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zxhlyh
2025-07-21 17:45:26 +08:00
parent caa2de3344 8246f946c2
commit 4d36e784b7
117 changed files with 1639 additions and 419 deletions
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4
api/core/app/apps/message_based_app_generator.py
View File

@ -1,7 +1,6 @@
import json
import logging
from collections.abc import Generator
from datetime import UTC, datetime
from typing import Optional, Union, cast
from core.app.app_config.entities import EasyUIBasedAppConfig, EasyUIBasedAppModelConfigFrom
@ -25,6 +24,7 @@ from core.app.entities.task_entities import (
from core.app.task_pipeline.easy_ui_based_generate_task_pipeline import EasyUIBasedGenerateTaskPipeline
from core.prompt.utils.prompt_template_parser import PromptTemplateParser
from extensions.ext_database import db
from libs.datetime_utils import naive_utc_now
from models import Account
from models.enums import CreatorUserRole
from models.model import App, AppMode, AppModelConfig, Conversation, EndUser, Message, MessageFile
@ -184,7 +184,7 @@ class MessageBasedAppGenerator(BaseAppGenerator):
db.session.commit()
db.session.refresh(conversation)
else:
conversation.updated_at = datetime.now(UTC).replace(tzinfo=None)
conversation.updated_at = naive_utc_now()
db.session.commit()
message = Message(

46
api/core/file/file_manager.py
View File

@ -7,6 +7,7 @@ from core.model_runtime.entities import (
AudioPromptMessageContent,
DocumentPromptMessageContent,
ImagePromptMessageContent,
TextPromptMessageContent,
VideoPromptMessageContent,
)
from core.model_runtime.entities.message_entities import PromptMessageContentUnionTypes
@ -44,11 +45,44 @@ def to_prompt_message_content(
*,
image_detail_config: ImagePromptMessageContent.DETAIL | None = None,
) -> PromptMessageContentUnionTypes:
"""
Convert a file to prompt message content.
This function converts files to their appropriate prompt message content types.
For supported file types (IMAGE, AUDIO, VIDEO, DOCUMENT), it creates the
corresponding message content with proper encoding/URL.
For unsupported file types, instead of raising an error, it returns a
TextPromptMessageContent with a descriptive message about the file.
Args:
f: The file to convert
image_detail_config: Optional detail configuration for image files
Returns:
PromptMessageContentUnionTypes: The appropriate message content type
Raises:
ValueError: If file extension or mime_type is missing
"""
if f.extension is None:
raise ValueError("Missing file extension")
if f.mime_type is None:
raise ValueError("Missing file mime_type")
prompt_class_map: Mapping[FileType, type[PromptMessageContentUnionTypes]] = {
FileType.IMAGE: ImagePromptMessageContent,
FileType.AUDIO: AudioPromptMessageContent,
FileType.VIDEO: VideoPromptMessageContent,
FileType.DOCUMENT: DocumentPromptMessageContent,
}
# Check if file type is supported
if f.type not in prompt_class_map:
# For unsupported file types, return a text description
return TextPromptMessageContent(data=f"[Unsupported file type: {f.filename} ({f.type.value})]")
# Process supported file types
params = {
"base64_data": _get_encoded_string(f) if dify_config.MULTIMODAL_SEND_FORMAT == "base64" else "",
"url": _to_url(f) if dify_config.MULTIMODAL_SEND_FORMAT == "url" else "",
@ -58,17 +92,7 @@ def to_prompt_message_content(
if f.type == FileType.IMAGE:
params["detail"] = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
prompt_class_map: Mapping[FileType, type[PromptMessageContentUnionTypes]] = {
FileType.IMAGE: ImagePromptMessageContent,
FileType.AUDIO: AudioPromptMessageContent,
FileType.VIDEO: VideoPromptMessageContent,
FileType.DOCUMENT: DocumentPromptMessageContent,
}
try:
return prompt_class_map[f.type].model_validate(params)
except KeyError:
raise ValueError(f"file type {f.type} is not supported")
return prompt_class_map[f.type].model_validate(params)
def download(f: File, /):

2
api/core/mcp/auth/auth_provider.py
View File

@ -8,7 +8,7 @@ from core.mcp.types import (
OAuthTokens,
)
from models.tools import MCPToolProvider
from services.tools.mcp_tools_mange_service import MCPToolManageService
from services.tools.mcp_tools_manage_service import MCPToolManageService
LATEST_PROTOCOL_VERSION = "1.0"

19
api/core/mcp/mcp_client.py
View File

@ -68,15 +68,17 @@ class MCPClient:
}
parsed_url = urlparse(self.server_url)
path = parsed_url.path
method_name = path.rstrip("/").split("/")[-1] if path else ""
try:
path = parsed_url.path or ""
method_name = path.removesuffix("/").lower()
if method_name in connection_methods:
client_factory = connection_methods[method_name]
self.connect_server(client_factory, method_name)
except KeyError:
else:
try:
logger.debug(f"Not supported method {method_name} found in URL path, trying default 'mcp' method.")
self.connect_server(sse_client, "sse")
except MCPConnectionError:
logger.debug("MCP connection failed with 'sse', falling back to 'mcp' method.")
self.connect_server(streamablehttp_client, "mcp")
def connect_server(
@ -91,7 +93,7 @@ class MCPClient:
else {}
)
self._streams_context = client_factory(url=self.server_url, headers=headers)
if self._streams_context is None:
if not self._streams_context:
raise MCPConnectionError("Failed to create connection context")
# Use exit_stack to manage context managers properly
@ -141,10 +143,11 @@ class MCPClient:
try:
# ExitStack will handle proper cleanup of all managed context managers
self.exit_stack.close()
except Exception as e:
logging.exception("Error during cleanup")
raise ValueError(f"Error during cleanup: {e}")
finally:
self._session = None
self._session_context = None
self._streams_context = None
self._initialized = False
except Exception as e:
logging.exception("Error during cleanup")
raise ValueError(f"Error during cleanup: {e}")

79
api/core/ops/arize_phoenix_trace/arize_phoenix_trace.py
View File

@ -3,7 +3,7 @@ import json
import logging
import os
from datetime import datetime, timedelta
from typing import Optional, Union, cast
from typing import Any, Optional, Union, cast
from openinference.semconv.trace import OpenInferenceSpanKindValues, SpanAttributes
from opentelemetry import trace
@ -142,11 +142,8 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
raise
def workflow_trace(self, trace_info: WorkflowTraceInfo):
if trace_info.message_data is None:
return
workflow_metadata = {
"workflow_id": trace_info.workflow_run_id or "",
"workflow_run_id": trace_info.workflow_run_id or "",
"message_id": trace_info.message_id or "",
"workflow_app_log_id": trace_info.workflow_app_log_id or "",
"status": trace_info.workflow_run_status or "",
@ -156,7 +153,7 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
}
workflow_metadata.update(trace_info.metadata)
trace_id = uuid_to_trace_id(trace_info.message_id)
trace_id = uuid_to_trace_id(trace_info.workflow_run_id)
span_id = RandomIdGenerator().generate_span_id()
context = SpanContext(
trace_id=trace_id,
@ -213,7 +210,7 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
if model:
node_metadata["ls_model_name"] = model
outputs = json.loads(node_execution.outputs).get("usage", {})
outputs = json.loads(node_execution.outputs).get("usage", {}) if "outputs" in node_execution else {}
usage_data = process_data.get("usage", {}) if "usage" in process_data else outputs.get("usage", {})
if usage_data:
node_metadata["total_tokens"] = usage_data.get("total_tokens", 0)
@ -236,31 +233,34 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
SpanAttributes.SESSION_ID: trace_info.conversation_id or "",
},
start_time=datetime_to_nanos(created_at),
context=trace.set_span_in_context(trace.NonRecordingSpan(context)),
)
try:
if node_execution.node_type == "llm":
llm_attributes: dict[str, Any] = {
SpanAttributes.INPUT_VALUE: json.dumps(process_data.get("prompts", []), ensure_ascii=False),
}
provider = process_data.get("model_provider")
model = process_data.get("model_name")
if provider:
node_span.set_attribute(SpanAttributes.LLM_PROVIDER, provider)
llm_attributes[SpanAttributes.LLM_PROVIDER] = provider
if model:
node_span.set_attribute(SpanAttributes.LLM_MODEL_NAME, model)
outputs = json.loads(node_execution.outputs).get("usage", {})
llm_attributes[SpanAttributes.LLM_MODEL_NAME] = model
outputs = (
json.loads(node_execution.outputs).get("usage", {}) if "outputs" in node_execution else {}
)
usage_data = (
process_data.get("usage", {}) if "usage" in process_data else outputs.get("usage", {})
)
if usage_data:
node_span.set_attribute(
SpanAttributes.LLM_TOKEN_COUNT_TOTAL, usage_data.get("total_tokens", 0)
)
node_span.set_attribute(
SpanAttributes.LLM_TOKEN_COUNT_PROMPT, usage_data.get("prompt_tokens", 0)
)
node_span.set_attribute(
SpanAttributes.LLM_TOKEN_COUNT_COMPLETION, usage_data.get("completion_tokens", 0)
llm_attributes[SpanAttributes.LLM_TOKEN_COUNT_TOTAL] = usage_data.get("total_tokens", 0)
llm_attributes[SpanAttributes.LLM_TOKEN_COUNT_PROMPT] = usage_data.get("prompt_tokens", 0)
llm_attributes[SpanAttributes.LLM_TOKEN_COUNT_COMPLETION] = usage_data.get(
"completion_tokens", 0
)
llm_attributes.update(self._construct_llm_attributes(process_data.get("prompts", [])))
node_span.set_attributes(llm_attributes)
finally:
node_span.end(end_time=datetime_to_nanos(finished_at))
finally:
@ -352,25 +352,7 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
SpanAttributes.METADATA: json.dumps(message_metadata, ensure_ascii=False),
SpanAttributes.SESSION_ID: trace_info.message_data.conversation_id,
}
if isinstance(trace_info.inputs, list):
for i, msg in enumerate(trace_info.inputs):
if isinstance(msg, dict):
llm_attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.{i}.message.content"] = msg.get("text", "")
llm_attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.{i}.message.role"] = msg.get(
"role", "user"
)
# todo: handle assistant and tool role messages, as they don't always
# have a text field, but may have a tool_calls field instead
# e.g. 'tool_calls': [{'id': '98af3a29-b066-45a5-b4b1-46c74ddafc58',
# 'type': 'function', 'function': {'name': 'current_time', 'arguments': '{}'}}]}
elif isinstance(trace_info.inputs, dict):
llm_attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.content"] = json.dumps(trace_info.inputs)
llm_attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.role"] = "user"
elif isinstance(trace_info.inputs, str):
llm_attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.content"] = trace_info.inputs
llm_attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.role"] = "user"
llm_attributes.update(self._construct_llm_attributes(trace_info.inputs))
if trace_info.total_tokens is not None and trace_info.total_tokens > 0:
llm_attributes[SpanAttributes.LLM_TOKEN_COUNT_TOTAL] = trace_info.total_tokens
if trace_info.message_tokens is not None and trace_info.message_tokens > 0:
@ -724,3 +706,24 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
.all()
)
return workflow_nodes
def _construct_llm_attributes(self, prompts: dict | list | str | None) -> dict[str, str]:
"""Helper method to construct LLM attributes with passed prompts."""
attributes = {}
if isinstance(prompts, list):
for i, msg in enumerate(prompts):
if isinstance(msg, dict):
attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.{i}.message.content"] = msg.get("text", "")
attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.{i}.message.role"] = msg.get("role", "user")
# todo: handle assistant and tool role messages, as they don't always
# have a text field, but may have a tool_calls field instead
# e.g. 'tool_calls': [{'id': '98af3a29-b066-45a5-b4b1-46c74ddafc58',
# 'type': 'function', 'function': {'name': 'current_time', 'arguments': '{}'}}]}
elif isinstance(prompts, dict):
attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.content"] = json.dumps(prompts)
attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.role"] = "user"
elif isinstance(prompts, str):
attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.content"] = prompts
attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.role"] = "user"
return attributes

15
api/core/rag/datasource/vdb/analyticdb/analyticdb_vector_openapi.py
View File

@ -233,6 +233,12 @@ class AnalyticdbVectorOpenAPI:
def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
document_ids_filter = kwargs.get("document_ids_filter")
where_clause = ""
if document_ids_filter:
document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
where_clause += f"metadata_->>'document_id' IN ({document_ids})"
score_threshold = kwargs.get("score_threshold") or 0.0
request = gpdb_20160503_models.QueryCollectionDataRequest(
dbinstance_id=self.config.instance_id,
@ -245,7 +251,7 @@ class AnalyticdbVectorOpenAPI:
vector=query_vector,
content=None,
top_k=kwargs.get("top_k", 4),
filter=None,
filter=where_clause,
)
response = self._client.query_collection_data(request)
documents = []
@ -265,6 +271,11 @@ class AnalyticdbVectorOpenAPI:
def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
document_ids_filter = kwargs.get("document_ids_filter")
where_clause = ""
if document_ids_filter:
document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
where_clause += f"metadata_->>'document_id' IN ({document_ids})"
score_threshold = float(kwargs.get("score_threshold") or 0.0)
request = gpdb_20160503_models.QueryCollectionDataRequest(
dbinstance_id=self.config.instance_id,
@ -277,7 +288,7 @@ class AnalyticdbVectorOpenAPI:
vector=None,
content=query,
top_k=kwargs.get("top_k", 4),
filter=None,
filter=where_clause,
)
response = self._client.query_collection_data(request)
documents = []

11
api/core/rag/datasource/vdb/elasticsearch/elasticsearch_vector.py
View File

@ -147,10 +147,17 @@ class ElasticSearchVector(BaseVector):
return docs
def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
query_str = {"match": {Field.CONTENT_KEY.value: query}}
query_str: dict[str, Any] = {"match": {Field.CONTENT_KEY.value: query}}
document_ids_filter = kwargs.get("document_ids_filter")
if document_ids_filter:
query_str["filter"] = {"terms": {"metadata.document_id": document_ids_filter}} # type: ignore
query_str = {
"bool": {
"must": {"match": {Field.CONTENT_KEY.value: query}},
"filter": {"terms": {"metadata.document_id": document_ids_filter}},
}
}
results = self._client.search(index=self._collection_name, query=query_str, size=kwargs.get("top_k", 4))
docs = []
for hit in results["hits"]["hits"]:

1
api/core/rag/splitter/fixed_text_splitter.py
View File

@ -102,6 +102,7 @@ class FixedRecursiveCharacterTextSplitter(EnhanceRecursiveCharacterTextSplitter)
splits = text.split()
else:
splits = text.split(separator)
splits = [item + separator if i < len(splits) else item for i, item in enumerate(splits)]
else:
splits = list(text)
splits = [s for s in splits if (s not in {"", "\n"})]

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

@ -21,7 +21,7 @@ from core.tools.plugin_tool.tool import PluginTool
from core.tools.utils.uuid_utils import is_valid_uuid
from core.tools.workflow_as_tool.provider import WorkflowToolProviderController
from core.workflow.entities.variable_pool import VariablePool
from services.tools.mcp_tools_mange_service import MCPToolManageService
from services.tools.mcp_tools_manage_service import MCPToolManageService
if TYPE_CHECKING:
from core.workflow.nodes.tool.entities import ToolEntity

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

@ -270,7 +270,14 @@ class AgentNode(BaseNode):
)
extra = tool.get("extra", {})
runtime_variable_pool = variable_pool if self._node_data.version != "1" else None
# This is an issue that caused problems before.
# Logically, we shouldn't use the node_data.version field for judgment
# But for backward compatibility with historical data
# this version field judgment is still preserved here.
runtime_variable_pool: VariablePool | None = None
if node_data.version != "1" or node_data.tool_node_version != "1":
runtime_variable_pool = variable_pool
tool_runtime = ToolManager.get_agent_tool_runtime(
self.tenant_id, self.app_id, entity, self.invoke_from, runtime_variable_pool
)

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

@ -13,6 +13,10 @@ class AgentNodeData(BaseNodeData):
agent_strategy_name: str
agent_strategy_label: str # redundancy
memory: MemoryConfig | None = None
# The version of the tool parameter.
# If this value is None, it indicates this is a previous version
# and requires using the legacy parameter parsing rules.
tool_node_version: str | None = None
class AgentInput(BaseModel):
value: Union[list[str], list[ToolSelector], Any]

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

@ -117,7 +117,7 @@ class KnowledgeRetrievalNodeData(BaseNodeData):
multiple_retrieval_config: Optional[MultipleRetrievalConfig] = None
single_retrieval_config: Optional[SingleRetrievalConfig] = None
metadata_filtering_mode: Optional[Literal["disabled", "automatic", "manual"]] = "disabled"
metadata_model_config: ModelConfig
metadata_model_config: Optional[ModelConfig] = None
metadata_filtering_conditions: Optional[MetadataFilteringCondition] = None
vision: VisionConfig = Field(default_factory=VisionConfig)

4
api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
View File

@ -509,6 +509,8 @@ class KnowledgeRetrievalNode(BaseNode):
# get all metadata field
metadata_fields = db.session.query(DatasetMetadata).filter(DatasetMetadata.dataset_id.in_(dataset_ids)).all()
all_metadata_fields = [metadata_field.name for metadata_field in metadata_fields]
if node_data.metadata_model_config is None:
raise ValueError("metadata_model_config is required")
# get metadata model instance and fetch model config
model_instance, model_config = self.get_model_config(node_data.metadata_model_config)
# fetch prompt messages
@ -701,7 +703,7 @@ class KnowledgeRetrievalNode(BaseNode):
)
def _get_prompt_template(self, node_data: KnowledgeRetrievalNodeData, metadata_fields: list, query: str):
model_mode = ModelMode(node_data.metadata_model_config.mode)
model_mode = ModelMode(node_data.metadata_model_config.mode) # type: ignore
input_text = query
prompt_messages: list[LLMNodeChatModelMessage] = []

6
api/core/workflow/nodes/node_mapping.py
View File

@ -75,6 +75,9 @@ NODE_TYPE_CLASSES_MAPPING: Mapping[NodeType, Mapping[str, type[BaseNode]]] = {
},
NodeType.TOOL: {
LATEST_VERSION: ToolNode,
# This is an issue that caused problems before.
# Logically, we shouldn't use two different versions to point to the same class here,
# but in order to maintain compatibility with historical data, this approach has been retained.
"2": ToolNode,
"1": ToolNode,
},
@ -125,6 +128,9 @@ NODE_TYPE_CLASSES_MAPPING: Mapping[NodeType, Mapping[str, type[BaseNode]]] = {
},
NodeType.AGENT: {
LATEST_VERSION: AgentNode,
# This is an issue that caused problems before.
# Logically, we shouldn't use two different versions to point to the same class here,
# but in order to maintain compatibility with historical data, this approach has been retained.
"2": AgentNode,
"1": AgentNode,
},

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

@ -59,6 +59,10 @@ class ToolNodeData(BaseNodeData, ToolEntity):
return typ
tool_parameters: dict[str, ToolInput]
# The version of the tool parameter.
# If this value is None, it indicates this is a previous version
# and requires using the legacy parameter parsing rules.
tool_node_version: str | None = None
@field_validator("tool_parameters", mode="before")
@classmethod

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

@ -70,7 +70,13 @@ class ToolNode(BaseNode):
try:
from core.tools.tool_manager import ToolManager
variable_pool = self.graph_runtime_state.variable_pool if self._node_data.version != "1" else None
# This is an issue that caused problems before.
# Logically, we shouldn't use the node_data.version field for judgment
# But for backward compatibility with historical data
# this version field judgment is still preserved here.
variable_pool: VariablePool | None = None
if node_data.version != "1" or node_data.tool_node_version != "1":
variable_pool = self.graph_runtime_state.variable_pool
tool_runtime = ToolManager.get_workflow_tool_runtime(
self.tenant_id, self.app_id, self.node_id, self._node_data, self.invoke_from, variable_pool
)

8
api/core/workflow/workflow_cycle_manager.py
View File

@ -1,6 +1,6 @@
from collections.abc import Mapping
from dataclasses import dataclass
from datetime import UTC, datetime
from datetime import datetime
from typing import Any, Optional, Union
from uuid import uuid4
@ -71,7 +71,7 @@ class WorkflowCycleManager:
workflow_version=self._workflow_info.version,
graph=self._workflow_info.graph_data,
inputs=inputs,
started_at=datetime.now(UTC).replace(tzinfo=None),
started_at=naive_utc_now(),
)
return self._save_and_cache_workflow_execution(execution)
@ -356,7 +356,7 @@ class WorkflowCycleManager:
created_at: Optional[datetime] = None,
) -> WorkflowNodeExecution:
"""Create a node execution from an event."""
now = datetime.now(UTC).replace(tzinfo=None)
now = naive_utc_now()
created_at = created_at or now
metadata = {
@ -403,7 +403,7 @@ class WorkflowCycleManager:
handle_special_values: bool = False,
) -> None:
"""Update node execution with completion data."""
finished_at = datetime.now(UTC).replace(tzinfo=None)
finished_at = naive_utc_now()
elapsed_time = (finished_at - event.start_at).total_seconds()
# Process data