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feat: add backwards invoke node api

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pull/9184/head
Yeuoly 2 years ago
parent 592f85f7a9
commit 68c10a1672
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GPG Key ID: A66E7E320FB19F61
5 changed files with 335 additions and 42 deletions
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70
api/controllers/inner_api/plugin/plugin.py
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@ -8,13 +8,15 @@ from controllers.inner_api.plugin.wraps import get_tenant, plugin_data
from controllers.inner_api.wraps import plugin_inner_api_only
from core.plugin.backwards_invocation.app import PluginAppBackwardsInvocation
from core.plugin.backwards_invocation.model import PluginModelBackwardsInvocation
from core.plugin.backwards_invocation.node import PluginNodeBackwardsInvocation
from core.plugin.encrypt import PluginEncrypter
from core.plugin.entities.request import (
RequestInvokeApp,
RequestInvokeEncrypt,
RequestInvokeLLM,
RequestInvokeModeration,
RequestInvokeNode,
RequestInvokeParameterExtractorNode,
RequestInvokeQuestionClassifierNode,
RequestInvokeRerank,
RequestInvokeSpeech2Text,
RequestInvokeTextEmbedding,
@ -96,23 +98,46 @@ class PluginInvokeToolApi(Resource):
yield (
ToolInvokeMessage(
type=ToolInvokeMessage.MessageType.TEXT,
message=ToolInvokeMessage.TextMessage(text='helloworld'),
message=ToolInvokeMessage.TextMessage(text="helloworld"),
)
.model_dump_json()
.encode()
+ b'\n\n'
+ b"\n\n"
)
return compact_generate_response(generator())
class PluginInvokeNodeApi(Resource):
class PluginInvokeParameterExtractorNodeApi(Resource):
@setup_required
@plugin_inner_api_only
@get_tenant
@plugin_data(payload_type=RequestInvokeNode)
def post(self, user_id: str, tenant_model: Tenant, payload: RequestInvokeNode):
pass
@plugin_data(payload_type=RequestInvokeParameterExtractorNode)
def post(self, user_id: str, tenant_model: Tenant, payload: RequestInvokeParameterExtractorNode):
return PluginNodeBackwardsInvocation.invoke_parameter_extractor(
tenant_id=tenant_model.id,
user_id=user_id,
parameters=payload.parameters,
model_config=payload.model,
instruction=payload.instruction,
query=payload.query,
)
class PluginInvokeQuestionClassifierNodeApi(Resource):
@setup_required
@plugin_inner_api_only
@get_tenant
@plugin_data(payload_type=RequestInvokeQuestionClassifierNode)
def post(self, user_id: str, tenant_model: Tenant, payload: RequestInvokeQuestionClassifierNode):
return PluginNodeBackwardsInvocation.invoke_question_classifier(
tenant_id=tenant_model.id,
user_id=user_id,
query=payload.query,
model_config=payload.model,
classes=payload.classes,
instruction=payload.instruction,
)
class PluginInvokeAppApi(Resource):
@ -127,15 +152,13 @@ class PluginInvokeAppApi(Resource):
tenant_id=tenant_model.id,
conversation_id=payload.conversation_id,
query=payload.query,
stream=payload.response_mode == 'streaming',
stream=payload.response_mode == "streaming",
inputs=payload.inputs,
files=payload.files
)
return compact_generate_response(
PluginAppBackwardsInvocation.convert_to_event_stream(response)
files=payload.files,
)
return compact_generate_response(PluginAppBackwardsInvocation.convert_to_event_stream(response))
class PluginInvokeEncryptApi(Resource):
@setup_required
@ -149,13 +172,14 @@ class PluginInvokeEncryptApi(Resource):
return PluginEncrypter.invoke_encrypt(tenant_model, payload)
api.add_resource(PluginInvokeLLMApi, '/invoke/llm')
api.add_resource(PluginInvokeTextEmbeddingApi, '/invoke/text-embedding')
api.add_resource(PluginInvokeRerankApi, '/invoke/rerank')
api.add_resource(PluginInvokeTTSApi, '/invoke/tts')
api.add_resource(PluginInvokeSpeech2TextApi, '/invoke/speech2text')
api.add_resource(PluginInvokeModerationApi, '/invoke/moderation')
api.add_resource(PluginInvokeToolApi, '/invoke/tool')
api.add_resource(PluginInvokeNodeApi, '/invoke/node')
api.add_resource(PluginInvokeAppApi, '/invoke/app')
api.add_resource(PluginInvokeEncryptApi, '/invoke/encrypt')
api.add_resource(PluginInvokeLLMApi, "/invoke/llm")
api.add_resource(PluginInvokeTextEmbeddingApi, "/invoke/text-embedding")
api.add_resource(PluginInvokeRerankApi, "/invoke/rerank")
api.add_resource(PluginInvokeTTSApi, "/invoke/tts")
api.add_resource(PluginInvokeSpeech2TextApi, "/invoke/speech2text")
api.add_resource(PluginInvokeModerationApi, "/invoke/moderation")
api.add_resource(PluginInvokeToolApi, "/invoke/tool")
api.add_resource(PluginInvokeParameterExtractorNodeApi, "/invoke/parameter-extractor")
api.add_resource(PluginInvokeQuestionClassifierNodeApi, "/invoke/question-classifier")
api.add_resource(PluginInvokeAppApi, "/invoke/app")
api.add_resource(PluginInvokeEncryptApi, "/invoke/encrypt")

114
api/core/plugin/backwards_invocation/node.py
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@ -0,0 +1,114 @@
from core.plugin.backwards_invocation.base import BaseBackwardsInvocation
from core.workflow.nodes.parameter_extractor.entities import (
ModelConfig as ParameterExtractorModelConfig,
)
from core.workflow.nodes.parameter_extractor.entities import (
ParameterConfig,
ParameterExtractorNodeData,
)
from core.workflow.nodes.question_classifier.entities import (
ClassConfig,
QuestionClassifierNodeData,
)
from core.workflow.nodes.question_classifier.entities import (
ModelConfig as QuestionClassifierModelConfig,
)
from services.workflow_service import WorkflowService
class PluginNodeBackwardsInvocation(BaseBackwardsInvocation):
@classmethod
def invoke_parameter_extractor(
cls,
tenant_id: str,
user_id: str,
parameters: list[ParameterConfig],
model_config: ParameterExtractorModelConfig,
instruction: str,
query: str,
) -> dict:
"""
Invoke parameter extractor node.
:param tenant_id: str
:param user_id: str
:param parameters: list[ParameterConfig]
:param model_config: ModelConfig
:param instruction: str
:param query: str
:return: dict with __reason, __is_success, and other parameters
"""
workflow_service = WorkflowService()
node_id = "1919810"
node_data = ParameterExtractorNodeData(
title="parameter_extractor",
desc="parameter_extractor",
parameters=parameters,
reasoning_mode="function_call",
query=[node_id, "query"],
model=model_config,
instruction=instruction, # instruct with variables are not supported
)
node_data_dict = node_data.model_dump()
execution = workflow_service.run_free_workflow_node(
node_data_dict,
tenant_id=tenant_id,
user_id=user_id,
node_id=node_id,
user_inputs={
f"{node_id}.query": query,
},
)
output = execution.outputs_dict
return output or {
"__reason": "No parameters extracted",
"__is_success": False,
}
@classmethod
def invoke_question_classifier(
cls,
tenant_id: str,
user_id: str,
model_config: QuestionClassifierModelConfig,
classes: list[ClassConfig],
instruction: str,
query: str,
) -> dict:
"""
Invoke question classifier node.
:param tenant_id: str
:param user_id: str
:param model_config: ModelConfig
:param classes: list[ClassConfig]
:param instruction: str
:param query: str
:return: dict with class_name
"""
workflow_service = WorkflowService()
node_id = "1919810"
node_data = QuestionClassifierNodeData(
title="question_classifier",
desc="question_classifier",
query_variable_selector=[node_id, "query"],
model=model_config,
classes=classes,
instruction=instruction, # instruct with variables are not supported
)
node_data_dict = node_data.model_dump()
execution = workflow_service.run_free_workflow_node(
node_data_dict,
tenant_id=tenant_id,
user_id=user_id,
node_id=node_id,
user_inputs={
f"{node_id}.query": query,
},
)
output = execution.outputs_dict
return output or {
"class_name": classes[0].name,
}

30
api/core/plugin/entities/request.py
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@ -14,6 +14,16 @@ from core.model_runtime.entities.message_entities import (
UserPromptMessage,
)
from core.model_runtime.entities.model_entities import ModelType
from core.workflow.nodes.question_classifier.entities import (
ClassConfig,
ModelConfig as QuestionClassifierModelConfig,
)
from core.workflow.nodes.parameter_extractor.entities import (
ModelConfig as ParameterExtractorModelConfig,
)
from core.workflow.nodes.parameter_extractor.entities import (
ParameterConfig,
)
class RequestInvokeTool(BaseModel):
@ -92,11 +102,27 @@ class RequestInvokeModeration(BaseModel):
"""
class RequestInvokeNode(BaseModel):
class RequestInvokeParameterExtractorNode(BaseModel):
"""
Request to invoke node
Request to invoke parameter extractor node
"""
parameters: list[ParameterConfig]
model: ParameterExtractorModelConfig
instruction: str
query: str
class RequestInvokeQuestionClassifierNode(BaseModel):
"""
Request to invoke question classifier node
"""
query: str
model: QuestionClassifierModelConfig
classes: list[ClassConfig]
instruction: str
class RequestInvokeApp(BaseModel):
"""

82
api/core/workflow/workflow_entry.py
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@ -205,6 +205,88 @@ class WorkflowEntry:
except Exception as e:
raise WorkflowNodeRunFailedError(node_instance=node_instance, error=str(e))
@classmethod
def run_free_node(
cls, node_data: dict, node_id: str, tenant_id: str, user_id: str, user_inputs: dict[str, Any]
) -> tuple[BaseNode, Generator[RunEvent | InNodeEvent, None, None]]:
"""
Run free node
NOTE: only parameter_extractor/question_classifier are supported
:param node_data: node data
:param user_id: user id
:param user_inputs: user inputs
:return:
"""
# generate a fake graph
node_config = {"id": node_id, "width": 114, "height": 514, "type": "custom", "data": node_data}
graph_dict = {
"nodes": [node_config],
}
node_type = NodeType.value_of(node_data.get("type", ""))
if node_type not in {NodeType.PARAMETER_EXTRACTOR, NodeType.QUESTION_CLASSIFIER}:
raise ValueError(f"Node type {node_type} not supported")
node_cls = node_classes.get(node_type)
if not node_cls:
raise ValueError(f"Node class not found for node type {node_type}")
graph = Graph.init(graph_config=graph_dict)
# init variable pool
variable_pool = VariablePool(
system_variables={},
user_inputs={},
environment_variables=[],
)
node_cls = cast(type[BaseNode], node_cls)
# init workflow run state
node_instance: BaseNode = node_cls(
id=str(uuid.uuid4()),
config=node_config,
graph_init_params=GraphInitParams(
tenant_id=tenant_id,
app_id="",
workflow_type=WorkflowType.WORKFLOW,
workflow_id="",
graph_config=graph_dict,
user_id=user_id,
user_from=UserFrom.ACCOUNT,
invoke_from=InvokeFrom.DEBUGGER,
call_depth=0,
),
graph=graph,
graph_runtime_state=GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter()),
)
try:
# variable selector to variable mapping
try:
variable_mapping = node_cls.extract_variable_selector_to_variable_mapping(
graph_config=graph_dict, config=node_config
)
except NotImplementedError:
variable_mapping = {}
cls.mapping_user_inputs_to_variable_pool(
variable_mapping=variable_mapping,
user_inputs=user_inputs,
variable_pool=variable_pool,
tenant_id=tenant_id,
node_type=node_type,
node_data=node_instance.node_data,
)
# run node
generator = node_instance.run()
return node_instance, generator
except Exception as e:
raise WorkflowNodeRunFailedError(node_instance=node_instance, error=str(e))
@classmethod
def handle_special_values(cls, value: Optional[Mapping[str, Any]]) -> Optional[dict]:
"""

81
api/services/workflow_service.py
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@ -1,8 +1,8 @@
import json
import time
from collections.abc import Sequence
from collections.abc import Callable, Generator, Sequence
from datetime import datetime, timezone
from typing import Optional
from typing import Any, Optional
from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfigManager
from core.app.apps.workflow.app_config_manager import WorkflowAppConfigManager
@ -10,7 +10,9 @@ from core.app.segments import Variable
from core.model_runtime.utils.encoders import jsonable_encoder
from core.workflow.entities.node_entities import NodeRunResult, NodeType
from core.workflow.errors import WorkflowNodeRunFailedError
from core.workflow.nodes.event import RunCompletedEvent
from core.workflow.graph_engine.entities.event import InNodeEvent
from core.workflow.nodes.base_node import BaseNode
from core.workflow.nodes.event import RunCompletedEvent, RunEvent
from core.workflow.nodes.node_mapping import node_classes
from core.workflow.workflow_entry import WorkflowEntry
from events.app_event import app_draft_workflow_was_synced, app_published_workflow_was_updated
@ -216,13 +218,64 @@ class WorkflowService:
# run draft workflow node
start_at = time.perf_counter()
try:
node_instance, generator = WorkflowEntry.single_step_run(
workflow_node_execution = self._handle_node_run_result(
getter=lambda: WorkflowEntry.single_step_run(
workflow=draft_workflow,
node_id=node_id,
user_inputs=user_inputs,
user_id=account.id,
)
),
start_at=start_at,
tenant_id=app_model.tenant_id,
node_id=node_id,
)
db.session.add(workflow_node_execution)
db.session.commit()
return workflow_node_execution
def run_free_workflow_node(
self, node_data: dict, tenant_id: str, user_id: str, node_id: str, user_inputs: dict[str, Any]
) -> WorkflowNodeExecution:
"""
Run draft workflow node
"""
# run draft workflow node
start_at = time.perf_counter()
workflow_node_execution = self._handle_node_run_result(
getter=lambda: WorkflowEntry.run_free_node(
node_id=node_id,
node_data=node_data,
tenant_id=tenant_id,
user_id=user_id,
user_inputs=user_inputs,
),
start_at=start_at,
tenant_id=tenant_id,
node_id=node_id
)
return workflow_node_execution
def _handle_node_run_result(
self,
getter: Callable[[], tuple[BaseNode, Generator[RunEvent | InNodeEvent, None, None]]],
start_at: float,
tenant_id: str,
node_id: str,
):
"""
Handle node run result
:param getter: Callable[[], tuple[BaseNode, Generator[RunEvent | InNodeEvent, None, None]]]
:param start_at: float
:param tenant_id: str
:param node_id: str
"""
try:
node_instance, generator = getter()
node_run_result: NodeRunResult | None = None
for event in generator:
@ -245,9 +298,7 @@ class WorkflowService:
error = e.error
workflow_node_execution = WorkflowNodeExecution()
workflow_node_execution.tenant_id = app_model.tenant_id
workflow_node_execution.app_id = app_model.id
workflow_node_execution.workflow_id = draft_workflow.id
workflow_node_execution.tenant_id = tenant_id
workflow_node_execution.triggered_from = WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP.value
workflow_node_execution.index = 1
workflow_node_execution.node_id = node_id
@ -255,7 +306,6 @@ class WorkflowService:
workflow_node_execution.title = node_instance.node_data.title
workflow_node_execution.elapsed_time = time.perf_counter() - start_at
workflow_node_execution.created_by_role = CreatedByRole.ACCOUNT.value
workflow_node_execution.created_by = account.id
workflow_node_execution.created_at = datetime.now(timezone.utc).replace(tzinfo=None)
workflow_node_execution.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
@ -277,9 +327,6 @@ class WorkflowService:
workflow_node_execution.status = WorkflowNodeExecutionStatus.FAILED.value
workflow_node_execution.error = error
db.session.add(workflow_node_execution)
db.session.commit()
return workflow_node_execution
def convert_to_workflow(self, app_model: App, account: Account, args: dict) -> App:
@ -302,10 +349,10 @@ class WorkflowService:
new_app = workflow_converter.convert_to_workflow(
app_model=app_model,
account=account,
name=args.get("name"),
icon_type=args.get("icon_type"),
icon=args.get("icon"),
icon_background=args.get("icon_background"),
name=args.get("name", ""),
icon_type=args.get("icon_type", ""),
icon=args.get("icon", ""),
icon_background=args.get("icon_background", ""),
)
return new_app

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