File size: 4,171 Bytes
a8b3f00 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
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.stepfun.llm.llm import StepfunLargeLanguageModel
def test_validate_credentials():
model = StepfunLargeLanguageModel()
with pytest.raises(CredentialsValidateFailedError):
model.validate_credentials(model="step-1-8k", credentials={"api_key": "invalid_key"})
model.validate_credentials(model="step-1-8k", credentials={"api_key": os.environ.get("STEPFUN_API_KEY")})
def test_invoke_model():
model = StepfunLargeLanguageModel()
response = model.invoke(
model="step-1-8k",
credentials={"api_key": os.environ.get("STEPFUN_API_KEY")},
prompt_messages=[UserPromptMessage(content="Hello World!")],
model_parameters={"temperature": 0.9, "top_p": 0.7},
stop=["Hi"],
stream=False,
user="abc-123",
)
assert isinstance(response, LLMResult)
assert len(response.message.content) > 0
def test_invoke_stream_model():
model = StepfunLargeLanguageModel()
response = model.invoke(
model="step-1-8k",
credentials={"api_key": os.environ.get("STEPFUN_API_KEY")},
prompt_messages=[
SystemPromptMessage(
content="You are a helpful AI assistant.",
),
UserPromptMessage(content="Hello World!"),
],
model_parameters={"temperature": 0.9, "top_p": 0.7},
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
def test_get_customizable_model_schema():
model = StepfunLargeLanguageModel()
schema = model.get_customizable_model_schema(
model="step-1-8k", credentials={"api_key": os.environ.get("STEPFUN_API_KEY")}
)
assert isinstance(schema, AIModelEntity)
def test_invoke_chat_model_with_tools():
model = StepfunLargeLanguageModel()
result = model.invoke(
model="step-1-8k",
credentials={"api_key": os.environ.get("STEPFUN_API_KEY")},
prompt_messages=[
SystemPromptMessage(
content="You are a helpful AI assistant.",
),
UserPromptMessage(
content="what's the weather today in Shanghai?",
),
],
model_parameters={"temperature": 0.9, "max_tokens": 100},
tools=[
PromptMessageTool(
name="get_weather",
description="Determine weather in my 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"],
},
),
PromptMessageTool(
name="get_stock_price",
description="Get the current stock price",
parameters={
"type": "object",
"properties": {"symbol": {"type": "string", "description": "The stock symbol"}},
"required": ["symbol"],
},
),
],
stream=False,
user="abc-123",
)
assert isinstance(result, LLMResult)
assert isinstance(result.message, AssistantPromptMessage)
assert len(result.message.tool_calls) > 0
|