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test: migrate workflow converter tests to testcontainers (#34038)
Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
This commit is contained in:
Desel72
GitHub
@ -1,11 +1,16 @@
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from __future__ import annotations
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import json
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from unittest.mock import patch
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from unittest.mock import MagicMock, patch
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import pytest
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from faker import Faker
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from sqlalchemy.orm import Session
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from core.app.app_config.entities import (
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AdvancedChatMessageEntity,
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AdvancedChatPromptTemplateEntity,
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AdvancedCompletionPromptTemplateEntity,
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DatasetEntity,
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DatasetRetrieveConfigEntity,
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ExternalDataVariableEntity,
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@ -14,9 +19,10 @@ from core.app.app_config.entities import (
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)
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from core.prompt.utils.prompt_template_parser import PromptTemplateParser
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from dify_graph.model_runtime.entities.llm_entities import LLMMode
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from dify_graph.model_runtime.entities.message_entities import PromptMessageRole
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from dify_graph.variables.input_entities import VariableEntity, VariableEntityType
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from models import Account, Tenant
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from models.api_based_extension import APIBasedExtension
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from models.api_based_extension import APIBasedExtension, APIBasedExtensionPoint
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from models.model import App, AppMode, AppModelConfig
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from models.workflow import Workflow
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from services.workflow.workflow_converter import WorkflowConverter
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@ -548,3 +554,198 @@ class TestWorkflowConverter:
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# Verify single retrieval config is None for multiple strategy
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assert node["data"]["single_retrieval_config"] is None
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@pytest.fixture
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def default_variables():
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return [
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VariableEntity(variable="text_input", label="text-input", type=VariableEntityType.TEXT_INPUT),
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VariableEntity(variable="paragraph", label="paragraph", type=VariableEntityType.PARAGRAPH),
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VariableEntity(variable="select", label="select", type=VariableEntityType.SELECT),
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]
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class TestConvertToHttpRequestNodeVariants:
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"""Tests for chatbot vs workflow differences in HTTP request node conversion."""
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@staticmethod
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def _setup(app_mode, default_variables):
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app_model = App(
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tenant_id="tenant_id",
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mode=app_mode,
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name="test",
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icon_type="emoji",
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icon="🤖",
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icon_background="#FFFFFF",
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)
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ext = APIBasedExtension(tenant_id="tenant_id", name="api-1", api_key="enc", api_endpoint="https://dify.ai")
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ext.id = "ext_id"
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converter = WorkflowConverter()
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converter._get_api_based_extension = MagicMock(return_value=ext)
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from core.helper import encrypter
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encrypter.decrypt_token = MagicMock(return_value="api_key")
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ext_vars = [
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ExternalDataVariableEntity(
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variable="external_variable", type="api", config={"api_based_extension_id": "ext_id"}
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)
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]
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nodes, _ = converter._convert_to_http_request_node(
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app_model=app_model,
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variables=default_variables,
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external_data_variables=ext_vars,
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)
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return nodes
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def test_chatbot_query_uses_sys_query(self, default_variables):
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nodes = self._setup(AppMode.CHAT, default_variables)
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body = json.loads(nodes[0]["data"]["body"]["data"])
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assert body["params"]["query"] == "{{#sys.query#}}"
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assert body["point"] == APIBasedExtensionPoint.APP_EXTERNAL_DATA_TOOL_QUERY
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assert nodes[1]["data"]["type"] == "code"
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def test_workflow_query_is_empty(self, default_variables):
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nodes = self._setup(AppMode.WORKFLOW, default_variables)
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body = json.loads(nodes[0]["data"]["body"]["data"])
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assert body["params"]["query"] == ""
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class TestConvertToKnowledgeRetrievalNodeVariants:
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"""Tests for chatbot vs workflow differences in knowledge retrieval node."""
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@staticmethod
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def _dataset_config(query_variable=None):
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return DatasetEntity(
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dataset_ids=["ds1", "ds2"],
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retrieve_config=DatasetRetrieveConfigEntity(
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query_variable=query_variable,
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retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.MULTIPLE,
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top_k=5,
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score_threshold=0.8,
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reranking_model={"reranking_provider_name": "cohere", "reranking_model_name": "rerank-english-v2.0"},
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reranking_enabled=True,
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),
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)
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@staticmethod
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def _model_config():
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return ModelConfigEntity(provider="openai", model="gpt-4", mode="chat", parameters={}, stop=[])
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def test_chatbot_uses_sys_query(self):
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node = WorkflowConverter()._convert_to_knowledge_retrieval_node(
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new_app_mode=AppMode.ADVANCED_CHAT,
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dataset_config=self._dataset_config(),
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model_config=self._model_config(),
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)
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assert node["data"]["query_variable_selector"] == ["sys", "query"]
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def test_workflow_uses_start_variable(self):
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node = WorkflowConverter()._convert_to_knowledge_retrieval_node(
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new_app_mode=AppMode.WORKFLOW,
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dataset_config=self._dataset_config(query_variable="query"),
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model_config=self._model_config(),
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)
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assert node["data"]["query_variable_selector"] == ["start", "query"]
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class TestConvertToLlmNode:
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"""Tests for LLM node conversion across model modes and prompt types."""
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@staticmethod
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def _model_config(model, mode):
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return ModelConfigEntity(
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provider="openai",
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model=model,
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mode=mode.value,
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parameters={},
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stop=[],
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)
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@staticmethod
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def _graph(default_variables):
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start = WorkflowConverter()._convert_to_start_node(default_variables)
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return {"nodes": [start], "edges": []}
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def test_simple_chat_model(self, default_variables):
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prompt = PromptTemplateEntity(
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prompt_type=PromptTemplateEntity.PromptType.SIMPLE,
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simple_prompt_template="You are helpful {{text_input}}, {{paragraph}}, {{select}}.",
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)
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node = WorkflowConverter()._convert_to_llm_node(
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original_app_mode=AppMode.CHAT,
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new_app_mode=AppMode.ADVANCED_CHAT,
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model_config=self._model_config("gpt-4", LLMMode.CHAT),
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graph=self._graph(default_variables),
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prompt_template=prompt,
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)
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assert node["data"]["type"] == "llm"
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assert node["data"]["model"]["mode"] == LLMMode.CHAT.value
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assert node["data"]["context"]["enabled"] is False
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expected = "You are helpful {{#start.text_input#}}, {{#start.paragraph#}}, {{#start.select#}}.\n"
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assert node["data"]["prompt_template"][0]["text"] == expected
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def test_simple_completion_model(self, default_variables):
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prompt = PromptTemplateEntity(
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prompt_type=PromptTemplateEntity.PromptType.SIMPLE,
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simple_prompt_template="You are helpful {{text_input}}, {{paragraph}}, {{select}}.",
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)
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node = WorkflowConverter()._convert_to_llm_node(
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original_app_mode=AppMode.CHAT,
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new_app_mode=AppMode.ADVANCED_CHAT,
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model_config=self._model_config("gpt-3.5-turbo-instruct", LLMMode.COMPLETION),
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graph=self._graph(default_variables),
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prompt_template=prompt,
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)
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assert node["data"]["model"]["mode"] == LLMMode.COMPLETION.value
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expected = "You are helpful {{#start.text_input#}}, {{#start.paragraph#}}, {{#start.select#}}.\n"
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assert node["data"]["prompt_template"]["text"] == expected
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def test_advanced_chat_model(self, default_variables):
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prompt = PromptTemplateEntity(
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prompt_type=PromptTemplateEntity.PromptType.ADVANCED,
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advanced_chat_prompt_template=AdvancedChatPromptTemplateEntity(
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messages=[
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AdvancedChatMessageEntity(
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text="You are helpful named {{name}}.\n\nContext:\n{{#context#}}",
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role=PromptMessageRole.SYSTEM,
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),
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AdvancedChatMessageEntity(text="Hi.", role=PromptMessageRole.USER),
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AdvancedChatMessageEntity(text="Hello!", role=PromptMessageRole.ASSISTANT),
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]
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),
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)
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node = WorkflowConverter()._convert_to_llm_node(
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original_app_mode=AppMode.CHAT,
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new_app_mode=AppMode.ADVANCED_CHAT,
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model_config=self._model_config("gpt-4", LLMMode.CHAT),
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graph=self._graph(default_variables),
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prompt_template=prompt,
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)
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assert isinstance(node["data"]["prompt_template"], list)
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assert len(node["data"]["prompt_template"]) == 3
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def test_advanced_completion_model(self, default_variables):
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prompt = PromptTemplateEntity(
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prompt_type=PromptTemplateEntity.PromptType.ADVANCED,
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advanced_completion_prompt_template=AdvancedCompletionPromptTemplateEntity(
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prompt="You are helpful named {{name}}.\n\nContext:\n{{#context#}}\n\nHuman: hi\nAssistant: ",
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role_prefix=AdvancedCompletionPromptTemplateEntity.RolePrefixEntity(
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user="Human", assistant="Assistant"
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),
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),
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)
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node = WorkflowConverter()._convert_to_llm_node(
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original_app_mode=AppMode.CHAT,
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new_app_mode=AppMode.ADVANCED_CHAT,
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model_config=self._model_config("gpt-3.5-turbo-instruct", LLMMode.COMPLETION),
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graph=self._graph(default_variables),
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prompt_template=prompt,
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)
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assert isinstance(node["data"]["prompt_template"], dict)
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assert "text" in node["data"]["prompt_template"]
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@ -1,431 +0,0 @@
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# test for api/services/workflow/workflow_converter.py
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import json
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from unittest.mock import MagicMock
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import pytest
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from core.app.app_config.entities import (
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AdvancedChatMessageEntity,
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AdvancedChatPromptTemplateEntity,
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AdvancedCompletionPromptTemplateEntity,
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DatasetEntity,
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DatasetRetrieveConfigEntity,
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ExternalDataVariableEntity,
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ModelConfigEntity,
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PromptTemplateEntity,
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)
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from core.helper import encrypter
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from dify_graph.model_runtime.entities.llm_entities import LLMMode
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from dify_graph.model_runtime.entities.message_entities import PromptMessageRole
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from dify_graph.variables.input_entities import VariableEntity, VariableEntityType
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from models.api_based_extension import APIBasedExtension, APIBasedExtensionPoint
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from models.model import AppMode
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from services.workflow.workflow_converter import WorkflowConverter
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@pytest.fixture
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def default_variables():
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value = [
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VariableEntity(
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variable="text_input",
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label="text-input",
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type=VariableEntityType.TEXT_INPUT,
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),
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VariableEntity(
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variable="paragraph",
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label="paragraph",
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type=VariableEntityType.PARAGRAPH,
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),
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VariableEntity(
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variable="select",
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label="select",
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type=VariableEntityType.SELECT,
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),
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]
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return value
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def test__convert_to_start_node(default_variables):
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# act
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result = WorkflowConverter()._convert_to_start_node(default_variables)
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# assert
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assert isinstance(result["data"]["variables"][0]["type"], str)
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assert result["data"]["variables"][0]["type"] == "text-input"
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assert result["data"]["variables"][0]["variable"] == "text_input"
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assert result["data"]["variables"][1]["variable"] == "paragraph"
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assert result["data"]["variables"][2]["variable"] == "select"
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def test__convert_to_http_request_node_for_chatbot(default_variables):
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"""
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Test convert to http request nodes for chatbot
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:return:
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"""
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app_model = MagicMock()
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app_model.id = "app_id"
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app_model.tenant_id = "tenant_id"
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app_model.mode = AppMode.CHAT
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api_based_extension_id = "api_based_extension_id"
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mock_api_based_extension = APIBasedExtension(
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tenant_id="tenant_id",
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name="api-1",
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api_key="encrypted_api_key",
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api_endpoint="https://dify.ai",
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)
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mock_api_based_extension.id = api_based_extension_id
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workflow_converter = WorkflowConverter()
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workflow_converter._get_api_based_extension = MagicMock(return_value=mock_api_based_extension)
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encrypter.decrypt_token = MagicMock(return_value="api_key")
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external_data_variables = [
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ExternalDataVariableEntity(
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variable="external_variable", type="api", config={"api_based_extension_id": api_based_extension_id}
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)
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]
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nodes, _ = workflow_converter._convert_to_http_request_node(
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app_model=app_model, variables=default_variables, external_data_variables=external_data_variables
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)
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assert len(nodes) == 2
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assert nodes[0]["data"]["type"] == "http-request"
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http_request_node = nodes[0]
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assert http_request_node["data"]["method"] == "post"
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assert http_request_node["data"]["url"] == mock_api_based_extension.api_endpoint
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assert http_request_node["data"]["authorization"]["type"] == "api-key"
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assert http_request_node["data"]["authorization"]["config"] == {"type": "bearer", "api_key": "api_key"}
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assert http_request_node["data"]["body"]["type"] == "json"
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body_data = http_request_node["data"]["body"]["data"]
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assert body_data
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body_data_json = json.loads(body_data)
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assert body_data_json["point"] == APIBasedExtensionPoint.APP_EXTERNAL_DATA_TOOL_QUERY
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body_params = body_data_json["params"]
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assert body_params["app_id"] == app_model.id
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assert body_params["tool_variable"] == external_data_variables[0].variable
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assert len(body_params["inputs"]) == 3
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assert body_params["query"] == "{{#sys.query#}}" # for chatbot
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code_node = nodes[1]
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assert code_node["data"]["type"] == "code"
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def test__convert_to_http_request_node_for_workflow_app(default_variables):
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"""
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Test convert to http request nodes for workflow app
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:return:
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"""
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app_model = MagicMock()
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app_model.id = "app_id"
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app_model.tenant_id = "tenant_id"
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app_model.mode = AppMode.WORKFLOW
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api_based_extension_id = "api_based_extension_id"
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mock_api_based_extension = APIBasedExtension(
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tenant_id="tenant_id",
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name="api-1",
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api_key="encrypted_api_key",
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api_endpoint="https://dify.ai",
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)
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mock_api_based_extension.id = api_based_extension_id
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workflow_converter = WorkflowConverter()
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workflow_converter._get_api_based_extension = MagicMock(return_value=mock_api_based_extension)
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encrypter.decrypt_token = MagicMock(return_value="api_key")
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external_data_variables = [
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ExternalDataVariableEntity(
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variable="external_variable", type="api", config={"api_based_extension_id": api_based_extension_id}
|
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)
|
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]
|
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|
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nodes, _ = workflow_converter._convert_to_http_request_node(
|
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app_model=app_model, variables=default_variables, external_data_variables=external_data_variables
|
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)
|
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|
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assert len(nodes) == 2
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assert nodes[0]["data"]["type"] == "http-request"
|
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|
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http_request_node = nodes[0]
|
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|
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assert http_request_node["data"]["method"] == "post"
|
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assert http_request_node["data"]["url"] == mock_api_based_extension.api_endpoint
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assert http_request_node["data"]["authorization"]["type"] == "api-key"
|
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assert http_request_node["data"]["authorization"]["config"] == {"type": "bearer", "api_key": "api_key"}
|
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assert http_request_node["data"]["body"]["type"] == "json"
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|
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body_data = http_request_node["data"]["body"]["data"]
|
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|
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assert body_data
|
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|
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body_data_json = json.loads(body_data)
|
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assert body_data_json["point"] == APIBasedExtensionPoint.APP_EXTERNAL_DATA_TOOL_QUERY
|
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|
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body_params = body_data_json["params"]
|
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assert body_params["app_id"] == app_model.id
|
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assert body_params["tool_variable"] == external_data_variables[0].variable
|
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assert len(body_params["inputs"]) == 3
|
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assert body_params["query"] == ""
|
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|
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code_node = nodes[1]
|
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assert code_node["data"]["type"] == "code"
|
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|
||||
|
||||
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"},
|
||||
reranking_enabled=True,
|
||||
),
|
||||
)
|
||||
|
||||
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
|
||||
)
|
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assert node is not None
|
||||
|
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assert node["data"]["type"] == "knowledge-retrieval"
|
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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"},
|
||||
reranking_enabled=True,
|
||||
),
|
||||
)
|
||||
|
||||
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 is not None
|
||||
|
||||
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
|
||||
assert template is not None
|
||||
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
|
||||
assert template is not None
|
||||
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 prompt_template.advanced_chat_prompt_template is not None
|
||||
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\nHuman: 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)
|
||||
assert prompt_template.advanced_completion_prompt_template is not None
|
||||
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