.get("GPUSTACK_API_KEY"),
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"mode": "chat",
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},
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prompt_messages=[UserPromptMessage(content="ping")],
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model_parameters={"temperature": 0.7, "top_p": 1.0, "max_tokens": 10},
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stop=[],
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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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def test_invoke_stream_chat_model():
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model = GPUStackLanguageModel()
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response = model.invoke(
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model="llama-3.2-1b-instruct",
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credentials={
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"endpoint_url": os.environ.get("GPUSTACK_SERVER_URL"),
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"api_key": os.environ.get("GPUSTACK_API_KEY"),
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"mode": "chat",
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},
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prompt_messages=[UserPromptMessage(content="Hello World!")],
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model_parameters={"temperature": 0.7, "top_p": 1.0, "max_tokens": 10},
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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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def test_get_num_tokens():
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model = GPUStackLanguageModel()
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num_tokens = model.get_num_tokens(
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model="????",
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credentials={
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"endpoint_url": os.environ.get("GPUSTACK_SERVER_URL"),
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"api_key": os.environ.get("GPUSTACK_API_KEY"),
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"mode": "chat",
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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(content="Hello World!"),
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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": {"type": "string", "enum": ["c", "f"]},
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},
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"required": ["location"],
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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 == 80
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num_tokens = model.get_num_tokens(
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model="????",
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credentials={
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"endpoint_url": os.environ.get("GPUSTACK_SERVER_URL"),
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"api_key": os.environ.get("GPUSTACK_API_KEY"),
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"mode": "chat",
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},
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prompt_messages=[UserPromptMessage(content="Hello World!")],
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)
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assert isinstance(num_tokens, int)
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assert num_tokens == 10
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