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chore(api): adapt Graphon 0.2.2 upgrade (#35377)

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Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
This commit is contained in:
99
2026-04-18 19:16:24 +08:00
committed by GitHub
parent ae9c4244d6
commit 3e876e173a
134 changed files with 2154 additions and 1134 deletions
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2
api/core/agent/base_agent_runner.py
View File

@ -42,7 +42,7 @@ from graphon.model_runtime.entities import (
)
from graphon.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
from graphon.model_runtime.entities.model_entities import ModelFeature
from graphon.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from graphon.model_runtime.model_providers.base.large_language_model import LargeLanguageModel
from models.enums import CreatorUserRole
from models.model import Conversation, Message, MessageAgentThought, MessageFile

2
api/core/app/app_config/easy_ui_based_app/model_config/converter.py
View File

@ -7,7 +7,7 @@ from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotIni
from core.plugin.impl.model_runtime_factory import create_plugin_provider_manager
from graphon.model_runtime.entities.llm_entities import LLMMode
from graphon.model_runtime.entities.model_entities import ModelPropertyKey, ModelType
from graphon.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from graphon.model_runtime.model_providers.base.large_language_model import LargeLanguageModel
class ModelConfigConverter:

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

@ -18,7 +18,7 @@ from core.moderation.base import ModerationError
from extensions.ext_database import db
from graphon.model_runtime.entities.llm_entities import LLMMode
from graphon.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey
from graphon.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from graphon.model_runtime.model_providers.base.large_language_model import LargeLanguageModel
from models.model import App, Conversation, Message
logger = logging.getLogger(__name__)

2
api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py
View File

@ -59,7 +59,7 @@ from graphon.model_runtime.entities.message_entities import (
AssistantPromptMessage,
TextPromptMessageContent,
)
from graphon.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from graphon.model_runtime.model_providers.base.large_language_model import LargeLanguageModel
from libs.datetime_utils import naive_utc_now
from models.model import AppMode, Conversation, Message, MessageAgentThought, MessageFile, UploadFile

7
api/core/app/workflow/file_runtime.py
View File

@ -12,13 +12,14 @@ from typing import TYPE_CHECKING, Literal
from configs import dify_config
from core.app.file_access import DatabaseFileAccessController, FileAccessControllerProtocol
from core.db.session_factory import session_factory
from core.helper.ssrf_proxy import ssrf_proxy
from core.helper.ssrf_proxy import graphon_ssrf_proxy
from core.tools.signature import sign_tool_file
from core.workflow.file_reference import parse_file_reference
from extensions.ext_storage import storage
from graphon.file import FileTransferMethod
from graphon.file.protocols import HttpResponseProtocol, WorkflowFileRuntimeProtocol
from graphon.file.protocols import WorkflowFileRuntimeProtocol
from graphon.file.runtime import set_workflow_file_runtime
from graphon.http.protocols import HttpResponseProtocol
if TYPE_CHECKING:
from graphon.file import File
@ -43,7 +44,7 @@ class DifyWorkflowFileRuntime(WorkflowFileRuntimeProtocol):
return dify_config.MULTIMODAL_SEND_FORMAT
def http_get(self, url: str, *, follow_redirects: bool = True) -> HttpResponseProtocol:
return ssrf_proxy.get(url, follow_redirects=follow_redirects)
return graphon_ssrf_proxy.get(url, follow_redirects=follow_redirects)
def storage_load(self, path: str, *, stream: bool = False) -> bytes | Generator:
return storage.load(path, stream=stream)

2
api/core/app/workflow/layers/persistence.py
View File

@ -349,7 +349,7 @@ class WorkflowPersistenceLayer(GraphEngineLayer):
execution.total_tokens = runtime_state.total_tokens
execution.total_steps = runtime_state.node_run_steps
execution.outputs = execution.outputs or runtime_state.outputs
execution.exceptions_count = runtime_state.exceptions_count
execution.exceptions_count = max(execution.exceptions_count, runtime_state.exceptions_count)
def _update_node_execution(
self,

4
api/core/datasource/datasource_manager.py
View File

@ -352,11 +352,11 @@ class DatasourceManager:
raise ValueError(f"UploadFile not found for file_id={file_id}, tenant_id={tenant_id}")
file_info = File(
id=upload_file.id,
file_id=upload_file.id,
filename=upload_file.name,
extension="." + upload_file.extension,
mime_type=upload_file.mime_type,
type=FileType.CUSTOM,
file_type=FileType.CUSTOM,
transfer_method=FileTransferMethod.LOCAL_FILE,
remote_url=upload_file.source_url,
reference=build_file_reference(record_id=str(upload_file.id)),

6
api/core/entities/provider_configuration.py
View File

@ -31,7 +31,7 @@ from graphon.model_runtime.entities.provider_entities import (
FormType,
ProviderEntity,
)
from graphon.model_runtime.model_providers.__base.ai_model import AIModel
from graphon.model_runtime.model_providers.base.ai_model import AIModel
from graphon.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
from graphon.model_runtime.runtime import ModelRuntime
from libs.datetime_utils import naive_utc_now
@ -363,7 +363,7 @@ class ProviderConfiguration(BaseModel):
)
for key, value in validated_credentials.items():
if key in provider_credential_secret_variables:
if key in provider_credential_secret_variables and isinstance(value, str):
validated_credentials[key] = encrypter.encrypt_token(self.tenant_id, value)
return validated_credentials
@ -912,7 +912,7 @@ class ProviderConfiguration(BaseModel):
)
for key, value in validated_credentials.items():
if key in provider_credential_secret_variables:
if key in provider_credential_secret_variables and isinstance(value, str):
validated_credentials[key] = encrypter.encrypt_token(self.tenant_id, value)
return validated_credentials

2
api/core/helper/code_executor/template_transformer.py
View File

@ -102,7 +102,7 @@ class TemplateTransformer(ABC):
@classmethod
def serialize_inputs(cls, inputs: Mapping[str, Any]) -> str:
inputs_json_str = dumps_with_segments(inputs, ensure_ascii=False).encode()
inputs_json_str = dumps_with_segments(inputs).encode()
input_base64_encoded = b64encode(inputs_json_str).decode("utf-8")
return input_base64_encoded

2
api/core/helper/moderation.py
View File

@ -8,7 +8,7 @@ from core.plugin.impl.model_runtime_factory import create_plugin_model_provider_
from extensions.ext_hosting_provider import hosting_configuration
from graphon.model_runtime.entities.model_entities import ModelType
from graphon.model_runtime.errors.invoke import InvokeBadRequestError
from graphon.model_runtime.model_providers.__base.moderation_model import ModerationModel
from graphon.model_runtime.model_providers.base.moderation_model import ModerationModel
from models.provider import ProviderType
logger = logging.getLogger(__name__)

44
api/core/helper/ssrf_proxy.py
View File

@ -12,6 +12,7 @@ from pydantic import TypeAdapter, ValidationError
from configs import dify_config
from core.helper.http_client_pooling import get_pooled_http_client
from core.tools.errors import ToolSSRFError
from graphon.http.response import HttpResponse
logger = logging.getLogger(__name__)
@ -267,4 +268,47 @@ class SSRFProxy:
return patch(url=url, max_retries=max_retries, **kwargs)
def _to_graphon_http_response(response: httpx.Response) -> HttpResponse:
"""Convert an ``httpx`` response into Graphon's transport-agnostic wrapper."""
return HttpResponse(
status_code=response.status_code,
headers=dict(response.headers),
content=response.content,
url=str(response.url) if response.url else None,
reason_phrase=response.reason_phrase,
fallback_text=response.text,
)
class GraphonSSRFProxy:
"""Adapter exposing SSRF helpers behind Graphon's ``HttpClientProtocol``."""
@property
def max_retries_exceeded_error(self) -> type[Exception]:
return max_retries_exceeded_error
@property
def request_error(self) -> type[Exception]:
return request_error
def get(self, url: str, max_retries: int = SSRF_DEFAULT_MAX_RETRIES, **kwargs: Any) -> HttpResponse:
return _to_graphon_http_response(get(url=url, max_retries=max_retries, **kwargs))
def head(self, url: str, max_retries: int = SSRF_DEFAULT_MAX_RETRIES, **kwargs: Any) -> HttpResponse:
return _to_graphon_http_response(head(url=url, max_retries=max_retries, **kwargs))
def post(self, url: str, max_retries: int = SSRF_DEFAULT_MAX_RETRIES, **kwargs: Any) -> HttpResponse:
return _to_graphon_http_response(post(url=url, max_retries=max_retries, **kwargs))
def put(self, url: str, max_retries: int = SSRF_DEFAULT_MAX_RETRIES, **kwargs: Any) -> HttpResponse:
return _to_graphon_http_response(put(url=url, max_retries=max_retries, **kwargs))
def delete(self, url: str, max_retries: int = SSRF_DEFAULT_MAX_RETRIES, **kwargs: Any) -> HttpResponse:
return _to_graphon_http_response(delete(url=url, max_retries=max_retries, **kwargs))
def patch(self, url: str, max_retries: int = SSRF_DEFAULT_MAX_RETRIES, **kwargs: Any) -> HttpResponse:
return _to_graphon_http_response(patch(url=url, max_retries=max_retries, **kwargs))
ssrf_proxy = SSRFProxy()
graphon_ssrf_proxy = GraphonSSRFProxy()

38
api/core/model_manager.py
View File

@ -1,6 +1,6 @@
import logging
from collections.abc import Callable, Generator, Iterable, Mapping, Sequence
from typing import IO, Any, Literal, Optional, Union, cast, overload
from typing import IO, Any, Literal, Optional, ParamSpec, TypeVar, Union, cast, overload
from configs import dify_config
from core.entities import PluginCredentialType
@ -18,15 +18,17 @@ from graphon.model_runtime.entities.model_entities import AIModelEntity, ModelFe
from graphon.model_runtime.entities.rerank_entities import MultimodalRerankInput, RerankResult
from graphon.model_runtime.entities.text_embedding_entities import EmbeddingResult
from graphon.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeConnectionError, InvokeRateLimitError
from graphon.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from graphon.model_runtime.model_providers.__base.moderation_model import ModerationModel
from graphon.model_runtime.model_providers.__base.rerank_model import RerankModel
from graphon.model_runtime.model_providers.__base.speech2text_model import Speech2TextModel
from graphon.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
from graphon.model_runtime.model_providers.__base.tts_model import TTSModel
from graphon.model_runtime.model_providers.base.large_language_model import LargeLanguageModel
from graphon.model_runtime.model_providers.base.moderation_model import ModerationModel
from graphon.model_runtime.model_providers.base.rerank_model import RerankModel
from graphon.model_runtime.model_providers.base.speech2text_model import Speech2TextModel
from graphon.model_runtime.model_providers.base.text_embedding_model import TextEmbeddingModel
from graphon.model_runtime.model_providers.base.tts_model import TTSModel
from models.provider import ProviderType
logger = logging.getLogger(__name__)
P = ParamSpec("P")
R = TypeVar("R")
class ModelInstance:
@ -168,7 +170,7 @@ class ModelInstance:
return cast(
Union[LLMResult, Generator],
self._round_robin_invoke(
function=self.model_type_instance.invoke,
self.model_type_instance.invoke,
model=self.model_name,
credentials=self.credentials,
prompt_messages=list(prompt_messages),
@ -193,7 +195,7 @@ class ModelInstance:
if not isinstance(self.model_type_instance, LargeLanguageModel):
raise Exception("Model type instance is not LargeLanguageModel")
return self._round_robin_invoke(
function=self.model_type_instance.get_num_tokens,
self.model_type_instance.get_num_tokens,
model=self.model_name,
credentials=self.credentials,
prompt_messages=list(prompt_messages),
@ -213,7 +215,7 @@ class ModelInstance:
if not isinstance(self.model_type_instance, TextEmbeddingModel):
raise Exception("Model type instance is not TextEmbeddingModel")
return self._round_robin_invoke(
function=self.model_type_instance.invoke,
self.model_type_instance.invoke,
model=self.model_name,
credentials=self.credentials,
texts=texts,
@ -235,7 +237,7 @@ class ModelInstance:
if not isinstance(self.model_type_instance, TextEmbeddingModel):
raise Exception("Model type instance is not TextEmbeddingModel")
return self._round_robin_invoke(
function=self.model_type_instance.invoke,
self.model_type_instance.invoke,
model=self.model_name,
credentials=self.credentials,
multimodel_documents=multimodel_documents,
@ -252,7 +254,7 @@ class ModelInstance:
if not isinstance(self.model_type_instance, TextEmbeddingModel):
raise Exception("Model type instance is not TextEmbeddingModel")
return self._round_robin_invoke(
function=self.model_type_instance.get_num_tokens,
self.model_type_instance.get_num_tokens,
model=self.model_name,
credentials=self.credentials,
texts=texts,
@ -277,7 +279,7 @@ class ModelInstance:
if not isinstance(self.model_type_instance, RerankModel):
raise Exception("Model type instance is not RerankModel")
return self._round_robin_invoke(
function=self.model_type_instance.invoke,
self.model_type_instance.invoke,
model=self.model_name,
credentials=self.credentials,
query=query,
@ -305,7 +307,7 @@ class ModelInstance:
if not isinstance(self.model_type_instance, RerankModel):
raise Exception("Model type instance is not RerankModel")
return self._round_robin_invoke(
function=self.model_type_instance.invoke_multimodal_rerank,
self.model_type_instance.invoke_multimodal_rerank,
model=self.model_name,
credentials=self.credentials,
query=query,
@ -324,7 +326,7 @@ class ModelInstance:
if not isinstance(self.model_type_instance, ModerationModel):
raise Exception("Model type instance is not ModerationModel")
return self._round_robin_invoke(
function=self.model_type_instance.invoke,
self.model_type_instance.invoke,
model=self.model_name,
credentials=self.credentials,
text=text,
@ -340,7 +342,7 @@ class ModelInstance:
if not isinstance(self.model_type_instance, Speech2TextModel):
raise Exception("Model type instance is not Speech2TextModel")
return self._round_robin_invoke(
function=self.model_type_instance.invoke,
self.model_type_instance.invoke,
model=self.model_name,
credentials=self.credentials,
file=file,
@ -357,14 +359,14 @@ class ModelInstance:
if not isinstance(self.model_type_instance, TTSModel):
raise Exception("Model type instance is not TTSModel")
return self._round_robin_invoke(
function=self.model_type_instance.invoke,
self.model_type_instance.invoke,
model=self.model_name,
credentials=self.credentials,
content_text=content_text,
voice=voice,
)
def _round_robin_invoke[**P, R](self, function: Callable[P, R], *args: P.args, **kwargs: P.kwargs) -> R:
def _round_robin_invoke(self, function: Callable[P, R], *args: P.args, **kwargs: P.kwargs) -> R:
"""
Round-robin invoke
:param function: function to invoke

6
api/core/plugin/impl/model_runtime.py
View File

@ -66,15 +66,15 @@ class PluginModelRuntime(ModelRuntime):
if not provider_schema.icon_small:
raise ValueError(f"Provider {provider} does not have small icon.")
file_name = (
provider_schema.icon_small.zh_Hans if lang.lower() == "zh_hans" else provider_schema.icon_small.en_US
provider_schema.icon_small.zh_hans if lang.lower() == "zh_hans" else provider_schema.icon_small.en_us
)
elif icon_type.lower() == "icon_small_dark":
if not provider_schema.icon_small_dark:
raise ValueError(f"Provider {provider} does not have small dark icon.")
file_name = (
provider_schema.icon_small_dark.zh_Hans
provider_schema.icon_small_dark.zh_hans
if lang.lower() == "zh_hans"
else provider_schema.icon_small_dark.en_US
else provider_schema.icon_small_dark.en_us
)
else:
raise ValueError(f"Unsupported icon type: {icon_type}.")

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

@ -10,7 +10,7 @@ from graphon.model_runtime.entities.message_entities import (
SystemPromptMessage,
UserPromptMessage,
)
from graphon.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from graphon.model_runtime.model_providers.base.large_language_model import LargeLanguageModel
class AgentHistoryPromptTransform(PromptTransform):

2
api/core/rag/embedding/cached_embedding.py
View File

@ -14,7 +14,7 @@ from core.rag.embedding.embedding_base import Embeddings
from extensions.ext_database import db
from extensions.ext_redis import redis_client
from graphon.model_runtime.entities.model_entities import ModelPropertyKey
from graphon.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
from graphon.model_runtime.model_providers.base.text_embedding_model import TextEmbeddingModel
from libs import helper
from models.dataset import Embedding

26
api/core/rag/extractor/word_extractor.py
View File

@ -3,6 +3,7 @@
Supports local file paths and remote URLs (downloaded via `core.helper.ssrf_proxy`).
"""
import inspect
import logging
import mimetypes
import os
@ -36,8 +37,11 @@ class WordExtractor(BaseExtractor):
file_path: Path to the file to load.
"""
_closed: bool
def __init__(self, file_path: str, tenant_id: str, user_id: str):
"""Initialize with file path."""
self._closed = False
self.file_path = file_path
self.tenant_id = tenant_id
self.user_id = user_id
@ -65,9 +69,27 @@ class WordExtractor(BaseExtractor):
elif not os.path.isfile(self.file_path):
raise ValueError(f"File path {self.file_path} is not a valid file or url")
def close(self) -> None:
"""Best-effort cleanup for downloaded temporary files."""
if getattr(self, "_closed", False):
return
self._closed = True
temp_file = getattr(self, "temp_file", None)
if temp_file is None:
return
try:
close_result = temp_file.close()
if inspect.isawaitable(close_result):
close_awaitable = getattr(close_result, "close", None)
if callable(close_awaitable):
close_awaitable()
except Exception:
logger.debug("Failed to cleanup downloaded word temp file", exc_info=True)
def __del__(self):
if hasattr(self, "temp_file"):
self.temp_file.close()
self.close()
def extract(self) -> list[Document]:
"""Load given path as single page."""

4
api/core/rag/index_processor/processor/paragraph_index_processor.py
View File

@ -609,11 +609,11 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
try:
# Create File object directly (similar to DatasetRetrieval)
file_obj = File(
id=upload_file.id,
file_id=upload_file.id,
filename=upload_file.name,
extension="." + upload_file.extension,
mime_type=upload_file.mime_type,
type=FileType.IMAGE,
file_type=FileType.IMAGE,
transfer_method=FileTransferMethod.LOCAL_FILE,
remote_url=upload_file.source_url,
reference=build_file_reference(

6
api/core/rag/retrieval/dataset_retrieval.py
View File

@ -68,7 +68,7 @@ from graphon.file import File, FileTransferMethod, FileType
from graphon.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMUsage
from graphon.model_runtime.entities.message_entities import PromptMessage, PromptMessageRole, PromptMessageTool
from graphon.model_runtime.entities.model_entities import ModelFeature, ModelType
from graphon.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from graphon.model_runtime.model_providers.base.large_language_model import LargeLanguageModel
from libs.helper import parse_uuid_str_or_none
from libs.json_in_md_parser import parse_and_check_json_markdown
from models import UploadFile
@ -517,11 +517,11 @@ class DatasetRetrieval:
if attachments_with_bindings:
for _, upload_file in attachments_with_bindings:
attachment_info = File(
id=upload_file.id,
file_id=upload_file.id,
filename=upload_file.name,
extension="." + upload_file.extension,
mime_type=upload_file.mime_type,
type=FileType.IMAGE,
file_type=FileType.IMAGE,
transfer_method=FileTransferMethod.LOCAL_FILE,
remote_url=upload_file.source_url,
reference=build_file_reference(

2
api/core/rag/splitter/fixed_text_splitter.py
View File

@ -9,7 +9,7 @@ from typing import Any, Literal
from core.model_manager import ModelInstance
from core.rag.splitter.text_splitter import RecursiveCharacterTextSplitter
from graphon.model_runtime.model_providers.__base.tokenizers.gpt2_tokenizer import GPT2Tokenizer
from graphon.model_runtime.model_providers.base.tokenizers.gpt2_tokenizer import GPT2Tokenizer
class EnhanceRecursiveCharacterTextSplitter(RecursiveCharacterTextSplitter):

2
api/core/repositories/human_input_repository.py
View File

@ -8,7 +8,7 @@ from sqlalchemy import select
from sqlalchemy.orm import Session, selectinload
from core.db.session_factory import session_factory
from core.workflow.human_input_compat import (
from core.workflow.human_input_adapter import (
BoundRecipient,
DeliveryChannelConfig,
EmailDeliveryMethod,

2
api/core/tools/tool_file_manager.py
View File

@ -28,7 +28,7 @@ class ToolFileManager:
def _build_graph_file_reference(tool_file: ToolFile) -> File:
extension = guess_extension(tool_file.mimetype) or ".bin"
return File(
type=get_file_type_by_mime_type(tool_file.mimetype),
file_type=get_file_type_by_mime_type(tool_file.mimetype),
transfer_method=FileTransferMethod.TOOL_FILE,
remote_url=tool_file.original_url,
reference=build_file_reference(record_id=str(tool_file.id)),

7
api/core/tools/tool_manager.py
View File

@ -1082,7 +1082,12 @@ class ToolManager:
continue
tool_input = ToolNodeData.ToolInput.model_validate(tool_configurations.get(parameter.name, {}))
if tool_input.type == "variable":
variable = variable_pool.get(tool_input.value)
variable_selector = tool_input.value
if not isinstance(variable_selector, list) or not all(
isinstance(selector_part, str) for selector_part in variable_selector
):
raise ToolParameterError("Variable tool input must be a variable selector")
variable = variable_pool.get(variable_selector)
if variable is None:
raise ToolParameterError(f"Variable {tool_input.value} does not exist")
parameter_value = variable.value

2
api/core/tools/utils/model_invocation_utils.py
View File

@ -21,7 +21,7 @@ from graphon.model_runtime.errors.invoke import (
InvokeRateLimitError,
InvokeServerUnavailableError,
)
from graphon.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from graphon.model_runtime.model_providers.base.large_language_model import LargeLanguageModel
from graphon.model_runtime.utils.encoders import jsonable_encoder
from models.tools import ToolModelInvoke

5
api/core/tools/workflow_as_tool/tool.py
View File

@ -357,7 +357,10 @@ class WorkflowTool(Tool):
def _update_file_mapping(self, file_dict: dict[str, Any]) -> dict[str, Any]:
file_id = resolve_file_record_id(file_dict.get("reference") or file_dict.get("related_id"))
transfer_method = FileTransferMethod.value_of(file_dict.get("transfer_method"))
transfer_method_value = file_dict.get("transfer_method")
if not isinstance(transfer_method_value, str):
raise ValueError("Workflow file mapping is missing a valid transfer_method")
transfer_method = FileTransferMethod.value_of(transfer_method_value)
match transfer_method:
case FileTransferMethod.TOOL_FILE:
file_dict["tool_file_id"] = file_id

88
api/core/workflow/human_input_compat.py → api/core/workflow/human_input_adapter.py
View File

@ -1,8 +1,8 @@
"""Workflow-layer adapters for legacy human-input payload keys.
"""Workflow-to-Graphon adapters for persisted node payloads.
Stored workflow graphs and editor payloads may still use Dify-specific human
input recipient keys. Normalize them here before handing configs to
`graphon` so graph-owned models only see graph-neutral field names.
Stored workflow graphs and editor payloads still contain a small set of
Dify-owned field spellings and value shapes. Adapt them here before handing the
payload to Graphon so Graphon-owned models only see current contracts.
"""
from __future__ import annotations
@ -185,7 +185,7 @@ def _copy_mapping(value: object) -> dict[str, Any] | None:
return None
def normalize_human_input_node_data_for_graph(node_data: Mapping[str, Any] | BaseModel) -> dict[str, Any]:
def adapt_human_input_node_data_for_graph(node_data: Mapping[str, Any] | BaseModel) -> dict[str, Any]:
normalized = _copy_mapping(node_data)
if normalized is None:
raise TypeError(f"human-input node data must be a mapping, got {type(node_data).__name__}")
@ -215,7 +215,7 @@ def normalize_human_input_node_data_for_graph(node_data: Mapping[str, Any] | Bas
def parse_human_input_delivery_methods(node_data: Mapping[str, Any] | BaseModel) -> list[DeliveryChannelConfig]:
normalized = normalize_human_input_node_data_for_graph(node_data)
normalized = adapt_human_input_node_data_for_graph(node_data)
raw_delivery_methods = normalized.get("delivery_methods")
if not isinstance(raw_delivery_methods, list):
return []
@ -229,17 +229,20 @@ def is_human_input_webapp_enabled(node_data: Mapping[str, Any] | BaseModel) -> b
return False
def normalize_node_data_for_graph(node_data: Mapping[str, Any] | BaseModel) -> dict[str, Any]:
def adapt_node_data_for_graph(node_data: Mapping[str, Any] | BaseModel) -> dict[str, Any]:
normalized = _copy_mapping(node_data)
if normalized is None:
raise TypeError(f"node data must be a mapping, got {type(node_data).__name__}")
if normalized.get("type") != BuiltinNodeTypes.HUMAN_INPUT:
return normalized
return normalize_human_input_node_data_for_graph(normalized)
node_type = normalized.get("type")
if node_type == BuiltinNodeTypes.HUMAN_INPUT:
return adapt_human_input_node_data_for_graph(normalized)
if node_type == BuiltinNodeTypes.TOOL:
return _adapt_tool_node_data_for_graph(normalized)
return normalized
def normalize_node_config_for_graph(node_config: Mapping[str, Any] | BaseModel) -> dict[str, Any]:
def adapt_node_config_for_graph(node_config: Mapping[str, Any] | BaseModel) -> dict[str, Any]:
normalized = _copy_mapping(node_config)
if normalized is None:
raise TypeError(f"node config must be a mapping, got {type(node_config).__name__}")
@ -248,10 +251,65 @@ def normalize_node_config_for_graph(node_config: Mapping[str, Any] | BaseModel)
if data_mapping is None:
return normalized
normalized["data"] = normalize_node_data_for_graph(data_mapping)
normalized["data"] = adapt_node_data_for_graph(data_mapping)
return normalized
def _adapt_tool_node_data_for_graph(node_data: Mapping[str, Any]) -> dict[str, Any]:
normalized = dict(node_data)
raw_tool_configurations = normalized.get("tool_configurations")
if not isinstance(raw_tool_configurations, Mapping):
return normalized
existing_tool_parameters = normalized.get("tool_parameters")
normalized_tool_parameters = dict(existing_tool_parameters) if isinstance(existing_tool_parameters, Mapping) else {}
normalized_tool_configurations: dict[str, Any] = {}
found_legacy_tool_inputs = False
for name, value in raw_tool_configurations.items():
if not isinstance(value, Mapping):
normalized_tool_configurations[name] = value
continue
input_type = value.get("type")
input_value = value.get("value")
if input_type not in {"mixed", "variable", "constant"}:
normalized_tool_configurations[name] = value
continue
found_legacy_tool_inputs = True
normalized_tool_parameters.setdefault(name, dict(value))
flattened_value = _flatten_legacy_tool_configuration_value(
input_type=input_type,
input_value=input_value,
)
if flattened_value is not None:
normalized_tool_configurations[name] = flattened_value
if not found_legacy_tool_inputs:
return normalized
normalized["tool_parameters"] = normalized_tool_parameters
normalized["tool_configurations"] = normalized_tool_configurations
return normalized
def _flatten_legacy_tool_configuration_value(*, input_type: Any, input_value: Any) -> str | int | float | bool | None:
if input_type in {"mixed", "constant"} and isinstance(input_value, str | int | float | bool):
return input_value
if (
input_type == "variable"
and isinstance(input_value, list)
and all(isinstance(item, str) for item in input_value)
):
return "{{#" + ".".join(input_value) + "#}}"
return None
def _normalize_email_recipients(recipients: Mapping[str, Any]) -> dict[str, Any]:
normalized = dict(recipients)
@ -291,9 +349,9 @@ __all__ = [
"MemberRecipient",
"WebAppDeliveryMethod",
"_WebAppDeliveryConfig",
"adapt_human_input_node_data_for_graph",
"adapt_node_config_for_graph",
"adapt_node_data_for_graph",
"is_human_input_webapp_enabled",
"normalize_human_input_node_data_for_graph",
"normalize_node_config_for_graph",
"normalize_node_data_for_graph",
"parse_human_input_delivery_methods",
]

30
api/core/workflow/node_factory.py
View File

@ -15,12 +15,12 @@ from core.helper.code_executor.code_executor import (
CodeExecutionError,
CodeExecutor,
)
from core.helper.ssrf_proxy import ssrf_proxy
from core.helper.ssrf_proxy import graphon_ssrf_proxy
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_manager import ModelInstance
from core.prompt.entities.advanced_prompt_entities import MemoryConfig
from core.trigger.constants import TRIGGER_NODE_TYPES
from core.workflow.human_input_compat import normalize_node_config_for_graph
from core.workflow.human_input_adapter import adapt_node_config_for_graph
from core.workflow.node_runtime import (
DifyFileReferenceFactory,
DifyHumanInputNodeRuntime,
@ -46,7 +46,7 @@ from graphon.enums import BuiltinNodeTypes, NodeType
from graphon.file.file_manager import file_manager
from graphon.graph.graph import NodeFactory
from graphon.model_runtime.memory import PromptMessageMemory
from graphon.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from graphon.model_runtime.model_providers.base.large_language_model import LargeLanguageModel
from graphon.nodes.base.node import Node
from graphon.nodes.code.code_node import WorkflowCodeExecutor
from graphon.nodes.code.entities import CodeLanguage
@ -121,6 +121,7 @@ def get_node_type_classes_mapping() -> Mapping[NodeType, Mapping[str, type[Node]
def resolve_workflow_node_class(*, node_type: NodeType, node_version: str) -> type[Node]:
"""Resolve the production node class for the requested type/version."""
node_mapping = get_node_type_classes_mapping().get(node_type)
if not node_mapping:
raise ValueError(f"No class mapping found for node type: {node_type}")
@ -297,7 +298,7 @@ class DifyNodeFactory(NodeFactory):
)
self._jinja2_template_renderer = CodeExecutorJinja2TemplateRenderer()
self._template_transform_max_output_length = dify_config.TEMPLATE_TRANSFORM_MAX_LENGTH
self._http_request_http_client = ssrf_proxy
self._http_request_http_client = graphon_ssrf_proxy
self._bound_tool_file_manager_factory = lambda: DifyToolFileManager(
self._dify_context,
conversation_id_getter=self._conversation_id,
@ -364,10 +365,14 @@ class DifyNodeFactory(NodeFactory):
(including pydantic ValidationError, which subclasses ValueError),
if node type is unknown, or if no implementation exists for the resolved version
"""
typed_node_config = NodeConfigDictAdapter.validate_python(normalize_node_config_for_graph(node_config))
typed_node_config = NodeConfigDictAdapter.validate_python(adapt_node_config_for_graph(node_config))
node_id = typed_node_config["id"]
node_data = typed_node_config["data"]
node_class = self._resolve_node_class(node_type=node_data.type, node_version=str(node_data.version))
# Graph configs are initially validated against permissive shared node data.
# Re-validate using the resolved node class so workflow-local node schemas
# stay explicit and constructors receive the concrete typed payload.
resolved_node_data = self._validate_resolved_node_data(node_class, node_data)
node_type = node_data.type
node_init_kwargs_factories: Mapping[NodeType, Callable[[], dict[str, object]]] = {
BuiltinNodeTypes.CODE: lambda: {
@ -391,7 +396,7 @@ class DifyNodeFactory(NodeFactory):
},
BuiltinNodeTypes.LLM: lambda: self._build_llm_compatible_node_init_kwargs(
node_class=node_class,
node_data=node_data,
node_data=resolved_node_data,
wrap_model_instance=True,
include_http_client=True,
include_llm_file_saver=True,
@ -405,7 +410,7 @@ class DifyNodeFactory(NodeFactory):
},
BuiltinNodeTypes.QUESTION_CLASSIFIER: lambda: self._build_llm_compatible_node_init_kwargs(
node_class=node_class,
node_data=node_data,
node_data=resolved_node_data,
wrap_model_instance=True,
include_http_client=True,
include_llm_file_saver=True,
@ -415,7 +420,7 @@ class DifyNodeFactory(NodeFactory):
),
BuiltinNodeTypes.PARAMETER_EXTRACTOR: lambda: self._build_llm_compatible_node_init_kwargs(
node_class=node_class,
node_data=node_data,
node_data=resolved_node_data,
wrap_model_instance=True,
include_http_client=False,
include_llm_file_saver=False,
@ -436,8 +441,8 @@ class DifyNodeFactory(NodeFactory):
}
node_init_kwargs = node_init_kwargs_factories.get(node_type, lambda: {})()
return node_class(
id=node_id,
config=typed_node_config,
node_id=node_id,
config=resolved_node_data,
graph_init_params=self.graph_init_params,
graph_runtime_state=self.graph_runtime_state,
**node_init_kwargs,
@ -448,7 +453,10 @@ class DifyNodeFactory(NodeFactory):
"""
Re-validate the permissive graph payload with the concrete NodeData model declared by the resolved node class.
"""
return node_class.validate_node_data(node_data)
validate_node_data = getattr(node_class, "validate_node_data", None)
if callable(validate_node_data):
return cast("BaseNodeData", validate_node_data(node_data))
return node_data
@staticmethod
def _resolve_node_class(*, node_type: NodeType, node_version: str) -> type[Node]:

50
api/core/workflow/node_runtime.py
View File

@ -2,7 +2,7 @@ from __future__ import annotations
from collections.abc import Callable, Generator, Mapping, Sequence
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, cast
from typing import TYPE_CHECKING, Any, Literal, cast, overload
from sqlalchemy import select
from sqlalchemy.orm import Session
@ -41,7 +41,7 @@ from graphon.model_runtime.entities.llm_entities import (
)
from graphon.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
from graphon.model_runtime.entities.model_entities import AIModelEntity
from graphon.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from graphon.model_runtime.model_providers.base.large_language_model import LargeLanguageModel
from graphon.nodes.human_input.entities import HumanInputNodeData
from graphon.nodes.llm.runtime_protocols import (
PreparedLLMProtocol,
@ -64,7 +64,7 @@ from models.dataset import SegmentAttachmentBinding
from models.model import UploadFile
from services.tools.builtin_tools_manage_service import BuiltinToolManageService
from .human_input_compat import (
from .human_input_adapter import (
BoundRecipient,
DeliveryChannelConfig,
DeliveryMethodType,
@ -173,6 +173,28 @@ class DifyPreparedLLM(PreparedLLMProtocol):
def get_llm_num_tokens(self, prompt_messages: Sequence[PromptMessage]) -> int:
return self._model_instance.get_llm_num_tokens(prompt_messages)
@overload
def invoke_llm(
self,
*,
prompt_messages: Sequence[PromptMessage],
model_parameters: Mapping[str, Any],
tools: Sequence[PromptMessageTool] | None,
stop: Sequence[str] | None,
stream: Literal[False],
) -> LLMResult: ...
@overload
def invoke_llm(
self,
*,
prompt_messages: Sequence[PromptMessage],
model_parameters: Mapping[str, Any],
tools: Sequence[PromptMessageTool] | None,
stop: Sequence[str] | None,
stream: Literal[True],
) -> Generator[LLMResultChunk, None, None]: ...
def invoke_llm(
self,
*,
@ -190,6 +212,28 @@ class DifyPreparedLLM(PreparedLLMProtocol):
stream=stream,
)
@overload
def invoke_llm_with_structured_output(
self,
*,
prompt_messages: Sequence[PromptMessage],
json_schema: Mapping[str, Any],
model_parameters: Mapping[str, Any],
stop: Sequence[str] | None,
stream: Literal[False],
) -> LLMResultWithStructuredOutput: ...
@overload
def invoke_llm_with_structured_output(
self,
*,
prompt_messages: Sequence[PromptMessage],
json_schema: Mapping[str, Any],
model_parameters: Mapping[str, Any],
stop: Sequence[str] | None,
stream: Literal[True],
) -> Generator[LLMResultChunkWithStructuredOutput, None, None]: ...
def invoke_llm_with_structured_output(
self,
*,

9
api/core/workflow/nodes/agent/agent_node.py
View File

@ -5,7 +5,6 @@ from typing import TYPE_CHECKING, Any
from core.app.entities.app_invoke_entities import DIFY_RUN_CONTEXT_KEY, DifyRunContext
from core.workflow.system_variables import SystemVariableKey, get_system_text
from graphon.entities.graph_config import NodeConfigDict
from graphon.enums import BuiltinNodeTypes, WorkflowNodeExecutionStatus
from graphon.node_events import NodeEventBase, NodeRunResult, StreamCompletedEvent
from graphon.nodes.base.node import Node
@ -35,18 +34,18 @@ class AgentNode(Node[AgentNodeData]):
def __init__(
self,
id: str,
config: NodeConfigDict,
node_id: str,
config: AgentNodeData,
*,
graph_init_params: GraphInitParams,
graph_runtime_state: GraphRuntimeState,
*,
strategy_resolver: AgentStrategyResolver,
presentation_provider: AgentStrategyPresentationProvider,
runtime_support: AgentRuntimeSupport,
message_transformer: AgentMessageTransformer,
) -> None:
super().__init__(
id=id,
node_id=node_id,
config=config,
graph_init_params=graph_init_params,
graph_runtime_state=graph_runtime_state,

10
api/core/workflow/nodes/datasource/datasource_node.py
View File

@ -7,7 +7,6 @@ from core.datasource.entities.datasource_entities import DatasourceProviderType
from core.plugin.impl.exc import PluginDaemonClientSideError
from core.workflow.file_reference import resolve_file_record_id
from core.workflow.system_variables import SystemVariableKey, get_system_segment
from graphon.entities.graph_config import NodeConfigDict
from graphon.enums import (
BuiltinNodeTypes,
NodeExecutionType,
@ -36,13 +35,14 @@ class DatasourceNode(Node[DatasourceNodeData]):
def __init__(
self,
id: str,
config: NodeConfigDict,
node_id: str,
config: DatasourceNodeData,
*,
graph_init_params: "GraphInitParams",
graph_runtime_state: "GraphRuntimeState",
):
) -> None:
super().__init__(
id=id,
node_id=node_id,
config=config,
graph_init_params=graph_init_params,
graph_runtime_state=graph_runtime_state,

13
api/core/workflow/nodes/knowledge_index/knowledge_index_node.py
View File

@ -7,7 +7,6 @@ from core.rag.index_processor.index_processor_base import SummaryIndexSettingDic
from core.rag.summary_index.summary_index import SummaryIndex
from core.workflow.nodes.knowledge_index import KNOWLEDGE_INDEX_NODE_TYPE
from core.workflow.system_variables import SystemVariableKey, get_system_segment, get_system_text
from graphon.entities.graph_config import NodeConfigDict
from graphon.enums import NodeExecutionType, WorkflowNodeExecutionStatus
from graphon.node_events import NodeRunResult
from graphon.nodes.base.node import Node
@ -32,12 +31,18 @@ class KnowledgeIndexNode(Node[KnowledgeIndexNodeData]):
def __init__(
self,
id: str,
config: NodeConfigDict,
node_id: str,
config: KnowledgeIndexNodeData,
*,
graph_init_params: "GraphInitParams",
graph_runtime_state: "GraphRuntimeState",
) -> None:
super().__init__(id, config, graph_init_params, graph_runtime_state)
super().__init__(
node_id=node_id,
config=config,
graph_init_params=graph_init_params,
graph_runtime_state=graph_runtime_state,
)
self.index_processor = IndexProcessor()
self.summary_index_service = SummaryIndex()

33
api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
View File

@ -14,7 +14,6 @@ from core.rag.data_post_processor.data_post_processor import RerankingModelDict,
from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
from core.workflow.file_reference import parse_file_reference
from graphon.entities import GraphInitParams
from graphon.entities.graph_config import NodeConfigDict
from graphon.enums import (
BuiltinNodeTypes,
WorkflowNodeExecutionMetadataKey,
@ -50,6 +49,18 @@ if TYPE_CHECKING:
logger = logging.getLogger(__name__)
def _normalize_metadata_filter_scalar(value: object) -> str | int | float | None:
if value is None or isinstance(value, (str, float)):
return value
if isinstance(value, int) and not isinstance(value, bool):
return value
return str(value)
def _normalize_metadata_filter_sequence_item(value: object) -> str:
return value if isinstance(value, str) else str(value)
class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeData]):
node_type = BuiltinNodeTypes.KNOWLEDGE_RETRIEVAL
@ -59,13 +70,14 @@ class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeD
def __init__(
self,
id: str,
config: NodeConfigDict,
node_id: str,
config: KnowledgeRetrievalNodeData,
*,
graph_init_params: "GraphInitParams",
graph_runtime_state: "GraphRuntimeState",
):
) -> None:
super().__init__(
id=id,
node_id=node_id,
config=config,
graph_init_params=graph_init_params,
graph_runtime_state=graph_runtime_state,
@ -282,18 +294,21 @@ class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeD
resolved_conditions: list[Condition] = []
for cond in conditions.conditions or []:
value = cond.value
resolved_value: str | Sequence[str] | int | float | None
if isinstance(value, str):
segment_group = variable_pool.convert_template(value)
if len(segment_group.value) == 1:
resolved_value = segment_group.value[0].to_object()
resolved_value = _normalize_metadata_filter_scalar(segment_group.value[0].to_object())
else:
resolved_value = segment_group.text
elif isinstance(value, Sequence) and all(isinstance(v, str) for v in value):
resolved_values = []
for v in value: # type: ignore
resolved_values: list[str] = []
for v in value:
segment_group = variable_pool.convert_template(v)
if len(segment_group.value) == 1:
resolved_values.append(segment_group.value[0].to_object())
resolved_values.append(
_normalize_metadata_filter_sequence_item(segment_group.value[0].to_object())
)
else:
resolved_values.append(segment_group.text)
resolved_value = resolved_values