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Merge branch 'develop' of github.com:SuanmoSuanyangTechnology/MemoryBear into develop

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Mark
2026-02-06 18:56:35 +08:00
parent 73a432879a 09afec17f9
commit 232cef7cb9
8 changed files with 229 additions and 195 deletions
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3
api/app/controllers/app_controller.py
View File

@@ -802,7 +802,8 @@ async def draft_run_compare(
web_search=True,
memory=True,
parallel=payload.parallel,
timeout=payload.timeout or 60
timeout=payload.timeout or 60,
files=payload.files
):
yield event

13
api/app/controllers/memory_reflection_controller.py
View File

@@ -104,14 +104,18 @@ async def start_workspace_reflection(
) -> dict:
"""启动工作空间中所有匹配应用的反思功能"""
workspace_id = current_user.current_workspace_id
reflection_service = MemoryReflectionService(db)
try:
api_logger.info(f"用户 {current_user.username} 启动workspace反思，workspace_id: {workspace_id}")
service = WorkspaceAppService(db)
# 使用独立的数据库会话来获取工作空间应用详情，避免事务失败
from app.db import get_db_context
with get_db_context() as query_db:
service = WorkspaceAppService(query_db)
result = service.get_workspace_apps_detailed(workspace_id)
reflection_results = []
for data in result['apps_detailed_info']:
# 跳过没有配置的应用
if not data['memory_configs']:
@@ -133,11 +137,14 @@ async def start_workspace_reflection(
api_logger.debug(f"配置 {config_id_str} 没有匹配的release")
continue
# 为每个用户执行反思
# 为每个用户执行反思 - 使用独立的数据库会话
for user in end_users:
api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config_id_str}")
# 为每个用户创建独立的数据库会话，避免事务失败影响其他用户
with get_db_context() as user_db:
try:
reflection_service = MemoryReflectionService(user_db)
reflection_result = await reflection_service.start_text_reflection(
config_data=config,
end_user_id=user['id']

4
api/app/core/memory/storage_services/reflection_engine/self_reflexion.py
View File

@@ -462,8 +462,8 @@ class ReflectionEngine:
List[Any]: 反思数据列表
"""
print("=== 获取反思数据 ===")
print(f" 主机ID: {host_id}")
if self.config.reflexion_range == ReflectionRange.PARTIAL:
neo4j_query = neo4j_query_part.format(host_id)
neo4j_statement = neo4j_statement_part.format(host_id)

2
api/app/schemas/app_schema.py
View File

@@ -488,7 +488,7 @@ class DraftRunCompareRequest(BaseModel):
max_length=5,
description="要对比的模型列表（1-5个）"
)
files: Optional[List[FileInput]] = Field(default=None, description="附件列表（支持多文件）")
parallel: bool = Field(True, description="是否并行执行")
stream: bool = Field(False, description="是否流式返回")
timeout: Optional[int] = Field(60, ge=10, le=300, description="超时时间（秒）")

51
api/app/services/draft_run_service.py
View File

@@ -16,26 +16,26 @@ from sqlalchemy import select
from sqlalchemy.orm import Session
from app.celery_app import celery_app
from app.core.agent.agent_middleware import AgentMiddleware
from app.core.error_codes import BizCode
from app.core.exceptions import BusinessException
from app.core.logging_config import get_business_logger
from app.core.rag.nlp.search import knowledge_retrieval
from app.models import AgentConfig, ModelApiKey, ModelConfig
from app.repositories.model_repository import ModelApiKeyRepository
from app.models import AgentConfig, ModelConfig
from app.repositories.tool_repository import ToolRepository
from app.schemas.prompt_schema import PromptMessageRole, render_prompt_message
from app.schemas.app_schema import FileInput
from app.schemas.prompt_schema import PromptMessageRole, render_prompt_message
from app.services import task_service
from app.services.langchain_tool_server import Search
from app.services.memory_agent_service import MemoryAgentService
from app.services.model_parameter_merger import ModelParameterMerger
from app.services.model_service import ModelApiKeyService
from app.services.tool_service import ToolService
from app.services.multimodal_service import MultimodalService
from app.core.agent.agent_middleware import AgentMiddleware
from app.services.tool_service import ToolService
logger = get_business_logger()
class KnowledgeRetrievalInput(BaseModel):
"""知识库检索工具输入参数"""
query: str = Field(description="需要检索的问题或关键词")
@@ -48,9 +48,12 @@ class WebSearchInput(BaseModel):
class LongTermMemoryInput(BaseModel):
"""长期记忆工具输入参数"""
question: str = Field(description="经过优化重写的查询问题。请将用户的原始问题重写为更合适的检索形式，包含关键词，上下文和具体描述，注意错词检查并且改写")
question: str = Field(
description="经过优化重写的查询问题。请将用户的原始问题重写为更合适的检索形式，包含关键词，上下文和具体描述，注意错词检查并且改写")
def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str, storage_type: Optional[str] = None,user_rag_memory_id: Optional[str] = None):
def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str, storage_type: Optional[str] = None,
user_rag_memory_id: Optional[str] = None):
"""创建记忆工具,
@@ -66,6 +69,7 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str
# 兼容新旧字段名：优先使用 memory_config_id，回退到 memory_content
config_id = memory_config.get("memory_config_id") or memory_config.get("memory_content", None)
logger.info(f"创建长期记忆工具，配置: end_user_id={end_user_id}, config_id={config_id}, storage_type={storage_type}")
@tool(args_schema=LongTermMemoryInput)
def long_term_memory(question: str) -> str:
"""
@@ -133,6 +137,7 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str
except Exception as e:
logger.error("长期记忆检索失败", extra={"error": str(e), "error_type": type(e).__name__})
return f"记忆检索失败: {str(e)}"
return long_term_memory
@@ -189,6 +194,7 @@ def create_knowledge_retrieval_tool(kb_config,kb_ids,user_id):
检索到的相关知识内容
"""
logger.info(f"创建知识库检索工具，用户：{user_id}")
@tool(args_schema=KnowledgeRetrievalInput)
def knowledge_retrieval_tool(query: str) -> str:
"""从知识库中检索相关信息。当用户的问题需要参考知识库、文档或历史记录时，使用此工具进行检索。
@@ -200,7 +206,6 @@ def create_knowledge_retrieval_tool(kb_config,kb_ids,user_id):
检索到的相关知识内容
"""
try:
retrieve_chunks_result = knowledge_retrieval(query, kb_config)
@@ -226,6 +231,7 @@ def create_knowledge_retrieval_tool(kb_config,kb_ids,user_id):
return knowledge_retrieval_tool
class DraftRunService:
"""试运行服务类"""
@@ -296,7 +302,6 @@ class DraftRunService:
agent_config=agent_config
)
items_params = variables
system_prompt = render_prompt_message(
agent_config.system_prompt, # 修正拼写错误
@@ -358,7 +363,8 @@ class DraftRunService:
# 应用动态过滤
if skill_configs:
tools, activated_skill_ids = middleware.filter_tools(tools, message, skill_configs, tool_to_skill_map)
tools, activated_skill_ids = middleware.filter_tools(tools, message, skill_configs,
tool_to_skill_map)
logger.debug(f"过滤后剩余 {len(tools)} 个工具")
active_prompts = AgentMiddleware.get_active_prompts(
activated_skill_ids, skill_configs
@@ -391,7 +397,8 @@ class DraftRunService:
memory_config = agent_config.memory
if user_id:
# 创建长期记忆工具
memory_tool = create_long_term_memory_tool(memory_config, user_id,storage_type,user_rag_memory_id)
memory_tool = create_long_term_memory_tool(memory_config, user_id, storage_type,
user_rag_memory_id)
tools.append(memory_tool)
logger.debug(
@@ -626,14 +633,14 @@ class DraftRunService:
# 应用动态过滤
if skill_configs:
tools, activated_skill_ids = middleware.filter_tools(tools, message, skill_configs, tool_to_skill_map)
tools, activated_skill_ids = middleware.filter_tools(tools, message, skill_configs,
tool_to_skill_map)
logger.debug(f"过滤后剩余 {len(tools)} 个工具")
active_prompts = AgentMiddleware.get_active_prompts(
activated_skill_ids, skill_configs
)
system_prompt = f"{system_prompt}\n\n{active_prompts}"
# 添加知识库检索工具
if agent_config.knowledge_retrieval:
kb_config = agent_config.knowledge_retrieval
@@ -658,7 +665,8 @@ class DraftRunService:
memory_config = agent_config.memory
if user_id:
# 创建长期记忆工具
memory_tool = create_long_term_memory_tool(memory_config, user_id,storage_type,user_rag_memory_id)
memory_tool = create_long_term_memory_tool(memory_config, user_id, storage_type,
user_rag_memory_id)
tools.append(memory_tool)
logger.debug(
@@ -863,7 +871,6 @@ class DraftRunService:
BusinessException: 当指定的会话不存在时
"""
from app.models import Conversation as ConversationModel
from app.schemas.conversation_schema import ConversationCreate
from app.services.conversation_service import ConversationService
conversation_service = ConversationService(self.db)
@@ -1157,6 +1164,7 @@ class DraftRunService:
user_rag_memory_id: Optional[str] = None,
web_search: bool = True,
memory: bool = True,
files: list[FileInput] | None = None
) -> Dict[str, Any]:
"""多模型对比试运行
@@ -1206,7 +1214,8 @@ class DraftRunService:
storage_type=storage_type,
user_rag_memory_id=user_rag_memory_id,
web_search=web_search,
memory=memory
memory=memory,
files=files
),
timeout=timeout
)
@@ -1363,7 +1372,8 @@ class DraftRunService:
web_search: bool = True,
memory: bool = True,
parallel: bool = True,
timeout: int = 60
timeout: int = 60,
files: list[FileInput] | None = None
) -> AsyncGenerator[str, None]:
"""多模型对比试运行（流式返回）
@@ -1383,6 +1393,7 @@ class DraftRunService:
memory: 是否启用记忆
parallel: 是否并行执行
timeout: 超时时间（秒）
files: 多模态文件
Yields:
str: SSE 格式的事件数据
@@ -1441,7 +1452,8 @@ class DraftRunService:
storage_type=storage_type,
user_rag_memory_id=user_rag_memory_id,
web_search=web_search,
memory=memory
memory=memory,
files=files
):
# 解析原始事件
try:
@@ -1696,4 +1708,3 @@ async def draft_run(
similarity_threshold=similarity_threshold,
top_k=top_k
)

10
api/app/services/memory_agent_service.py
View File

@@ -313,8 +313,11 @@ class MemoryAgentService:
start_time = time.time()
# Load configuration from database with workspace fallback
# Use a separate database session to avoid transaction failures
try:
config_service = MemoryConfigService(db)
from app.db import get_db_context
with get_db_context() as config_db:
config_service = MemoryConfigService(config_db)
memory_config = config_service.load_memory_config(
config_id=config_id,
workspace_id=workspace_id,
@@ -454,7 +457,10 @@ class MemoryAgentService:
config_load_start = time.time()
try:
config_service = MemoryConfigService(db)
# Use a separate database session to avoid transaction failures
from app.db import get_db_context
with get_db_context() as config_db:
config_service = MemoryConfigService(config_db)
memory_config = config_service.load_memory_config(
config_id=config_id,
workspace_id=workspace_id,

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

@@ -364,6 +364,7 @@ class MemoryReflectionService:
reflexion_range_value = config_data.get("reflexion_range")
if reflexion_range_value is None or reflexion_range_value == "":
reflexion_range_value = "partial"
# Map legacy/invalid values to valid enum values
reflexion_range_mapping = {
"retrieval": "partial", # Map old 'retrieval' to 'partial'
@@ -378,13 +379,19 @@ class MemoryReflectionService:
baseline_value = "TIME"
baseline = ReflectionBaseline(baseline_value)
# iteration_period =
# iteration_period
iteration_period = config_data.get("iteration_period", 24)
if isinstance(iteration_period, str):
try:
iteration_period = int(iteration_period)
except (ValueError, TypeError):
iteration_period = 24 # 默认24小时
# 获取 model_id 并转换为字符串（如果是 UUID 对象）
reflection_model_id = config_data.get("reflection_model_id", "")
if reflection_model_id:
reflection_model_id = str(reflection_model_id)
return ReflectionConfig(
enabled=config_data.get("enable_self_reflexion", False),
iteration_period=str(iteration_period), # ReflectionConfig期望字符串
@@ -392,7 +399,7 @@ class MemoryReflectionService:
baseline=baseline,
memory_verify=config_data.get("memory_verify", False),
quality_assessment=config_data.get("quality_assessment", False),
model_id=config_data.get("reflection_model_id", "")
model_id=reflection_model_id
)
async def _execute_reflection_engine(

6
web/src/views/KnowledgeBase/components/CreateModal.tsx
View File

@@ -211,7 +211,8 @@ const CreateModal = forwardRef<CreateModalRef, CreateModalRefProps>(({
// Process parser_config data, set default values if not present
const recordAny = record as any;
baseValues.parser_config = record.parser_config || {
baseValues.parser_config = {
...record.parser_config,
graphrag: {
use_graphrag: false,
scene_name: '',
@@ -219,6 +220,7 @@ const CreateModal = forwardRef<CreateModalRef, CreateModalRefProps>(({
method: 'general',
resolution: false,
community: false,
...(record.parser_config?.graphrag || {})
}
};
@@ -656,7 +658,7 @@ const CreateModal = forwardRef<CreateModalRef, CreateModalRefProps>(({
{currentType !== 'Folder' && dynamicTypeList.map((tp) => {
const fieldKey = typeToFieldKey(tp);
// When tp is 'llm', merge llm and chat options
const options = tp.toLowerCase() === 'llm'
const options = tp.toLowerCase() === 'llm' || tp.toLowerCase() === 'image2text'
? [...(modelOptionsByType['llm'] || []), ...(modelOptionsByType['chat'] || [])]
: modelOptionsByType[tp] || [];
return (