FEAT: NEW WORKFLOW ENGINE (#3160)
Co-authored-by: Joel <iamjoel007@gmail.com> Co-authored-by: Yeuoly <admin@srmxy.cn> Co-authored-by: JzoNg <jzongcode@gmail.com> Co-authored-by: StyleZhang <jasonapring2015@outlook.com> Co-authored-by: jyong <jyong@dify.ai> Co-authored-by: nite-knite <nkCoding@gmail.com> Co-authored-by: jyong <718720800@qq.com>
This commit is contained in:
takatost
0
api/tests/unit_tests/services/workflow/__init__.py
Normal file
0
api/tests/unit_tests/services/workflow/__init__.py
Normal file
@@ -0,0 +1,462 @@
|
||||
# test for api/services/workflow/workflow_converter.py
|
||||
import json
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
import pytest
|
||||
|
||||
from core.app.app_config.entities import VariableEntity, ExternalDataVariableEntity, DatasetEntity, \
|
||||
DatasetRetrieveConfigEntity, ModelConfigEntity, PromptTemplateEntity, AdvancedChatPromptTemplateEntity, \
|
||||
AdvancedChatMessageEntity, AdvancedCompletionPromptTemplateEntity
|
||||
from core.helper import encrypter
|
||||
from core.model_runtime.entities.llm_entities import LLMMode
|
||||
from core.model_runtime.entities.message_entities import PromptMessageRole
|
||||
from models.api_based_extension import APIBasedExtension, APIBasedExtensionPoint
|
||||
from models.model import AppMode
|
||||
from services.workflow.workflow_converter import WorkflowConverter
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def default_variables():
|
||||
return [
|
||||
VariableEntity(
|
||||
variable="text_input",
|
||||
label="text-input",
|
||||
type=VariableEntity.Type.TEXT_INPUT
|
||||
),
|
||||
VariableEntity(
|
||||
variable="paragraph",
|
||||
label="paragraph",
|
||||
type=VariableEntity.Type.PARAGRAPH
|
||||
),
|
||||
VariableEntity(
|
||||
variable="select",
|
||||
label="select",
|
||||
type=VariableEntity.Type.SELECT
|
||||
)
|
||||
]
|
||||
|
||||
|
||||
def test__convert_to_start_node(default_variables):
|
||||
# act
|
||||
result = WorkflowConverter()._convert_to_start_node(default_variables)
|
||||
|
||||
# assert
|
||||
assert isinstance(result["data"]["variables"][0]["type"], str)
|
||||
assert result["data"]["variables"][0]["type"] == "text-input"
|
||||
assert result["data"]["variables"][0]["variable"] == "text_input"
|
||||
assert result["data"]["variables"][1]["variable"] == "paragraph"
|
||||
assert result["data"]["variables"][2]["variable"] == "select"
|
||||
|
||||
|
||||
def test__convert_to_http_request_node_for_chatbot(default_variables):
|
||||
"""
|
||||
Test convert to http request nodes for chatbot
|
||||
:return:
|
||||
"""
|
||||
app_model = MagicMock()
|
||||
app_model.id = "app_id"
|
||||
app_model.tenant_id = "tenant_id"
|
||||
app_model.mode = AppMode.CHAT.value
|
||||
|
||||
api_based_extension_id = "api_based_extension_id"
|
||||
mock_api_based_extension = APIBasedExtension(
|
||||
id=api_based_extension_id,
|
||||
name="api-1",
|
||||
api_key="encrypted_api_key",
|
||||
api_endpoint="https://dify.ai",
|
||||
)
|
||||
|
||||
workflow_converter = WorkflowConverter()
|
||||
workflow_converter._get_api_based_extension = MagicMock(return_value=mock_api_based_extension)
|
||||
|
||||
encrypter.decrypt_token = MagicMock(return_value="api_key")
|
||||
|
||||
external_data_variables = [
|
||||
ExternalDataVariableEntity(
|
||||
variable="external_variable",
|
||||
type="api",
|
||||
config={
|
||||
"api_based_extension_id": api_based_extension_id
|
||||
}
|
||||
)
|
||||
]
|
||||
|
||||
nodes = workflow_converter._convert_to_http_request_node(
|
||||
app_model=app_model,
|
||||
variables=default_variables,
|
||||
external_data_variables=external_data_variables
|
||||
)
|
||||
|
||||
assert len(nodes) == 2
|
||||
assert nodes[0]["data"]["type"] == "http-request"
|
||||
|
||||
http_request_node = nodes[0]
|
||||
|
||||
assert http_request_node["data"]["method"] == "post"
|
||||
assert http_request_node["data"]["url"] == mock_api_based_extension.api_endpoint
|
||||
assert http_request_node["data"]["authorization"]["type"] == "api-key"
|
||||
assert http_request_node["data"]["authorization"]["config"] == {
|
||||
"type": "bearer",
|
||||
"api_key": "api_key"
|
||||
}
|
||||
assert http_request_node["data"]["body"]["type"] == "json"
|
||||
|
||||
body_data = http_request_node["data"]["body"]["data"]
|
||||
|
||||
assert body_data
|
||||
|
||||
body_data_json = json.loads(body_data)
|
||||
assert body_data_json["point"] == APIBasedExtensionPoint.APP_EXTERNAL_DATA_TOOL_QUERY.value
|
||||
|
||||
body_params = body_data_json["params"]
|
||||
assert body_params["app_id"] == app_model.id
|
||||
assert body_params["tool_variable"] == external_data_variables[0].variable
|
||||
assert len(body_params["inputs"]) == 3
|
||||
assert body_params["query"] == "{{#sys.query#}}" # for chatbot
|
||||
|
||||
code_node = nodes[1]
|
||||
assert code_node["data"]["type"] == "code"
|
||||
|
||||
|
||||
def test__convert_to_http_request_node_for_workflow_app(default_variables):
|
||||
"""
|
||||
Test convert to http request nodes for workflow app
|
||||
:return:
|
||||
"""
|
||||
app_model = MagicMock()
|
||||
app_model.id = "app_id"
|
||||
app_model.tenant_id = "tenant_id"
|
||||
app_model.mode = AppMode.WORKFLOW.value
|
||||
|
||||
api_based_extension_id = "api_based_extension_id"
|
||||
mock_api_based_extension = APIBasedExtension(
|
||||
id=api_based_extension_id,
|
||||
name="api-1",
|
||||
api_key="encrypted_api_key",
|
||||
api_endpoint="https://dify.ai",
|
||||
)
|
||||
|
||||
workflow_converter = WorkflowConverter()
|
||||
workflow_converter._get_api_based_extension = MagicMock(return_value=mock_api_based_extension)
|
||||
|
||||
encrypter.decrypt_token = MagicMock(return_value="api_key")
|
||||
|
||||
external_data_variables = [
|
||||
ExternalDataVariableEntity(
|
||||
variable="external_variable",
|
||||
type="api",
|
||||
config={
|
||||
"api_based_extension_id": api_based_extension_id
|
||||
}
|
||||
)
|
||||
]
|
||||
|
||||
nodes = workflow_converter._convert_to_http_request_node(
|
||||
app_model=app_model,
|
||||
variables=default_variables,
|
||||
external_data_variables=external_data_variables
|
||||
)
|
||||
|
||||
assert len(nodes) == 2
|
||||
assert nodes[0]["data"]["type"] == "http-request"
|
||||
|
||||
http_request_node = nodes[0]
|
||||
|
||||
assert http_request_node["data"]["method"] == "post"
|
||||
assert http_request_node["data"]["url"] == mock_api_based_extension.api_endpoint
|
||||
assert http_request_node["data"]["authorization"]["type"] == "api-key"
|
||||
assert http_request_node["data"]["authorization"]["config"] == {
|
||||
"type": "bearer",
|
||||
"api_key": "api_key"
|
||||
}
|
||||
assert http_request_node["data"]["body"]["type"] == "json"
|
||||
|
||||
body_data = http_request_node["data"]["body"]["data"]
|
||||
|
||||
assert body_data
|
||||
|
||||
body_data_json = json.loads(body_data)
|
||||
assert body_data_json["point"] == APIBasedExtensionPoint.APP_EXTERNAL_DATA_TOOL_QUERY.value
|
||||
|
||||
body_params = body_data_json["params"]
|
||||
assert body_params["app_id"] == app_model.id
|
||||
assert body_params["tool_variable"] == external_data_variables[0].variable
|
||||
assert len(body_params["inputs"]) == 3
|
||||
assert body_params["query"] == ""
|
||||
|
||||
code_node = nodes[1]
|
||||
assert code_node["data"]["type"] == "code"
|
||||
|
||||
|
||||
def test__convert_to_knowledge_retrieval_node_for_chatbot():
|
||||
new_app_mode = AppMode.ADVANCED_CHAT
|
||||
|
||||
dataset_config = DatasetEntity(
|
||||
dataset_ids=["dataset_id_1", "dataset_id_2"],
|
||||
retrieve_config=DatasetRetrieveConfigEntity(
|
||||
retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.MULTIPLE,
|
||||
top_k=5,
|
||||
score_threshold=0.8,
|
||||
reranking_model={
|
||||
'reranking_provider_name': 'cohere',
|
||||
'reranking_model_name': 'rerank-english-v2.0'
|
||||
}
|
||||
)
|
||||
)
|
||||
|
||||
model_config = ModelConfigEntity(
|
||||
provider='openai',
|
||||
model='gpt-4',
|
||||
mode='chat',
|
||||
parameters={},
|
||||
stop=[]
|
||||
)
|
||||
|
||||
node = WorkflowConverter()._convert_to_knowledge_retrieval_node(
|
||||
new_app_mode=new_app_mode,
|
||||
dataset_config=dataset_config,
|
||||
model_config=model_config
|
||||
)
|
||||
|
||||
assert node["data"]["type"] == "knowledge-retrieval"
|
||||
assert node["data"]["query_variable_selector"] == ["sys", "query"]
|
||||
assert node["data"]["dataset_ids"] == dataset_config.dataset_ids
|
||||
assert (node["data"]["retrieval_mode"]
|
||||
== dataset_config.retrieve_config.retrieve_strategy.value)
|
||||
assert node["data"]["multiple_retrieval_config"] == {
|
||||
"top_k": dataset_config.retrieve_config.top_k,
|
||||
"score_threshold": dataset_config.retrieve_config.score_threshold,
|
||||
"reranking_model": dataset_config.retrieve_config.reranking_model
|
||||
}
|
||||
|
||||
|
||||
def test__convert_to_knowledge_retrieval_node_for_workflow_app():
|
||||
new_app_mode = AppMode.WORKFLOW
|
||||
|
||||
dataset_config = DatasetEntity(
|
||||
dataset_ids=["dataset_id_1", "dataset_id_2"],
|
||||
retrieve_config=DatasetRetrieveConfigEntity(
|
||||
query_variable="query",
|
||||
retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.MULTIPLE,
|
||||
top_k=5,
|
||||
score_threshold=0.8,
|
||||
reranking_model={
|
||||
'reranking_provider_name': 'cohere',
|
||||
'reranking_model_name': 'rerank-english-v2.0'
|
||||
}
|
||||
)
|
||||
)
|
||||
|
||||
model_config = ModelConfigEntity(
|
||||
provider='openai',
|
||||
model='gpt-4',
|
||||
mode='chat',
|
||||
parameters={},
|
||||
stop=[]
|
||||
)
|
||||
|
||||
node = WorkflowConverter()._convert_to_knowledge_retrieval_node(
|
||||
new_app_mode=new_app_mode,
|
||||
dataset_config=dataset_config,
|
||||
model_config=model_config
|
||||
)
|
||||
|
||||
assert node["data"]["type"] == "knowledge-retrieval"
|
||||
assert node["data"]["query_variable_selector"] == ["start", dataset_config.retrieve_config.query_variable]
|
||||
assert node["data"]["dataset_ids"] == dataset_config.dataset_ids
|
||||
assert (node["data"]["retrieval_mode"]
|
||||
== dataset_config.retrieve_config.retrieve_strategy.value)
|
||||
assert node["data"]["multiple_retrieval_config"] == {
|
||||
"top_k": dataset_config.retrieve_config.top_k,
|
||||
"score_threshold": dataset_config.retrieve_config.score_threshold,
|
||||
"reranking_model": dataset_config.retrieve_config.reranking_model
|
||||
}
|
||||
|
||||
|
||||
def test__convert_to_llm_node_for_chatbot_simple_chat_model(default_variables):
|
||||
new_app_mode = AppMode.ADVANCED_CHAT
|
||||
model = "gpt-4"
|
||||
model_mode = LLMMode.CHAT
|
||||
|
||||
workflow_converter = WorkflowConverter()
|
||||
start_node = workflow_converter._convert_to_start_node(default_variables)
|
||||
graph = {
|
||||
"nodes": [
|
||||
start_node
|
||||
],
|
||||
"edges": [] # no need
|
||||
}
|
||||
|
||||
model_config_mock = MagicMock(spec=ModelConfigEntity)
|
||||
model_config_mock.provider = 'openai'
|
||||
model_config_mock.model = model
|
||||
model_config_mock.mode = model_mode.value
|
||||
model_config_mock.parameters = {}
|
||||
model_config_mock.stop = []
|
||||
|
||||
prompt_template = PromptTemplateEntity(
|
||||
prompt_type=PromptTemplateEntity.PromptType.SIMPLE,
|
||||
simple_prompt_template="You are a helpful assistant {{text_input}}, {{paragraph}}, {{select}}."
|
||||
)
|
||||
|
||||
llm_node = workflow_converter._convert_to_llm_node(
|
||||
original_app_mode=AppMode.CHAT,
|
||||
new_app_mode=new_app_mode,
|
||||
model_config=model_config_mock,
|
||||
graph=graph,
|
||||
prompt_template=prompt_template
|
||||
)
|
||||
|
||||
assert llm_node["data"]["type"] == "llm"
|
||||
assert llm_node["data"]["model"]['name'] == model
|
||||
assert llm_node["data"]['model']["mode"] == model_mode.value
|
||||
template = prompt_template.simple_prompt_template
|
||||
for v in default_variables:
|
||||
template = template.replace('{{' + v.variable + '}}', '{{#start.' + v.variable + '#}}')
|
||||
assert llm_node["data"]["prompt_template"][0]['text'] == template + '\n'
|
||||
assert llm_node["data"]['context']['enabled'] is False
|
||||
|
||||
|
||||
def test__convert_to_llm_node_for_chatbot_simple_completion_model(default_variables):
|
||||
new_app_mode = AppMode.ADVANCED_CHAT
|
||||
model = "gpt-3.5-turbo-instruct"
|
||||
model_mode = LLMMode.COMPLETION
|
||||
|
||||
workflow_converter = WorkflowConverter()
|
||||
start_node = workflow_converter._convert_to_start_node(default_variables)
|
||||
graph = {
|
||||
"nodes": [
|
||||
start_node
|
||||
],
|
||||
"edges": [] # no need
|
||||
}
|
||||
|
||||
model_config_mock = MagicMock(spec=ModelConfigEntity)
|
||||
model_config_mock.provider = 'openai'
|
||||
model_config_mock.model = model
|
||||
model_config_mock.mode = model_mode.value
|
||||
model_config_mock.parameters = {}
|
||||
model_config_mock.stop = []
|
||||
|
||||
prompt_template = PromptTemplateEntity(
|
||||
prompt_type=PromptTemplateEntity.PromptType.SIMPLE,
|
||||
simple_prompt_template="You are a helpful assistant {{text_input}}, {{paragraph}}, {{select}}."
|
||||
)
|
||||
|
||||
llm_node = workflow_converter._convert_to_llm_node(
|
||||
original_app_mode=AppMode.CHAT,
|
||||
new_app_mode=new_app_mode,
|
||||
model_config=model_config_mock,
|
||||
graph=graph,
|
||||
prompt_template=prompt_template
|
||||
)
|
||||
|
||||
assert llm_node["data"]["type"] == "llm"
|
||||
assert llm_node["data"]["model"]['name'] == model
|
||||
assert llm_node["data"]['model']["mode"] == model_mode.value
|
||||
template = prompt_template.simple_prompt_template
|
||||
for v in default_variables:
|
||||
template = template.replace('{{' + v.variable + '}}', '{{#start.' + v.variable + '#}}')
|
||||
assert llm_node["data"]["prompt_template"]['text'] == template + '\n'
|
||||
assert llm_node["data"]['context']['enabled'] is False
|
||||
|
||||
|
||||
def test__convert_to_llm_node_for_chatbot_advanced_chat_model(default_variables):
|
||||
new_app_mode = AppMode.ADVANCED_CHAT
|
||||
model = "gpt-4"
|
||||
model_mode = LLMMode.CHAT
|
||||
|
||||
workflow_converter = WorkflowConverter()
|
||||
start_node = workflow_converter._convert_to_start_node(default_variables)
|
||||
graph = {
|
||||
"nodes": [
|
||||
start_node
|
||||
],
|
||||
"edges": [] # no need
|
||||
}
|
||||
|
||||
model_config_mock = MagicMock(spec=ModelConfigEntity)
|
||||
model_config_mock.provider = 'openai'
|
||||
model_config_mock.model = model
|
||||
model_config_mock.mode = model_mode.value
|
||||
model_config_mock.parameters = {}
|
||||
model_config_mock.stop = []
|
||||
|
||||
prompt_template = PromptTemplateEntity(
|
||||
prompt_type=PromptTemplateEntity.PromptType.ADVANCED,
|
||||
advanced_chat_prompt_template=AdvancedChatPromptTemplateEntity(messages=[
|
||||
AdvancedChatMessageEntity(text="You are a helpful assistant named {{name}}.\n\nContext:\n{{#context#}}",
|
||||
role=PromptMessageRole.SYSTEM),
|
||||
AdvancedChatMessageEntity(text="Hi.", role=PromptMessageRole.USER),
|
||||
AdvancedChatMessageEntity(text="Hello!", role=PromptMessageRole.ASSISTANT),
|
||||
])
|
||||
)
|
||||
|
||||
llm_node = workflow_converter._convert_to_llm_node(
|
||||
original_app_mode=AppMode.CHAT,
|
||||
new_app_mode=new_app_mode,
|
||||
model_config=model_config_mock,
|
||||
graph=graph,
|
||||
prompt_template=prompt_template
|
||||
)
|
||||
|
||||
assert llm_node["data"]["type"] == "llm"
|
||||
assert llm_node["data"]["model"]['name'] == model
|
||||
assert llm_node["data"]['model']["mode"] == model_mode.value
|
||||
assert isinstance(llm_node["data"]["prompt_template"], list)
|
||||
assert len(llm_node["data"]["prompt_template"]) == len(prompt_template.advanced_chat_prompt_template.messages)
|
||||
template = prompt_template.advanced_chat_prompt_template.messages[0].text
|
||||
for v in default_variables:
|
||||
template = template.replace('{{' + v.variable + '}}', '{{#start.' + v.variable + '#}}')
|
||||
assert llm_node["data"]["prompt_template"][0]['text'] == template
|
||||
|
||||
|
||||
def test__convert_to_llm_node_for_workflow_advanced_completion_model(default_variables):
|
||||
new_app_mode = AppMode.ADVANCED_CHAT
|
||||
model = "gpt-3.5-turbo-instruct"
|
||||
model_mode = LLMMode.COMPLETION
|
||||
|
||||
workflow_converter = WorkflowConverter()
|
||||
start_node = workflow_converter._convert_to_start_node(default_variables)
|
||||
graph = {
|
||||
"nodes": [
|
||||
start_node
|
||||
],
|
||||
"edges": [] # no need
|
||||
}
|
||||
|
||||
model_config_mock = MagicMock(spec=ModelConfigEntity)
|
||||
model_config_mock.provider = 'openai'
|
||||
model_config_mock.model = model
|
||||
model_config_mock.mode = model_mode.value
|
||||
model_config_mock.parameters = {}
|
||||
model_config_mock.stop = []
|
||||
|
||||
prompt_template = PromptTemplateEntity(
|
||||
prompt_type=PromptTemplateEntity.PromptType.ADVANCED,
|
||||
advanced_completion_prompt_template=AdvancedCompletionPromptTemplateEntity(
|
||||
prompt="You are a helpful assistant named {{name}}.\n\nContext:\n{{#context#}}\n\n"
|
||||
"Human: hi\nAssistant: ",
|
||||
role_prefix=AdvancedCompletionPromptTemplateEntity.RolePrefixEntity(
|
||||
user="Human",
|
||||
assistant="Assistant"
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
llm_node = workflow_converter._convert_to_llm_node(
|
||||
original_app_mode=AppMode.CHAT,
|
||||
new_app_mode=new_app_mode,
|
||||
model_config=model_config_mock,
|
||||
graph=graph,
|
||||
prompt_template=prompt_template
|
||||
)
|
||||
|
||||
assert llm_node["data"]["type"] == "llm"
|
||||
assert llm_node["data"]["model"]['name'] == model
|
||||
assert llm_node["data"]['model']["mode"] == model_mode.value
|
||||
assert isinstance(llm_node["data"]["prompt_template"], dict)
|
||||
template = prompt_template.advanced_completion_prompt_template.prompt
|
||||
for v in default_variables:
|
||||
template = template.replace('{{' + v.variable + '}}', '{{#start.' + v.variable + '#}}')
|
||||
assert llm_node["data"]["prompt_template"]['text'] == template
|
||||
Reference in New Issue
Block a user