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Merge branch 'release/v0.2.6' into develop

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* release/v0.2.6:
  fix(web): ontology class default tag bugfix
  fix(version): Version 0.2.6 Release Notes
  fix(web): chat file delete bugfix
  feat: support model load balancing and add message_id to API responses
  feat: support model load balancing and add message_id to API responses
  [changes] Work space isolation
  [add] Recently, memory activities have adopted Redis caching.
  [changes] Work space isolation
  [add] Recently, memory activities have adopted Redis caching.
  fix(web): upload add loading
  [changes] The enumeration check has been changed to a string.
  [changes] The enumeration check has been changed to a string.
  feat(web): http-request add headers variable
  fix(workflow): ensure file messages are written to messages in non-stream mode
  fix(workflow): fix Dify compatibility issues
  [changes] Memory write completion active failure interest cache
  feat(workflow): support multimodal context
  [changes] AI review and correction of code
  [add] Semantic pruning is unified with the ontology engineering scenario.
  feat(chat): add message_id field to chat API response
This commit is contained in:
Mark
2026-03-07 11:09:39 +08:00
parent c01ad5a19e df18868888
commit d080b44ac3
38 changed files with 684 additions and 163 deletions
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2
api/app/cache/memory/__init__.py vendored
View File

@@ -4,7 +4,9 @@ Memory 缓存模块
提供记忆系统相关的缓存功能
"""
from .interest_memory import InterestMemoryCache
from .activity_stats_cache import ActivityStatsCache
__all__ = [
"InterestMemoryCache",
"ActivityStatsCache",
]

124
api/app/cache/memory/activity_stats_cache.py vendored Normal file
View File

@@ -0,0 +1,124 @@
"""
Recent Activity Stats Cache
记忆提取活动统计缓存模块
用于缓存每次记忆提取流程的统计数据，按 workspace_id 存储，24小时后释放
查询命令：cache:memory:activity_stats:by_workspace:7de31a97-40a6-4fc0-b8d3-15c89f523843
"""
import json
import logging
from typing import Optional, Dict, Any
from datetime import datetime
from app.aioRedis import aio_redis
logger = logging.getLogger(__name__)
# 缓存过期时间：24小时
ACTIVITY_STATS_CACHE_EXPIRE = 86400
class ActivityStatsCache:
"""记忆提取活动统计缓存类"""
PREFIX = "cache:memory:activity_stats"
@classmethod
def _get_key(cls, workspace_id: str) -> str:
"""生成 Redis key
Args:
workspace_id: 工作空间ID
Returns:
完整的 Redis key
"""
return f"{cls.PREFIX}:by_workspace:{workspace_id}"
@classmethod
async def set_activity_stats(
cls,
workspace_id: str,
stats: Dict[str, Any],
expire: int = ACTIVITY_STATS_CACHE_EXPIRE,
) -> bool:
"""设置记忆提取活动统计缓存
Args:
workspace_id: 工作空间ID
stats: 统计数据，格式：
{
"chunk_count": int,
"statements_count": int,
"triplet_entities_count": int,
"triplet_relations_count": int,
"temporal_count": int,
}
expire: 过期时间（秒），默认24小时
Returns:
是否设置成功
"""
try:
key = cls._get_key(workspace_id)
payload = {
"stats": stats,
"generated_at": datetime.now().isoformat(),
"workspace_id": workspace_id,
"cached": True,
}
value = json.dumps(payload, ensure_ascii=False)
await aio_redis.set(key, value, ex=expire)
logger.info(f"设置活动统计缓存成功: {key}, 过期时间: {expire}秒")
return True
except Exception as e:
logger.error(f"设置活动统计缓存失败: {e}", exc_info=True)
return False
@classmethod
async def get_activity_stats(
cls,
workspace_id: str,
) -> Optional[Dict[str, Any]]:
"""获取记忆提取活动统计缓存
Args:
workspace_id: 工作空间ID
Returns:
统计数据字典，缓存不存在或已过期返回 None
"""
try:
key = cls._get_key(workspace_id)
value = await aio_redis.get(key)
if value:
payload = json.loads(value)
logger.info(f"命中活动统计缓存: {key}")
return payload
logger.info(f"活动统计缓存不存在或已过期: {key}")
return None
except Exception as e:
logger.error(f"获取活动统计缓存失败: {e}", exc_info=True)
return None
@classmethod
async def delete_activity_stats(
cls,
workspace_id: str,
) -> bool:
"""删除记忆提取活动统计缓存
Args:
workspace_id: 工作空间ID
Returns:
是否删除成功
"""
try:
key = cls._get_key(workspace_id)
result = await aio_redis.delete(key)
logger.info(f"删除活动统计缓存: {key}, 结果: {result}")
return result > 0
except Exception as e:
logger.error(f"删除活动统计缓存失败: {e}", exc_info=True)
return False

7
api/app/controllers/memory_storage_controller.py
View File

@@ -544,10 +544,11 @@ async def clear_hot_memory_tags_cache(
@router.get("/analytics/recent_activity_stats", response_model=ApiResponse)
async def get_recent_activity_stats_api(
current_user: User = Depends(get_current_user),
) -> dict:
api_logger.info("Recent activity stats requested")
) -> dict:
workspace_id = str(current_user.current_workspace_id) if current_user.current_workspace_id else None
api_logger.info(f"Recent activity stats requested: workspace_id={workspace_id}")
try:
result = await analytics_recent_activity_stats()
result = await analytics_recent_activity_stats(workspace_id=workspace_id)
return success(data=result, msg="查询成功")
except Exception as e:
api_logger.error(f"Recent activity stats failed: {str(e)}")

4
api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py
View File

@@ -111,7 +111,7 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
"error_type": type(e).__name__,
"error_message": str(e),
"content_length": len(content),
"llm_model_id": memory_config.llm_model_id if memory_config else None
"llm_model_id": str(memory_config.llm_model_id) if memory_config else None
}
logger.error(f"Split_The_Problem error details: {error_details}")
@@ -221,7 +221,7 @@ async def Problem_Extension(state: ReadState) -> ReadState:
"error_type": type(e).__name__,
"error_message": str(e),
"questions_count": len(databasets),
"llm_model_id": memory_config.llm_model_id if memory_config else None
"llm_model_id": str(memory_config.llm_model_id) if memory_config else None
}
logger.error(f"Problem_Extension error details: {error_details}")

10
api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py
View File

@@ -1,3 +1,4 @@
from app.cache.memory.interest_memory import InterestMemoryCache
from app.core.memory.agent.utils.llm_tools import WriteState
from app.core.memory.agent.utils.write_tools import write
from app.core.logging_config import get_agent_logger
@@ -40,6 +41,15 @@ async def write_node(state: WriteState) -> WriteState:
)
logger.info(f"Write completed successfully! Config: {memory_config.config_name}")
# 写入 neo4j 成功后，删除该用户的兴趣分布缓存，确保下次请求重新生成
for lang in ["zh", "en"]:
deleted = await InterestMemoryCache.delete_interest_distribution(
end_user_id=end_user_id,
language=lang,
)
if deleted:
logger.info(f"Invalidated interest distribution cache: end_user_id={end_user_id}, language={lang}")
write_result = {
"status": "success",
"data": structured_messages,

4
api/app/core/memory/agent/utils/get_dialogs.py
View File

@@ -82,7 +82,9 @@ async def get_chunked_dialogs(
pruning_config = PruningConfig(
pruning_switch=memory_config.pruning_enabled,
pruning_scene=memory_config.pruning_scene or "education",
pruning_threshold=memory_config.pruning_threshold
pruning_threshold=memory_config.pruning_threshold,
scene_id=str(memory_config.scene_id) if memory_config.scene_id else None,
ontology_classes=memory_config.ontology_classes,
)
logger.info(f"[剪枝] 加载配置: switch={pruning_config.pruning_switch}, scene={pruning_config.pruning_scene}, threshold={pruning_config.pruning_threshold}")

19
api/app/core/memory/agent/utils/write_tools.py
View File

@@ -225,5 +225,24 @@ async def write(
with open(log_file, "a", encoding="utf-8") as f:
f.write(f"=== Pipeline Run Completed: {timestamp} ===\n\n")
# 将提取统计写入 Redis，按 workspace_id 存储
try:
from app.cache.memory.activity_stats_cache import ActivityStatsCache
stats_to_cache = {
"chunk_count": len(all_chunk_nodes) if all_chunk_nodes else 0,
"statements_count": len(all_statement_nodes) if all_statement_nodes else 0,
"triplet_entities_count": len(all_entity_nodes) if all_entity_nodes else 0,
"triplet_relations_count": len(all_entity_entity_edges) if all_entity_entity_edges else 0,
"temporal_count": 0,
}
await ActivityStatsCache.set_activity_stats(
workspace_id=str(memory_config.workspace_id),
stats=stats_to_cache,
)
logger.info(f"[WRITE] 活动统计已写入 Redis: workspace_id={memory_config.workspace_id}")
except Exception as cache_err:
logger.warning(f"[WRITE] 写入活动统计缓存失败（不影响主流程）: {cache_err}", exc_info=True)
logger.info("=== Pipeline Complete ===")
logger.info(f"Total execution time: {total_time:.2f} seconds")

15
api/app/core/memory/models/config_models.py
View File

@@ -10,7 +10,7 @@ Classes:
TemporalSearchParams: Parameters for temporal search queries
"""
from typing import Optional
from typing import Optional, List
from pydantic import BaseModel, Field
@@ -55,17 +55,26 @@ class PruningConfig(BaseModel):
Attributes:
pruning_switch: Enable or disable semantic pruning
pruning_scene: Scene type for pruning ('education', 'online_service', 'outbound')
pruning_scene: Scene name for pruning, either a built-in key
('education', 'online_service', 'outbound') or a custom scene_name
from ontology_scene table
pruning_threshold: Pruning ratio (0-0.9, max 0.9 to avoid complete removal)
scene_id: Optional ontology scene UUID, used to load custom ontology classes
ontology_classes: List of class_name strings from ontology_class table,
injected into the prompt when pruning_scene is not a built-in scene
"""
pruning_switch: bool = Field(False, description="Enable semantic pruning when True.")
pruning_scene: str = Field(
"education",
description="Scene for pruning: one of 'education', 'online_service', 'outbound'.",
description="Scene for pruning: built-in key or custom scene_name from ontology_scene.",
)
pruning_threshold: float = Field(
0.5, ge=0.0, le=0.9,
description="Pruning ratio within 0-0.9 (max 0.9 to avoid termination).")
scene_id: Optional[str] = Field(None, description="Ontology scene UUID (optional).")
ontology_classes: Optional[List[str]] = Field(
None, description="Class names from ontology_class table for custom scenes."
)
class TemporalSearchParams(BaseModel):

27
api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py
View File

@@ -86,19 +86,26 @@ class SemanticPruner:
self._detailed_prune_logging = True # 是否启用详细日志
self._max_debug_msgs_per_dialog = 20 # 每个对话最多记录前N条消息的详细日志
# 加载场景特定配置
# 加载场景特定配置（内置场景走专门规则，自定义场景 fallback 到通用规则）
self.scene_config: ScenePatterns = SceneConfigRegistry.get_config(
self.config.pruning_scene,
fallback_to_generic=True
)
# 检查场景是否有专门支持
is_supported = SceneConfigRegistry.is_scene_supported(self.config.pruning_scene)
if is_supported:
self._log(f"[剪枝-初始化] 场景={self.config.pruning_scene} 使用专门配置")
# 判断是否为内置专门场景
self._is_builtin_scene = SceneConfigRegistry.is_scene_supported(self.config.pruning_scene)
# 自定义场景的本体类型列表（用于注入提示词）
self._ontology_classes = getattr(self.config, "ontology_classes", None) or []
if self._is_builtin_scene:
self._log(f"[剪枝-初始化] 场景={self.config.pruning_scene} 使用内置专门配置")
else:
self._log(f"[剪枝-初始化] 场景={self.config.pruning_scene} 未预定义，使用通用配置（保守策略）")
self._log(f"[剪枝-初始化] 支持的场景: {SceneConfigRegistry.get_all_scenes()}")
self._log(f"[剪枝-初始化] 场景={self.config.pruning_scene} 为自定义场景，使用通用规则 + 本体类型提示词注入")
if self._ontology_classes:
self._log(f"[剪枝-初始化] 注入本体类型: {self._ontology_classes}")
else:
self._log(f"[剪枝-初始化] 未找到本体类型，将使用通用提示词")
# Load Jinja2 template
self.template = prompt_env.get_template("extracat_Pruning.jinja2")
@@ -424,12 +431,16 @@ class SemanticPruner:
self._log(f"[剪枝-缓存] LRU缓存已满，删除最旧条目")
rendered = self.template.render(
pruning_scene=self.config.pruning_scene,
pruning_scene=self.config.pruning_scene,
is_builtin_scene=self._is_builtin_scene,
ontology_classes=self._ontology_classes,
dialog_text=dialog_text,
language=self.language
)
log_template_rendering("extracat_Pruning.jinja2", {
"pruning_scene": self.config.pruning_scene,
"is_builtin_scene": self._is_builtin_scene,
"ontology_classes_count": len(self._ontology_classes),
"language": self.language
})
log_prompt_rendering("pruning-extract", rendered)

42
api/app/core/memory/utils/prompt/prompts/extracat_Pruning.jinja2
View File

@@ -1,6 +1,6 @@
{#
对话级抽取与相关性判定模板（用于剪枝加速）
输入：pruning_scene, dialog_text
输入：pruning_scene, is_builtin_scene, ontology_classes, dialog_text, language
输出：严格 JSON（不要包含任何多余文本），字段：
- is_related: bool，是否与所选场景相关
- times: [string]，从对话中抽取的时间相关文本（日期、时间、时间段、有效期等）
@@ -16,7 +16,8 @@
- 仅输出上述键；避免多余解释或字段。
#}
{% set scene_instructions = {
{# ── 内置场景的固定说明 ── #}
{% set builtin_scene_instructions = {
'education': {
'zh': '教育场景：教学、课程、考试、作业、老师/学生互动、学习资源、学校管理等。',
'en': 'Education Scenario: Teaching, courses, exams, homework, teacher/student interaction, learning resources, school management, etc.'
@@ -31,16 +32,40 @@
}
} %}
{% set scene_key = pruning_scene %}
{% if scene_key not in scene_instructions %}
{% set scene_key = 'education' %}
{# ── 确定最终使用的场景说明 ── #}
{% if is_builtin_scene %}
{# 内置专门场景：使用固定说明 #}
{% set scene_key = pruning_scene %}
{% if scene_key not in builtin_scene_instructions %}{% set scene_key = 'education' %}{% endif %}
{% set instruction = builtin_scene_instructions[scene_key][language] if language in ['zh', 'en'] else builtin_scene_instructions[scene_key]['zh'] %}
{% set custom_types_str = '' %}
{% else %}
{# 自定义场景：使用场景名称 + 本体类型列表构建说明 #}
{% if ontology_classes and ontology_classes | length > 0 %}
{% if language == 'en' %}
{% set custom_types_str = ontology_classes | join(', ') %}
{% set instruction = 'Custom scene "' ~ pruning_scene ~ '": The dialogue is related to this scene if it involves any of the following entity types: ' ~ custom_types_str ~ '.' %}
{% else %}
{% set custom_types_str = ontology_classes | join('、') %}
{% set instruction = '自定义场景「' ~ pruning_scene ~ '」：对话涉及以下任意实体类型时视为相关：' ~ custom_types_str ~ '。' %}
{% endif %}
{% else %}
{# 无本体类型时退化为通用说明 #}
{% if language == 'en' %}
{% set instruction = 'Custom scene "' ~ pruning_scene ~ '": Determine whether the dialogue content is relevant to this scene based on overall context.' %}
{% else %}
{% set instruction = '自定义场景「' ~ pruning_scene ~ '」：根据对话整体内容判断是否与该场景相关。' %}
{% endif %}
{% set custom_types_str = '' %}
{% endif %}
{% endif %}
{% set instruction = scene_instructions[scene_key][language] if language in ['zh', 'en'] else scene_instructions[scene_key]['zh'] %}
{% if language == "zh" %}
请在下方对话全文基础上，按该场景进行一次性抽取并判定相关性：
场景说明：{{ instruction }}
{% if not is_builtin_scene and custom_types_str %}
重要提示：只要对话中出现与上述实体类型（{{ custom_types_str }}）相关的内容，即判定为相关（is_related=true）。
{% endif %}
对话全文：
"""
@@ -60,6 +85,9 @@
{% else %}
Based on the full dialogue below, perform one-time extraction and relevance determination according to this scenario:
Scenario Description: {{ instruction }}
{% if not is_builtin_scene and custom_types_str %}
Important: If the dialogue contains content related to any of the entity types above ({{ custom_types_str }}), mark it as relevant (is_related=true).
{% endif %}
Full Dialogue:
"""

16
api/app/core/workflow/adapters/dify/converter.py
View File

@@ -129,11 +129,11 @@ class DifyConverter(BaseConverter):
@staticmethod
def _convert_file(var):
pass
return None
@staticmethod
def _convert_array_file(var):
pass
return []
@staticmethod
def variable_type_map(source_type) -> VariableType | None:
@@ -198,7 +198,7 @@ class DifyConverter(BaseConverter):
"over-write": AssignmentOperator.COVER,
"remove-last": AssignmentOperator.REMOVE_LAST,
"remove-first": AssignmentOperator.REMOVE_FIRST,
"set": AssignmentOperator.ASSIGN,
}
return operator_map.get(operator, operator)
@@ -267,10 +267,10 @@ class DifyConverter(BaseConverter):
type=var_type,
required=var["required"],
default=self.convert_variable_type(
var_type, var["default"]
var_type, var.get("default")
),
description=var["label"],
max_length=var.get("max_length"),
max_length=var.get("max_length", 50),
)
start_vars.append(var_def)
result = StartNodeConfig.model_construct(
@@ -333,7 +333,7 @@ class DifyConverter(BaseConverter):
MessageConfig(
role="user",
content=self.trans_variable_format(
node_data["memory"].get("query_prompt_template", "{{#sys.query#}}")
node_data["memory"].get("query_prompt_template") or "{{#sys.query#}}"
)
)
)
@@ -612,7 +612,7 @@ class DifyConverter(BaseConverter):
),
headers=headers,
params=params,
verify_ssl=node_data["ssl_verify"],
verify_ssl=node_data.get("ssl_verify", False),
timeouts=HttpTimeOutConfig.model_construct(
connect_timeout=node_data["timeout"]["max_connect_timeout"] or 5,
read_timeout=node_data["timeout"]["max_read_timeout"] or 5,
@@ -696,7 +696,7 @@ class DifyConverter(BaseConverter):
group_variables = {}
group_type = {}
if not advanced_settings or not advanced_settings["group_enabled"]:
group_variables["output"] = [
group_variables = [
self._process_list_variable_litearl(variable)
for variable in node_data["variables"]
]

10
api/app/core/workflow/adapters/dify/dify_adapter.py
View File

@@ -83,6 +83,12 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
require_fields = frozenset({'app', 'kind', 'version', 'workflow'})
if not all(field in self.config for field in require_fields):
return False
if self.config.get("app",{}).get("mode") == "workflow":
self.errors.append(ExceptionDefineition(
type=ExceptionType.PLATFORM,
detail="workflow mode is not supported"
))
return False
for node in self.origin_nodes:
if not self._valid_nodes(node):
@@ -134,6 +140,8 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
for node in self.origin_nodes:
if self.map_node_type(node["data"]["type"]) == NodeType.LLM:
self.node_output_map[f"{node['id']}.text"] = f"{node['id']}.output"
elif self.map_node_type(node["data"]["type"]) == NodeType.KNOWLEDGE_RETRIEVAL:
self.node_output_map[f"{node['id']}.result"] = f"{node['id']}.output"
def _convert_cycle_node_position(self, node_id: str, position: dict):
for node in self.origin_nodes:
@@ -184,7 +192,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
type=ExceptionType.NODE,
node_id=node["id"],
node_name=node["data"]["title"],
detail=f"node type {node_type} is unsupported",
detail=f"node type {node_type if node_type else 'notes'} is unsupported",
))
return converter(node)
except Exception as e:

2
api/app/core/workflow/engine/graph_builder.py
View File

@@ -320,7 +320,7 @@ class GraphBuilder:
# Used later to determine which branch to take based on the node's output
# Assumes node output `node.<node_id>.output` matches the edge's label
# For example, if node.123.output == 'CASE1', take the branch labeled 'CASE1'
related_edge[idx]['condition'] = f"node.{node_id}.output == '{related_edge[idx]['label']}'"
related_edge[idx]['condition'] = f"node['{node_id}']['output'] == '{related_edge[idx]['label']}'"
if node_instance:
# Wrap node's run method to avoid closure issues

84
api/app/core/workflow/executor.py
View File

@@ -158,18 +158,36 @@ class WorkflowExecutor:
full_content += self.variable_pool.get_value(f"{end_id}.output", default="", strict=False)
# Append messages for user and assistant
result["messages"].extend(
[
{
"role": "user",
"content": input_data.get("message", '')
},
{
"role": "assistant",
"content": full_content
}
]
)
if input_data.get("files"):
result["messages"].extend(
[
{
"role": "user",
"content": input_data.get("message", '')
},
{
"role": "user",
"content": input_data.get("files")
},
{
"role": "assistant",
"content": full_content
}
]
)
else:
result["messages"].extend(
[
{
"role": "user",
"content": input_data.get("message", '')
},
{
"role": "assistant",
"content": full_content
}
]
)
# Calculate elapsed time
end_time = datetime.datetime.now()
elapsed_time = (end_time - start_time).total_seconds()
@@ -308,18 +326,36 @@ class WorkflowExecutor:
elapsed_time = (end_time - start_time).total_seconds()
# Append messages for user and assistant
result["messages"].extend(
[
{
"role": "user",
"content": input_data.get("message", '')
},
{
"role": "assistant",
"content": full_content
}
]
)
if input_data.get("files"):
result["messages"].extend(
[
{
"role": "user",
"content": input_data.get("message", '')
},
{
"role": "user",
"content": input_data.get("files")
},
{
"role": "assistant",
"content": full_content
}
]
)
else:
result["messages"].extend(
[
{
"role": "user",
"content": input_data.get("message", '')
},
{
"role": "assistant",
"content": full_content
}
]
)
logger.info(
f"Workflow execution completed (streaming), "
f"elapsed: {elapsed_time:.2f}ms, execution_id: {self.execution_context.execution_id}"

28
api/app/core/workflow/nodes/base_config.py
View File

@@ -85,20 +85,20 @@ class BaseNodeConfig(BaseModel):
- tags: 节点标签（用于分类和搜索）
"""
name: str | None = Field(
default=None,
description="节点名称（显示名称），如果不设置则使用节点 ID"
)
description: str | None = Field(
default=None,
description="节点描述，说明节点的作用"
)
tags: list[str] = Field(
default_factory=list,
description="节点标签，用于分类和搜索"
)
# name: str | None = Field(
# default=None,
# description="节点名称（显示名称），如果不设置则使用节点 ID"
# )
#
# description: str | None = Field(
# default=None,
# description="节点描述，说明节点的作用"
# )
#
# tags: list[str] = Field(
# default_factory=list,
# description="节点标签，用于分类和搜索"
# )
class Config:
"""Pydantic 配置"""

37
api/app/core/workflow/nodes/base_node.py
View File

@@ -1,7 +1,7 @@
import asyncio
import logging
import uuid
from abc import ABC, abstractmethod
from datetime import datetime
from functools import cached_property
from typing import Any, AsyncGenerator
@@ -13,6 +13,7 @@ from app.core.workflow.engine.variable_pool import VariablePool
from app.core.workflow.nodes.enums import BRANCH_NODES
from app.core.workflow.variable.base_variable import VariableType, FileObject
from app.db import get_db_read
from app.models import ModelConfig, ModelApiKey, LoadBalanceStrategy
from app.schemas import FileInput
from app.services.multimodal_service import MultimodalService
@@ -617,17 +618,31 @@ class BaseNode(ABC):
return variable_pool.has(selector)
@staticmethod
async def process_message(provider: str, content: str | FileObject, enable_file=False) -> dict | str | None:
async def process_message(
provider: str,
is_omni: bool,
content: str | dict | FileObject,
enable_file=False
) -> list | str | None:
if isinstance(content, dict):
content = FileObject(
type=content.get("type"),
url=content.get("url"),
transfer_method=content.get("transfer_method"),
origin_file_type=content.get("origin_file_type"),
file_id=content.get("file_id"),
is_file=True
)
if isinstance(content, str):
if enable_file:
return {"text": content}
return [{"type": "text", "text": content}]
return content
elif isinstance(content, FileObject):
if content.content_cache.get(provider):
return content.content_cache[provider]
with get_db_read() as db:
multimodel_service = MultimodalService(db, provider)
multimodel_service = MultimodalService(db, provider, is_omni=is_omni)
message = await multimodel_service.process_files(
[FileInput.model_construct(
type=content.type,
@@ -637,10 +652,9 @@ class BaseNode(ABC):
upload_file_id=content.file_id
)]
)
if message:
content.content_cache[provider] = message[0]
return message[0]
content.content_cache[provider] = message
return message
return None
raise TypeError(f'Unexpect input value type - {type(content)}')
@@ -658,3 +672,12 @@ class BaseNode(ABC):
elif isinstance(content, str):
return content
return result
@staticmethod
def model_balance(model_config: ModelConfig) -> ModelApiKey:
api_keys = [key for key in model_config.api_keys if key.is_active]
if not api_keys:
raise ValueError("No active API keys available for model")
if model_config.load_balance_strategy == LoadBalanceStrategy.ROUND_ROBIN:
return min(api_keys, key=lambda x: (int(x.usage_count or "0"), x.last_used_at or datetime.min))
return api_keys[0]

36
api/app/core/workflow/nodes/llm/node.py
View File

@@ -112,11 +112,12 @@ class LLMNode(BaseNode):
raise BusinessException("模型配置缺少 API Key", BizCode.INVALID_PARAMETER)
# 在 Session 关闭前提取所有需要的数据
api_config = config.api_keys[0]
api_config = self.model_balance(config)
model_name = api_config.model_name
provider = api_config.provider
api_key = api_config.api_key
api_base = api_config.api_base
is_omni = api_config.is_omni
model_type = config.type
# 4. 创建 LLM 实例（使用已提取的数据）
@@ -129,7 +130,8 @@ class LLMNode(BaseNode):
provider=provider,
api_key=api_key,
base_url=api_base,
extra_params=extra_params
extra_params=extra_params,
is_omni=is_omni
),
type=ModelType(model_type)
)
@@ -151,39 +153,53 @@ class LLMNode(BaseNode):
if role == "system":
messages.append({
"role": "system",
"content": content
"content": await self.process_message(provider, is_omni, content, self.typed_config.vision)
})
elif role in ["user", "human"]:
messages.append({
"role": "user",
"content": content
"content": await self.process_message(provider, is_omni, content, self.typed_config.vision)
})
elif role in ["ai", "assistant"]:
messages.append({
"role": "assistant",
"content": content
"content": await self.process_message(provider, is_omni, content, self.typed_config.vision)
})
else:
logger.warning(f"未知的消息角色: {role}，默认使用 user")
messages.append({
"role": "user",
"content": content
"content": await self.process_message(provider, is_omni, content, self.typed_config.vision)
})
if self.typed_config.vision_input and self.typed_config.vision:
file_content = []
files = variable_pool.get_instance(self.typed_config.vision_input)
for file in files.value:
content = await self.process_message(provider, file.value, self.typed_config.vision)
content = await self.process_message(provider, is_omni, file.value, self.typed_config.vision)
if content:
file_content.append(content)
file_content.extend(content)
if messages and messages[-1]["role"] == 'user':
messages[-1]['content'] = [messages[-1]["content"]] + file_content
messages[-1]['content'] = messages[-1]["content"] + file_content
else:
messages.append({"role": "user", "content": file_content})
if self.typed_config.memory.enable:
messages = messages[:-1] + state["messages"][-self.typed_config.memory.window_size:] + messages[-1:]
history_message = []
for message in state["messages"][-self.typed_config.memory.window_size:]:
if isinstance(message["content"], list):
file_content = []
for file in message["content"]:
content = await self.process_message(provider, is_omni, file, self.typed_config.vision)
if content:
file_content.extend(content)
history_message.append(
{"role": message["role"], "content": file_content}
)
else:
message["content"] = await self.process_message(provider, is_omni, message["content"], self.typed_config.vision)
history_message.append(message)
messages = messages[:-1] + history_message + messages[-1:]
self.messages = messages
else:
# 使用简单的 prompt 格式（向后兼容）

4
api/app/core/workflow/nodes/parameter_extractor/node.py
View File

@@ -95,11 +95,12 @@ class ParameterExtractorNode(BaseNode):
if not config.api_keys or len(config.api_keys) == 0:
raise BusinessException("Model configuration is missing API Key", BizCode.INVALID_PARAMETER)
api_config = config.api_keys[0]
api_config = self.model_balance(config)
model_name = api_config.model_name
provider = api_config.provider
api_key = api_config.api_key
api_base = api_config.api_base
is_omni = api_config.is_omni
model_type = config.type
llm = RedBearLLM(
@@ -108,6 +109,7 @@ class ParameterExtractorNode(BaseNode):
provider=provider,
api_key=api_key,
base_url=api_base,
is_omni=is_omni
),
type=ModelType(model_type)
)

4
api/app/core/workflow/nodes/question_classifier/node.py
View File

@@ -56,11 +56,12 @@ class QuestionClassifierNode(BaseNode):
if not config.api_keys or len(config.api_keys) == 0:
raise BusinessException("模型配置缺少 API Key", BizCode.INVALID_PARAMETER)
api_config = config.api_keys[0]
api_config = self.model_balance(config)
model_name = api_config.model_name
provider = api_config.provider
api_key = api_config.api_key
base_url = api_config.api_base
is_omni = api_config.is_omni
model_type = config.type
return RedBearLLM(
@@ -69,6 +70,7 @@ class QuestionClassifierNode(BaseNode):
provider=provider,
api_key=api_key,
base_url=base_url,
is_omni=is_omni
),
type=ModelType(model_type)
)

1
api/app/repositories/memory_config_repository.py
View File

@@ -233,6 +233,7 @@ class MemoryConfigRepository:
config_desc=params.config_desc,
workspace_id=params.workspace_id,
scene_id=params.scene_id,
pruning_scene=params.pruning_scene,
llm_id=params.llm_id,
embedding_id=params.embedding_id,
rerank_id=params.rerank_id,

1
api/app/schemas/conversation_schema.py
View File

@@ -86,6 +86,7 @@ class ChatResponse(BaseModel):
"""聊天响应（非流式）"""
conversation_id: uuid.UUID
message: str
message_id: str
usage: Optional[Dict[str, Any]] = None
elapsed_time: Optional[float] = None

1
api/app/schemas/memory_config_schema.py
View File

@@ -417,6 +417,7 @@ class MemoryConfig:
# Ontology scene association
scene_id: Optional[UUID] = None
ontology_classes: Optional[list] = field(default=None)
def __post_init__(self):
"""Validate configuration after initialization."""

9
api/app/schemas/memory_storage_schema.py
View File

@@ -232,14 +232,15 @@ class ConfigParamsCreate(BaseModel): # 创建配置参数模型（仅 body，
# 本体场景关联（可选）
scene_id: Optional[uuid.UUID] = Field(None, description="本体场景ID（UUID），关联ontology_scene表")
# 语义剪枝场景（由 service 层根据 scene_id 自动推导，值为关联场景的 scene_name，前端无需传入）
pruning_scene: Optional[str] = Field(None, description="语义剪枝场景，由 scene_id 对应的 scene_name 自动填充")
# 模型配置字段（可选，用于手动指定或自动填充）
llm_id: Optional[str] = Field(None, description="LLM模型配置ID")
embedding_id: Optional[str] = Field(None, description="嵌入模型配置ID")
rerank_id: Optional[str] = Field(None, description="重排序模型配置ID")
reflection_model_id: Optional[str] = Field(None, description="反思模型ID，默认与llm_id一致")
emotion_model_id: Optional[str] = Field(None, description="情绪分析模型ID，默认与llm_id一致")
class ConfigParamsDelete(BaseModel): # 删除配置参数模型（请求体）
model_config = ConfigDict(populate_by_name=True, extra="forbid")
# config_name: str = Field("配置名称", description="配置名称（字符串）")
@@ -274,8 +275,8 @@ class ConfigUpdateExtracted(BaseModel): # 更新记忆萃取引擎配置参数
# 剪枝配置：与 runtime.json 中 pruning 段对应
pruning_enabled: Optional[bool] = Field(None, description="是否启动智能语义剪枝")
pruning_scene: Optional[Literal["education", "online_service", "outbound"]] = Field(
None, description="智能剪枝场景：education/online_service/outbound"
pruning_scene: Optional[str] = Field(
None, description="智能剪枝场景：education/online_service/outbound 或本体工程自定义场景"
)
pruning_threshold: Optional[float] = Field(
None, ge=0.0, le=0.9, description="智能语义剪枝阈值（0-0.9）"

19
api/app/services/app_chat_service.py
View File

@@ -144,7 +144,7 @@ class AppChatService:
)
# 保存消息
self.conversation_service.save_conversation_messages(
message_id = self.conversation_service.save_conversation_messages(
conversation_id=conversation_id,
user_message=message,
assistant_message=result["content"],
@@ -163,6 +163,7 @@ class AppChatService:
return {
"conversation_id": conversation_id,
"message_id": str(message_id),
"message": result["content"],
"usage": result.get("usage", {
"prompt_tokens": 0,
@@ -191,7 +192,11 @@ class AppChatService:
try:
start_time = time.time()
config_id = None
yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation_id)}, ensure_ascii=False)}\n\n"
message_id = uuid.uuid4()
yield f"event: start\ndata: {json.dumps({
'conversation_id': str(conversation_id),
"message_id": str(message_id)
}, ensure_ascii=False)}\n\n"
variables = self.agent_service.prepare_variables(variables, config.variables)
# 获取模型配置ID
@@ -296,6 +301,7 @@ class AppChatService:
)
self.conversation_service.add_message(
message_id=message_id,
conversation_id=conversation_id,
role="assistant",
content=full_content,
@@ -373,7 +379,7 @@ class AppChatService:
content=message
)
self.conversation_service.add_message(
ai_message = self.conversation_service.add_message(
conversation_id=conversation_id,
role="assistant",
content=result.get("message", ""),
@@ -391,6 +397,7 @@ class AppChatService:
return {
"conversation_id": conversation_id,
"message": result.get("message", ""),
"message_id": str(ai_message.id),
"usage": {
"prompt_tokens": 0,
"completion_tokens": 0,
@@ -419,9 +426,9 @@ class AppChatService:
variables = {}
try:
message_id = uuid.uuid4()
# 发送开始事件
yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation_id)}, ensure_ascii=False)}\n\n"
yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation_id), "message_id": str(message_id)}, ensure_ascii=False)}\n\n"
full_content = ""
total_tokens = 0
@@ -429,6 +436,7 @@ class AppChatService:
# 2. 创建编排器
orchestrator = MultiAgentOrchestrator(self.db, config)
# 3. 流式执行任务
async for event in orchestrator.execute_stream(
message=message,
@@ -472,6 +480,7 @@ class AppChatService:
)
self.conversation_service.add_message(
message_id=message_id,
conversation_id=conversation_id,
role="assistant",
content=full_content,

8
api/app/services/conversation_service.py
View File

@@ -178,7 +178,8 @@ class ConversationService:
conversation_id: uuid.UUID,
role: str,
content: str,
meta_data: Optional[dict] = None
meta_data: Optional[dict] = None,
message_id: Optional[uuid.UUID] = None,
) -> Message:
"""
Add a message to a conversation using UnitOfWork.
@@ -188,6 +189,7 @@ class ConversationService:
role (str): Role of the message sender ('user' or 'assistant').
content (str): Message content.
meta_data (Optional[dict]): Optional metadata.
message_id (Optional[uuid.UUID]): Optional custom message UUID.
Returns:
Message: Newly created Message instance.
@@ -198,6 +200,7 @@ class ConversationService:
)
message = Message(
id=message_id if message_id else uuid.uuid4(),
conversation_id=conversation_id,
role=role,
content=content,
@@ -317,7 +320,7 @@ class ConversationService:
content=user_message
)
self.add_message(
ai_message = self.add_message(
conversation_id=conversation_id,
role="assistant",
content=assistant_message,
@@ -332,6 +335,7 @@ class ConversationService:
"assistant_message_length": len(assistant_message)
}
)
return ai_message.id
def delete_conversation(
self,

35
api/app/services/memory_config_service.py
View File

@@ -107,6 +107,40 @@ def _validate_config_id(config_id, db: Session = None):
)
# 专门场景的内置 key 集合，直接从 SceneConfigRegistry 派生，避免重复维护
# 使用懒加载函数避免模块级循环导入
def _get_builtin_pruning_scenes() -> set:
from app.core.memory.storage_services.extraction_engine.data_preprocessing.scene_config import SceneConfigRegistry
return set(SceneConfigRegistry.get_all_scenes())
def _load_ontology_classes(db: Session, scene_id, pruning_scene: Optional[str]) -> Optional[list]:
"""当 pruning_scene 不是内置场景时，从 ontology_class 表加载类型名称列表。
Args:
db: 数据库会话
scene_id: 本体场景 UUID
pruning_scene: 语义剪枝场景名称
Returns:
class_name 字符串列表，或 None（内置场景 / 无数据时）
"""
if not scene_id:
return None
# 内置场景走 SceneConfigRegistry，不需要注入类型列表
if pruning_scene in _get_builtin_pruning_scenes():
return None
try:
from app.repositories.ontology_class_repository import OntologyClassRepository
repo = OntologyClassRepository(db)
classes = repo.get_classes_by_scene(scene_id)
names = [c.class_name for c in classes if c.class_name]
return names if names else None
except Exception as e:
logger.warning(f"Failed to load ontology classes for scene_id={scene_id}: {e}")
return None
class MemoryConfigService:
"""
Centralized service for memory configuration loading and validation.
@@ -359,6 +393,7 @@ class MemoryConfigService:
pruning_threshold=float(memory_config.pruning_threshold) if memory_config.pruning_threshold is not None else 0.5,
# Ontology scene association
scene_id=memory_config.scene_id,
ontology_classes=_load_ontology_classes(self.db, memory_config.scene_id, memory_config.pruning_scene),
)
elapsed_ms = (time.time() - start_time) * 1000

110
api/app/services/memory_storage_service.py
View File

@@ -146,6 +146,10 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
if not params.emotion_model_id:
params.emotion_model_id = params.llm_id
# 根据关联的本体场景推导 pruning_scene（语义剪枝场景与本体工程场景保持一致）
if params.scene_id and not getattr(params, 'pruning_scene', None):
params.pruning_scene = self._resolve_pruning_scene_from_scene_id(params.scene_id)
config = MemoryConfigRepository.create(self.db, params)
self.db.commit()
return {"affected": 1, "config_id": config.config_id}
@@ -161,6 +165,23 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
finally:
db_session.close()
def _resolve_pruning_scene_from_scene_id(self, scene_id) -> Optional[str]:
"""根据本体场景ID获取对应的 scene_name，作为语义剪枝场景值
Args:
scene_id: 本体场景UUID
Returns:
scene_name 字符串，查询失败时返回 None
"""
try:
from app.models.ontology_scene import OntologyScene
scene = self.db.query(OntologyScene).filter_by(scene_id=scene_id).first()
return scene.scene_name if scene else None
except Exception as e:
logger.warning(f"_resolve_pruning_scene_from_scene_id failed for scene_id={scene_id}: {e}", exc_info=True)
return None
# --- Delete ---
def delete(self, key: ConfigParamsDelete) -> Dict[str, Any]: # 删除配置参数（按配置ID）
success = MemoryConfigRepository.delete(self.db, key.config_id)
@@ -196,6 +217,19 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
def get_all(self, workspace_id = None) -> List[Dict[str, Any]]: # 获取所有配置参数
results = MemoryConfigRepository.get_all(self.db, workspace_id)
# 检查并修正 pruning_scene 与 scene_name 不一致的记录
needs_commit = False
for config, scene_name in results:
if scene_name and config.pruning_scene != scene_name:
logger.info(
f"修正 pruning_scene: config_id={config.config_id} "
f"'{config.pruning_scene}' -> '{scene_name}'"
)
config.pruning_scene = scene_name
needs_commit = True
if needs_commit:
self.db.commit()
# 将 ORM 对象转换为字典列表
data_list = []
for config, scene_name in results:
@@ -749,8 +783,37 @@ async def analytics_hot_memory_tags(
await connector.close()
async def analytics_recent_activity_stats() -> Dict[str, Any]:
stats, _msg = get_recent_activity_stats()
async def analytics_recent_activity_stats(workspace_id: Optional[str] = None) -> Dict[str, Any]:
"""获取最近记忆提取活动统计。
优先从 Redis 缓存读取（按 workspace_id），缓存不存在时降级到日志文件解析。
Args:
workspace_id: 工作空间ID，用于从 Redis 读取对应缓存
Returns:
包含 total、stats、latest_relative、source 的统计字典
"""
stats = None
source = "log"
# 优先从 Redis 读取
if workspace_id:
try:
from app.cache.memory.activity_stats_cache import ActivityStatsCache
cached = await ActivityStatsCache.get_activity_stats(workspace_id)
if cached:
stats = cached.get("stats", {})
source = "redis"
logger.info(f"[ANALYTICS] 从 Redis 读取活动统计: workspace_id={workspace_id}")
except Exception as e:
logger.warning(f"[ANALYTICS] 读取 Redis 活动统计失败，降级到日志: {e}")
# 降级：从日志文件解析
if stats is None:
stats, _msg = get_recent_activity_stats()
source = "log"
total = (
stats.get("chunk_count", 0)
+ stats.get("statements_count", 0)
@@ -758,26 +821,29 @@ async def analytics_recent_activity_stats() -> Dict[str, Any]:
+ stats.get("triplet_relations_count", 0)
+ stats.get("temporal_count", 0)
)
# 精简：仅提供“最新一次活动多久前”
latest_relative = None
try:
info = stats.get("log_path", "")
idx = info.rfind("最新：")
if idx != -1:
latest_path = info[idx + 3 :].strip()
if latest_path and os.path.exists(latest_path):
import time
diff = max(0.0, time.time() - os.path.getmtime(latest_path))
m = int(diff // 60)
if m < 1:
latest_relative = "刚刚"
elif m < 60:
latest_relative = "一会前"
else:
latest_relative = "较早前"
except Exception:
pass
data = {"total": total, "stats": stats, "latest_relative": latest_relative}
# 计算"最新一次活动多久前"（仅日志来源时有效）
latest_relative = None
if source == "log":
try:
info = stats.get("log_path", "")
idx = info.rfind("最新：")
if idx != -1:
latest_path = info[idx + 3:].strip()
if latest_path and os.path.exists(latest_path):
import time
diff = max(0.0, time.time() - os.path.getmtime(latest_path))
m = int(diff // 60)
if m < 1:
latest_relative = "刚刚"
elif m < 60:
latest_relative = "一会前"
else:
latest_relative = "较早前"
except Exception:
pass
data = {"total": total, "stats": stats, "latest_relative": latest_relative, "source": source}
return data

19
api/app/services/pilot_run_service.py
View File

@@ -326,6 +326,25 @@ async def run_pilot_extraction(
logger.info("Pilot run completed: Skipping Neo4j save")
# 将提取统计写入 Redis，按 workspace_id 存储
try:
from app.cache.memory.activity_stats_cache import ActivityStatsCache
stats_to_cache = {
"chunk_count": len(chunk_nodes) if chunk_nodes else 0,
"statements_count": len(statement_nodes) if statement_nodes else 0,
"triplet_entities_count": len(entity_nodes) if entity_nodes else 0,
"triplet_relations_count": len(entity_edges) if entity_edges else 0,
"temporal_count": 0, # temporal 数据在日志中，此处暂置0
}
await ActivityStatsCache.set_activity_stats(
workspace_id=str(memory_config.workspace_id),
stats=stats_to_cache,
)
logger.info(f"[PILOT_RUN] 活动统计已写入 Redis: workspace_id={memory_config.workspace_id}")
except Exception as cache_err:
logger.warning(f"[PILOT_RUN] 写入活动统计缓存失败（不影响主流程）: {cache_err}", exc_info=True)
except Exception as e:
logger.error(f"Pilot run failed: {e}", exc_info=True)
raise

2
api/app/services/workflow_import_service.py
View File

@@ -56,7 +56,7 @@ class WorkflowImportService:
success=False,
temp_id=None,
workflow_id=None,
errors=[InvalidConfiguration()]
errors=[InvalidConfiguration()] + adapter.errors
)
workflow_config = adapter.parse_workflow()

87
api/app/services/workflow_service.py
View File

@@ -25,7 +25,7 @@ from app.repositories.workflow_repository import (
WorkflowExecutionRepository,
WorkflowNodeExecutionRepository
)
from app.schemas import DraftRunRequest, FileInput
from app.schemas import DraftRunRequest, FileInput, FileType
from app.services.conversation_service import ConversationService
from app.services.multi_agent_service import convert_uuids_to_str
from app.services.multimodal_service import MultimodalService
@@ -496,6 +496,7 @@ class WorkflowService:
"event": "start",
"data": {
"conversation_id": payload.get("conversation_id"),
"message_id": payload.get("message_id")
}
}
case "workflow_end":
@@ -600,6 +601,7 @@ class WorkflowService:
try:
files = await self._handle_file_input(payload.files)
input_data["files"] = files
message_id = uuid.uuid4()
# 更新状态为运行中
self.update_execution_status(execution.execution_id, "running")
@@ -624,24 +626,45 @@ class WorkflowService:
workspace_id=str(workspace_id),
user_id=payload.user_id
)
# 更新执行结果
if result.get("status") == "completed":
token_usage = result.get("token_usage", {}) or {}
final_messages = result.get("messages", [])[init_message_length:]
human_message = ""
assistant_message = ""
for message in final_messages:
if message["role"] == "user":
if isinstance(message["content"], str):
human_message += message["content"]
elif isinstance(message["content"], list):
for file in message["content"]:
if file.get("type") == FileType.IMAGE:
human_message += f"![image]({file.get('url', '')})"
else:
human_message += f"[{file.get('type')}]({file.get('url', '')})"
if message["role"] == "assistant":
assistant_message = message["content"]
self.conversation_service.add_message(
conversation_id=conversation_id_uuid,
role="user",
content=human_message,
meta_data=None
)
self.conversation_service.add_message(
message_id=message_id,
conversation_id=conversation_id_uuid,
role="assistant",
content=assistant_message,
meta_data={"usage": token_usage}
)
self.update_execution_status(
execution.execution_id,
"completed",
output_data=result,
token_usage=token_usage.get("total_tokens", None)
)
final_messages = result.get("messages", [])[init_message_length:]
for message in final_messages:
self.conversation_service.add_message(
conversation_id=conversation_id_uuid,
role=message["role"],
content=message["content"],
meta_data=None if message["role"] == "user" else {"usage": token_usage}
)
logger.info(f"Workflow Run Success, "
f"execution_id: {execution.execution_id}, message count: {len(final_messages)}")
else:
@@ -650,6 +673,8 @@ class WorkflowService:
"failed",
error_message=result.get("error")
)
logger.error(f"Workflow Run Failed, execution_id: {execution.execution_id},"
f" error: {result.get('error')}")
# 返回增强的响应结构
return {
@@ -659,6 +684,7 @@ class WorkflowService:
# "messages": result.get("messages"),
"output": result.get("output"), # 最终输出（字符串）
"message": result.get("output"), # 最终输出（字符串）
"message_id": str(message_id),
# "output_data": result.get("node_outputs", {}), # 所有节点输出（详细数据）
"conversation_id": result.get("conversation_id"), # 所有节点输出（详细数据）payload., # 会话 ID
"error_message": result.get("error"),
@@ -756,7 +782,7 @@ class WorkflowService:
input_data["conv_messages"] = last_state.get("messages") or []
break
init_message_length = len(input_data.get("conv_messages", []))
message_id = uuid.uuid4()
async for event in execute_workflow_stream(
workflow_config=workflow_config_dict,
input_data=input_data,
@@ -765,24 +791,43 @@ class WorkflowService:
user_id=payload.user_id,
):
if event.get("event") == "workflow_end":
status = event.get("data", {}).get("status")
token_usage = event.get("data", {}).get("token_usage", {}) or {}
if status == "completed":
final_messages = event.get("data", {}).get("messages", [])[init_message_length:]
human_message = ""
assistant_message = ""
for message in final_messages:
if message["role"] == "user":
if isinstance(message["content"], str):
human_message += message["content"]
elif isinstance(message["content"], list):
for file in message["content"]:
if file.get("type") == FileType.IMAGE:
human_message += f"![image]({file.get('url', '')})"
else:
human_message += f"[{file.get('type')}]({file.get('url', '')})"
if message["role"] == "assistant":
assistant_message = message["content"]
self.conversation_service.add_message(
conversation_id=conversation_id_uuid,
role="user",
content=human_message,
meta_data=None
)
self.conversation_service.add_message(
message_id=message_id,
conversation_id=conversation_id_uuid,
role="assistant",
content=assistant_message,
meta_data={"usage": token_usage}
)
self.update_execution_status(
execution.execution_id,
"completed",
output_data=event.get("data"),
token_usage=token_usage.get("total_tokens", None)
)
final_messages = event.get("data", {}).get("messages", [])[init_message_length:]
for message in final_messages:
self.conversation_service.add_message(
conversation_id=conversation_id_uuid,
role=message["role"],
content=message["content"],
meta_data=None if message["role"] == "user" else {"usage": token_usage}
)
logger.info(f"Workflow Run Success, "
f"execution_id: {execution.execution_id}, message count: {len(final_messages)}")
elif status == "failed":
@@ -793,6 +838,8 @@ class WorkflowService:
)
else:
logger.error(f"unexpect workflow run status, status: {status}")
elif event.get("event") == "workflow_start":
event["data"]["message_id"] = str(message_id)
event = self._emit(public, event)
if event:
yield event

11
api/app/services/workspace_service.py
View File

@@ -152,6 +152,7 @@ def create_workspace(
# Initialize default ontology scenes for the workspace (先创建本体场景)
default_scene_id = None
default_scene_name = None
try:
initializer = DefaultOntologyInitializer(db)
success, error_msg = initializer.initialize_default_scenes(
@@ -163,7 +164,7 @@ def create_workspace(
f"为工作空间 {db_workspace.id} 创建默认本体场景成功 (language={language})"
)
# 获取默认场景ID，优先使用"在线教育"场景，如果不存在则使用"情感陪伴"场景
# 获取默认场景ID，优先使用"在线教育"场景，如果不存在则使用"情感陪伴"场景
from app.repositories.ontology_scene_repository import OntologySceneRepository
from app.config.default_ontology_config import (
ONLINE_EDUCATION_SCENE,
@@ -179,6 +180,7 @@ def create_workspace(
if education_scene:
default_scene_id = education_scene.scene_id
default_scene_name = education_scene.scene_name
business_logger.info(
f"获取到教育场景ID用于默认记忆配置: {default_scene_id} (scene_name={education_scene_name})"
)
@@ -189,6 +191,7 @@ def create_workspace(
if companion_scene:
default_scene_id = companion_scene.scene_id
default_scene_name = companion_scene.scene_name
business_logger.info(
f"教育场景不存在，使用情感陪伴场景ID用于默认记忆配置: {default_scene_id} (scene_name={companion_scene_name})"
)
@@ -219,6 +222,7 @@ def create_workspace(
embedding_id=embedding,
rerank_id=rerank,
scene_id=default_scene_id, # 传入默认场景ID（优先教育场景，其次情感陪伴场景）
pruning_scene_name=default_scene_name, # 传入场景名称作为语义剪枝场景值
)
business_logger.info(
f"为工作空间 {db_workspace.id} 创建默认记忆配置成功 (scene_id={default_scene_id})"
@@ -1159,6 +1163,7 @@ def _create_default_memory_config(
embedding_id: Optional[uuid.UUID] = None,
rerank_id: Optional[uuid.UUID] = None,
scene_id: Optional[uuid.UUID] = None,
pruning_scene_name: Optional[str] = None,
) -> None:
"""Create a default memory config for a newly created workspace.
@@ -1170,6 +1175,7 @@ def _create_default_memory_config(
embedding_id: Optional embedding model ID
rerank_id: Optional rerank model ID
scene_id: Optional ontology scene ID (默认关联教育场景)
pruning_scene_name: Optional pruning scene name，取自 ontology_scene.scene_name
"""
from app.models.memory_config_model import MemoryConfig
@@ -1183,7 +1189,8 @@ def _create_default_memory_config(
llm_id=str(llm_id) if llm_id else None,
embedding_id=str(embedding_id) if embedding_id else None,
rerank_id=str(rerank_id) if rerank_id else None,
scene_id=scene_id, # 关联本体场景ID
scene_id=scene_id, # 关联本体场景ID（默认为"在线教育"场景）
pruning_scene=pruning_scene_name, # 语义剪枝场景直接使用 scene_name
state=True, # Active by default
is_default=True, # Mark as workspace default
)

32
api/app/version_info.json
View File

@@ -1,4 +1,36 @@
{
"v0.2.6": {
"introduction": {
"codeName": "听剑",
"releaseDate": "2026-3-6",
"upgradePosition": "🐻 多模态交互全面升级，记忆剪枝与工作流迁移双线并进，锋芒初露，兼收并蓄",
"coreUpgrades": [
"1. 工作流与应用框架<br>* 工作流导入适配（Dify）：支持 Dify 工作流定义无缝迁移<br>* 字段字数限制与校验规则：可配置字符限制与产品级校验<br>* 应用复制（Agent、工作流、集群）：一键复制完整应用配置<br>* 对话变量（调试+分享）：支持有状态多轮交互<br>* Chat 接口流式输出 message_id：流式响应包含消息追踪标识",
"2. 多模态与交互 💬<br>* 音频输入与输出：应用支持音频模态<br>* 文件类型输入支持：扩展支持语音、文件、视频上传",
"3. 模型与智能 🧠<br>* 模型视觉与 Omni 区分：精确区分视觉与 Omni 模型能力<br>* 教育记忆与陪伴玩具场景预设：垂直领域本体配置开箱即用<br>* 本体配置默认标识：支持基线配置标记<br>* 记忆配置默认标识：自动应用默认记忆设置",
"4. 记忆智能 🔬<br>* 记忆剪枝模块：智能裁剪冗余低价值记忆<br>* RAG 快速检索集成记忆：深度思考与正常回复双模式检索",
"5. 稳健性与缺陷修复 🔧<br>* 模型管理：修复自定义模型 API Key 批量配置错误<br>* 知识库管理：修复非源文档下载原始内容接口错误，更新分享停用提示文案<br>* 用户记忆：优化档案提取准确性（姓名、职业、兴趣分布）<br>* 长期记忆：修复情景记忆卡片重复和用户归属错误<br>* 工作空间首页：修复知识库数量、应用数量、总记忆容量、API 调用次数、知识库类型分布等数据不一致问题<br>* 基础设施：修正 Celery 环境变量配置，修复数据库连接池 idle-in-transaction 泄漏",
"<br>",
"v0.2.6 标志着 MemoryBear 在多模态交互、跨平台工作流迁移和智能记忆管理方面的重要突破。下一版本将聚焦 A2A 协议支持实现多智能体协作、多模态记忆能力扩展至语音与视觉领域，以及应用导入导出功能支持跨环境便携部署。",
"MemoryBear，让记忆有熊力 🐻✨"
]
},
"introduction_en": {
"codeName": "TingJian",
"releaseDate": "2026-3-6",
"upgradePosition": "🐻 Full multimodal interaction upgrade with memory pruning and workflow migration — sharpened edge, broader reach",
"coreUpgrades": [
"1. Workflow & Application Framework<br>* Workflow Import Adaptation (Dify): Seamless Dify workflow migration<br>* Field Character Limits & Validation: Configurable limits with product-defined rules<br>* Application Cloning (Agent, Workflow, Cluster): One-click full config duplication<br>* Conversation Variables (Debug + Share): Stateful multi-turn interactions<br>* Streaming message_id in Chat API: Message tracking in streaming responses",
"2. Multimodal & Interaction 💬<br>* Audio Input & Output: Audio modality support for applications<br>* File Type Input Support: Voice, file, and video upload support",
"3. Model & Intelligence 🧠<br>* Model Vision & Omni Differentiation: Precise capability routing<br>* Education Memory & Companion Toy Presets: Domain-specific ontology configs<br>* Ontology Default Identifier: Baseline configuration flagging<br>* Memory Configuration Default Identifier: Auto-apply default settings",
"4. Memory Intelligence 🔬<br>* Memory Pruning Module: Intelligent trimming of redundant memories<br>* RAG Quick Retrieval with Memory: Deep think and normal reply dual-mode retrieval",
"5. Robustness & Bug Fixes 🔧<br>* Model Management: Fixed custom model API key batch configuration error<br>* Knowledge Base: Fixed download original content API error for non-source documents, updated share disable prompt text<br>* User Memory: Improved profile extraction accuracy (name, occupation, interests)<br>* Long-Term Memory: Fixed duplicate episodic memory cards and wrong user attribution<br>* Dashboard: Fixed data inconsistencies in knowledge count, app count, memory capacity, API calls, and knowledge type distribution<br>* Infrastructure: Corrected Celery environment variables, fixed database connection pool idle-in-transaction leak",
"<br>",
"v0.2.6 marks a significant milestone for MemoryBear in multimodal interaction, cross-platform workflow migration, and intelligent memory management. The next release will focus on A2A protocol support for multi-agent collaboration, multimodal memory extending extraction to voice and visual domains, and application import/export for portable cross-environment deployment.",
"MemoryBear, Memory with Bear Power 🐻✨"
]
}
},
"v0.2.5": {
"introduction": {
"codeName": "行云",

6
web/src/components/Chat/ChatInput.tsx
View File

@@ -50,7 +50,11 @@ const ChatInput: FC<ChatInputProps> = ({
const handleDelete = (file: any) => {
fileChange?.(fileList?.filter(item => file.url ? item.url !== file.url : item.uid !== file.uid) || [])
fileChange?.(fileList?.filter(item => {
return item.thumbUrl && file.thumbUrl ? item.thumbUrl !== file.thumbUrl
: item.url && file.url ? item.url !== file.url
: item.uid !== file.uid
}) || [])
}
// Convert file object to preview URL
const previewFileList = useMemo(() => {

1
web/src/i18n/en.ts
View File

@@ -1361,6 +1361,7 @@ export const en = {
complex: 'Compatibility Analysis',
sureInfo: 'Information Confirmation',
completed: 'Import Completed',
baseInfo: 'Basic Information',
workflowName: 'Workflow Name',
fileName: 'File Name',
fileSize: 'File Size',

12
web/src/utils/request.ts
View File

@@ -356,12 +356,11 @@ export const request = {
* Get parent domain for cookie setting
* @returns Parent domain or IP address
*/
const isIp = (hostname: string) => /^\d+\.\d+\.\d+\.\d+$/.test(hostname)
const getParentDomain = () => {
const hostname = window.location.hostname
// Check if it's an IP address
if (/^\d+\.\d+\.\d+\.\d+$/.test(hostname)) {
return hostname
}
if (isIp(hostname)) return hostname
const parts = hostname.split('.')
return parts.length > 2 ? `.${parts.slice(-2).join('.')}` : hostname
}
@@ -371,7 +370,10 @@ const getParentDomain = () => {
*/
export const cookieUtils = {
set: (name: string, value: string, domain = getParentDomain()) => {
document.cookie = `${name}=${value}; domain=${domain}; path=/; secure; samesite=strict`
const ip = isIp(window.location.hostname)
const domainPart = ip ? '' : `; domain=${domain}`
const securePart = window.location.protocol === 'https:' ? '; secure' : ''
document.cookie = `${name}=${value}${domainPart}; path=/${securePart}; samesite=strict`
},
get: (name: string) => {
const value = `; ${document.cookie}`

6
web/src/views/ApplicationManagement/components/UploadWorkflowModal.tsx
View File

@@ -142,6 +142,7 @@ const UploadWorkflowModal = forwardRef<UploadWorkflowModalRef, UploadWorkflowMod
break;
case 2: // Step 3: Confirm information
if (data) {
setLoading(true);
// Complete import workflow
completeImportWorkflow({
temp_id: data.temp_id,
@@ -152,7 +153,8 @@ const UploadWorkflowModal = forwardRef<UploadWorkflowModalRef, UploadWorkflowMod
const response = res as { id: string };
setCurrent(3);
setAppId(response.id);
});
})
.finally(() => setLoading(false));
}
break;
default:
@@ -243,7 +245,7 @@ const UploadWorkflowModal = forwardRef<UploadWorkflowModalRef, UploadWorkflowMod
</Button>
];
}
}, [current]);
}, [current, loading]);
return (
<RbModal

4
web/src/views/Ontology/pages/Detail.tsx
View File

@@ -102,10 +102,10 @@ const Detail: FC = () => {
<PageHeader
name={<Space>
{data.scene_name}
<Tag color="warning">{t('common.default')}</Tag>
{data.is_system_default ? <Tag color="warning">{t('common.default')}</Tag> : undefined}
</Space>}
subTitle={<Tooltip title={data.scene_description}><div className="rb:h-4 rb:text-ellipsis rb:overflow-hidden rb:whitespace-nowrap">{data.scene_description}</div></Tooltip>}
extra={!data.is_system_default ? undefined : (<Space>
extra={data.is_system_default ? undefined : (<Space>
<Button type="primary" ghost className="rb:h-6! rb:px-2! rb:leading-5.5!" onClick={handleAdd}>+ {t('ontology.addClass')}</Button>
<Button className="rb:h-6! rb:px-2! rb:leading-5.5!" type="primary" onClick={handleExtract}>+ {t('ontology.extract')}</Button>
</Space>)}

8
web/src/views/Workflow/components/Properties/hooks/useVariableList.ts
View File

@@ -35,7 +35,8 @@ const NODE_VARIABLES = {
],
'http-request': [
{ label: 'body', dataType: 'string', field: 'body' },
{ label: 'status_code', dataType: 'number', field: 'status_code' }
{ label: 'status_code', dataType: 'number', field: 'status_code' },
{ label: 'headers', dataType: 'object', field: 'headers' },
],
'question-classifier': [{ label: 'class_name', dataType: 'string', field: 'class_name' }],
'memory-read': [
@@ -390,11 +391,6 @@ export const useVariableList = (
addVariable(list, keys, `${pid}_item`, 'item', itemType, `${pid}.item`, pd);
addVariable(list, keys, `${pid}_index`, 'index', 'number', `${pid}.index`, pd);
} else if (pd.type === 'iteration' && !pd.config.input.defaultValue) {
let itemType = 'object';
const iv = list.find(v => `{{${v.value}}}` === pd.config.input.defaultValue);
if (iv?.dataType.startsWith('array[')) {
itemType = iv.dataType.replace(/^array\[(.+)\]$/, '$1');
}
addVariable(list, keys, `${pid}_item`, 'item', 'string', `${pid}.item`, pd);
addVariable(list, keys, `${pid}_index`, 'index', 'number', `${pid}.index`, pd);
}