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Merge remote-tracking branch 'origin/feat/r2' into feat/r2

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jyong
2025-04-28 16:19:29 +08:00
parent 49d1846e63 c2d02f8f4d
commit 3c386c63a6
874 changed files with 31114 additions and 19811 deletions
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2
api/core/workflow/constants.py
View File

@ -1,4 +1,4 @@
SYSTEM_VARIABLE_NODE_ID = "sys"
ENVIRONMENT_VARIABLE_NODE_ID = "env"
CONVERSATION_VARIABLE_NODE_ID = "conversation"
PIPELINE_VARIABLE_NODE_ID = "pipeline"
PIPELINE_VARIABLE_NODE_ID = "pipeline"

13
api/core/workflow/graph_engine/entities/next_graph_node.py
View File

@ -1,13 +0,0 @@
from typing import Optional
from pydantic import BaseModel
from core.workflow.graph_engine.entities.graph import GraphParallel
class NextGraphNode(BaseModel):
node_id: str
"""next node id"""
parallel: Optional[GraphParallel] = None
"""parallel"""

22
api/core/workflow/nodes/agent/agent_node.py
View File

@ -7,8 +7,8 @@ from core.agent.plugin_entities import AgentStrategyParameter
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_manager import ModelInstance, ModelManager
from core.model_runtime.entities.model_entities import AIModelEntity, ModelType
from core.plugin.manager.exc import PluginDaemonClientSideError
from core.plugin.manager.plugin import PluginInstallationManager
from core.plugin.impl.exc import PluginDaemonClientSideError
from core.plugin.impl.plugin import PluginInstaller
from core.provider_manager import ProviderManager
from core.tools.entities.tool_entities import ToolParameter, ToolProviderType
from core.tools.tool_manager import ToolManager
@ -16,7 +16,7 @@ from core.variables.segments import StringSegment
from core.workflow.entities.node_entities import NodeRunResult
from core.workflow.entities.variable_pool import VariablePool
from core.workflow.enums import SystemVariableKey
from core.workflow.nodes.agent.entities import AgentNodeData, ParamsAutoGenerated
from core.workflow.nodes.agent.entities import AgentNodeData, AgentOldVersionModelFeatures, ParamsAutoGenerated
from core.workflow.nodes.base.entities import BaseNodeData
from core.workflow.nodes.enums import NodeType
from core.workflow.nodes.event.event import RunCompletedEvent
@ -251,7 +251,12 @@ class AgentNode(ToolNode):
prompt_message.model_dump(mode="json") for prompt_message in prompt_messages
]
value["history_prompt_messages"] = history_prompt_messages
value["entity"] = model_schema.model_dump(mode="json") if model_schema else None
if model_schema:
# remove structured output feature to support old version agent plugin
model_schema = self._remove_unsupported_model_features_for_old_version(model_schema)
value["entity"] = model_schema.model_dump(mode="json")
else:
value["entity"] = None
result[parameter_name] = value
return result
@ -292,7 +297,7 @@ class AgentNode(ToolNode):
Get agent strategy icon
:return:
"""
manager = PluginInstallationManager()
manager = PluginInstaller()
plugins = manager.list_plugins(self.tenant_id)
try:
current_plugin = next(
@ -348,3 +353,10 @@ class AgentNode(ToolNode):
)
model_schema = model_type_instance.get_model_schema(model_name, model_credentials)
return model_instance, model_schema
def _remove_unsupported_model_features_for_old_version(self, model_schema: AIModelEntity) -> AIModelEntity:
if model_schema.features:
for feature in model_schema.features:
if feature.value not in AgentOldVersionModelFeatures:
model_schema.features.remove(feature)
return model_schema

15
api/core/workflow/nodes/agent/entities.py
View File

@ -24,3 +24,18 @@ class AgentNodeData(BaseNodeData):
class ParamsAutoGenerated(Enum):
CLOSE = 0
OPEN = 1
class AgentOldVersionModelFeatures(Enum):
"""
Enum class for old SDK version llm feature.
"""
TOOL_CALL = "tool-call"
MULTI_TOOL_CALL = "multi-tool-call"
AGENT_THOUGHT = "agent-thought"
VISION = "vision"
STREAM_TOOL_CALL = "stream-tool-call"
DOCUMENT = "document"
VIDEO = "video"
AUDIO = "audio"

23
api/core/workflow/nodes/answer/answer_stream_processor.py
View File

@ -155,9 +155,28 @@ class AnswerStreamProcessor(StreamProcessor):
for answer_node_id, route_position in self.route_position.items():
if answer_node_id not in self.rest_node_ids:
continue
# exclude current node id
# Remove current node id from answer dependencies to support stream output if it is a success branch
answer_dependencies = self.generate_routes.answer_dependencies
if event.node_id in answer_dependencies[answer_node_id]:
edge_mapping = self.graph.edge_mapping.get(event.node_id)
success_edge = (
next(
(
edge
for edge in edge_mapping
if edge.run_condition
and edge.run_condition.type == "branch_identify"
and edge.run_condition.branch_identify == "success-branch"
),
None,
)
if edge_mapping
else None
)
if (
event.node_id in answer_dependencies[answer_node_id]
and success_edge
and success_edge.target_node_id == answer_node_id
):
answer_dependencies[answer_node_id].remove(event.node_id)
answer_dependencies_ids = answer_dependencies.get(answer_node_id, [])
# all depends on answer node id not in rest node ids

1
api/core/workflow/nodes/http_request/entities.py
View File

@ -90,6 +90,7 @@ class HttpRequestNodeData(BaseNodeData):
params: str
body: Optional[HttpRequestNodeBody] = None
timeout: Optional[HttpRequestNodeTimeout] = None
ssl_verify: Optional[bool] = dify_config.HTTP_REQUEST_NODE_SSL_VERIFY
class Response:

7
api/core/workflow/nodes/http_request/executor.py
View File

@ -1,3 +1,4 @@
import base64
import json
from collections.abc import Mapping
from copy import deepcopy
@ -87,6 +88,7 @@ class Executor:
self.method = node_data.method
self.auth = node_data.authorization
self.timeout = timeout
self.ssl_verify = node_data.ssl_verify
self.params = []
self.headers = {}
self.content = None
@ -259,7 +261,9 @@ class Executor:
if self.auth.config.type == "bearer":
headers[authorization.config.header] = f"Bearer {authorization.config.api_key}"
elif self.auth.config.type == "basic":
headers[authorization.config.header] = f"Basic {authorization.config.api_key}"
credentials = authorization.config.api_key
encoded_credentials = base64.b64encode(credentials.encode("utf-8")).decode("utf-8")
headers[authorization.config.header] = f"Basic {encoded_credentials}"
elif self.auth.config.type == "custom":
headers[authorization.config.header] = authorization.config.api_key or ""
@ -313,6 +317,7 @@ class Executor:
"headers": headers,
"params": self.params,
"timeout": (self.timeout.connect, self.timeout.read, self.timeout.write),
"ssl_verify": self.ssl_verify,
"follow_redirects": True,
"max_retries": self.max_retries,
}

1
api/core/workflow/nodes/http_request/node.py
View File

@ -51,6 +51,7 @@ class HttpRequestNode(BaseNode[HttpRequestNodeData]):
"max_read_timeout": dify_config.HTTP_REQUEST_MAX_READ_TIMEOUT,
"max_write_timeout": dify_config.HTTP_REQUEST_MAX_WRITE_TIMEOUT,
},
"ssl_verify": dify_config.HTTP_REQUEST_NODE_SSL_VERIFY,
},
"retry_config": {
"max_retries": dify_config.SSRF_DEFAULT_MAX_RETRIES,

11
api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
View File

@ -259,6 +259,7 @@ class KnowledgeRetrievalNode(LLMNode):
"_source": "knowledge",
"dataset_id": item.metadata.get("dataset_id"),
"dataset_name": item.metadata.get("dataset_name"),
"document_id": item.metadata.get("document_id") or item.metadata.get("title"),
"document_name": item.metadata.get("title"),
"data_source_type": "external",
"retriever_from": "workflow",
@ -348,7 +349,9 @@ class KnowledgeRetrievalNode(LLMNode):
)
)
metadata_condition = MetadataCondition(
logical_operator=node_data.metadata_filtering_conditions.logical_operator, # type: ignore
logical_operator=node_data.metadata_filtering_conditions.logical_operator
if node_data.metadata_filtering_conditions
else "or", # type: ignore
conditions=conditions,
)
elif node_data.metadata_filtering_mode == "manual":
@ -379,7 +382,10 @@ class KnowledgeRetrievalNode(LLMNode):
else:
raise ValueError("Invalid metadata filtering mode")
if filters:
if node_data.metadata_filtering_conditions.logical_operator == "and": # type: ignore
if (
node_data.metadata_filtering_conditions
and node_data.metadata_filtering_conditions.logical_operator == "and"
): # type: ignore
document_query = document_query.filter(and_(*filters))
else:
document_query = document_query.filter(or_(*filters))
@ -596,7 +602,6 @@ class KnowledgeRetrievalNode(LLMNode):
def _get_prompt_template(self, node_data: KnowledgeRetrievalNodeData, metadata_fields: list, query: str):
model_mode = ModelMode.value_of(node_data.metadata_model_config.mode) # type: ignore
input_text = query
memory_str = ""
prompt_messages: list[LLMNodeChatModelMessage] = []
if model_mode == ModelMode.CHAT:

5
api/core/workflow/nodes/list_operator/node.py
View File

@ -149,7 +149,10 @@ class ListOperatorNode(BaseNode[ListOperatorNodeData]):
def _extract_slice(
self, variable: Union[ArrayFileSegment, ArrayNumberSegment, ArrayStringSegment]
) -> Union[ArrayFileSegment, ArrayNumberSegment, ArrayStringSegment]:
value = int(self.graph_runtime_state.variable_pool.convert_template(self.node_data.extract_by.serial).text) - 1
value = int(self.graph_runtime_state.variable_pool.convert_template(self.node_data.extract_by.serial).text)
if value < 1:
raise ValueError(f"Invalid serial index: must be >= 1, got {value}")
value -= 1
if len(variable.value) > int(value):
result = variable.value[value]
else:

2
api/core/workflow/nodes/llm/entities.py
View File

@ -65,6 +65,8 @@ class LLMNodeData(BaseNodeData):
memory: Optional[MemoryConfig] = None
context: ContextConfig
vision: VisionConfig = Field(default_factory=VisionConfig)
structured_output: dict | None = None
structured_output_enabled: bool = False
@field_validator("prompt_config", mode="before")
@classmethod

293
api/core/workflow/nodes/llm/node.py
View File

@ -4,6 +4,8 @@ from collections.abc import Generator, Mapping, Sequence
from datetime import UTC, datetime
from typing import TYPE_CHECKING, Any, Optional, cast
import json_repair
from configs import dify_config
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
from core.entities.model_entities import ModelStatus
@ -22,14 +24,21 @@ from core.model_runtime.entities import (
from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
from core.model_runtime.entities.message_entities import (
AssistantPromptMessage,
PromptMessageContent,
PromptMessageContentUnionTypes,
PromptMessageRole,
SystemPromptMessage,
UserPromptMessage,
)
from core.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey, ModelType
from core.model_runtime.entities.model_entities import (
AIModelEntity,
ModelFeature,
ModelPropertyKey,
ModelType,
ParameterRule,
)
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.model_runtime.utils.encoders import jsonable_encoder
from core.model_runtime.utils.helper import convert_llm_result_chunk_to_str
from core.plugin.entities.plugin import ModelProviderID
from core.prompt.entities.advanced_prompt_entities import CompletionModelPromptTemplate, MemoryConfig
from core.prompt.utils.prompt_message_util import PromptMessageUtil
@ -57,6 +66,12 @@ from core.workflow.nodes.event import (
RunRetrieverResourceEvent,
RunStreamChunkEvent,
)
from core.workflow.utils.structured_output.entities import (
ResponseFormat,
SpecialModelType,
SupportStructuredOutputStatus,
)
from core.workflow.utils.structured_output.prompt import STRUCTURED_OUTPUT_PROMPT
from core.workflow.utils.variable_template_parser import VariableTemplateParser
from extensions.ext_database import db
from models.model import Conversation
@ -92,6 +107,12 @@ class LLMNode(BaseNode[LLMNodeData]):
_node_type = NodeType.LLM
def _run(self) -> Generator[NodeEvent | InNodeEvent, None, None]:
def process_structured_output(text: str) -> Optional[dict[str, Any] | list[Any]]:
"""Process structured output if enabled"""
if not self.node_data.structured_output_enabled or not self.node_data.structured_output:
return None
return self._parse_structured_output(text)
node_inputs: Optional[dict[str, Any]] = None
process_data = None
result_text = ""
@ -130,7 +151,6 @@ class LLMNode(BaseNode[LLMNodeData]):
if isinstance(event, RunRetrieverResourceEvent):
context = event.context
yield event
if context:
node_inputs["#context#"] = context
@ -192,7 +212,9 @@ class LLMNode(BaseNode[LLMNodeData]):
self.deduct_llm_quota(tenant_id=self.tenant_id, model_instance=model_instance, usage=usage)
break
outputs = {"text": result_text, "usage": jsonable_encoder(usage), "finish_reason": finish_reason}
structured_output = process_structured_output(result_text)
if structured_output:
outputs["structured_output"] = structured_output
yield RunCompletedEvent(
run_result=NodeRunResult(
status=WorkflowNodeExecutionStatus.SUCCEEDED,
@ -248,18 +270,7 @@ class LLMNode(BaseNode[LLMNodeData]):
def _handle_invoke_result(self, invoke_result: LLMResult | Generator) -> Generator[NodeEvent, None, None]:
if isinstance(invoke_result, LLMResult):
content = invoke_result.message.content
if content is None:
message_text = ""
elif isinstance(content, str):
message_text = content
elif isinstance(content, list):
# Assuming the list contains PromptMessageContent objects with a "data" attribute
message_text = "".join(
item.data if hasattr(item, "data") and isinstance(item.data, str) else str(item) for item in content
)
else:
message_text = str(content)
message_text = convert_llm_result_chunk_to_str(invoke_result.message.content)
yield ModelInvokeCompletedEvent(
text=message_text,
@ -274,7 +285,7 @@ class LLMNode(BaseNode[LLMNodeData]):
usage = None
finish_reason = None
for result in invoke_result:
text = result.delta.message.content
text = convert_llm_result_chunk_to_str(result.delta.message.content)
full_text += text
yield RunStreamChunkEvent(chunk_content=text, from_variable_selector=[self.node_id, "text"])
@ -513,7 +524,12 @@ class LLMNode(BaseNode[LLMNodeData]):
if not model_schema:
raise ModelNotExistError(f"Model {model_name} not exist.")
support_structured_output = self._check_model_structured_output_support()
if support_structured_output == SupportStructuredOutputStatus.SUPPORTED:
completion_params = self._handle_native_json_schema(completion_params, model_schema.parameter_rules)
elif support_structured_output == SupportStructuredOutputStatus.UNSUPPORTED:
# Set appropriate response format based on model capabilities
self._set_response_format(completion_params, model_schema.parameter_rules)
return model_instance, ModelConfigWithCredentialsEntity(
provider=provider_name,
model=model_name,
@ -568,8 +584,7 @@ class LLMNode(BaseNode[LLMNodeData]):
variable_pool: VariablePool,
jinja2_variables: Sequence[VariableSelector],
) -> tuple[Sequence[PromptMessage], Optional[Sequence[str]]]:
# FIXME: fix the type error cause prompt_messages is type quick a few times
prompt_messages: list[Any] = []
prompt_messages: list[PromptMessage] = []
if isinstance(prompt_template, list):
# For chat model
@ -631,12 +646,14 @@ class LLMNode(BaseNode[LLMNodeData]):
# For issue #11247 - Check if prompt content is a string or a list
prompt_content_type = type(prompt_content)
if prompt_content_type == str:
prompt_content = str(prompt_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
elif prompt_content_type == list:
prompt_content = prompt_content if isinstance(prompt_content, list) else []
for content_item in prompt_content:
if content_item.type == PromptMessageContentType.TEXT:
if "#histories#" in content_item.data:
@ -649,9 +666,10 @@ class LLMNode(BaseNode[LLMNodeData]):
# Add current query to the prompt message
if sys_query:
if prompt_content_type == str:
prompt_content = prompt_messages[0].content.replace("#sys.query#", sys_query)
prompt_content = str(prompt_messages[0].content).replace("#sys.query#", sys_query)
prompt_messages[0].content = prompt_content
elif prompt_content_type == list:
prompt_content = prompt_content if isinstance(prompt_content, list) else []
for content_item in prompt_content:
if content_item.type == PromptMessageContentType.TEXT:
content_item.data = sys_query + "\n" + content_item.data
@ -681,7 +699,7 @@ class LLMNode(BaseNode[LLMNodeData]):
filtered_prompt_messages = []
for prompt_message in prompt_messages:
if isinstance(prompt_message.content, list):
prompt_message_content = []
prompt_message_content: list[PromptMessageContentUnionTypes] = []
for content_item in prompt_message.content:
# Skip content if features are not defined
if not model_config.model_schema.features:
@ -724,10 +742,29 @@ class LLMNode(BaseNode[LLMNodeData]):
"No prompt found in the LLM configuration. "
"Please ensure a prompt is properly configured before proceeding."
)
support_structured_output = self._check_model_structured_output_support()
if support_structured_output == SupportStructuredOutputStatus.UNSUPPORTED:
filtered_prompt_messages = self._handle_prompt_based_schema(
prompt_messages=filtered_prompt_messages,
)
stop = model_config.stop
return filtered_prompt_messages, stop
def _parse_structured_output(self, result_text: str) -> dict[str, Any] | list[Any]:
structured_output: dict[str, Any] | list[Any] = {}
try:
parsed = json.loads(result_text)
if not isinstance(parsed, (dict | list)):
raise LLMNodeError(f"Failed to parse structured output: {result_text}")
structured_output = parsed
except json.JSONDecodeError as e:
# if the result_text is not a valid json, try to repair it
parsed = json_repair.loads(result_text)
if not isinstance(parsed, (dict | list)):
raise LLMNodeError(f"Failed to parse structured output: {result_text}")
structured_output = parsed
return structured_output
@classmethod
def deduct_llm_quota(cls, tenant_id: str, model_instance: ModelInstance, usage: LLMUsage) -> None:
provider_model_bundle = model_instance.provider_model_bundle
@ -926,8 +963,170 @@ class LLMNode(BaseNode[LLMNodeData]):
return prompt_messages
def _handle_native_json_schema(self, model_parameters: dict, rules: list[ParameterRule]) -> dict:
"""
Handle structured output for models with native JSON schema support.
def _combine_message_content_with_role(*, contents: Sequence[PromptMessageContent], role: PromptMessageRole):
:param model_parameters: Model parameters to update
:param rules: Model parameter rules
:return: Updated model parameters with JSON schema configuration
"""
# Process schema according to model requirements
schema = self._fetch_structured_output_schema()
schema_json = self._prepare_schema_for_model(schema)
# Set JSON schema in parameters
model_parameters["json_schema"] = json.dumps(schema_json, ensure_ascii=False)
# Set appropriate response format if required by the model
for rule in rules:
if rule.name == "response_format" and ResponseFormat.JSON_SCHEMA.value in rule.options:
model_parameters["response_format"] = ResponseFormat.JSON_SCHEMA.value
return model_parameters
def _handle_prompt_based_schema(self, prompt_messages: Sequence[PromptMessage]) -> list[PromptMessage]:
"""
Handle structured output for models without native JSON schema support.
This function modifies the prompt messages to include schema-based output requirements.
Args:
prompt_messages: Original sequence of prompt messages
Returns:
list[PromptMessage]: Updated prompt messages with structured output requirements
"""
# Convert schema to string format
schema_str = json.dumps(self._fetch_structured_output_schema(), ensure_ascii=False)
# Find existing system prompt with schema placeholder
system_prompt = next(
(prompt for prompt in prompt_messages if isinstance(prompt, SystemPromptMessage)),
None,
)
structured_output_prompt = STRUCTURED_OUTPUT_PROMPT.replace("{{schema}}", schema_str)
# Prepare system prompt content
system_prompt_content = (
structured_output_prompt + "\n\n" + system_prompt.content
if system_prompt and isinstance(system_prompt.content, str)
else structured_output_prompt
)
system_prompt = SystemPromptMessage(content=system_prompt_content)
# Extract content from the last user message
filtered_prompts = [prompt for prompt in prompt_messages if not isinstance(prompt, SystemPromptMessage)]
updated_prompt = [system_prompt] + filtered_prompts
return updated_prompt
def _set_response_format(self, model_parameters: dict, rules: list) -> None:
"""
Set the appropriate response format parameter based on model rules.
:param model_parameters: Model parameters to update
:param rules: Model parameter rules
"""
for rule in rules:
if rule.name == "response_format":
if ResponseFormat.JSON.value in rule.options:
model_parameters["response_format"] = ResponseFormat.JSON.value
elif ResponseFormat.JSON_OBJECT.value in rule.options:
model_parameters["response_format"] = ResponseFormat.JSON_OBJECT.value
def _prepare_schema_for_model(self, schema: dict) -> dict:
"""
Prepare JSON schema based on model requirements.
Different models have different requirements for JSON schema formatting.
This function handles these differences.
:param schema: The original JSON schema
:return: Processed schema compatible with the current model
"""
# Deep copy to avoid modifying the original schema
processed_schema = schema.copy()
# Convert boolean types to string types (common requirement)
convert_boolean_to_string(processed_schema)
# Apply model-specific transformations
if SpecialModelType.GEMINI in self.node_data.model.name:
remove_additional_properties(processed_schema)
return processed_schema
elif SpecialModelType.OLLAMA in self.node_data.model.provider:
return processed_schema
else:
# Default format with name field
return {"schema": processed_schema, "name": "llm_response"}
def _fetch_model_schema(self, provider: str) -> AIModelEntity | None:
"""
Fetch model schema
"""
model_name = self.node_data.model.name
model_manager = ModelManager()
model_instance = model_manager.get_model_instance(
tenant_id=self.tenant_id, model_type=ModelType.LLM, provider=provider, model=model_name
)
model_type_instance = model_instance.model_type_instance
model_type_instance = cast(LargeLanguageModel, model_type_instance)
model_credentials = model_instance.credentials
model_schema = model_type_instance.get_model_schema(model_name, model_credentials)
return model_schema
def _fetch_structured_output_schema(self) -> dict[str, Any]:
"""
Fetch the structured output schema from the node data.
Returns:
dict[str, Any]: The structured output schema
"""
if not self.node_data.structured_output:
raise LLMNodeError("Please provide a valid structured output schema")
structured_output_schema = json.dumps(self.node_data.structured_output.get("schema", {}), ensure_ascii=False)
if not structured_output_schema:
raise LLMNodeError("Please provide a valid structured output schema")
try:
schema = json.loads(structured_output_schema)
if not isinstance(schema, dict):
raise LLMNodeError("structured_output_schema must be a JSON object")
return schema
except json.JSONDecodeError:
raise LLMNodeError("structured_output_schema is not valid JSON format")
def _check_model_structured_output_support(self) -> SupportStructuredOutputStatus:
"""
Check if the current model supports structured output.
Returns:
SupportStructuredOutput: The support status of structured output
"""
# Early return if structured output is disabled
if (
not isinstance(self.node_data, LLMNodeData)
or not self.node_data.structured_output_enabled
or not self.node_data.structured_output
):
return SupportStructuredOutputStatus.DISABLED
# Get model schema and check if it exists
model_schema = self._fetch_model_schema(self.node_data.model.provider)
if not model_schema:
return SupportStructuredOutputStatus.DISABLED
# Check if model supports structured output feature
return (
SupportStructuredOutputStatus.SUPPORTED
if bool(model_schema.features and ModelFeature.STRUCTURED_OUTPUT in model_schema.features)
else SupportStructuredOutputStatus.UNSUPPORTED
)
def _combine_message_content_with_role(
*, contents: Optional[str | list[PromptMessageContentUnionTypes]] = None, role: PromptMessageRole
):
match role:
case PromptMessageRole.USER:
return UserPromptMessage(content=contents)
@ -1064,3 +1263,49 @@ def _handle_completion_template(
)
prompt_messages.append(prompt_message)
return prompt_messages
def remove_additional_properties(schema: dict) -> None:
"""
Remove additionalProperties fields from JSON schema.
Used for models like Gemini that don't support this property.
:param schema: JSON schema to modify in-place
"""
if not isinstance(schema, dict):
return
# Remove additionalProperties at current level
schema.pop("additionalProperties", None)
# Process nested structures recursively
for value in schema.values():
if isinstance(value, dict):
remove_additional_properties(value)
elif isinstance(value, list):
for item in value:
if isinstance(item, dict):
remove_additional_properties(item)
def convert_boolean_to_string(schema: dict) -> None:
"""
Convert boolean type specifications to string in JSON schema.
:param schema: JSON schema to modify in-place
"""
if not isinstance(schema, dict):
return
# Check for boolean type at current level
if schema.get("type") == "boolean":
schema["type"] = "string"
# Process nested dictionaries and lists recursively
for value in schema.values():
if isinstance(value, dict):
convert_boolean_to_string(value)
elif isinstance(value, list):
for item in value:
if isinstance(item, dict):
convert_boolean_to_string(item)

62
api/core/workflow/nodes/parameter_extractor/parameter_extractor_node.py
View File

@ -1,4 +1,5 @@
import json
import logging
import uuid
from collections.abc import Mapping, Sequence
from typing import Any, Optional, cast
@ -58,6 +59,30 @@ from .prompts import (
FUNCTION_CALLING_EXTRACTOR_USER_TEMPLATE,
)
logger = logging.getLogger(__name__)
def extract_json(text):
"""
From a given JSON started from '{' or '[' extract the complete JSON object.
"""
stack = []
for i, c in enumerate(text):
if c in {"{", "["}:
stack.append(c)
elif c in {"}", "]"}:
# check if stack is empty
if not stack:
return text[:i]
# check if the last element in stack is matching
if (c == "}" and stack[-1] == "{") or (c == "]" and stack[-1] == "["):
stack.pop()
if not stack:
return text[: i + 1]
else:
return text[:i]
return None
class ParameterExtractorNode(LLMNode):
"""
@ -161,6 +186,8 @@ class ParameterExtractorNode(LLMNode):
"usage": None,
"function": {} if not prompt_message_tools else jsonable_encoder(prompt_message_tools[0]),
"tool_call": None,
"model_provider": model_config.provider,
"model_name": model_config.model,
}
try:
@ -594,27 +621,6 @@ class ParameterExtractorNode(LLMNode):
Extract complete json response.
"""
def extract_json(text):
"""
From a given JSON started from '{' or '[' extract the complete JSON object.
"""
stack = []
for i, c in enumerate(text):
if c in {"{", "["}:
stack.append(c)
elif c in {"}", "]"}:
# check if stack is empty
if not stack:
return text[:i]
# check if the last element in stack is matching
if (c == "}" and stack[-1] == "{") or (c == "]" and stack[-1] == "["):
stack.pop()
if not stack:
return text[: i + 1]
else:
return text[:i]
return None
# extract json from the text
for idx in range(len(result)):
if result[idx] == "{" or result[idx] == "[":
@ -624,6 +630,7 @@ class ParameterExtractorNode(LLMNode):
return cast(dict, json.loads(json_str))
except Exception:
pass
logger.info(f"extra error: {result}")
return None
def _extract_json_from_tool_call(self, tool_call: AssistantPromptMessage.ToolCall) -> Optional[dict]:
@ -633,7 +640,18 @@ class ParameterExtractorNode(LLMNode):
if not tool_call or not tool_call.function.arguments:
return None
return cast(dict, json.loads(tool_call.function.arguments))
result = tool_call.function.arguments
# extract json from the arguments
for idx in range(len(result)):
if result[idx] == "{" or result[idx] == "[":
json_str = extract_json(result[idx:])
if json_str:
try:
return cast(dict, json.loads(json_str))
except Exception:
pass
logger.info(f"extra error: {result}")
return None
def _generate_default_result(self, data: ParameterExtractorNodeData) -> dict:
"""

2
api/core/workflow/nodes/question_classifier/question_classifier_node.py
View File

@ -130,6 +130,8 @@ class QuestionClassifierNode(LLMNode):
),
"usage": jsonable_encoder(usage),
"finish_reason": finish_reason,
"model_provider": model_config.provider,
"model_name": model_config.model,
}
outputs = {"class_name": category_name, "class_id": category_id}

34
api/core/workflow/nodes/question_classifier/template_prompts.py
View File

@ -1,21 +1,21 @@
QUESTION_CLASSIFIER_SYSTEM_PROMPT = """
### Job Description',
You are a text classification engine that analyzes text data and assigns categories based on user input or automatically determined categories.
### Task
Your task is to assign one categories ONLY to the input text and only one category may be assigned returned in the output. Additionally, you need to extract the key words from the text that are related to the classification.
### Format
The input text is in the variable input_text. Categories are specified as a category list with two filed category_id and category_name in the variable categories. Classification instructions may be included to improve the classification accuracy.
### Constraint
DO NOT include anything other than the JSON array in your response.
### Memory
Here are the chat histories between human and assistant, inside <histories></histories> XML tags.
<histories>
{histories}
</histories>
### Job Description',
You are a text classification engine that analyzes text data and assigns categories based on user input or automatically determined categories.
### Task
Your task is to assign one categories ONLY to the input text and only one category may be assigned returned in the output. Additionally, you need to extract the key words from the text that are related to the classification.
### Format
The input text is in the variable input_text. Categories are specified as a category list with two filed category_id and category_name in the variable categories. Classification instructions may be included to improve the classification accuracy.
### Constraint
DO NOT include anything other than the JSON array in your response.
### Memory
Here are the chat histories between human and assistant, inside <histories></histories> XML tags.
<histories>
{histories}
</histories>
""" # noqa: E501
QUESTION_CLASSIFIER_USER_PROMPT_1 = """
{ "input_text": ["I recently had a great experience with your company. The service was prompt and the staff was very friendly."],
{"input_text": ["I recently had a great experience with your company. The service was prompt and the staff was very friendly."],
"categories": [{"category_id":"f5660049-284f-41a7-b301-fd24176a711c","category_name":"Customer Service"},{"category_id":"8d007d06-f2c9-4be5-8ff6-cd4381c13c60","category_name":"Satisfaction"},{"category_id":"5fbbbb18-9843-466d-9b8e-b9bfbb9482c8","category_name":"Sales"},{"category_id":"23623c75-7184-4a2e-8226-466c2e4631e4","category_name":"Product"}],
"classification_instructions": ["classify the text based on the feedback provided by customer"]}
""" # noqa: E501
@ -43,9 +43,9 @@ QUESTION_CLASSIFIER_ASSISTANT_PROMPT_2 = """
"""
QUESTION_CLASSIFIER_USER_PROMPT_3 = """
'{{"input_text": ["{input_text}"],',
'"categories": {categories}, ',
'"classification_instructions": ["{classification_instructions}"]}}'
{{"input_text": ["{input_text}"],
"categories": {categories},
"classification_instructions": ["{classification_instructions}"]}}
"""
QUESTION_CLASSIFIER_COMPLETION_PROMPT = """

8
api/core/workflow/nodes/tool/tool_node.py
View File

@ -6,8 +6,8 @@ from sqlalchemy.orm import Session
from core.callback_handler.workflow_tool_callback_handler import DifyWorkflowCallbackHandler
from core.file import File, FileTransferMethod
from core.plugin.manager.exc import PluginDaemonClientSideError
from core.plugin.manager.plugin import PluginInstallationManager
from core.plugin.impl.exc import PluginDaemonClientSideError
from core.plugin.impl.plugin import PluginInstaller
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParameter
from core.tools.errors import ToolInvokeError
from core.tools.tool_engine import ToolEngine
@ -73,7 +73,7 @@ class ToolNode(BaseNode[ToolNodeData]):
metadata={NodeRunMetadataKey.TOOL_INFO: tool_info},
error=f"Failed to get tool runtime: {str(e)}",
error_type=type(e).__name__,
)
)
)
return
@ -307,7 +307,7 @@ class ToolNode(BaseNode[ToolNodeData]):
icon = tool_info.get("icon", "")
dict_metadata = dict(message.message.metadata)
if dict_metadata.get("provider"):
manager = PluginInstallationManager()
manager = PluginInstaller()
plugins = manager.list_plugins(self.tenant_id)
try:
current_plugin = next(

24
api/core/workflow/utils/structured_output/entities.py Normal file
View File

@ -0,0 +1,24 @@
from enum import StrEnum
class ResponseFormat(StrEnum):
"""Constants for model response formats"""
JSON_SCHEMA = "json_schema" # model's structured output mode. some model like gemini, gpt-4o, support this mode.
JSON = "JSON" # model's json mode. some model like claude support this mode.
JSON_OBJECT = "json_object" # json mode's another alias. some model like deepseek-chat, qwen use this alias.
class SpecialModelType(StrEnum):
"""Constants for identifying model types"""
GEMINI = "gemini"
OLLAMA = "ollama"
class SupportStructuredOutputStatus(StrEnum):
"""Constants for structured output support status"""
SUPPORTED = "supported"
UNSUPPORTED = "unsupported"
DISABLED = "disabled"

17
api/core/workflow/utils/structured_output/prompt.py Normal file
View File

@ -0,0 +1,17 @@
STRUCTURED_OUTPUT_PROMPT = """You’re a helpful AI assistant. You could answer questions and output in JSON format.
constraints:
- You must output in JSON format.
- Do not output boolean value, use string type instead.
- Do not output integer or float value, use number type instead.
eg:
Here is the JSON schema:
{"additionalProperties": false, "properties": {"age": {"type": "number"}, "name": {"type": "string"}}, "required": ["name", "age"], "type": "object"}
Here is the user's question:
My name is John Doe and I am 30 years old.
output:
{"name": "John Doe", "age": 30}
Here is the JSON schema:
{{schema}}
""" # noqa: E501