File size: 8,789 Bytes
4304c6d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
231
232
233
234
235
import json
import os
from unittest.mock import MagicMock

import pytest

from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
from core.entities.provider_configuration import ProviderConfiguration, ProviderModelBundle
from core.entities.provider_entities import CustomConfiguration, CustomProviderConfiguration, SystemConfiguration
from core.model_manager import ModelInstance
from core.model_runtime.entities.model_entities import ModelType
from core.model_runtime.model_providers import ModelProviderFactory
from core.workflow.entities.node_entities import SystemVariable
from core.workflow.entities.variable_pool import VariablePool
from core.workflow.nodes.base_node import UserFrom
from core.workflow.nodes.llm.llm_node import LLMNode
from extensions.ext_database import db
from models.provider import ProviderType
from models.workflow import WorkflowNodeExecutionStatus

"""FOR MOCK FIXTURES, DO NOT REMOVE"""
from tests.integration_tests.model_runtime.__mock.openai import setup_openai_mock
from tests.integration_tests.workflow.nodes.__mock.code_executor import setup_code_executor_mock


@pytest.mark.parametrize('setup_openai_mock', [['chat']], indirect=True)
def test_execute_llm(setup_openai_mock):
    node = LLMNode(
        tenant_id='1',
        app_id='1',
        workflow_id='1',
        user_id='1',
        user_from=UserFrom.ACCOUNT,
        config={
            'id': 'llm',
            'data': {
                'title': '123',
                'type': 'llm',
                'model': {
                    'provider': 'openai',
                    'name': 'gpt-3.5-turbo',
                    'mode': 'chat',
                    'completion_params': {}
                },
                'prompt_template': [
                    {
                        'role': 'system',
                        'text': 'you are a helpful assistant.\ntoday\'s weather is {{#abc.output#}}.'
                    },
                    {
                        'role': 'user',
                        'text': '{{#sys.query#}}'
                    }
                ],
                'memory': None,
                'context': {
                    'enabled': False
                },
                'vision': {
                    'enabled': False
                }
            }
        }
    )

    # construct variable pool
    pool = VariablePool(system_variables={
        SystemVariable.QUERY: 'what\'s the weather today?',
        SystemVariable.FILES: [],
        SystemVariable.CONVERSATION_ID: 'abababa',
        SystemVariable.USER_ID: 'aaa'
    }, user_inputs={})
    pool.append_variable(node_id='abc', variable_key_list=['output'], value='sunny')

    credentials = {
        'openai_api_key': os.environ.get('OPENAI_API_KEY')
    }

    provider_instance = ModelProviderFactory().get_provider_instance('openai')
    model_type_instance = provider_instance.get_model_instance(ModelType.LLM)
    provider_model_bundle = ProviderModelBundle(
        configuration=ProviderConfiguration(
            tenant_id='1',
            provider=provider_instance.get_provider_schema(),
            preferred_provider_type=ProviderType.CUSTOM,
            using_provider_type=ProviderType.CUSTOM,
            system_configuration=SystemConfiguration(
                enabled=False
            ),
            custom_configuration=CustomConfiguration(
                provider=CustomProviderConfiguration(
                    credentials=credentials
                )
            )
        ),
        provider_instance=provider_instance,
        model_type_instance=model_type_instance
    )
    model_instance = ModelInstance(provider_model_bundle=provider_model_bundle, model='gpt-3.5-turbo')
    model_config = ModelConfigWithCredentialsEntity(
        model='gpt-3.5-turbo',
        provider='openai',
        mode='chat',
        credentials=credentials,
        parameters={},
        model_schema=model_type_instance.get_model_schema('gpt-3.5-turbo'),
        provider_model_bundle=provider_model_bundle
    )

    # Mock db.session.close()
    db.session.close = MagicMock()

    node._fetch_model_config = MagicMock(return_value=tuple([model_instance, model_config]))

    # execute node
    result = node.run(pool)

    assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
    assert result.outputs['text'] is not None
    assert result.outputs['usage']['total_tokens'] > 0

@pytest.mark.parametrize('setup_code_executor_mock', [['none']], indirect=True)
@pytest.mark.parametrize('setup_openai_mock', [['chat']], indirect=True)
def test_execute_llm_with_jinja2(setup_code_executor_mock, setup_openai_mock):
    """

    Test execute LLM node with jinja2

    """
    node = LLMNode(
        tenant_id='1',
        app_id='1',
        workflow_id='1',
        user_id='1',
        user_from=UserFrom.ACCOUNT,
        config={
            'id': 'llm',
            'data': {
                'title': '123',
                'type': 'llm',
                'model': {
                    'provider': 'openai',
                    'name': 'gpt-3.5-turbo',
                    'mode': 'chat',
                    'completion_params': {}
                },
                'prompt_config': {
                    'jinja2_variables': [{
                        'variable': 'sys_query',
                        'value_selector': ['sys', 'query']
                    }, {
                        'variable': 'output',
                        'value_selector': ['abc', 'output']
                    }]
                },
                'prompt_template': [
                    {
                        'role': 'system',
                        'text': 'you are a helpful assistant.\ntoday\'s weather is {{#abc.output#}}',
                        'jinja2_text': 'you are a helpful assistant.\ntoday\'s weather is {{output}}.',
                        'edition_type': 'jinja2'
                    },
                    {
                        'role': 'user',
                        'text': '{{#sys.query#}}',
                        'jinja2_text': '{{sys_query}}',
                        'edition_type': 'basic'
                    }
                ],
                'memory': None,
                'context': {
                    'enabled': False
                },
                'vision': {
                    'enabled': False
                }
            }
        }
    )

    # construct variable pool
    pool = VariablePool(system_variables={
        SystemVariable.QUERY: 'what\'s the weather today?',
        SystemVariable.FILES: [],
        SystemVariable.CONVERSATION_ID: 'abababa',
        SystemVariable.USER_ID: 'aaa'
    }, user_inputs={})
    pool.append_variable(node_id='abc', variable_key_list=['output'], value='sunny')

    credentials = {
        'openai_api_key': os.environ.get('OPENAI_API_KEY')
    }

    provider_instance = ModelProviderFactory().get_provider_instance('openai')
    model_type_instance = provider_instance.get_model_instance(ModelType.LLM)
    provider_model_bundle = ProviderModelBundle(
        configuration=ProviderConfiguration(
            tenant_id='1',
            provider=provider_instance.get_provider_schema(),
            preferred_provider_type=ProviderType.CUSTOM,
            using_provider_type=ProviderType.CUSTOM,
            system_configuration=SystemConfiguration(
                enabled=False
            ),
            custom_configuration=CustomConfiguration(
                provider=CustomProviderConfiguration(
                    credentials=credentials
                )
            )
        ),
        provider_instance=provider_instance,
        model_type_instance=model_type_instance
    )

    model_instance = ModelInstance(provider_model_bundle=provider_model_bundle, model='gpt-3.5-turbo')

    model_config = ModelConfigWithCredentialsEntity(
        model='gpt-3.5-turbo',
        provider='openai',
        mode='chat',
        credentials=credentials,
        parameters={},
        model_schema=model_type_instance.get_model_schema('gpt-3.5-turbo'),
        provider_model_bundle=provider_model_bundle
    )

    # Mock db.session.close()
    db.session.close = MagicMock()

    node._fetch_model_config = MagicMock(return_value=tuple([model_instance, model_config]))

    # execute node
    result = node.run(pool)

    assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
    assert 'sunny' in json.dumps(result.process_data)
    assert 'what\'s the weather today?' in json.dumps(result.process_data)