File size: 7,950 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 |
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
|