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Merge branch 'fix/chore-fix' into dev/plugin-deploy

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This commit is contained in:
Yeuoly
2024-11-25 17:19:51 +08:00
parent dfc94cd7c2 ba3659a792
commit 69b61ef57b
250 changed files with 7636 additions and 1975 deletions
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1
api/core/app/app_config/easy_ui_based_app/dataset/manager.py
View File

@ -1,3 +1,4 @@
import uuid
from typing import Optional
from core.app.app_config.entities import DatasetEntity, DatasetRetrieveConfigEntity

36
api/core/app/app_config/easy_ui_based_app/model_config/converter.py
View File

@ -12,7 +12,7 @@ from core.provider_manager import ProviderManager
class ModelConfigConverter:
@classmethod
def convert(cls, app_config: EasyUIBasedAppConfig, skip_check: bool = False) -> ModelConfigWithCredentialsEntity:
def convert(cls, app_config: EasyUIBasedAppConfig) -> ModelConfigWithCredentialsEntity:
"""
Convert app model config dict to entity.
:param app_config: app config
@ -39,27 +39,23 @@ class ModelConfigConverter:
)
if model_credentials is None:
if not skip_check:
raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.")
else:
model_credentials = {}
raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.")
if not skip_check:
# check model
provider_model = provider_model_bundle.configuration.get_provider_model(
model=model_config.model, model_type=ModelType.LLM
)
# check model
provider_model = provider_model_bundle.configuration.get_provider_model(
model=model_config.model, model_type=ModelType.LLM
)
if provider_model is None:
model_name = model_config.model
raise ValueError(f"Model {model_name} not exist.")
if provider_model is None:
model_name = model_config.model
raise ValueError(f"Model {model_name} not exist.")
if provider_model.status == ModelStatus.NO_CONFIGURE:
raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.")
elif provider_model.status == ModelStatus.NO_PERMISSION:
raise ModelCurrentlyNotSupportError(f"Dify Hosted OpenAI {model_name} currently not support.")
elif provider_model.status == ModelStatus.QUOTA_EXCEEDED:
raise QuotaExceededError(f"Model provider {provider_name} quota exceeded.")
if provider_model.status == ModelStatus.NO_CONFIGURE:
raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.")
elif provider_model.status == ModelStatus.NO_PERMISSION:
raise ModelCurrentlyNotSupportError(f"Dify Hosted OpenAI {model_name} currently not support.")
elif provider_model.status == ModelStatus.QUOTA_EXCEEDED:
raise QuotaExceededError(f"Model provider {provider_name} quota exceeded.")
# model config
completion_params = model_config.parameters
@ -77,7 +73,7 @@ class ModelConfigConverter:
if model_schema and model_schema.model_properties.get(ModelPropertyKey.MODE):
model_mode = LLMMode.value_of(model_schema.model_properties[ModelPropertyKey.MODE]).value
if not skip_check and not model_schema:
if not model_schema:
raise ValueError(f"Model {model_name} not exist.")
return ModelConfigWithCredentialsEntity(

5
api/core/app/app_config/easy_ui_based_app/prompt_template/manager.py
View File

@ -1,4 +1,5 @@
from core.app.app_config.entities import (
AdvancedChatMessageEntity,
AdvancedChatPromptTemplateEntity,
AdvancedCompletionPromptTemplateEntity,
PromptTemplateEntity,
@ -25,7 +26,9 @@ class PromptTemplateConfigManager:
chat_prompt_messages = []
for message in chat_prompt_config.get("prompt", []):
chat_prompt_messages.append(
{"text": message["text"], "role": PromptMessageRole.value_of(message["role"])}
AdvancedChatMessageEntity(
**{"text": message["text"], "role": PromptMessageRole.value_of(message["role"])}
)
)
advanced_chat_prompt_template = AdvancedChatPromptTemplateEntity(messages=chat_prompt_messages)

4
api/core/app/app_config/entities.py
View File

@ -1,5 +1,5 @@
from collections.abc import Sequence
from enum import Enum
from enum import Enum, StrEnum
from typing import Any, Optional
from pydantic import BaseModel, Field, field_validator
@ -88,7 +88,7 @@ class PromptTemplateEntity(BaseModel):
advanced_completion_prompt_template: Optional[AdvancedCompletionPromptTemplateEntity] = None
class VariableEntityType(str, Enum):
class VariableEntityType(StrEnum):
TEXT_INPUT = "text-input"
SELECT = "select"
PARAGRAPH = "paragraph"

2
api/core/app/apps/advanced_chat/app_generator.py
View File

@ -138,7 +138,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
conversation_id=conversation.id if conversation else None,
inputs=conversation.inputs
if conversation
else self._prepare_user_inputs(user_inputs=inputs, app_config=app_config),
else self._prepare_user_inputs(user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.id),
query=query,
files=file_objs,
parent_message_id=args.get("parent_message_id") if invoke_from != InvokeFrom.SERVICE_API else UUID_NIL,

2
api/core/app/apps/agent_chat/app_generator.py
View File

@ -139,7 +139,7 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
conversation_id=conversation.id if conversation else None,
inputs=conversation.inputs
if conversation
else self._prepare_user_inputs(user_inputs=inputs, app_config=app_config),
else self._prepare_user_inputs(user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.id),
query=query,
files=file_objs,
parent_message_id=args.get("parent_message_id") if invoke_from != InvokeFrom.SERVICE_API else UUID_NIL,

14
api/core/app/apps/base_app_generator.py
View File

@ -1,5 +1,5 @@
import json
from collections.abc import Generator, Mapping
from collections.abc import Generator, Mapping, Sequence
from typing import TYPE_CHECKING, Any, Optional, Union
from core.app.app_config.entities import VariableEntityType
@ -7,7 +7,7 @@ from core.file import File, FileUploadConfig
from factories import file_factory
if TYPE_CHECKING:
from core.app.app_config.entities import AppConfig, VariableEntity
from core.app.app_config.entities import VariableEntity
class BaseAppGenerator:
@ -15,23 +15,23 @@ class BaseAppGenerator:
self,
*,
user_inputs: Optional[Mapping[str, Any]],
app_config: "AppConfig",
variables: Sequence["VariableEntity"],
tenant_id: str,
) -> Mapping[str, Any]:
user_inputs = user_inputs or {}
# Filter input variables from form configuration, handle required fields, default values, and option values
variables = app_config.variables
user_inputs = {
var.variable: self._validate_inputs(value=user_inputs.get(var.variable), variable_entity=var)
for var in variables
}
user_inputs = {k: self._sanitize_value(v) for k, v in user_inputs.items()}
# Convert files in inputs to File
entity_dictionary = {item.variable: item for item in app_config.variables}
entity_dictionary = {item.variable: item for item in variables}
# Convert single file to File
files_inputs = {
k: file_factory.build_from_mapping(
mapping=v,
tenant_id=app_config.tenant_id,
tenant_id=tenant_id,
config=FileUploadConfig(
allowed_file_types=entity_dictionary[k].allowed_file_types,
allowed_file_extensions=entity_dictionary[k].allowed_file_extensions,
@ -45,7 +45,7 @@ class BaseAppGenerator:
file_list_inputs = {
k: file_factory.build_from_mappings(
mappings=v,
tenant_id=app_config.tenant_id,
tenant_id=tenant_id,
config=FileUploadConfig(
allowed_file_types=entity_dictionary[k].allowed_file_types,
allowed_file_extensions=entity_dictionary[k].allowed_file_extensions,

2
api/core/app/apps/chat/app_generator.py
View File

@ -142,7 +142,7 @@ class ChatAppGenerator(MessageBasedAppGenerator):
conversation_id=conversation.id if conversation else None,
inputs=conversation.inputs
if conversation
else self._prepare_user_inputs(user_inputs=inputs, app_config=app_config),
else self._prepare_user_inputs(user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.id),
query=query,
files=file_objs,
parent_message_id=args.get("parent_message_id") if invoke_from != InvokeFrom.SERVICE_API else UUID_NIL,

4
api/core/app/apps/completion/app_generator.py
View File

@ -123,7 +123,9 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
app_config=app_config,
model_conf=ModelConfigConverter.convert(app_config),
file_upload_config=file_extra_config,
inputs=self._prepare_user_inputs(user_inputs=inputs, app_config=app_config),
inputs=self._prepare_user_inputs(
user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.id
),
query=query,
files=file_objs,
user_id=user.id,

4
api/core/app/apps/message_based_app_generator.py
View File

@ -1,7 +1,7 @@
import json
import logging
from collections.abc import Generator
from datetime import datetime, timezone
from datetime import UTC, datetime
from typing import Optional, Union
from sqlalchemy import and_
@ -200,7 +200,7 @@ class MessageBasedAppGenerator(BaseAppGenerator):
db.session.commit()
db.session.refresh(conversation)
else:
conversation.updated_at = datetime.now(timezone.utc).replace(tzinfo=None)
conversation.updated_at = datetime.now(UTC).replace(tzinfo=None)
db.session.commit()
message = Message(

4
api/core/app/apps/workflow/app_generator.py
View File

@ -108,7 +108,9 @@ class WorkflowAppGenerator(BaseAppGenerator):
task_id=str(uuid.uuid4()),
app_config=app_config,
file_upload_config=file_extra_config,
inputs=self._prepare_user_inputs(user_inputs=inputs, app_config=app_config),
inputs=self._prepare_user_inputs(
user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.tenant_id
),
files=system_files,
user_id=user.id,
stream=stream,

3
api/core/app/apps/workflow_app_runner.py
View File

@ -43,7 +43,6 @@ from core.workflow.graph_engine.entities.event import (
)
from core.workflow.graph_engine.entities.graph import Graph
from core.workflow.nodes import NodeType
from core.workflow.nodes.iteration import IterationNodeData
from core.workflow.nodes.node_mapping import node_type_classes_mapping
from core.workflow.workflow_entry import WorkflowEntry
from extensions.ext_database import db
@ -160,8 +159,6 @@ class WorkflowBasedAppRunner(AppRunner):
user_inputs=user_inputs,
variable_pool=variable_pool,
tenant_id=workflow.tenant_id,
node_type=node_type,
node_data=IterationNodeData(**iteration_node_config.get("data", {})),
)
return graph, variable_pool

4
api/core/app/entities/queue_entities.py
View File

@ -1,5 +1,5 @@
from datetime import datetime
from enum import Enum
from enum import Enum, StrEnum
from typing import Any, Optional
from pydantic import BaseModel, field_validator
@ -11,7 +11,7 @@ from core.workflow.nodes import NodeType
from core.workflow.nodes.base import BaseNodeData
class QueueEvent(str, Enum):
class QueueEvent(StrEnum):
"""
QueueEvent enum
"""

21
api/core/app/task_pipeline/workflow_cycle_manage.py
View File

@ -1,7 +1,7 @@
import json
import time
from collections.abc import Mapping, Sequence
from datetime import datetime, timezone
from datetime import UTC, datetime
from typing import Any, Optional, Union, cast
from sqlalchemy.orm import Session
@ -144,7 +144,7 @@ class WorkflowCycleManage:
workflow_run.elapsed_time = time.perf_counter() - start_at
workflow_run.total_tokens = total_tokens
workflow_run.total_steps = total_steps
workflow_run.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
workflow_run.finished_at = datetime.now(UTC).replace(tzinfo=None)
db.session.commit()
db.session.refresh(workflow_run)
@ -191,7 +191,7 @@ class WorkflowCycleManage:
workflow_run.elapsed_time = time.perf_counter() - start_at
workflow_run.total_tokens = total_tokens
workflow_run.total_steps = total_steps
workflow_run.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
workflow_run.finished_at = datetime.now(UTC).replace(tzinfo=None)
db.session.commit()
@ -211,15 +211,18 @@ class WorkflowCycleManage:
for workflow_node_execution in running_workflow_node_executions:
workflow_node_execution.status = WorkflowNodeExecutionStatus.FAILED.value
workflow_node_execution.error = error
workflow_node_execution.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
workflow_node_execution.finished_at = datetime.now(UTC).replace(tzinfo=None)
workflow_node_execution.elapsed_time = (
workflow_node_execution.finished_at - workflow_node_execution.created_at
).total_seconds()
db.session.commit()
db.session.refresh(workflow_run)
db.session.close()
with Session(db.engine, expire_on_commit=False) as session:
session.add(workflow_run)
session.refresh(workflow_run)
if trace_manager:
trace_manager.add_trace_task(
TraceTask(
@ -259,7 +262,7 @@ class WorkflowCycleManage:
NodeRunMetadataKey.ITERATION_ID: event.in_iteration_id,
}
)
workflow_node_execution.created_at = datetime.now(timezone.utc).replace(tzinfo=None)
workflow_node_execution.created_at = datetime.now(UTC).replace(tzinfo=None)
session.add(workflow_node_execution)
session.commit()
@ -282,7 +285,7 @@ class WorkflowCycleManage:
execution_metadata = (
json.dumps(jsonable_encoder(event.execution_metadata)) if event.execution_metadata else None
)
finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
finished_at = datetime.now(UTC).replace(tzinfo=None)
elapsed_time = (finished_at - event.start_at).total_seconds()
db.session.query(WorkflowNodeExecution).filter(WorkflowNodeExecution.id == workflow_node_execution.id).update(
@ -326,7 +329,7 @@ class WorkflowCycleManage:
inputs = WorkflowEntry.handle_special_values(event.inputs)
process_data = WorkflowEntry.handle_special_values(event.process_data)
outputs = WorkflowEntry.handle_special_values(event.outputs)
finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
finished_at = datetime.now(UTC).replace(tzinfo=None)
elapsed_time = (finished_at - event.start_at).total_seconds()
execution_metadata = (
json.dumps(jsonable_encoder(event.execution_metadata)) if event.execution_metadata else None
@ -654,7 +657,7 @@ class WorkflowCycleManage:
if event.error is None
else WorkflowNodeExecutionStatus.FAILED,
error=None,
elapsed_time=(datetime.now(timezone.utc).replace(tzinfo=None) - event.start_at).total_seconds(),
elapsed_time=(datetime.now(UTC).replace(tzinfo=None) - event.start_at).total_seconds(),
total_tokens=event.metadata.get("total_tokens", 0) if event.metadata else 0,
execution_metadata=event.metadata,
finished_at=int(time.time()),

12
api/core/entities/provider_configuration.py
View File

@ -246,7 +246,7 @@ class ProviderConfiguration(BaseModel):
if provider_record:
provider_record.encrypted_config = json.dumps(credentials)
provider_record.is_valid = True
provider_record.updated_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
provider_record.updated_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
db.session.commit()
else:
provider_record = Provider()
@ -401,7 +401,7 @@ class ProviderConfiguration(BaseModel):
if provider_model_record:
provider_model_record.encrypted_config = json.dumps(credentials)
provider_model_record.is_valid = True
provider_model_record.updated_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
provider_model_record.updated_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
db.session.commit()
else:
provider_model_record = ProviderModel()
@ -474,7 +474,7 @@ class ProviderConfiguration(BaseModel):
if model_setting:
model_setting.enabled = True
model_setting.updated_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
model_setting.updated_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
db.session.commit()
else:
model_setting = ProviderModelSetting()
@ -508,7 +508,7 @@ class ProviderConfiguration(BaseModel):
if model_setting:
model_setting.enabled = False
model_setting.updated_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
model_setting.updated_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
db.session.commit()
else:
model_setting = ProviderModelSetting()
@ -574,7 +574,7 @@ class ProviderConfiguration(BaseModel):
if model_setting:
model_setting.load_balancing_enabled = True
model_setting.updated_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
model_setting.updated_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
db.session.commit()
else:
model_setting = ProviderModelSetting()
@ -608,7 +608,7 @@ class ProviderConfiguration(BaseModel):
if model_setting:
model_setting.load_balancing_enabled = False
model_setting.updated_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
model_setting.updated_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
db.session.commit()
else:
model_setting = ProviderModelSetting()

12
api/core/file/enums.py
View File

@ -1,7 +1,7 @@
from enum import Enum
from enum import StrEnum
class FileType(str, Enum):
class FileType(StrEnum):
IMAGE = "image"
DOCUMENT = "document"
AUDIO = "audio"
@ -16,7 +16,7 @@ class FileType(str, Enum):
raise ValueError(f"No matching enum found for value '{value}'")
class FileTransferMethod(str, Enum):
class FileTransferMethod(StrEnum):
REMOTE_URL = "remote_url"
LOCAL_FILE = "local_file"
TOOL_FILE = "tool_file"
@ -29,7 +29,7 @@ class FileTransferMethod(str, Enum):
raise ValueError(f"No matching enum found for value '{value}'")
class FileBelongsTo(str, Enum):
class FileBelongsTo(StrEnum):
USER = "user"
ASSISTANT = "assistant"
@ -41,7 +41,7 @@ class FileBelongsTo(str, Enum):
raise ValueError(f"No matching enum found for value '{value}'")
class FileAttribute(str, Enum):
class FileAttribute(StrEnum):
TYPE = "type"
SIZE = "size"
NAME = "name"
@ -51,5 +51,5 @@ class FileAttribute(str, Enum):
EXTENSION = "extension"
class ArrayFileAttribute(str, Enum):
class ArrayFileAttribute(StrEnum):
LENGTH = "length"

69
api/core/file/file_manager.py
View File

@ -3,7 +3,12 @@ import base64
from configs import dify_config
from core.file import file_repository
from core.helper import ssrf_proxy
from core.model_runtime.entities import AudioPromptMessageContent, ImagePromptMessageContent, VideoPromptMessageContent
from core.model_runtime.entities import (
AudioPromptMessageContent,
DocumentPromptMessageContent,
ImagePromptMessageContent,
VideoPromptMessageContent,
)
from extensions.ext_database import db
from extensions.ext_storage import storage
@ -29,35 +34,17 @@ def get_attr(*, file: File, attr: FileAttribute):
return file.remote_url
case FileAttribute.EXTENSION:
return file.extension
case _:
raise ValueError(f"Invalid file attribute: {attr}")
def to_prompt_message_content(
f: File,
/,
*,
image_detail_config: ImagePromptMessageContent.DETAIL = ImagePromptMessageContent.DETAIL.LOW,
image_detail_config: ImagePromptMessageContent.DETAIL | None = None,
):
"""
Convert a File object to an ImagePromptMessageContent or AudioPromptMessageContent object.
This function takes a File object and converts it to an appropriate PromptMessageContent
object, which can be used as a prompt for image or audio-based AI models.
Args:
f (File): The File object to convert.
detail (Optional[ImagePromptMessageContent.DETAIL]): The detail level for image prompts.
If not provided, defaults to ImagePromptMessageContent.DETAIL.LOW.
Returns:
Union[ImagePromptMessageContent, AudioPromptMessageContent]: An object containing the file data and detail level
Raises:
ValueError: If the file type is not supported or if required data is missing.
"""
match f.type:
case FileType.IMAGE:
image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
if dify_config.MULTIMODAL_SEND_IMAGE_FORMAT == "url":
data = _to_url(f)
else:
@ -65,7 +52,7 @@ def to_prompt_message_content(
return ImagePromptMessageContent(data=data, detail=image_detail_config)
case FileType.AUDIO:
encoded_string = _file_to_encoded_string(f)
encoded_string = _get_encoded_string(f)
if f.extension is None:
raise ValueError("Missing file extension")
return AudioPromptMessageContent(data=encoded_string, format=f.extension.lstrip("."))
@ -74,9 +61,20 @@ def to_prompt_message_content(
data = _to_url(f)
else:
data = _to_base64_data_string(f)
if f.extension is None:
raise ValueError("Missing file extension")
return VideoPromptMessageContent(data=data, format=f.extension.lstrip("."))
case FileType.DOCUMENT:
data = _get_encoded_string(f)
if f.mime_type is None:
raise ValueError("Missing file mime_type")
return DocumentPromptMessageContent(
encode_format="base64",
mime_type=f.mime_type,
data=data,
)
case _:
raise ValueError("file type f.type is not supported")
raise ValueError(f"file type {f.type} is not supported")
def download(f: File, /):
@ -118,21 +116,16 @@ def _get_encoded_string(f: File, /):
case FileTransferMethod.REMOTE_URL:
response = ssrf_proxy.get(f.remote_url, follow_redirects=True)
response.raise_for_status()
content = response.content
encoded_string = base64.b64encode(content).decode("utf-8")
return encoded_string
data = response.content
case FileTransferMethod.LOCAL_FILE:
upload_file = file_repository.get_upload_file(session=db.session(), file=f)
data = _download_file_content(upload_file.key)
encoded_string = base64.b64encode(data).decode("utf-8")
return encoded_string
case FileTransferMethod.TOOL_FILE:
tool_file = file_repository.get_tool_file(session=db.session(), file=f)
data = _download_file_content(tool_file.file_key)
encoded_string = base64.b64encode(data).decode("utf-8")
return encoded_string
case _:
raise ValueError(f"Unsupported transfer method: {f.transfer_method}")
encoded_string = base64.b64encode(data).decode("utf-8")
return encoded_string
def _to_base64_data_string(f: File, /):
@ -140,18 +133,6 @@ def _to_base64_data_string(f: File, /):
return f"data:{f.mime_type};base64,{encoded_string}"
def _file_to_encoded_string(f: File, /):
match f.type:
case FileType.IMAGE:
return _to_base64_data_string(f)
case FileType.VIDEO:
return _to_base64_data_string(f)
case FileType.AUDIO:
return _get_encoded_string(f)
case _:
raise ValueError(f"file type {f.type} is not supported")
def _to_url(f: File, /):
if f.transfer_method == FileTransferMethod.REMOTE_URL:
if f.remote_url is None:

4
api/core/helper/code_executor/code_executor.py
View File

@ -1,6 +1,6 @@
import logging
from collections.abc import Mapping
from enum import Enum
from enum import StrEnum
from threading import Lock
from typing import Any, Optional
@ -31,7 +31,7 @@ class CodeExecutionResponse(BaseModel):
data: Data
class CodeLanguage(str, Enum):
class CodeLanguage(StrEnum):
PYTHON3 = "python3"
JINJA2 = "jinja2"
JAVASCRIPT = "javascript"

28
api/core/indexing_runner.py
View File

@ -86,7 +86,7 @@ class IndexingRunner:
except ProviderTokenNotInitError as e:
dataset_document.indexing_status = "error"
dataset_document.error = str(e.description)
dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
dataset_document.stopped_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
db.session.commit()
except ObjectDeletedError:
logging.warning("Document deleted, document id: {}".format(dataset_document.id))
@ -94,7 +94,7 @@ class IndexingRunner:
logging.exception("consume document failed")
dataset_document.indexing_status = "error"
dataset_document.error = str(e)
dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
dataset_document.stopped_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
db.session.commit()
def run_in_splitting_status(self, dataset_document: DatasetDocument):
@ -142,13 +142,13 @@ class IndexingRunner:
except ProviderTokenNotInitError as e:
dataset_document.indexing_status = "error"
dataset_document.error = str(e.description)
dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
dataset_document.stopped_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
db.session.commit()
except Exception as e:
logging.exception("consume document failed")
dataset_document.indexing_status = "error"
dataset_document.error = str(e)
dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
dataset_document.stopped_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
db.session.commit()
def run_in_indexing_status(self, dataset_document: DatasetDocument):
@ -200,13 +200,13 @@ class IndexingRunner:
except ProviderTokenNotInitError as e:
dataset_document.indexing_status = "error"
dataset_document.error = str(e.description)
dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
dataset_document.stopped_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
db.session.commit()
except Exception as e:
logging.exception("consume document failed")
dataset_document.indexing_status = "error"
dataset_document.error = str(e)
dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
dataset_document.stopped_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
db.session.commit()
def indexing_estimate(
@ -372,7 +372,7 @@ class IndexingRunner:
after_indexing_status="splitting",
extra_update_params={
DatasetDocument.word_count: sum(len(text_doc.page_content) for text_doc in text_docs),
DatasetDocument.parsing_completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
DatasetDocument.parsing_completed_at: datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
},
)
@ -464,7 +464,7 @@ class IndexingRunner:
doc_store.add_documents(documents)
# update document status to indexing
cur_time = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
cur_time = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
self._update_document_index_status(
document_id=dataset_document.id,
after_indexing_status="indexing",
@ -479,7 +479,7 @@ class IndexingRunner:
dataset_document_id=dataset_document.id,
update_params={
DocumentSegment.status: "indexing",
DocumentSegment.indexing_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
DocumentSegment.indexing_at: datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
},
)
@ -680,7 +680,7 @@ class IndexingRunner:
after_indexing_status="completed",
extra_update_params={
DatasetDocument.tokens: tokens,
DatasetDocument.completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
DatasetDocument.completed_at: datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
DatasetDocument.indexing_latency: indexing_end_at - indexing_start_at,
DatasetDocument.error: None,
},
@ -705,7 +705,7 @@ class IndexingRunner:
{
DocumentSegment.status: "completed",
DocumentSegment.enabled: True,
DocumentSegment.completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
DocumentSegment.completed_at: datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
}
)
@ -738,7 +738,7 @@ class IndexingRunner:
{
DocumentSegment.status: "completed",
DocumentSegment.enabled: True,
DocumentSegment.completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
DocumentSegment.completed_at: datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
}
)
@ -849,7 +849,7 @@ class IndexingRunner:
doc_store.add_documents(documents)
# update document status to indexing
cur_time = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
cur_time = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
self._update_document_index_status(
document_id=dataset_document.id,
after_indexing_status="indexing",
@ -864,7 +864,7 @@ class IndexingRunner:
dataset_document_id=dataset_document.id,
update_params={
DocumentSegment.status: "indexing",
DocumentSegment.indexing_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
DocumentSegment.indexing_at: datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
},
)
pass

15
api/core/memory/token_buffer_memory.py
View File

@ -1,8 +1,8 @@
from collections.abc import Sequence
from typing import Optional
from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
from core.file import file_manager
from core.file.models import FileType
from core.model_manager import ModelInstance
from core.model_runtime.entities import (
AssistantPromptMessage,
@ -27,7 +27,7 @@ class TokenBufferMemory:
def get_history_prompt_messages(
self, max_token_limit: int = 2000, message_limit: Optional[int] = None
) -> list[PromptMessage]:
) -> Sequence[PromptMessage]:
"""
Get history prompt messages.
:param max_token_limit: max token limit
@ -102,12 +102,11 @@ class TokenBufferMemory:
prompt_message_contents: list[PromptMessageContent] = []
prompt_message_contents.append(TextPromptMessageContent(data=message.query))
for file in file_objs:
if file.type in {FileType.IMAGE, FileType.AUDIO}:
prompt_message = file_manager.to_prompt_message_content(
file,
image_detail_config=detail,
)
prompt_message_contents.append(prompt_message)
prompt_message = file_manager.to_prompt_message_content(
file,
image_detail_config=detail,
)
prompt_message_contents.append(prompt_message)
prompt_messages.append(UserPromptMessage(content=prompt_message_contents))

4
api/core/model_manager.py
View File

@ -136,10 +136,10 @@ class ModelInstance:
def invoke_llm(
self,
prompt_messages: list[PromptMessage],
prompt_messages: Sequence[PromptMessage],
model_parameters: Optional[dict] = None,
tools: Sequence[PromptMessageTool] | None = None,
stop: Optional[list[str]] = None,
stop: Optional[Sequence[str]] = None,
stream: bool = True,
user: Optional[str] = None,
callbacks: Optional[list[Callback]] = None,

9
api/core/model_runtime/callbacks/base_callback.py
View File

@ -1,4 +1,5 @@
from abc import ABC, abstractmethod
from collections.abc import Sequence
from typing import Optional
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
@ -31,7 +32,7 @@ class Callback(ABC):
prompt_messages: list[PromptMessage],
model_parameters: dict,
tools: Optional[list[PromptMessageTool]] = None,
stop: Optional[list[str]] = None,
stop: Optional[Sequence[str]] = None,
stream: bool = True,
user: Optional[str] = None,
) -> None:
@ -60,7 +61,7 @@ class Callback(ABC):
prompt_messages: list[PromptMessage],
model_parameters: dict,
tools: Optional[list[PromptMessageTool]] = None,
stop: Optional[list[str]] = None,
stop: Optional[Sequence[str]] = None,
stream: bool = True,
user: Optional[str] = None,
):
@ -90,7 +91,7 @@ class Callback(ABC):
prompt_messages: list[PromptMessage],
model_parameters: dict,
tools: Optional[list[PromptMessageTool]] = None,
stop: Optional[list[str]] = None,
stop: Optional[Sequence[str]] = None,
stream: bool = True,
user: Optional[str] = None,
) -> None:
@ -120,7 +121,7 @@ class Callback(ABC):
prompt_messages: list[PromptMessage],
model_parameters: dict,
tools: Optional[list[PromptMessageTool]] = None,
stop: Optional[list[str]] = None,
stop: Optional[Sequence[str]] = None,
stream: bool = True,
user: Optional[str] = None,
) -> None:

2
api/core/model_runtime/entities/__init__.py
View File

@ -2,6 +2,7 @@ from .llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsa
from .message_entities import (
AssistantPromptMessage,
AudioPromptMessageContent,
DocumentPromptMessageContent,
ImagePromptMessageContent,
PromptMessage,
PromptMessageContent,
@ -37,4 +38,5 @@ __all__ = [
"LLMResultChunk",
"LLMResultChunkDelta",
"AudioPromptMessageContent",
"DocumentPromptMessageContent",
]

19
api/core/model_runtime/entities/message_entities.py
View File

@ -1,6 +1,7 @@
from abc import ABC
from enum import Enum
from typing import Optional
from collections.abc import Sequence
from enum import Enum, StrEnum
from typing import Literal, Optional
from pydantic import BaseModel, Field, field_validator
@ -48,7 +49,7 @@ class PromptMessageFunction(BaseModel):
function: PromptMessageTool
class PromptMessageContentType(Enum):
class PromptMessageContentType(StrEnum):
"""
Enum class for prompt message content type.
"""
@ -57,6 +58,7 @@ class PromptMessageContentType(Enum):
IMAGE = "image"
AUDIO = "audio"
VIDEO = "video"
DOCUMENT = "document"
class PromptMessageContent(BaseModel):
@ -93,7 +95,7 @@ class ImagePromptMessageContent(PromptMessageContent):
Model class for image prompt message content.
"""
class DETAIL(str, Enum):
class DETAIL(StrEnum):
LOW = "low"
HIGH = "high"
@ -101,13 +103,20 @@ class ImagePromptMessageContent(PromptMessageContent):
detail: DETAIL = DETAIL.LOW
class DocumentPromptMessageContent(PromptMessageContent):
type: PromptMessageContentType = PromptMessageContentType.DOCUMENT
encode_format: Literal["base64"]
mime_type: str
data: str
class PromptMessage(ABC, BaseModel):
"""
Model class for prompt message.
"""
role: PromptMessageRole
content: Optional[str | list[PromptMessageContent]] = None
content: Optional[str | Sequence[PromptMessageContent]] = None
name: Optional[str] = None
def is_empty(self) -> bool:

7
api/core/model_runtime/entities/model_entities.py
View File

@ -1,5 +1,5 @@
from decimal import Decimal
from enum import Enum
from enum import Enum, StrEnum
from typing import Any, Optional
from pydantic import BaseModel, ConfigDict
@ -82,9 +82,12 @@ class ModelFeature(Enum):
AGENT_THOUGHT = "agent-thought"
VISION = "vision"
STREAM_TOOL_CALL = "stream-tool-call"
DOCUMENT = "document"
VIDEO = "video"
AUDIO = "audio"
class DefaultParameterName(str, Enum):
class DefaultParameterName(StrEnum):
"""
Enum class for parameter template variable.
"""

16
api/core/model_runtime/model_providers/__base/large_language_model.py
View File

@ -1,6 +1,6 @@
import logging
import time
from collections.abc import Generator
from collections.abc import Generator, Sequence
from typing import Optional, Union
from pydantic import ConfigDict
@ -41,7 +41,7 @@ class LargeLanguageModel(AIModel):
prompt_messages: list[PromptMessage],
model_parameters: Optional[dict] = None,
tools: Optional[list[PromptMessageTool]] = None,
stop: Optional[list[str]] = None,
stop: Optional[Sequence[str]] = None,
stream: bool = True,
user: Optional[str] = None,
callbacks: Optional[list[Callback]] = None,
@ -96,7 +96,7 @@ class LargeLanguageModel(AIModel):
model_parameters=model_parameters,
prompt_messages=prompt_messages,
tools=tools,
stop=stop,
stop=list(stop) if stop else None,
stream=stream,
)
@ -176,7 +176,7 @@ class LargeLanguageModel(AIModel):
prompt_messages: list[PromptMessage],
model_parameters: dict,
tools: Optional[list[PromptMessageTool]] = None,
stop: Optional[list[str]] = None,
stop: Optional[Sequence[str]] = None,
stream: bool = True,
user: Optional[str] = None,
callbacks: Optional[list[Callback]] = None,
@ -318,7 +318,7 @@ class LargeLanguageModel(AIModel):
prompt_messages: list[PromptMessage],
model_parameters: dict,
tools: Optional[list[PromptMessageTool]] = None,
stop: Optional[list[str]] = None,
stop: Optional[Sequence[str]] = None,
stream: bool = True,
user: Optional[str] = None,
callbacks: Optional[list[Callback]] = None,
@ -364,7 +364,7 @@ class LargeLanguageModel(AIModel):
prompt_messages: list[PromptMessage],
model_parameters: dict,
tools: Optional[list[PromptMessageTool]] = None,
stop: Optional[list[str]] = None,
stop: Optional[Sequence[str]] = None,
stream: bool = True,
user: Optional[str] = None,
callbacks: Optional[list[Callback]] = None,
@ -411,7 +411,7 @@ class LargeLanguageModel(AIModel):
prompt_messages: list[PromptMessage],
model_parameters: dict,
tools: Optional[list[PromptMessageTool]] = None,
stop: Optional[list[str]] = None,
stop: Optional[Sequence[str]] = None,
stream: bool = True,
user: Optional[str] = None,
callbacks: Optional[list[Callback]] = None,
@ -459,7 +459,7 @@ class LargeLanguageModel(AIModel):
prompt_messages: list[PromptMessage],
model_parameters: dict,
tools: Optional[list[PromptMessageTool]] = None,
stop: Optional[list[str]] = None,
stop: Optional[Sequence[str]] = None,
stream: bool = True,
user: Optional[str] = None,
callbacks: Optional[list[Callback]] = None,

4
api/core/ops/entities/trace_entity.py
View File

@ -1,5 +1,5 @@
from datetime import datetime
from enum import Enum
from enum import StrEnum
from typing import Any, Optional, Union
from pydantic import BaseModel, ConfigDict, field_validator
@ -122,7 +122,7 @@ trace_info_info_map = {
}
class TraceTaskName(str, Enum):
class TraceTaskName(StrEnum):
CONVERSATION_TRACE = "conversation"
WORKFLOW_TRACE = "workflow"
MESSAGE_TRACE = "message"

6
api/core/ops/langfuse_trace/entities/langfuse_trace_entity.py
View File

@ -1,5 +1,5 @@
from datetime import datetime
from enum import Enum
from enum import StrEnum
from typing import Any, Optional, Union
from pydantic import BaseModel, ConfigDict, Field, field_validator
@ -39,7 +39,7 @@ def validate_input_output(v, field_name):
return v
class LevelEnum(str, Enum):
class LevelEnum(StrEnum):
DEBUG = "DEBUG"
WARNING = "WARNING"
ERROR = "ERROR"
@ -178,7 +178,7 @@ class LangfuseSpan(BaseModel):
return validate_input_output(v, field_name)
class UnitEnum(str, Enum):
class UnitEnum(StrEnum):
CHARACTERS = "CHARACTERS"
TOKENS = "TOKENS"
SECONDS = "SECONDS"

4
api/core/ops/langsmith_trace/entities/langsmith_trace_entity.py
View File

@ -1,5 +1,5 @@
from datetime import datetime
from enum import Enum
from enum import StrEnum
from typing import Any, Optional, Union
from pydantic import BaseModel, Field, field_validator
@ -8,7 +8,7 @@ from pydantic_core.core_schema import ValidationInfo
from core.ops.utils import replace_text_with_content
class LangSmithRunType(str, Enum):
class LangSmithRunType(StrEnum):
tool = "tool"
chain = "chain"
llm = "llm"

4
api/core/plugin/entities/bundle.py
View File

@ -1,4 +1,4 @@
from enum import Enum
from enum import StrEnum
from pydantic import BaseModel
@ -6,7 +6,7 @@ from core.plugin.entities.plugin import PluginDeclaration, PluginInstallationSou
class PluginBundleDependency(BaseModel):
class Type(str, Enum):
class Type(StrEnum):
Github = PluginInstallationSource.Github.value
Marketplace = PluginInstallationSource.Marketplace.value
Package = PluginInstallationSource.Package.value

8
api/core/plugin/entities/plugin.py
View File

@ -1,7 +1,7 @@
import datetime
import enum
import re
from collections.abc import Mapping
from enum import Enum
from typing import Any, Optional
from pydantic import BaseModel, Field, model_validator
@ -13,7 +13,7 @@ from core.tools.entities.common_entities import I18nObject
from core.tools.entities.tool_entities import ToolProviderEntity
class PluginInstallationSource(str, Enum):
class PluginInstallationSource(enum.StrEnum):
Github = "github"
Marketplace = "marketplace"
Package = "package"
@ -55,7 +55,7 @@ class PluginResourceRequirements(BaseModel):
permission: Optional[Permission]
class PluginCategory(str, Enum):
class PluginCategory(enum.StrEnum):
Tool = "tool"
Model = "model"
Extension = "extension"
@ -163,7 +163,7 @@ class GenericProviderID:
class PluginDependency(BaseModel):
class Type(str, Enum):
class Type(enum.StrEnum):
Github = PluginInstallationSource.Github.value
Marketplace = PluginInstallationSource.Marketplace.value
Package = PluginInstallationSource.Package.value

3
api/core/plugin/entities/plugin_daemon.py
View File

@ -1,3 +1,4 @@
import enum
from datetime import datetime
from enum import Enum
from typing import Generic, Optional, TypeVar
@ -119,7 +120,7 @@ class PluginDaemonInnerError(Exception):
self.message = message
class PluginInstallTaskStatus(str, Enum):
class PluginInstallTaskStatus(enum.StrEnum):
Pending = "pending"
Running = "running"
Success = "success"

10
api/core/plugin/manager/plugin.py
View File

@ -126,6 +126,16 @@ class PluginInstallationManager(BasePluginManager):
bool,
)
def delete_all_plugin_installation_task_items(self, tenant_id: str) -> bool:
"""
Delete all plugin installation task items.
"""
return self._request_with_plugin_daemon_response(
"POST",
f"plugin/{tenant_id}/management/install/tasks/delete_all",
bool,
)
def delete_plugin_installation_task_item(self, tenant_id: str, task_id: str, identifier: str) -> bool:
"""
Delete a plugin installation task item.

2
api/core/prompt/simple_prompt_transform.py
View File

@ -23,7 +23,7 @@ if TYPE_CHECKING:
from core.file.models import File
class ModelMode(str, enum.Enum):
class ModelMode(enum.StrEnum):
COMPLETION = "completion"
CHAT = "chat"

3
api/core/prompt/utils/prompt_message_util.py
View File

@ -1,3 +1,4 @@
from collections.abc import Sequence
from typing import cast
from core.model_runtime.entities import (
@ -14,7 +15,7 @@ from core.prompt.simple_prompt_transform import ModelMode
class PromptMessageUtil:
@staticmethod
def prompt_messages_to_prompt_for_saving(model_mode: str, prompt_messages: list[PromptMessage]) -> list[dict]:
def prompt_messages_to_prompt_for_saving(model_mode: str, prompt_messages: Sequence[PromptMessage]) -> list[dict]:
"""
Prompt messages to prompt for saving.
:param model_mode: model mode

2
api/core/rag/cleaner/clean_processor.py
View File

@ -12,7 +12,7 @@ class CleanProcessor:
# Unicode U+FFFE
text = re.sub("\ufffe", "", text)
rules = process_rule["rules"] if process_rule else None
rules = process_rule["rules"] if process_rule else {}
if "pre_processing_rules" in rules:
pre_processing_rules = rules["pre_processing_rules"]
for pre_processing_rule in pre_processing_rules:

4
api/core/rag/datasource/keyword/keyword_type.py
View File

@ -1,5 +1,5 @@
from enum import Enum
from enum import StrEnum
class KeyWordType(str, Enum):
class KeyWordType(StrEnum):
JIEBA = "jieba"

4
api/core/rag/datasource/vdb/vector_type.py
View File

@ -1,7 +1,7 @@
from enum import Enum
from enum import StrEnum
class VectorType(str, Enum):
class VectorType(StrEnum):
ANALYTICDB = "analyticdb"
CHROMA = "chroma"
MILVUS = "milvus"

4
api/core/rag/extractor/word_extractor.py
View File

@ -114,10 +114,10 @@ class WordExtractor(BaseExtractor):
mime_type=mime_type or "",
created_by=self.user_id,
created_by_role=CreatedByRole.ACCOUNT,
created_at=datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
created_at=datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
used=True,
used_by=self.user_id,
used_at=datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
used_at=datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
)
db.session.add(upload_file)

6
api/core/rag/rerank/rerank_model.py
View File

@ -27,11 +27,11 @@ class RerankModelRunner(BaseRerankRunner):
:return:
"""
docs = []
doc_id = set()
doc_ids = set()
unique_documents = []
for document in documents:
if document.provider == "dify" and document.metadata["doc_id"] not in doc_id:
doc_id.add(document.metadata["doc_id"])
if document.provider == "dify" and document.metadata["doc_id"] not in doc_ids:
doc_ids.add(document.metadata["doc_id"])
docs.append(document.page_content)
unique_documents.append(document)
elif document.provider == "external":

4
api/core/rag/rerank/rerank_type.py
View File

@ -1,6 +1,6 @@
from enum import Enum
from enum import StrEnum
class RerankMode(str, Enum):
class RerankMode(StrEnum):
RERANKING_MODEL = "reranking_model"
WEIGHTED_SCORE = "weighted_score"

7
api/core/rag/rerank/weight_rerank.py
View File

@ -37,11 +37,10 @@ class WeightRerankRunner(BaseRerankRunner):
:return:
"""
unique_documents = []
doc_id = set()
doc_ids = set()
for document in documents:
doc_id = document.metadata.get("doc_id")
if doc_id not in doc_id:
doc_id.add(doc_id)
if document.metadata["doc_id"] not in doc_ids:
doc_ids.add(document.metadata["doc_id"])
unique_documents.append(document)
documents = unique_documents

4
api/core/tools/builtin_tool/providers/time/tools/current_time.py
View File

@ -1,4 +1,4 @@
from datetime import datetime, timezone
from datetime import UTC, datetime
from typing import Any, Optional, Union
from pytz import timezone as pytz_timezone
@ -23,7 +23,7 @@ class CurrentTimeTool(BuiltinTool):
tz = tool_parameters.get("timezone", "UTC")
fm = tool_parameters.get("format") or "%Y-%m-%d %H:%M:%S %Z"
if tz == "UTC":
return self.create_text_message(f"{datetime.now(timezone.utc).strftime(fm)}")
return self.create_text_message(f"{datetime.now(UTC).strftime(fm)}")
try:
tz = pytz_timezone(tz)

5
api/core/tools/entities/tool_entities.py
View File

@ -1,4 +1,5 @@
import base64
import enum
from enum import Enum
from typing import Any, Optional, Union
@ -33,7 +34,7 @@ class ToolLabelEnum(Enum):
OTHER = "other"
class ToolProviderType(str, Enum):
class ToolProviderType(enum.StrEnum):
"""
Enum class for tool provider
"""
@ -205,7 +206,7 @@ class ToolParameterOption(BaseModel):
class ToolParameter(BaseModel):
class ToolParameterType(str, Enum):
class ToolParameterType(enum.StrEnum):
STRING = CommonParameterType.STRING.value
NUMBER = CommonParameterType.NUMBER.value
BOOLEAN = CommonParameterType.BOOLEAN.value

16
api/core/tools/tool_engine.py
View File

@ -1,7 +1,7 @@
import json
from collections.abc import Generator, Iterable
from copy import deepcopy
from datetime import datetime, timezone
from datetime import UTC, datetime
from mimetypes import guess_type
from typing import Any, Optional, Union, cast
@ -64,7 +64,12 @@ class ToolEngine:
if parameters and len(parameters) == 1:
tool_parameters = {parameters[0].name: tool_parameters}
else:
raise ValueError(f"tool_parameters should be a dict, but got a string: {tool_parameters}")
try:
tool_parameters = json.loads(tool_parameters)
except Exception as e:
pass
if not isinstance(tool_parameters, dict):
raise ValueError(f"tool_parameters should be a dict, but got a string: {tool_parameters}")
# invoke the tool
try:
@ -195,10 +200,7 @@ class ToolEngine:
"""
Invoke the tool with the given arguments.
"""
if not tool.runtime:
raise ValueError("missing runtime in tool")
started_at = datetime.now(timezone.utc)
started_at = datetime.now(UTC)
meta = ToolInvokeMeta(
time_cost=0.0,
error=None,
@ -216,7 +218,7 @@ class ToolEngine:
meta.error = str(e)
raise ToolEngineInvokeError(meta)
finally:
ended_at = datetime.now(timezone.utc)
ended_at = datetime.now(UTC)
meta.time_cost = (ended_at - started_at).total_seconds()
yield meta

12
api/core/variables/segments.py
View File

@ -118,11 +118,11 @@ class FileSegment(Segment):
@property
def log(self) -> str:
return str(self.value)
return ""
@property
def text(self) -> str:
return str(self.value)
return ""
class ArrayAnySegment(ArraySegment):
@ -155,3 +155,11 @@ class ArrayFileSegment(ArraySegment):
for item in self.value:
items.append(item.markdown)
return "\n".join(items)
@property
def log(self) -> str:
return ""
@property
def text(self) -> str:
return ""

4
api/core/variables/types.py
View File

@ -1,7 +1,7 @@
from enum import Enum
from enum import StrEnum
class SegmentType(str, Enum):
class SegmentType(StrEnum):
NONE = "none"
NUMBER = "number"
STRING = "string"

6
api/core/workflow/entities/node_entities.py
View File

@ -1,5 +1,5 @@
from collections.abc import Mapping
from enum import Enum
from enum import StrEnum
from typing import Any, Optional
from pydantic import BaseModel
@ -8,7 +8,7 @@ from core.model_runtime.entities.llm_entities import LLMUsage
from models.workflow import WorkflowNodeExecutionStatus
class NodeRunMetadataKey(str, Enum):
class NodeRunMetadataKey(StrEnum):
"""
Node Run Metadata Key.
"""
@ -36,7 +36,7 @@ class NodeRunResult(BaseModel):
inputs: Optional[Mapping[str, Any]] = None # node inputs
process_data: Optional[dict[str, Any]] = None # process data
outputs: Optional[dict[str, Any]] = None # node outputs
outputs: Optional[Mapping[str, Any]] = None # node outputs
metadata: Optional[dict[NodeRunMetadataKey, Any]] = None # node metadata
llm_usage: Optional[LLMUsage] = None # llm usage

4
api/core/workflow/enums.py
View File

@ -1,7 +1,7 @@
from enum import Enum
from enum import StrEnum
class SystemVariableKey(str, Enum):
class SystemVariableKey(StrEnum):
"""
System Variables.
"""

6
api/core/workflow/graph_engine/entities/runtime_route_state.py
View File

@ -1,5 +1,5 @@
import uuid
from datetime import datetime, timezone
from datetime import UTC, datetime
from enum import Enum
from typing import Optional
@ -63,7 +63,7 @@ class RouteNodeState(BaseModel):
raise Exception(f"Invalid route status {run_result.status}")
self.node_run_result = run_result
self.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
self.finished_at = datetime.now(UTC).replace(tzinfo=None)
class RuntimeRouteState(BaseModel):
@ -81,7 +81,7 @@ class RuntimeRouteState(BaseModel):
:param node_id: node id
"""
state = RouteNodeState(node_id=node_id, start_at=datetime.now(timezone.utc).replace(tzinfo=None))
state = RouteNodeState(node_id=node_id, start_at=datetime.now(UTC).replace(tzinfo=None))
self.node_state_mapping[state.id] = state
return state

4
api/core/workflow/nodes/enums.py
View File

@ -1,7 +1,7 @@
from enum import Enum
from enum import StrEnum
class NodeType(str, Enum):
class NodeType(StrEnum):
START = "start"
END = "end"
ANSWER = "answer"

2
api/core/workflow/nodes/http_request/executor.py
View File

@ -108,7 +108,7 @@ class Executor:
self.content = self.variable_pool.convert_template(data[0].value).text
case "json":
json_string = self.variable_pool.convert_template(data[0].value).text
json_object = json.loads(json_string)
json_object = json.loads(json_string, strict=False)
self.json = json_object
# self.json = self._parse_object_contains_variables(json_object)
case "binary":

4
api/core/workflow/nodes/iteration/entities.py
View File

@ -1,4 +1,4 @@
from enum import Enum
from enum import StrEnum
from typing import Any, Optional
from pydantic import Field
@ -6,7 +6,7 @@ from pydantic import Field
from core.workflow.nodes.base import BaseIterationNodeData, BaseIterationState, BaseNodeData
class ErrorHandleMode(str, Enum):
class ErrorHandleMode(StrEnum):
TERMINATED = "terminated"
CONTINUE_ON_ERROR = "continue-on-error"
REMOVE_ABNORMAL_OUTPUT = "remove-abnormal-output"

12
api/core/workflow/nodes/iteration/iteration_node.py
View File

@ -2,7 +2,7 @@ import logging
import uuid
from collections.abc import Generator, Mapping, Sequence
from concurrent.futures import Future, wait
from datetime import datetime, timezone
from datetime import UTC, datetime
from queue import Empty, Queue
from typing import TYPE_CHECKING, Any, Optional, cast
@ -135,7 +135,7 @@ class IterationNode(BaseNode[IterationNodeData]):
thread_pool_id=self.thread_pool_id,
)
start_at = datetime.now(timezone.utc).replace(tzinfo=None)
start_at = datetime.now(UTC).replace(tzinfo=None)
yield IterationRunStartedEvent(
iteration_id=self.id,
@ -367,7 +367,7 @@ class IterationNode(BaseNode[IterationNodeData]):
"""
run single iteration
"""
iter_start_at = datetime.now(timezone.utc).replace(tzinfo=None)
iter_start_at = datetime.now(UTC).replace(tzinfo=None)
try:
rst = graph_engine.run()
@ -440,7 +440,7 @@ class IterationNode(BaseNode[IterationNodeData]):
variable_pool.add([self.node_id, "index"], next_index)
if next_index < len(iterator_list_value):
variable_pool.add([self.node_id, "item"], iterator_list_value[next_index])
duration = (datetime.now(timezone.utc).replace(tzinfo=None) - iter_start_at).total_seconds()
duration = (datetime.now(UTC).replace(tzinfo=None) - iter_start_at).total_seconds()
iter_run_map[iteration_run_id] = duration
yield IterationRunNextEvent(
iteration_id=self.id,
@ -461,7 +461,7 @@ class IterationNode(BaseNode[IterationNodeData]):
if next_index < len(iterator_list_value):
variable_pool.add([self.node_id, "item"], iterator_list_value[next_index])
duration = (datetime.now(timezone.utc).replace(tzinfo=None) - iter_start_at).total_seconds()
duration = (datetime.now(UTC).replace(tzinfo=None) - iter_start_at).total_seconds()
iter_run_map[iteration_run_id] = duration
yield IterationRunNextEvent(
iteration_id=self.id,
@ -503,7 +503,7 @@ class IterationNode(BaseNode[IterationNodeData]):
if next_index < len(iterator_list_value):
variable_pool.add([self.node_id, "item"], iterator_list_value[next_index])
duration = (datetime.now(timezone.utc).replace(tzinfo=None) - iter_start_at).total_seconds()
duration = (datetime.now(UTC).replace(tzinfo=None) - iter_start_at).total_seconds()
iter_run_map[iteration_run_id] = duration
yield IterationRunNextEvent(
iteration_id=self.id,

18
api/core/workflow/nodes/llm/entities.py
View File

@ -39,7 +39,14 @@ class VisionConfig(BaseModel):
class PromptConfig(BaseModel):
jinja2_variables: Optional[list[VariableSelector]] = None
jinja2_variables: Sequence[VariableSelector] = Field(default_factory=list)
@field_validator("jinja2_variables", mode="before")
@classmethod
def convert_none_jinja2_variables(cls, v: Any):
if v is None:
return []
return v
class LLMNodeChatModelMessage(ChatModelMessage):
@ -53,7 +60,14 @@ class LLMNodeCompletionModelPromptTemplate(CompletionModelPromptTemplate):
class LLMNodeData(BaseNodeData):
model: ModelConfig
prompt_template: Sequence[LLMNodeChatModelMessage] | LLMNodeCompletionModelPromptTemplate
prompt_config: Optional[PromptConfig] = None
prompt_config: PromptConfig = Field(default_factory=PromptConfig)
memory: Optional[MemoryConfig] = None
context: ContextConfig
vision: VisionConfig = Field(default_factory=VisionConfig)
@field_validator("prompt_config", mode="before")
@classmethod
def convert_none_prompt_config(cls, v: Any):
if v is None:
return PromptConfig()
return v

14
api/core/workflow/nodes/llm/exc.py
View File

@ -24,3 +24,17 @@ class LLMModeRequiredError(LLMNodeError):
class NoPromptFoundError(LLMNodeError):
"""Raised when no prompt is found in the LLM configuration."""
class TemplateTypeNotSupportError(LLMNodeError):
def __init__(self, *, type_name: str):
super().__init__(f"Prompt type {type_name} is not supported.")
class MemoryRolePrefixRequiredError(LLMNodeError):
"""Raised when memory role prefix is required for completion model."""
class FileTypeNotSupportError(LLMNodeError):
def __init__(self, *, type_name: str):
super().__init__(f"{type_name} type is not supported by this model")

418
api/core/workflow/nodes/llm/node.py
View File

@ -1,4 +1,5 @@
import json
import logging
from collections.abc import Generator, Mapping, Sequence
from typing import TYPE_CHECKING, Any, Optional, cast
@ -6,21 +7,26 @@ from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEnti
from core.entities.model_entities import ModelStatus
from core.entities.provider_entities import QuotaUnit
from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
from core.file import FileType, file_manager
from core.helper.code_executor import CodeExecutor, CodeLanguage
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_manager import ModelInstance, ModelManager
from core.model_runtime.entities import (
AudioPromptMessageContent,
ImagePromptMessageContent,
PromptMessage,
PromptMessageContentType,
TextPromptMessageContent,
VideoPromptMessageContent,
)
from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
from core.model_runtime.entities.model_entities import ModelType
from core.model_runtime.entities.message_entities import (
AssistantPromptMessage,
PromptMessageRole,
SystemPromptMessage,
UserPromptMessage,
)
from core.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey, ModelType
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.model_runtime.utils.encoders import jsonable_encoder
from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
from core.prompt.entities.advanced_prompt_entities import CompletionModelPromptTemplate, MemoryConfig
from core.prompt.utils.prompt_message_util import PromptMessageUtil
from core.variables import (
@ -34,6 +40,8 @@ from core.variables import (
)
from core.workflow.constants import SYSTEM_VARIABLE_NODE_ID
from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult
from core.workflow.entities.variable_entities import VariableSelector
from core.workflow.entities.variable_pool import VariablePool
from core.workflow.enums import SystemVariableKey
from core.workflow.graph_engine.entities.event import InNodeEvent
from core.workflow.nodes.base import BaseNode
@ -58,18 +66,23 @@ from .entities import (
ModelConfig,
)
from .exc import (
FileTypeNotSupportError,
InvalidContextStructureError,
InvalidVariableTypeError,
LLMModeRequiredError,
LLMNodeError,
MemoryRolePrefixRequiredError,
ModelNotExistError,
NoPromptFoundError,
TemplateTypeNotSupportError,
VariableNotFoundError,
)
if TYPE_CHECKING:
from core.file.models import File
logger = logging.getLogger(__name__)
class LLMNode(BaseNode[LLMNodeData]):
_node_data_cls = LLMNodeData
@ -121,19 +134,19 @@ class LLMNode(BaseNode[LLMNodeData]):
# fetch memory
memory = self._fetch_memory(node_data_memory=self.node_data.memory, model_instance=model_instance)
# fetch prompt messages
query = None
if self.node_data.memory:
query = self.graph_runtime_state.variable_pool.get((SYSTEM_VARIABLE_NODE_ID, SystemVariableKey.QUERY))
if not query:
raise VariableNotFoundError("Query not found")
query = query.text
else:
query = None
query = self.node_data.memory.query_prompt_template
if query is None and (
query_variable := self.graph_runtime_state.variable_pool.get(
(SYSTEM_VARIABLE_NODE_ID, SystemVariableKey.QUERY)
)
):
query = query_variable.text
prompt_messages, stop = self._fetch_prompt_messages(
system_query=query,
inputs=inputs,
files=files,
user_query=query,
user_files=files,
context=context,
memory=memory,
model_config=model_config,
@ -141,6 +154,8 @@ class LLMNode(BaseNode[LLMNodeData]):
memory_config=self.node_data.memory,
vision_enabled=self.node_data.vision.enabled,
vision_detail=self.node_data.vision.configs.detail,
variable_pool=self.graph_runtime_state.variable_pool,
jinja2_variables=self.node_data.prompt_config.jinja2_variables,
)
process_data = {
@ -181,6 +196,17 @@ class LLMNode(BaseNode[LLMNodeData]):
)
)
return
except Exception as e:
logger.exception(f"Node {self.node_id} failed to run")
yield RunCompletedEvent(
run_result=NodeRunResult(
status=WorkflowNodeExecutionStatus.FAILED,
error=str(e),
inputs=node_inputs,
process_data=process_data,
)
)
return
outputs = {"text": result_text, "usage": jsonable_encoder(usage), "finish_reason": finish_reason}
@ -203,8 +229,8 @@ class LLMNode(BaseNode[LLMNodeData]):
self,
node_data_model: ModelConfig,
model_instance: ModelInstance,
prompt_messages: list[PromptMessage],
stop: Optional[list[str]] = None,
prompt_messages: Sequence[PromptMessage],
stop: Optional[Sequence[str]] = None,
) -> Generator[NodeEvent, None, None]:
db.session.close()
@ -519,9 +545,8 @@ class LLMNode(BaseNode[LLMNodeData]):
def _fetch_prompt_messages(
self,
*,
system_query: str | None = None,
inputs: dict[str, str] | None = None,
files: Sequence["File"],
user_query: str | None = None,
user_files: Sequence["File"],
context: str | None = None,
memory: TokenBufferMemory | None = None,
model_config: ModelConfigWithCredentialsEntity,
@ -529,58 +554,144 @@ class LLMNode(BaseNode[LLMNodeData]):
memory_config: MemoryConfig | None = None,
vision_enabled: bool = False,
vision_detail: ImagePromptMessageContent.DETAIL,
) -> tuple[list[PromptMessage], Optional[list[str]]]:
inputs = inputs or {}
variable_pool: VariablePool,
jinja2_variables: Sequence[VariableSelector],
) -> tuple[Sequence[PromptMessage], Optional[Sequence[str]]]:
prompt_messages = []
prompt_transform = AdvancedPromptTransform(with_variable_tmpl=True)
prompt_messages = prompt_transform.get_prompt(
prompt_template=prompt_template,
inputs=inputs,
query=system_query or "",
files=files,
context=context,
memory_config=memory_config,
memory=memory,
model_config=model_config,
)
stop = model_config.stop
if isinstance(prompt_template, list):
# For chat model
prompt_messages.extend(
_handle_list_messages(
messages=prompt_template,
context=context,
jinja2_variables=jinja2_variables,
variable_pool=variable_pool,
vision_detail_config=vision_detail,
)
)
# Get memory messages for chat mode
memory_messages = _handle_memory_chat_mode(
memory=memory,
memory_config=memory_config,
model_config=model_config,
)
# Extend prompt_messages with memory messages
prompt_messages.extend(memory_messages)
# Add current query to the prompt messages
if user_query:
message = LLMNodeChatModelMessage(
text=user_query,
role=PromptMessageRole.USER,
edition_type="basic",
)
prompt_messages.extend(
_handle_list_messages(
messages=[message],
context="",
jinja2_variables=[],
variable_pool=variable_pool,
vision_detail_config=vision_detail,
)
)
elif isinstance(prompt_template, LLMNodeCompletionModelPromptTemplate):
# For completion model
prompt_messages.extend(
_handle_completion_template(
template=prompt_template,
context=context,
jinja2_variables=jinja2_variables,
variable_pool=variable_pool,
)
)
# Get memory text for completion model
memory_text = _handle_memory_completion_mode(
memory=memory,
memory_config=memory_config,
model_config=model_config,
)
# Insert histories into the prompt
prompt_content = prompt_messages[0].content
if "#histories#" in prompt_content:
prompt_content = prompt_content.replace("#histories#", memory_text)
else:
prompt_content = memory_text + "\n" + prompt_content
prompt_messages[0].content = prompt_content
# Add current query to the prompt message
if user_query:
prompt_content = prompt_messages[0].content.replace("#sys.query#", user_query)
prompt_messages[0].content = prompt_content
else:
raise TemplateTypeNotSupportError(type_name=str(type(prompt_template)))
if vision_enabled and user_files:
file_prompts = []
for file in user_files:
file_prompt = file_manager.to_prompt_message_content(file, image_detail_config=vision_detail)
file_prompts.append(file_prompt)
if (
len(prompt_messages) > 0
and isinstance(prompt_messages[-1], UserPromptMessage)
and isinstance(prompt_messages[-1].content, list)
):
prompt_messages[-1] = UserPromptMessage(content=prompt_messages[-1].content + file_prompts)
else:
prompt_messages.append(UserPromptMessage(content=file_prompts))
# Filter prompt messages
filtered_prompt_messages = []
for prompt_message in prompt_messages:
if prompt_message.is_empty():
continue
if not isinstance(prompt_message.content, str):
if isinstance(prompt_message.content, list):
prompt_message_content = []
for content_item in prompt_message.content or []:
# Skip image if vision is disabled
if not vision_enabled and content_item.type == PromptMessageContentType.IMAGE:
for content_item in prompt_message.content:
# Skip content if features are not defined
if not model_config.model_schema.features:
if content_item.type != PromptMessageContentType.TEXT:
continue
prompt_message_content.append(content_item)
continue
if isinstance(content_item, ImagePromptMessageContent):
# Override vision config if LLM node has vision config,
# cuz vision detail is related to the configuration from FileUpload feature.
content_item.detail = vision_detail
prompt_message_content.append(content_item)
elif isinstance(
content_item, TextPromptMessageContent | AudioPromptMessageContent | VideoPromptMessageContent
# Skip content if corresponding feature is not supported
if (
(
content_item.type == PromptMessageContentType.IMAGE
and ModelFeature.VISION not in model_config.model_schema.features
)
or (
content_item.type == PromptMessageContentType.DOCUMENT
and ModelFeature.DOCUMENT not in model_config.model_schema.features
)
or (
content_item.type == PromptMessageContentType.VIDEO
and ModelFeature.VIDEO not in model_config.model_schema.features
)
or (
content_item.type == PromptMessageContentType.AUDIO
and ModelFeature.AUDIO not in model_config.model_schema.features
)
):
prompt_message_content.append(content_item)
if len(prompt_message_content) > 1:
prompt_message.content = prompt_message_content
elif (
len(prompt_message_content) == 1 and prompt_message_content[0].type == PromptMessageContentType.TEXT
):
raise FileTypeNotSupportError(type_name=content_item.type)
prompt_message_content.append(content_item)
if len(prompt_message_content) == 1 and prompt_message_content[0].type == PromptMessageContentType.TEXT:
prompt_message.content = prompt_message_content[0].data
else:
prompt_message.content = prompt_message_content
if prompt_message.is_empty():
continue
filtered_prompt_messages.append(prompt_message)
if not filtered_prompt_messages:
if len(filtered_prompt_messages) == 0:
raise NoPromptFoundError(
"No prompt found in the LLM configuration. "
"Please ensure a prompt is properly configured before proceeding."
)
stop = model_config.stop
return filtered_prompt_messages, stop
@classmethod
@ -715,3 +826,198 @@ class LLMNode(BaseNode[LLMNodeData]):
}
},
}
def _combine_text_message_with_role(*, text: str, role: PromptMessageRole):
match role:
case PromptMessageRole.USER:
return UserPromptMessage(content=[TextPromptMessageContent(data=text)])
case PromptMessageRole.ASSISTANT:
return AssistantPromptMessage(content=[TextPromptMessageContent(data=text)])
case PromptMessageRole.SYSTEM:
return SystemPromptMessage(content=[TextPromptMessageContent(data=text)])
raise NotImplementedError(f"Role {role} is not supported")
def _render_jinja2_message(
*,
template: str,
jinjia2_variables: Sequence[VariableSelector],
variable_pool: VariablePool,
):
if not template:
return ""
jinjia2_inputs = {}
for jinja2_variable in jinjia2_variables:
variable = variable_pool.get(jinja2_variable.value_selector)
jinjia2_inputs[jinja2_variable.variable] = variable.to_object() if variable else ""
code_execute_resp = CodeExecutor.execute_workflow_code_template(
language=CodeLanguage.JINJA2,
code=template,
inputs=jinjia2_inputs,
)
result_text = code_execute_resp["result"]
return result_text
def _handle_list_messages(
*,
messages: Sequence[LLMNodeChatModelMessage],
context: Optional[str],
jinja2_variables: Sequence[VariableSelector],
variable_pool: VariablePool,
vision_detail_config: ImagePromptMessageContent.DETAIL,
) -> Sequence[PromptMessage]:
prompt_messages = []
for message in messages:
if message.edition_type == "jinja2":
result_text = _render_jinja2_message(
template=message.jinja2_text or "",
jinjia2_variables=jinja2_variables,
variable_pool=variable_pool,
)
prompt_message = _combine_text_message_with_role(text=result_text, role=message.role)
prompt_messages.append(prompt_message)
else:
# Get segment group from basic message
if context:
template = message.text.replace("{#context#}", context)
else:
template = message.text
segment_group = variable_pool.convert_template(template)
# Process segments for images
file_contents = []
for segment in segment_group.value:
if isinstance(segment, ArrayFileSegment):
for file in segment.value:
if file.type in {FileType.IMAGE, FileType.VIDEO, FileType.AUDIO, FileType.DOCUMENT}:
file_content = file_manager.to_prompt_message_content(
file, image_detail_config=vision_detail_config
)
file_contents.append(file_content)
if isinstance(segment, FileSegment):
file = segment.value
if file.type in {FileType.IMAGE, FileType.VIDEO, FileType.AUDIO, FileType.DOCUMENT}:
file_content = file_manager.to_prompt_message_content(
file, image_detail_config=vision_detail_config
)
file_contents.append(file_content)
# Create message with text from all segments
plain_text = segment_group.text
if plain_text:
prompt_message = _combine_text_message_with_role(text=plain_text, role=message.role)
prompt_messages.append(prompt_message)
if file_contents:
# Create message with image contents
prompt_message = UserPromptMessage(content=file_contents)
prompt_messages.append(prompt_message)
return prompt_messages
def _calculate_rest_token(
*, prompt_messages: list[PromptMessage], model_config: ModelConfigWithCredentialsEntity
) -> int:
rest_tokens = 2000
model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
if model_context_tokens:
model_instance = ModelInstance(
provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
)
curr_message_tokens = model_instance.get_llm_num_tokens(prompt_messages)
max_tokens = 0
for parameter_rule in model_config.model_schema.parameter_rules:
if parameter_rule.name == "max_tokens" or (
parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
):
max_tokens = (
model_config.parameters.get(parameter_rule.name)
or model_config.parameters.get(str(parameter_rule.use_template))
or 0
)
rest_tokens = model_context_tokens - max_tokens - curr_message_tokens
rest_tokens = max(rest_tokens, 0)
return rest_tokens
def _handle_memory_chat_mode(
*,
memory: TokenBufferMemory | None,
memory_config: MemoryConfig | None,
model_config: ModelConfigWithCredentialsEntity,
) -> Sequence[PromptMessage]:
memory_messages = []
# Get messages from memory for chat model
if memory and memory_config:
rest_tokens = _calculate_rest_token(prompt_messages=[], model_config=model_config)
memory_messages = memory.get_history_prompt_messages(
max_token_limit=rest_tokens,
message_limit=memory_config.window.size if memory_config.window.enabled else None,
)
return memory_messages
def _handle_memory_completion_mode(
*,
memory: TokenBufferMemory | None,
memory_config: MemoryConfig | None,
model_config: ModelConfigWithCredentialsEntity,
) -> str:
memory_text = ""
# Get history text from memory for completion model
if memory and memory_config:
rest_tokens = _calculate_rest_token(prompt_messages=[], model_config=model_config)
if not memory_config.role_prefix:
raise MemoryRolePrefixRequiredError("Memory role prefix is required for completion model.")
memory_text = memory.get_history_prompt_text(
max_token_limit=rest_tokens,
message_limit=memory_config.window.size if memory_config.window.enabled else None,
human_prefix=memory_config.role_prefix.user,
ai_prefix=memory_config.role_prefix.assistant,
)
return memory_text
def _handle_completion_template(
*,
template: LLMNodeCompletionModelPromptTemplate,
context: Optional[str],
jinja2_variables: Sequence[VariableSelector],
variable_pool: VariablePool,
) -> Sequence[PromptMessage]:
"""Handle completion template processing outside of LLMNode class.
Args:
template: The completion model prompt template
context: Optional context string
jinja2_variables: Variables for jinja2 template rendering
variable_pool: Variable pool for template conversion
Returns:
Sequence of prompt messages
"""
prompt_messages = []
if template.edition_type == "jinja2":
result_text = _render_jinja2_message(
template=template.jinja2_text or "",
jinjia2_variables=jinja2_variables,
variable_pool=variable_pool,
)
else:
if context:
template_text = template.text.replace("{#context#}", context)
else:
template_text = template.text
result_text = variable_pool.convert_template(template_text).text
prompt_message = _combine_text_message_with_role(text=result_text, role=PromptMessageRole.USER)
prompt_messages.append(prompt_message)
return prompt_messages

6
api/core/workflow/nodes/question_classifier/question_classifier_node.py
View File

@ -89,12 +89,14 @@ class QuestionClassifierNode(LLMNode):
)
prompt_messages, stop = self._fetch_prompt_messages(
prompt_template=prompt_template,
system_query=query,
user_query=query,
memory=memory,
model_config=model_config,
files=files,
user_files=files,
vision_enabled=node_data.vision.enabled,
vision_detail=node_data.vision.configs.detail,
variable_pool=variable_pool,
jinja2_variables=[],
)
# handle invoke result

4
api/core/workflow/nodes/variable_assigner/node_data.py
View File

@ -1,11 +1,11 @@
from collections.abc import Sequence
from enum import Enum
from enum import StrEnum
from typing import Optional
from core.workflow.nodes.base import BaseNodeData
class WriteMode(str, Enum):
class WriteMode(StrEnum):
OVER_WRITE = "over-write"
APPEND = "append"
CLEAR = "clear"

97
api/core/workflow/workflow_entry.py
View File

@ -5,10 +5,9 @@ from collections.abc import Generator, Mapping, Sequence
from typing import Any, Optional, cast
from configs import dify_config
from core.app.app_config.entities import FileUploadConfig
from core.app.apps.base_app_queue_manager import GenerateTaskStoppedError
from core.app.entities.app_invoke_entities import InvokeFrom
from core.file.models import File, FileTransferMethod, ImageConfig
from core.file.models import File
from core.workflow.callbacks import WorkflowCallback
from core.workflow.entities.variable_pool import VariablePool
from core.workflow.errors import WorkflowNodeRunFailedError
@ -18,9 +17,8 @@ from core.workflow.graph_engine.entities.graph_init_params import GraphInitParam
from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
from core.workflow.graph_engine.graph_engine import GraphEngine
from core.workflow.nodes import NodeType
from core.workflow.nodes.base import BaseNode, BaseNodeData
from core.workflow.nodes.base import BaseNode
from core.workflow.nodes.event import NodeEvent
from core.workflow.nodes.llm import LLMNodeData
from core.workflow.nodes.node_mapping import node_type_classes_mapping
from factories import file_factory
from models.enums import UserFrom
@ -115,7 +113,12 @@ class WorkflowEntry:
@classmethod
def single_step_run(
cls, workflow: Workflow, node_id: str, user_id: str, user_inputs: dict
cls,
*,
workflow: Workflow,
node_id: str,
user_id: str,
user_inputs: dict,
) -> tuple[BaseNode, Generator[NodeEvent | InNodeEvent, None, None]]:
"""
Single step run workflow node
@ -135,13 +138,9 @@ class WorkflowEntry:
raise ValueError("nodes not found in workflow graph")
# fetch node config from node id
node_config = None
for node in nodes:
if node.get("id") == node_id:
node_config = node
break
if not node_config:
try:
node_config = next(filter(lambda node: node["id"] == node_id, nodes))
except StopIteration:
raise ValueError("node id not found in workflow graph")
# Get node class
@ -153,11 +152,7 @@ class WorkflowEntry:
raise ValueError(f"Node class not found for node type {node_type}")
# init variable pool
variable_pool = VariablePool(
system_variables={},
user_inputs={},
environment_variables=workflow.environment_variables,
)
variable_pool = VariablePool(environment_variables=workflow.environment_variables)
# init graph
graph = Graph.init(graph_config=workflow.graph_dict)
@ -183,28 +178,24 @@ class WorkflowEntry:
try:
# variable selector to variable mapping
try:
variable_mapping = node_cls.extract_variable_selector_to_variable_mapping(
graph_config=workflow.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=workflow.tenant_id,
node_type=node_type,
node_data=node_instance.node_data,
variable_mapping = node_cls.extract_variable_selector_to_variable_mapping(
graph_config=workflow.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=workflow.tenant_id,
)
try:
# run node
generator = node_instance.run()
return node_instance, generator
except Exception as e:
raise WorkflowNodeRunFailedError(node_instance=node_instance, error=str(e))
return node_instance, generator
@classmethod
def run_free_node(
@ -332,12 +323,11 @@ class WorkflowEntry:
@classmethod
def mapping_user_inputs_to_variable_pool(
cls,
*,
variable_mapping: Mapping[str, Sequence[str]],
user_inputs: dict,
variable_pool: VariablePool,
tenant_id: str,
node_type: NodeType,
node_data: BaseNodeData,
) -> None:
for node_variable, variable_selector in variable_mapping.items():
# fetch node id and variable key from node_variable
@ -355,40 +345,21 @@ class WorkflowEntry:
# fetch variable node id from variable selector
variable_node_id = variable_selector[0]
variable_key_list = variable_selector[1:]
variable_key_list = cast(list[str], variable_key_list)
variable_key_list = list(variable_key_list)
# get input value
input_value = user_inputs.get(node_variable)
if not input_value:
input_value = user_inputs.get(node_variable_key)
# FIXME: temp fix for image type
if node_type == NodeType.LLM:
new_value = []
if isinstance(input_value, list):
node_data = cast(LLMNodeData, node_data)
detail = node_data.vision.configs.detail if node_data.vision.configs else None
for item in input_value:
if isinstance(item, dict) and "type" in item and item["type"] == "image":
transfer_method = FileTransferMethod.value_of(item.get("transfer_method"))
mapping = {
"id": item.get("id"),
"transfer_method": transfer_method,
"upload_file_id": item.get("upload_file_id"),
"url": item.get("url"),
}
config = FileUploadConfig(image_config=ImageConfig(detail=detail) if detail else None)
file = file_factory.build_from_mapping(
mapping=mapping,
tenant_id=tenant_id,
config=config,
)
new_value.append(file)
if new_value:
input_value = new_value
if isinstance(input_value, dict) and "type" in input_value and "transfer_method" in input_value:
input_value = file_factory.build_from_mapping(mapping=input_value, tenant_id=tenant_id)
if (
isinstance(input_value, list)
and all(isinstance(item, dict) for item in input_value)
and all("type" in item and "transfer_method" in item for item in input_value)
):
input_value = file_factory.build_from_mappings(mappings=input_value, tenant_id=tenant_id)
# append variable and value to variable pool
variable_pool.add([variable_node_id] + variable_key_list, input_value)