Add brain memory services and APIs

Introduce the backend pieces for brain memory ingestion, routing, and
system telemetry so the new knowledge workflows can project data into a
brain view. The supporting tests lock in the new behavior and keep the
expanded backend surface stable.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/app/config.py

@@ -1,14 +1,28 @@
+from pathlib import Path
 from pydantic_settings import BaseSettings, SettingsConfigDict
 from typing import Literal
 
 
+BASE_DIR = Path(__file__).resolve().parent.parent
+ENV_FILE = BASE_DIR / ".env"
+
+
+def _resolve_path(value: str) -> str:
+    path = Path(value)
+    if path.is_absolute():
+        return str(path)
+    return str((BASE_DIR / path).resolve())
+
+
 class Settings(BaseSettings):
-    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")
+    model_config = SettingsConfigDict(env_file=str(ENV_FILE), env_file_encoding="utf-8", extra="ignore")
 
     # === 应用基础 ===
     APP_NAME: str = "Jarvis"
     APP_VERSION: str = "0.1.0"
     DEBUG: bool = False
+    HOST: str = "127.0.0.1"
+    PORT: int = 9527
 
     # === 安全 ===
     SECRET_KEY: str = "change-me-in-production"
@@ -67,3 +81,7 @@ class Settings(BaseSettings):
 
 
 settings = Settings()
+settings.DATABASE_URL = settings.DATABASE_URL.replace("./data", _resolve_path("./data"), 1)
+settings.DATA_DIR = _resolve_path(settings.DATA_DIR)
+settings.CHROMA_PERSIST_DIR = _resolve_path(settings.CHROMA_PERSIST_DIR)
+settings.UPLOAD_DIR = _resolve_path(settings.UPLOAD_DIR)

backend/app/logging_utils.py

@@ -0,0 +1,282 @@
+import json
+import logging
+import time
+import traceback
+import uuid
+from contextvars import ContextVar
+from datetime import datetime, timezone
+from typing import Any
+
+from fastapi import Request
+from fastapi.exceptions import RequestValidationError
+from fastapi.responses import JSONResponse
+from starlette.exceptions import HTTPException as StarletteHTTPException
+
+from app.config import settings
+from app.database import async_session
+from app.services.log_service import LogService
+
+request_id_ctx: ContextVar[str] = ContextVar("request_id", default="-")
+request_user_ctx: ContextVar[str] = ContextVar("request_user", default="anonymous")
+request_path_ctx: ContextVar[str] = ContextVar("request_path", default="-")
+request_method_ctx: ContextVar[str] = ContextVar("request_method", default="-")
+
+logger = logging.getLogger("jarvis.request")
+
+SENSITIVE_KEYS = {"api_key", "authorization", "password", "current_password", "token", "access_token"}
+DB_LOG_EXCLUDED_PATH_PREFIXES = ("/api/logs",)
+
+
+class RequestContextFilter(logging.Filter):
+    def filter(self, record: logging.LogRecord) -> bool:
+        record.request_id = request_id_ctx.get()
+        record.user_id = request_user_ctx.get()
+        record.path = request_path_ctx.get()
+        record.method = request_method_ctx.get()
+        return True
+
+
+class JsonFormatter(logging.Formatter):
+    def format(self, record: logging.LogRecord) -> str:
+        payload = {
+            "time": datetime.now(timezone.utc).isoformat(),
+            "level": record.levelname,
+            "logger": record.name,
+            "message": record.getMessage(),
+            "request_id": getattr(record, "request_id", request_id_ctx.get()),
+            "user_id": getattr(record, "user_id", request_user_ctx.get()),
+            "method": getattr(record, "method", request_method_ctx.get()),
+            "path": getattr(record, "path", request_path_ctx.get()),
+        }
+        status_code = getattr(record, "status_code", None)
+        duration_ms = getattr(record, "duration_ms", None)
+        extra_details = getattr(record, "details", None)
+        if status_code is not None:
+            payload["status_code"] = status_code
+        if duration_ms is not None:
+            payload["duration_ms"] = duration_ms
+        if extra_details is not None:
+            payload["details"] = extra_details
+        if record.exc_info:
+            payload["exception"] = self.formatException(record.exc_info)
+        return json.dumps(payload, ensure_ascii=False)
+
+
+class TextFormatter(logging.Formatter):
+    def format(self, record: logging.LogRecord) -> str:
+        record.request_id = getattr(record, "request_id", request_id_ctx.get())
+        record.user_id = getattr(record, "user_id", request_user_ctx.get())
+        record.path = getattr(record, "path", request_path_ctx.get())
+        record.method = getattr(record, "method", request_method_ctx.get())
+        if not hasattr(record, "status_code"):
+            record.status_code = "-"
+        if not hasattr(record, "duration_ms"):
+            record.duration_ms = "-"
+        return super().format(record)
+
+
+def setup_logging(debug: bool = False) -> None:
+    root_logger = logging.getLogger()
+    if getattr(root_logger, "_jarvis_configured", False):
+        return
+
+    handler = logging.StreamHandler()
+    handler.addFilter(RequestContextFilter())
+    if debug:
+        formatter = TextFormatter(
+            "%(asctime)s | %(levelname)s | %(name)s | request_id=%(request_id)s | user=%(user_id)s | %(method)s %(path)s | status=%(status_code)s | duration=%(duration_ms)s | %(message)s"
+        )
+    else:
+        formatter = JsonFormatter()
+    handler.setFormatter(formatter)
+
+    root_logger.handlers.clear()
+    root_logger.addHandler(handler)
+    root_logger.setLevel(logging.DEBUG if debug else logging.INFO)
+    logging.getLogger("uvicorn.access").setLevel(logging.WARNING)
+    logging.getLogger("sqlalchemy.engine").setLevel(logging.INFO if debug else logging.WARNING)
+    root_logger._jarvis_configured = True
+
+
+def mask_sensitive(value: Any) -> Any:
+    if isinstance(value, dict):
+        return {k: ("[masked]" if k.lower() in SENSITIVE_KEYS else mask_sensitive(v)) for k, v in value.items()}
+    if isinstance(value, list):
+        return [mask_sensitive(item) for item in value]
+    return value
+
+
+def summarize_llm_config(config: dict | None) -> dict:
+    if not config:
+        return {}
+    summary: dict[str, Any] = {}
+    for key, value in config.items():
+        if isinstance(value, list):
+            summary[key] = {
+                "count": len(value),
+                "items": [
+                    {
+                        "name": item.get("name", ""),
+                        "provider": item.get("provider", ""),
+                        "model": item.get("model", ""),
+                        "has_base_url": bool(item.get("base_url")),
+                        "has_api_key": bool(item.get("api_key")),
+                        "enabled": item.get("enabled"),
+                    }
+                    for item in value
+                ],
+            }
+        else:
+            summary[key] = mask_sensitive(value)
+    return summary
+
+
+def should_persist_request_log(path: str) -> bool:
+    return not any(path.startswith(prefix) for prefix in DB_LOG_EXCLUDED_PATH_PREFIXES)
+
+
+async def persist_system_log(**kwargs) -> None:
+    try:
+        async with async_session() as session:
+            await LogService(session).system_log(**kwargs)
+    except Exception:
+        logger.exception("persist_system_log_failed")
+
+
+def build_cors_headers(request: Request) -> dict[str, str]:
+    origin = request.headers.get("origin")
+    if not origin:
+        return {}
+    if "*" in settings.CORS_ORIGINS or origin in settings.CORS_ORIGINS:
+        return {
+            "Access-Control-Allow-Origin": origin,
+            "Access-Control-Allow-Credentials": "true",
+            "Vary": "Origin",
+        }
+    return {}
+
+
+async def request_logging_middleware(request: Request, call_next):
+    request_id = request.headers.get("X-Request-ID") or str(uuid.uuid4())
+    request.state.request_id = request_id
+    request_id_token = request_id_ctx.set(request_id)
+    path_token = request_path_ctx.set(request.url.path)
+    method_token = request_method_ctx.set(request.method)
+    start = time.perf_counter()
+    response = None
+
+    logger.info(
+        "request_started",
+        extra={
+            "details": {
+                "query": dict(request.query_params),
+                "client": request.client.host if request.client else None,
+            }
+        },
+    )
+
+    try:
+        response = await call_next(request)
+        duration_ms = int((time.perf_counter() - start) * 1000)
+        user_id = getattr(request.state, "user_id", "anonymous")
+        request_user_ctx.set(user_id)
+        response.headers["X-Request-ID"] = request_id
+        logger.info(
+            "request_completed",
+            extra={
+                "status_code": response.status_code,
+                "duration_ms": duration_ms,
+            },
+        )
+        if should_persist_request_log(request.url.path):
+            await persist_system_log(
+                message="request_completed",
+                source="http",
+                user_id=user_id if user_id != "anonymous" else None,
+                request_id=request_id,
+                route=request.url.path,
+                method=request.method,
+                status_code=response.status_code,
+                operation="http.request",
+                duration_ms=duration_ms,
+                details={
+                    "query": dict(request.query_params),
+                    "client": request.client.host if request.client else None,
+                },
+            )
+        return response
+    finally:
+        request_id_ctx.reset(request_id_token)
+        request_path_ctx.reset(path_token)
+        request_method_ctx.reset(method_token)
+        request_user_ctx.set("anonymous")
+
+
+async def log_http_exception(request: Request, exc: StarletteHTTPException):
+    request_id = getattr(request.state, "request_id", request_id_ctx.get())
+    logger.warning(
+        "http_exception",
+        extra={
+            "status_code": exc.status_code,
+            "details": {"detail": exc.detail},
+        },
+    )
+    headers = {"X-Request-ID": request_id, **build_cors_headers(request)}
+    return JSONResponse(
+        status_code=exc.status_code,
+        content={"detail": exc.detail, "request_id": request_id},
+        headers=headers,
+    )
+
+
+async def log_validation_exception(request: Request, exc: RequestValidationError):
+    request_id = getattr(request.state, "request_id", request_id_ctx.get())
+    logger.warning(
+        "validation_exception",
+        extra={
+            "status_code": 422,
+            "details": {"errors": exc.errors()},
+        },
+    )
+    headers = {"X-Request-ID": request_id, **build_cors_headers(request)}
+    return JSONResponse(
+        status_code=422,
+        content={"detail": exc.errors(), "request_id": request_id},
+        headers=headers,
+    )
+
+
+async def log_unhandled_exception(request: Request, exc: Exception):
+    request_id = getattr(request.state, "request_id", request_id_ctx.get())
+    user_id = getattr(request.state, "user_id", None)
+    details = {
+        "error_type": exc.__class__.__name__,
+        "error": str(exc),
+        "traceback": traceback.format_exc(),
+    }
+    logger.error(
+        "unhandled_exception",
+        extra={
+            "status_code": 500,
+            "details": details,
+        },
+    )
+    if should_persist_request_log(request.url.path):
+        await persist_system_log(
+            message="unhandled_exception",
+            source="http",
+            user_id=user_id if user_id not in (None, "anonymous") else None,
+            request_id=request_id,
+            route=request.url.path,
+            method=request.method,
+            status_code=500,
+            error_type=exc.__class__.__name__,
+            operation="http.request",
+            details=details,
+        )
+    headers = {"X-Request-ID": request_id, **build_cors_headers(request)}
+    return JSONResponse(
+        status_code=500,
+        content={"detail": "服务器内部错误", "request_id": request_id},
+        headers=headers,
+    )

backend/app/main.py

@@ -1,6 +1,8 @@
 from contextlib import asynccontextmanager
 from fastapi import FastAPI
+from fastapi.exceptions import RequestValidationError
 from fastapi.middleware.cors import CORSMiddleware
+from starlette.exceptions import HTTPException as StarletteHTTPException
 from app.database import init_db
 import app.models  # noqa: F401 - 注册所有模型
 from app.routers import (
@@ -16,20 +18,37 @@ from app.routers import (
     folder_router,
     skill_router,
     log_router,
+    system_router,
+    brain_router,
 )
 from app.routers.scheduler import router as scheduler_router
 from app.services.scheduler_service import start_scheduler, stop_scheduler, get_scheduler_status
 from app.config import settings
+from app.logging_utils import (
+    setup_logging,
+    request_logging_middleware,
+    log_http_exception,
+    log_validation_exception,
+    log_unhandled_exception,
+    persist_system_log,
+)
 import os
 
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     # 启动
+    setup_logging(settings.DEBUG)
     os.makedirs(settings.DATA_DIR, exist_ok=True)
     os.makedirs(settings.UPLOAD_DIR, exist_ok=True)
     os.makedirs(settings.CHROMA_PERSIST_DIR, exist_ok=True)
     await init_db()
+    await persist_system_log(
+        message="application_started",
+        source="app",
+        operation="app.startup",
+        details={"version": settings.APP_VERSION},
+    )
     start_scheduler()
     yield
     # 关闭
@@ -50,6 +69,10 @@ app.add_middleware(
     allow_methods=["*"],
     allow_headers=["*"],
 )
+app.middleware("http")(request_logging_middleware)
+app.add_exception_handler(StarletteHTTPException, log_http_exception)
+app.add_exception_handler(RequestValidationError, log_validation_exception)
+app.add_exception_handler(Exception, log_unhandled_exception)
 
 # 注册路由
 app.include_router(auth_router)
@@ -64,6 +87,8 @@ app.include_router(settings_router)
 app.include_router(folder_router)
 app.include_router(skill_router)
 app.include_router(log_router)
+app.include_router(system_router)
+app.include_router(brain_router)
 app.include_router(scheduler_router)
 
 

backend/app/models/__init__.py

@@ -7,6 +7,15 @@ from app.models.agent import Agent, AgentMessage
 from app.models.conversation import Conversation, Message
 from app.models.knowledge_graph import KGNode, KGEdge
 from app.models.memory import MemorySummary, UserMemory
+from app.models.brain import (
+    BrainEvent,
+    BrainCandidate,
+    BrainMemory,
+    BrainTag,
+    brain_event_tags,
+    brain_memory_tags,
+    brain_memory_sources,
+)
 from app.models.todo import DailyTodo, TodoSource
 from app.models.log import Log, LogType, LogLevel
 
@@ -27,6 +36,13 @@ __all__ = [
     "KGEdge",
     "MemorySummary",
     "UserMemory",
+    "BrainEvent",
+    "BrainCandidate",
+    "BrainMemory",
+    "BrainTag",
+    "brain_event_tags",
+    "brain_memory_tags",
+    "brain_memory_sources",
     "DailyTodo",
     "TodoSource",
     "Log",

backend/app/models/brain.py

@@ -0,0 +1,93 @@
+from sqlalchemy import Column, DateTime, Float, ForeignKey, Integer, String, Table, Text
+from sqlalchemy.dialects.sqlite import JSON
+
+from app.database import Base
+from app.models.base import BaseModel, utc_now
+
+
+brain_event_tags = Table(
+    "brain_event_tags",
+    Base.metadata,
+    Column("event_id", String(36), ForeignKey("brain_events.id"), primary_key=True),
+    Column("tag_id", String(36), ForeignKey("brain_tags.id"), primary_key=True),
+)
+
+brain_memory_tags = Table(
+    "brain_memory_tags",
+    Base.metadata,
+    Column("memory_id", String(36), ForeignKey("brain_memories.id"), primary_key=True),
+    Column("tag_id", String(36), ForeignKey("brain_tags.id"), primary_key=True),
+)
+
+brain_memory_sources = Table(
+    "brain_memory_sources",
+    Base.metadata,
+    Column("memory_id", String(36), ForeignKey("brain_memories.id"), primary_key=True),
+    Column("event_id", String(36), ForeignKey("brain_events.id"), primary_key=True),
+)
+
+
+class BrainEvent(BaseModel):
+    __tablename__ = "brain_events"
+
+    user_id = Column(String(36), ForeignKey("users.id"), nullable=False, index=True)
+    source_type = Column(String(50), nullable=False, index=True)
+    source_id = Column(String(36), nullable=False, index=True)
+    event_type = Column(String(50), nullable=False, index=True)
+    title = Column(String(255), nullable=True)
+    content_summary = Column(Text, nullable=True)
+    raw_excerpt = Column(Text, nullable=True)
+    metadata_ = Column(JSON, nullable=True)
+    importance_signal = Column(Float, default=0.0, nullable=False)
+    is_user_pinned = Column(Integer, default=0, nullable=False)
+    occurred_at = Column(DateTime, default=utc_now, nullable=False, index=True)
+    processed_at = Column(DateTime, nullable=True)
+    status = Column(String(20), default="pending", nullable=False, index=True)
+
+
+class BrainCandidate(BaseModel):
+    __tablename__ = "brain_candidates"
+
+    user_id = Column(String(36), ForeignKey("users.id"), nullable=False, index=True)
+    candidate_type = Column(String(50), nullable=False, index=True)
+    title = Column(String(255), nullable=False)
+    summary = Column(Text, nullable=False)
+    importance_score = Column(Float, default=0.0, nullable=False)
+    confidence_score = Column(Float, default=0.0, nullable=False)
+    time_scope = Column(String(20), default="short_term", nullable=False)
+    valid_from = Column(DateTime, nullable=True)
+    valid_to = Column(DateTime, nullable=True)
+    source_event_ids = Column(JSON, nullable=True)
+    reasoning_trace = Column(Text, nullable=True)
+    status = Column(String(20), default="new", nullable=False, index=True)
+    reviewed_at = Column(DateTime, nullable=True)
+
+
+class BrainMemory(BaseModel):
+    __tablename__ = "brain_memories"
+
+    user_id = Column(String(36), ForeignKey("users.id"), nullable=False, index=True)
+    memory_type = Column(String(50), nullable=False, index=True)
+    title = Column(String(255), nullable=False)
+    content = Column(Text, nullable=False)
+    importance = Column(Integer, default=5, nullable=False)
+    confidence = Column(Float, default=0.0, nullable=False)
+    timeline_date = Column(DateTime, nullable=True)
+    first_learned_at = Column(DateTime, default=utc_now, nullable=False)
+    last_reinforced_at = Column(DateTime, nullable=True)
+    reinforcement_count = Column(Integer, default=0, nullable=False)
+    status = Column(String(20), default="active", nullable=False, index=True)
+    origin_candidate_id = Column(String(36), ForeignKey("brain_candidates.id"), nullable=True)
+    origin_source_types = Column(JSON, nullable=True)
+    metadata_ = Column(JSON, nullable=True)
+
+
+class BrainTag(BaseModel):
+    __tablename__ = "brain_tags"
+
+    user_id = Column(String(36), ForeignKey("users.id"), nullable=False, index=True)
+    name = Column(String(100), nullable=False, index=True)
+    category = Column(String(50), nullable=False)
+    priority = Column(String(20), default="secondary", nullable=False, index=True)
+    score = Column(Float, default=0.0, nullable=False)
+    last_seen_at = Column(DateTime, nullable=True)

backend/app/models/log.py

@@ -1,4 +1,4 @@
-from sqlalchemy import Column, String, Text, DateTime, Index, Enum as SQLEnum
+from sqlalchemy import Column, String, Text, Integer, Index
 from app.models.base import BaseModel
 import enum
 
@@ -22,12 +22,20 @@ class Log(BaseModel):
     level = Column(String(20), default=LogLevel.INFO.value, index=True)  # debug/info/warning/error
     type = Column(String(20), default=LogType.SYSTEM.value, index=True)   # agent/system/chat
     user_id = Column(String(36), nullable=True, index=True)  # 关联用户
+    request_id = Column(String(64), nullable=True, index=True)
+    route = Column(String(255), nullable=True, index=True)
+    method = Column(String(16), nullable=True, index=True)
+    status_code = Column(Integer, nullable=True, index=True)
+    error_type = Column(String(100), nullable=True)
+    operation = Column(String(100), nullable=True, index=True)
     message = Column(Text, nullable=False)  # 日志内容
     details = Column(Text, nullable=True)  # 详细信息(JSON)
     source = Column(String(100), nullable=True)  # 来源模块
-    duration_ms = Column(String(20), nullable=True)  # 执行耗时
+    duration_ms = Column(Integer, nullable=True)  # 执行耗时
 
     __table_args__ = (
         Index('idx_logs_type_level', 'type', 'level'),
         Index('idx_logs_created_at', 'created_at'),
+        Index('idx_logs_request_id', 'request_id'),
+        Index('idx_logs_operation_status', 'operation', 'status_code'),
     )

backend/app/routers/__init__.py

@@ -10,3 +10,5 @@ from app.routers.settings import router as settings_router
 from app.routers.folder import router as folder_router
 from app.routers.skill import router as skill_router
 from app.routers.log import router as log_router
+from app.routers.system import router as system_router
+from app.routers.brain import router as brain_router

backend/app/routers/brain.py

@@ -0,0 +1,61 @@
+from fastapi import APIRouter, Depends
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.database import get_db
+from app.models.user import User
+from app.routers.auth import get_current_user
+from app.schemas.brain import (
+    BrainEventOut,
+    BrainLearnRunOut,
+    BrainMemoryOut,
+    BrainOverviewOut,
+    BrainTagGroupsOut,
+)
+from app.services.brain_service import BrainService
+
+router = APIRouter(prefix="/api/brain", tags=["知识大脑"])
+
+
+@router.get("/overview", response_model=BrainOverviewOut)
+async def get_brain_overview(
+    current_user: User = Depends(get_current_user),
+    db: AsyncSession = Depends(get_db),
+):
+    service = BrainService(db)
+    return await service.get_overview(current_user.id)
+
+
+@router.get("/memories", response_model=list[BrainMemoryOut])
+async def list_brain_memories(
+    current_user: User = Depends(get_current_user),
+    db: AsyncSession = Depends(get_db),
+):
+    service = BrainService(db)
+    return await service.list_memories(current_user.id)
+
+
+@router.get("/tags", response_model=BrainTagGroupsOut)
+async def list_brain_tags(
+    current_user: User = Depends(get_current_user),
+    db: AsyncSession = Depends(get_db),
+):
+    service = BrainService(db)
+    return await service.list_tags(current_user.id)
+
+
+@router.get("/events", response_model=list[BrainEventOut])
+async def list_brain_events(
+    current_user: User = Depends(get_current_user),
+    db: AsyncSession = Depends(get_db),
+):
+    service = BrainService(db)
+    return await service.list_events(current_user.id)
+
+
+@router.post("/learn/run", response_model=BrainLearnRunOut)
+async def run_brain_learning(
+    current_user: User = Depends(get_current_user),
+    db: AsyncSession = Depends(get_db),
+):
+    service = BrainService(db)
+    return await service.run_learning(current_user.id)

backend/app/routers/conversation.py

@@ -92,11 +92,12 @@ async def chat(
 ):
     """简单版对话（非流式）"""
     agent_svc = AgentService(db)
-    conv_id, msg_id, content = await agent_svc.chat_simple(
+    conv_id, msg_id, content, model_name = await agent_svc.chat_simple(
         user_id=current_user.id,
         message=data.message,
         conversation_id=data.conversation_id,
         file_ids=data.file_ids,
+        model_name=data.model_name,
     )
 
     # 更新对话消息计数
@@ -111,6 +112,7 @@ async def chat(
         message_id=msg_id,
         content=content,
         agent_name="jarvis",
+        model_name=model_name,
     )
 
 
@@ -128,24 +130,24 @@ async def chat_stream(
             user_id=current_user.id,
             message=data.message,
             conversation_id=data.conversation_id,
+            file_ids=data.file_ids,
+            model_name=data.model_name,
         )
 
-        # 先发送元数据
         yield f"event: metadata\ndata: {json.dumps({'conversation_id': conv_id, 'message_id': msg_id})}\n\n"
 
-        # 流式发送内容
-        collected = ""
         try:
-            async for chunk in stream:
-                if chunk:
-                    collected += chunk
-                    yield f"event: chunk\ndata: {json.dumps({'content': chunk})}\n\n"
-
-            # 更新数据库中的消息
-            await agent_svc.save_response(msg_id, collected)
-
+            async for event in stream:
+                event_type = event.get('type', 'progress')
+                if event_type == 'chunk':
+                    yield f"event: chunk\ndata: {json.dumps({'content': event.get('content', '')}, ensure_ascii=False)}\n\n"
+                elif event_type == 'error':
+                    yield f"event: error\ndata: {json.dumps({'error': event.get('error', '未知错误')}, ensure_ascii=False)}\n\n"
+                else:
+                    payload = {k: v for k, v in event.items() if k != 'type'}
+                    yield f"event: progress\ndata: {json.dumps(payload, ensure_ascii=False)}\n\n"
         except Exception as e:
-            yield f"event: error\ndata: {json.dumps({'error': str(e)})}\n\n"
+            yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
         finally:
             yield f"event: done\ndata: {json.dumps({'message_id': msg_id})}\n\n"
 

backend/app/routers/folder.py

@@ -6,7 +6,7 @@ from app.database import get_db
 from app.models.folder import Folder
 from app.models.user import User
 from app.schemas.folder import FolderCreate, FolderUpdate, FolderOut, FolderTreeOut
-from app.services.auth_service import get_current_user
+from app.routers.auth import get_current_user
 
 router = APIRouter(prefix="/api/folders", tags=["文件夹"])
 

backend/app/routers/settings.py

@@ -1,4 +1,6 @@
-from fastapi import APIRouter, Depends, HTTPException
+import logging
+import time
+from fastapi import APIRouter, Depends, HTTPException, Request
 from sqlalchemy.ext.asyncio import AsyncSession
 from app.database import get_db
 from app.models.user import User
@@ -6,22 +8,40 @@ from app.routers.auth import get_current_user
 from app.schemas.settings import (
     SettingsOut, ProfileUpdateIn, LLMConfigIn, SchedulerConfigIn, LLMTestIn
 )
+from app.services.log_service import LogService
 from app.services.settings_service import (
     get_user_settings, update_user_profile, update_llm_config,
     update_scheduler_config, test_llm_connection
 )
+from app.logging_utils import summarize_llm_config
+
+logger = logging.getLogger(__name__)
 
 router = APIRouter(prefix="/api/settings", tags=["设置"])
 
 
 @router.get("", response_model=SettingsOut)
 async def get_settings(
+    request: Request,
     current_user: User = Depends(get_current_user),
     db: AsyncSession = Depends(get_db),
 ):
+    request.state.user_id = current_user.id
     settings = await get_user_settings(current_user.id, db)
     if not settings:
         raise HTTPException(status_code=404, detail="用户不存在")
+
+    await LogService(db).system_log(
+        message="加载用户设置",
+        source="settings",
+        user_id=current_user.id,
+        request_id=request.state.request_id,
+        route=request.url.path,
+        method=request.method,
+        status_code=200,
+        operation="settings.get",
+        details={"llm_config": summarize_llm_config(settings.get("llm_config"))},
+    )
     return settings
 
 
@@ -46,42 +66,128 @@ async def update_profile(
 @router.put("/llm")
 async def update_llm(
     data: LLMConfigIn,
+    request: Request,
     current_user: User = Depends(get_current_user),
     db: AsyncSession = Depends(get_db),
 ):
+    request.state.user_id = current_user.id
+    log_service = LogService(db)
+    start = time.perf_counter()
+    payload = data.model_dump(exclude_none=True)
     try:
-        config = await update_llm_config(current_user.id, data.model_dump(exclude_none=True), db)
+        config = await update_llm_config(current_user.id, payload, db)
+        await log_service.system_log(
+            message="更新 LLM 配置成功",
+            source="settings",
+            user_id=current_user.id,
+            request_id=request.state.request_id,
+            route=request.url.path,
+            method=request.method,
+            status_code=200,
+            operation="settings.update_llm",
+            duration_ms=int((time.perf_counter() - start) * 1000),
+            details={
+                "request": summarize_llm_config(payload),
+                "stored": summarize_llm_config(config),
+            },
+        )
         return {"llm_config": config}
     except ValueError as e:
+        await log_service.system_log(
+            message="更新 LLM 配置失败",
+            level="warning",
+            source="settings",
+            user_id=current_user.id,
+            request_id=request.state.request_id,
+            route=request.url.path,
+            method=request.method,
+            status_code=400,
+            error_type=e.__class__.__name__,
+            operation="settings.update_llm",
+            duration_ms=int((time.perf_counter() - start) * 1000),
+            details={"request": summarize_llm_config(payload), "detail": str(e)},
+        )
         raise HTTPException(status_code=400, detail=str(e))
 
 
 @router.post("/llm/test")
 async def test_llm(
     data: LLMTestIn,
+    request: Request,
     current_user: User = Depends(get_current_user),
+    db: AsyncSession = Depends(get_db),
 ):
+    request.state.user_id = current_user.id
+    start = time.perf_counter()
     result = await test_llm_connection(
         provider=data.provider,
         model=data.model,
         base_url=data.base_url,
         api_key=data.api_key
     )
+    await LogService(db).system_log(
+        message="测试 LLM 连接",
+        level="info" if result.get("success") else "warning",
+        source="settings",
+        user_id=current_user.id,
+        request_id=request.state.request_id,
+        route=request.url.path,
+        method=request.method,
+        status_code=200,
+        error_type=None if result.get("success") else "llm_test_failed",
+        operation="settings.test_llm",
+        duration_ms=int((time.perf_counter() - start) * 1000),
+        details={
+            "provider": data.provider,
+            "model": data.model,
+            "has_base_url": bool(data.base_url),
+            "has_api_key": bool(data.api_key),
+            "success": result.get("success"),
+            "error": result.get("error"),
+        },
+    )
     return result
 
 
 @router.put("/scheduler")
 async def update_scheduler(
     data: SchedulerConfigIn,
+    request: Request,
     current_user: User = Depends(get_current_user),
     db: AsyncSession = Depends(get_db),
 ):
+    request.state.user_id = current_user.id
+    payload = data.model_dump(exclude_none=True)
     try:
         config = await update_scheduler_config(
             current_user.id,
-            data.model_dump(exclude_none=True),
+            payload,
             db
         )
+        await LogService(db).system_log(
+            message="更新调度配置成功",
+            source="settings",
+            user_id=current_user.id,
+            request_id=request.state.request_id,
+            route=request.url.path,
+            method=request.method,
+            status_code=200,
+            operation="settings.update_scheduler",
+            details={"request": payload, "stored": config},
+        )
         return {"scheduler_config": config}
     except ValueError as e:
+        await LogService(db).system_log(
+            message="更新调度配置失败",
+            level="warning",
+            source="settings",
+            user_id=current_user.id,
+            request_id=request.state.request_id,
+            route=request.url.path,
+            method=request.method,
+            status_code=400,
+            error_type=e.__class__.__name__,
+            operation="settings.update_scheduler",
+            details={"request": payload, "detail": str(e)},
+        )
         raise HTTPException(status_code=400, detail=str(e))

backend/app/routers/system.py

@@ -0,0 +1,9 @@
+from fastapi import APIRouter
+from app.services.system_service import SystemService
+
+router = APIRouter(prefix='/api/system', tags=['system'])
+
+
+@router.get('/status')
+async def get_system_status():
+    return SystemService().get_status()

backend/app/schemas/brain.py

@@ -0,0 +1,57 @@
+from datetime import datetime
+
+from pydantic import BaseModel
+
+
+class BrainOverviewOut(BaseModel):
+    active_memory_count: int
+    important_tag_count: int
+    secondary_tag_count: int
+    recent_memory_titles: list[str]
+
+
+class BrainMemoryOut(BaseModel):
+    id: str
+    memory_type: str
+    title: str
+    content: str
+    importance: int
+    confidence: float
+    status: str
+    created_at: datetime
+
+    model_config = {"from_attributes": True}
+
+
+class BrainTagOut(BaseModel):
+    id: str
+    name: str
+    category: str
+    priority: str
+    score: float
+
+    model_config = {"from_attributes": True}
+
+
+class BrainEventOut(BaseModel):
+    id: str
+    source_type: str
+    source_id: str
+    event_type: str
+    title: str | None
+    content_summary: str | None
+    status: str
+    created_at: datetime
+
+    model_config = {"from_attributes": True}
+
+
+class BrainTagGroupsOut(BaseModel):
+    important: list[BrainTagOut]
+    secondary: list[BrainTagOut]
+
+
+class BrainLearnRunOut(BaseModel):
+    events_considered: int
+    candidates_created: int
+    memories_promoted: int

backend/app/schemas/conversation.py

@@ -12,6 +12,7 @@ class MessageOut(BaseModel):
     content: str
     model: str | None
     tokens_used: int | None
+    attachments: list[dict] | None = None
     created_at: datetime
 
     model_config = {"from_attributes": True}
@@ -35,7 +36,8 @@ class ChatRequest(BaseModel):
     message: str
     conversation_id: str | None = None
     agent_id: str | None = None
-    file_ids: list[str] = []  # 新增
+    model_name: str | None = None
+    file_ids: list[str] = []
 
 
 class ChatResponse(BaseModel):
@@ -43,3 +45,4 @@ class ChatResponse(BaseModel):
     message_id: str
     content: str
     agent_name: str
+    model_name: str | None = None

backend/app/services/agent_service.py

@@ -6,15 +6,60 @@ Jarvis Agent 服务层
 import json
 import uuid
 from datetime import datetime
-from typing import AsyncGenerator
+from typing import Any, AsyncGenerator
+import asyncio
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select
 from langchain_core.messages import HumanMessage, AIMessage
+from langchain_openai import ChatOpenAI
+from langchain_anthropic import ChatAnthropic
+from langchain_ollama import ChatOllama
+import httpx
+
+from app.database import async_session
 
 from app.models.conversation import Conversation, Message
+from app.models.user import User
 from app.agents.graph import get_agent_graph
 from app.agents.context import set_current_user, clear_current_user
 from app.services import memory_service
+from app.services.brain_service import BrainService
+
+
+def _create_llm_from_config(config: dict):
+    """根据用户模型配置创建 LLM 实例"""
+    provider = config.get("provider", "openai")
+    model = config.get("model", "")
+    api_key = config.get("api_key", "")
+    base_url = config.get("base_url", "")
+
+    if provider == "openai" or provider == "deepseek" or provider == "custom":
+        return ChatOpenAI(
+            api_key=api_key,
+            model=model,
+            base_url=base_url or None,
+            timeout=httpx.Timeout(60.0, connect=10.0),
+        )
+    elif provider == "claude":
+        return ChatAnthropic(
+            api_key=api_key,
+            model=model,
+            timeout=httpx.Timeout(60.0, connect=10.0),
+        )
+    elif provider == "ollama":
+        return ChatOllama(
+            base_url=base_url or "http://localhost:11434",
+            model=model,
+            timeout=httpx.Timeout(120.0, connect=10.0),
+        )
+    else:
+        # 默认使用 OpenAI
+        return ChatOpenAI(
+            api_key=api_key,
+            model=model,
+            base_url=base_url or None,
+            timeout=httpx.Timeout(60.0, connect=10.0),
+        )
 
 
 class AgentService:
@@ -23,12 +68,70 @@ class AgentService:
     def __init__(self, db: AsyncSession):
         self.db = db
 
+    async def _try_auto_summarize_background(self, user_id: str, conversation_id: str) -> None:
+        async with async_session() as session:
+            await memory_service.try_auto_summarize(session, user_id, conversation_id)
+
+    def _build_progress_event(
+        self,
+        stage: str,
+        label: str,
+        *,
+        agent: str | None = None,
+        tool_name: str | None = None,
+        step: str | None = None,
+        steps: list[str] | None = None,
+    ) -> dict[str, Any]:
+        return {
+            "type": "progress",
+            "stage": stage,
+            "label": label,
+            "agent": agent,
+            "tool_name": tool_name,
+            "step": step,
+            "steps": steps or [],
+        }
+
+    async def _get_user_llm_config(self, user_id: str, model_name: str | None = None) -> dict | None:
+        """获取用户的 LLM 模型配置"""
+        result = await self.db.execute(select(User).where(User.id == user_id))
+        user = result.scalar_one_or_none()
+        if not user or not user.llm_config:
+            return None
+
+        llm_config = user.llm_config
+
+        # 如果指定了模型名称，查找对应的配置
+        if model_name:
+            for model_type in ["chat", "vlm"]:
+                models = llm_config.get(model_type, [])
+                for m in models:
+                    if m.get("name") == model_name:
+                        return m
+            # 没找到，返回 None 让调用方知道配置不存在
+            return None
+
+        # 如果没指定模型名，返回默认启用的 chat 模型
+        chat_models = llm_config.get("chat", [])
+        for m in chat_models:
+            if m.get("enabled"):
+                return m
+
+        vlm_models = llm_config.get("vlm", [])
+        for m in vlm_models:
+            if m.get("enabled"):
+                return m
+
+        return None
+
     async def chat(
         self,
         user_id: str,
         message: str,
         conversation_id: str | None = None,
-    ) -> tuple[str, str, AsyncGenerator[str, None]]:
+        file_ids: list[str] | None = None,
+        model_name: str | None = None,
+    ) -> tuple[str, str, AsyncGenerator[dict[str, Any], None]]:
         """
         处理对话请求（流式）
 
@@ -53,22 +156,54 @@ class AgentService:
         else:
             conversation_id = conv.id
 
+        # 如果有文件，读取内容作为上下文
+        file_context = ""
+        if file_ids:
+            from app.services.document_service import DocumentService
+            doc_svc = DocumentService(self.db)
+            for file_id in file_ids:
+                content = await doc_svc.get_document_content(user_id, file_id)
+                if content:
+                    file_context += f"\n\n[用户上传文件内容]\n{content}\n[/文件内容]"
+
+        full_message = f"{message}\n{file_context}" if file_context else message
+
         # 存储用户消息
         user_msg = Message(
             conversation_id=conversation_id,
             role="user",
             content=message,
+            attachments=[{"file_ids": file_ids}] if file_ids else None,
         )
         self.db.add(user_msg)
         await self.db.commit()
         await self.db.refresh(user_msg)
 
+        brain_service = BrainService(self.db)
+        await brain_service.create_event(
+            user_id,
+            source_type="conversation",
+            source_id=conversation_id,
+            event_type="message_created",
+            title="User message",
+            content_summary=message[:500],
+            raw_excerpt=message[:2000],
+            metadata_={"role": "user"},
+            importance_signal=1.0,
+        )
+        await self.db.commit()
+
         # 预创建助手消息（后续更新内容）
+        user_llm_config = await self._get_user_llm_config(user_id, model_name)
+        model_name_used = model_name
+        if user_llm_config:
+            model_name_used = user_llm_config.get("name", model_name)
+
         assistant_msg = Message(
             conversation_id=conversation_id,
             role="assistant",
             content="",
-            model="jarvis",
+            model=model_name_used or "jarvis",
         )
         self.db.add(assistant_msg)
         await self.db.commit()
@@ -85,7 +220,7 @@ class AgentService:
             try:
                 graph = get_agent_graph()
                 langgraph_state = {
-                    "messages": [HumanMessage(content=message)],  # type: ignore[arg-type]
+                    "messages": [HumanMessage(content=full_message)],  # type: ignore[arg-type]
                     "user_id": user_id,
                     "conversation_id": conversation_id,
                     "current_agent": "master",
@@ -102,33 +237,81 @@ class AgentService:
                     "final_response": None,
                     "should_respond": True,
                     "memory_context": memory_ctx,
+                    "user_llm_config": user_llm_config,
                 }
 
+                yield self._build_progress_event("thinking", "Jarvis 正在分析请求", agent="master", step="理解你的问题")
+
                 collected = ""
                 async for event in graph.astream_events(langgraph_state, version="v2"):
                     kind = event.get("event")
-                    if kind == "on_chat_model_end":
-                        content = event.get("data", {}).get("output", {})
-                        if isinstance(content, dict):
-                            content = content.get("content", "")
-                        if content:
-                            delta = content[len(collected):]
-                            if delta:
-                                collected += delta
-                                yield delta
+                    event_name = event.get("name", "")
+                    metadata = event.get("metadata", {})
+                    data = event.get("data", {})
+
+                    if kind == "on_chain_start" and event_name in {"master", "planner", "executor", "librarian", "analyst"}:
+                        stage_map = {
+                            "master": ("thinking", "Jarvis 正在理解请求"),
+                            "planner": ("planning", "Jarvis 正在拆解步骤"),
+                            "executor": ("tool", "Jarvis 正在执行操作"),
+                            "librarian": ("tool", "Jarvis 正在检索知识"),
+                            "analyst": ("thinking", "Jarvis 正在分析信息"),
+                        }
+                        stage, label = stage_map[event_name]
+                        yield self._build_progress_event(stage, label, agent=event_name, step=label)
+                    elif kind == "on_tool_start":
+                        tool_input = data.get("input")
+                        step = None
+                        if isinstance(tool_input, dict) and tool_input:
+                            step = f"调用工具 {event_name}"
+                        yield self._build_progress_event("tool", f"Jarvis 正在调用工具 {event_name}", agent="executor", tool_name=event_name, step=step)
                     elif kind == "on_tool_end":
-                        name = event.get("name", "")
-                        yield f"\n[工具执行: {name}]\n"
+                        yield self._build_progress_event("tool", f"工具 {event_name} 已完成", agent="executor", tool_name=event_name, step=f"已获得 {event_name} 结果")
+                    elif kind == "on_chain_end" and event_name == "planner":
+                        output = data.get("output") or {}
+                        plan_steps = output.get("plan_steps") or []
+                        steps = [item.get("description", "") for item in plan_steps if item.get("description")]
+                        yield self._build_progress_event("planning", "Jarvis 已生成处理步骤", agent="planner", step=steps[0] if steps else "正在整理计划", steps=steps[:4])
+                    elif kind == "on_chat_model_stream":
+                        chunk = data.get("chunk")
+                        content = getattr(chunk, "content", "") if chunk else ""
+                        if isinstance(content, list):
+                            text_parts = []
+                            for item in content:
+                                if isinstance(item, dict):
+                                    text_parts.append(item.get("text", ""))
+                                else:
+                                    text_parts.append(str(item))
+                            content = "".join(text_parts)
+                        if content:
+                            collected += content
+                            yield {"type": "chunk", "content": content}
+                    elif kind == "on_chat_model_end" and not collected:
+                        output = data.get("output")
+                        content = getattr(output, "content", "") if output else ""
+                        if isinstance(content, list):
+                            text_parts = []
+                            for item in content:
+                                if isinstance(item, dict):
+                                    text_parts.append(item.get("text", ""))
+                                else:
+                                    text_parts.append(str(item))
+                            content = "".join(text_parts)
+                        if content:
+                            collected = content
+                            yield {"type": "chunk", "content": content}
+                    elif kind == "on_chain_end" and event_name in {"executor", "librarian", "analyst"}:
+                        yield self._build_progress_event("responding", "Jarvis 正在整理最终回答", agent=event_name, step="生成回复")
             except Exception as e:
-                yield f"\n执行出错: {str(e)}"
+                fallback = f"抱歉，发生错误: {str(e)}"
+                collected = fallback
+                yield {"type": "error", "error": str(e)}
+                yield {"type": "chunk", "content": fallback}
             finally:
                 clear_current_user()
-                # 异步触发自动摘要和记忆提取（不阻塞响应）
-                import asyncio
                 try:
-                    loop = asyncio.get_running_loop()
-                    loop.create_task(
-                        memory_service.try_auto_summarize(self.db, user_id, conversation_id)
+                    asyncio.get_running_loop().create_task(
+                        self._try_auto_summarize_background(user_id, conversation_id)
                     )
                 except Exception:
                     pass
@@ -143,6 +326,18 @@ class AgentService:
                     if msg:
                         msg.content = collected
                         await self.db.commit()
+                        await brain_service.create_event(
+                            user_id,
+                            source_type="conversation",
+                            source_id=conversation_id,
+                            event_type="message_created",
+                            title="Assistant message",
+                            content_summary=collected[:500],
+                            raw_excerpt=collected[:2000],
+                            metadata_={"role": "assistant"},
+                            importance_signal=1.0,
+                        )
+                        await self.db.commit()
                 except Exception:
                     pass
 
@@ -154,12 +349,13 @@ class AgentService:
         message: str,
         conversation_id: str | None = None,
         file_ids: list[str] | None = None,
-    ) -> tuple[str, str, str]:
+        model_name: str | None = None,
+    ) -> tuple[str, str, str, str | None]:
         """
         简单同步版对话（无流式）
 
         Returns:
-            (conversation_id, message_id, response_content)
+            (conversation_id, message_id, response_content, model_name_used)
         """
         # 获取或创建对话
         if conversation_id:
@@ -203,11 +399,31 @@ class AgentService:
         await self.db.commit()
         await self.db.refresh(user_msg)
 
+        brain_service = BrainService(self.db)
+        await brain_service.create_event(
+            user_id,
+            source_type="conversation",
+            source_id=conversation_id,
+            event_type="message_created",
+            title="User message",
+            content_summary=message[:500],
+            raw_excerpt=message[:2000],
+            metadata_={"role": "user"},
+            importance_signal=1.0,
+        )
+        await self.db.commit()
+
         # 加载记忆上下文
         memory_ctx = await memory_service.build_memory_context(
             self.db, user_id, conversation_id, message
         )
 
+        # 获取用户配置的 LLM
+        user_llm_config = await self._get_user_llm_config(user_id, model_name)
+        model_name_used = model_name
+        if user_llm_config:
+            model_name_used = user_llm_config.get("name", model_name)
+
         # 调用 LangGraph Agent
         set_current_user(user_id)
         graph = get_agent_graph()
@@ -229,6 +445,7 @@ class AgentService:
             "final_response": None,
             "should_respond": True,
             "memory_context": memory_ctx,
+            "user_llm_config": user_llm_config,  # 传递用户 LLM 配置
         }
 
         try:
@@ -238,11 +455,9 @@ class AgentService:
             response_content = f"抱歉，发生错误: {str(e)}"
         finally:
             clear_current_user()
-            # 异步触发自动摘要
-            import asyncio
             try:
                 asyncio.get_running_loop().create_task(
-                    memory_service.try_auto_summarize(self.db, user_id, conversation_id)
+                    self._try_auto_summarize_background(user_id, conversation_id)
                 )
             except Exception:
                 pass
@@ -252,10 +467,23 @@ class AgentService:
             conversation_id=conversation_id,
             role="assistant",
             content=response_content,
-            model="jarvis",
+            model=model_name_used or "jarvis",
         )
         self.db.add(assistant_msg)
         await self.db.commit()
         await self.db.refresh(assistant_msg)
 
-        return conversation_id, assistant_msg.id, response_content
+        await brain_service.create_event(
+            user_id,
+            source_type="conversation",
+            source_id=conversation_id,
+            event_type="message_created",
+            title="Assistant message",
+            content_summary=response_content[:500],
+            raw_excerpt=response_content[:2000],
+            metadata_={"role": "assistant"},
+            importance_signal=1.0,
+        )
+        await self.db.commit()
+
+        return conversation_id, assistant_msg.id, response_content, model_name_used

backend/app/services/brain_service.py

@@ -0,0 +1,204 @@
+from sqlalchemy import func, or_, select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.brain import BrainCandidate, BrainEvent, BrainMemory, BrainTag
+from app.services.graph_service import GraphService
+
+
+class BrainService:
+    def __init__(self, db: AsyncSession):
+        self.db = db
+
+    async def create_event(
+        self,
+        user_id: str,
+        *,
+        source_type: str,
+        source_id: str,
+        event_type: str,
+        title: str | None = None,
+        content_summary: str | None = None,
+        raw_excerpt: str | None = None,
+        metadata_: dict | None = None,
+        importance_signal: float = 0.0,
+    ) -> BrainEvent:
+        event = BrainEvent(
+            user_id=user_id,
+            source_type=source_type,
+            source_id=source_id,
+            event_type=event_type,
+            title=title,
+            content_summary=content_summary,
+            raw_excerpt=raw_excerpt,
+            metadata_=metadata_,
+            importance_signal=importance_signal,
+            status="pending",
+        )
+        self.db.add(event)
+        await self.db.flush()
+        return event
+
+    async def recall_memories(self, user_id: str, current_query: str, top_k: int = 3) -> list[BrainMemory]:
+        query_tokens = [token.strip().lower() for token in current_query.split() if token.strip()]
+        statement = select(BrainMemory).where(
+            BrainMemory.user_id == user_id,
+            BrainMemory.status == "active",
+        )
+        if query_tokens:
+            statement = statement.where(
+                or_(
+                    *[
+                        or_(
+                            BrainMemory.title.ilike(f"%{token}%"),
+                            BrainMemory.content.ilike(f"%{token}%"),
+                        )
+                        for token in query_tokens
+                    ]
+                )
+            )
+        result = await self.db.execute(
+            statement.order_by(BrainMemory.importance.desc(), BrainMemory.created_at.desc()).limit(top_k)
+        )
+        memories = list(result.scalars().all())
+        if memories or query_tokens:
+            return memories
+
+        fallback_result = await self.db.execute(
+            select(BrainMemory)
+            .where(BrainMemory.user_id == user_id, BrainMemory.status == "active")
+            .order_by(BrainMemory.importance.desc(), BrainMemory.created_at.desc())
+            .limit(top_k)
+        )
+        return list(fallback_result.scalars().all())
+
+    async def get_overview(self, user_id: str) -> dict:
+        active_memory_count = (
+            await self.db.execute(
+                select(func.count()).select_from(BrainMemory).where(
+                    BrainMemory.user_id == user_id,
+                    BrainMemory.status == "active",
+                )
+            )
+        ).scalar() or 0
+
+        important_tag_count = (
+            await self.db.execute(
+                select(func.count()).select_from(BrainTag).where(
+                    BrainTag.user_id == user_id,
+                    BrainTag.priority == "important",
+                )
+            )
+        ).scalar() or 0
+
+        secondary_tag_count = (
+            await self.db.execute(
+                select(func.count()).select_from(BrainTag).where(
+                    BrainTag.user_id == user_id,
+                    BrainTag.priority == "secondary",
+                )
+            )
+        ).scalar() or 0
+
+        recent_memory_result = await self.db.execute(
+            select(BrainMemory.title)
+            .where(BrainMemory.user_id == user_id, BrainMemory.status == "active")
+            .order_by(BrainMemory.importance.desc(), BrainMemory.created_at.desc())
+            .limit(5)
+        )
+        recent_memory_titles = list(recent_memory_result.scalars().all())
+
+        return {
+            "active_memory_count": active_memory_count,
+            "important_tag_count": important_tag_count,
+            "secondary_tag_count": secondary_tag_count,
+            "recent_memory_titles": recent_memory_titles,
+        }
+
+    async def list_memories(self, user_id: str) -> list[BrainMemory]:
+        result = await self.db.execute(
+            select(BrainMemory)
+            .where(BrainMemory.user_id == user_id, BrainMemory.status == "active")
+            .order_by(BrainMemory.importance.desc(), BrainMemory.created_at.desc())
+        )
+        return list(result.scalars().all())
+
+    async def list_tags(self, user_id: str) -> dict:
+        important_result = await self.db.execute(
+            select(BrainTag)
+            .where(BrainTag.user_id == user_id, BrainTag.priority == "important")
+            .order_by(BrainTag.score.desc(), BrainTag.created_at.desc())
+        )
+        secondary_result = await self.db.execute(
+            select(BrainTag)
+            .where(BrainTag.user_id == user_id, BrainTag.priority == "secondary")
+            .order_by(BrainTag.score.desc(), BrainTag.created_at.desc())
+        )
+        return {
+            "important": list(important_result.scalars().all()),
+            "secondary": list(secondary_result.scalars().all()),
+        }
+
+    async def list_events(self, user_id: str) -> list[BrainEvent]:
+        result = await self.db.execute(
+            select(BrainEvent)
+            .where(BrainEvent.user_id == user_id)
+            .order_by(BrainEvent.created_at.desc())
+        )
+        return list(result.scalars().all())
+
+    async def run_learning(self, user_id: str) -> dict:
+        pending_events_result = await self.db.execute(
+            select(BrainEvent)
+            .where(BrainEvent.user_id == user_id, BrainEvent.status == "pending")
+            .order_by(BrainEvent.created_at.asc())
+        )
+        pending_events = list(pending_events_result.scalars().all())
+        pending_count = len(pending_events)
+
+        candidates_created = 0
+        memories_promoted = 0
+
+        if pending_events:
+            candidate = BrainCandidate(
+                user_id=user_id,
+                candidate_type="daily_learning",
+                title="Daily learning synthesis",
+                summary=f"Processed {pending_count} pending brain events.",
+                importance_score=float(pending_count),
+                confidence_score=1.0,
+                status="promoted",
+                source_event_ids=[event.id for event in pending_events],
+            )
+            self.db.add(candidate)
+            await self.db.flush()
+            candidates_created = 1
+
+            memory = BrainMemory(
+                user_id=user_id,
+                memory_type="daily_learning",
+                title="Daily learning synthesis",
+                content=f"Processed {pending_count} pending brain events.",
+                importance=max(pending_count, 1),
+                confidence=1.0,
+                status="active",
+                origin_candidate_id=candidate.id,
+                origin_source_types=sorted({event.source_type for event in pending_events}),
+            )
+            self.db.add(memory)
+            memories_promoted = 1
+
+            for event in pending_events:
+                event.status = "processed"
+                event.processed_at = memory.created_at
+
+            await self.db.commit()
+        else:
+            await self.db.commit()
+
+        await GraphService(self.db).build_graph(user_id)
+
+        return {
+            "events_considered": pending_count,
+            "candidates_created": candidates_created,
+            "memories_promoted": memories_promoted,
+        }

backend/app/services/graph_service.py

@@ -4,11 +4,8 @@
 
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select, func
+from app.models.brain import BrainMemory, BrainTag
 from app.models.knowledge_graph import KGNode, KGEdge
-from app.models.document import Document, DocumentChunk
-from app.services.llm_service import get_llm
-from langchain_core.messages import HumanMessage
-import json
 import logging
 
 logger = logging.getLogger(__name__)
@@ -75,110 +72,93 @@ confidence: 0.0-1.0，表示推断置信度
 class GraphService:
     def __init__(self, db: AsyncSession):
         self.db = db
-        self.llm = get_llm()
 
     async def build_graph(self, user_id: str, document_ids: list[str] | None = None):
-        """
-        从文档构建/更新知识图谱
-        - 遍历所有 chunk
-        - LLM 实体识别
-        - LLM 关系抽取
-        - 去重合并
-        """
-        query = (
-            select(DocumentChunk)
-            .join(Document)
-            .where(Document.user_id == user_id)
-            .where(Document.is_indexed == True)
+        """从知识大脑投影图谱。"""
+        existing_nodes_result = await self.db.execute(select(KGNode).where(KGNode.user_id == user_id))
+        for node in existing_nodes_result.scalars().all():
+            await self.db.delete(node)
+        await self.db.flush()
+
+        memory_result = await self.db.execute(
+            select(BrainMemory)
+            .where(BrainMemory.user_id == user_id, BrainMemory.status == "active")
+            .order_by(BrainMemory.importance.desc(), BrainMemory.created_at.desc())
         )
-        if document_ids:
-            query = query.where(DocumentChunk.document_id.in_(document_ids))
+        memories = list(memory_result.scalars().all())
 
-        result = await self.db.execute(query)
-        chunks = list(result.scalars().all())
+        tag_result = await self.db.execute(
+            select(BrainTag)
+            .where(BrainTag.user_id == user_id)
+            .order_by(BrainTag.score.desc(), BrainTag.created_at.desc())
+        )
+        tags = list(tag_result.scalars().all())
 
-        logger.info(f"[GraphService] 开始构建图谱，共 {len(chunks)} 个 chunks")
+        logger.info(f"[GraphService] 开始从 brain 数据投影图谱，memories={len(memories)}, tags={len(tags)}")
 
-        for chunk in chunks:
-            try:
-                await self._process_chunk(chunk, user_id)
-            except Exception as e:
-                logger.error(f"[GraphService] 处理 chunk {chunk.id} 失败: {e}")
-                continue
-
-        logger.info(f"[GraphService] 图谱构建完成")
-
-    async def _process_chunk(self, chunk: DocumentChunk, user_id: str):
-        """处理单个 chunk，提取实体和关系"""
-        prompt = ENTITY_EXTRACTION_PROMPT.format(text=chunk.content[:2000])
-        response = await self.llm.invoke([HumanMessage(content=prompt)])
-
-        try:
-            data = json.loads(response.content)
-        except json.JSONDecodeError:
-            return
-
-        entities = data.get("entities", [])
-        relations = data.get("relations", [])
-
-        if not entities:
-            return
-
-        # 先查找已存在的节点
-        existing_nodes = {}
-        for entity_data in entities:
-            name = entity_data["name"]
-            result = await self.db.execute(
-                select(KGNode)
-                .where(KGNode.user_id == user_id)
-                .where(KGNode.name == name)
+        node_map: dict[str, KGNode] = {}
+        for memory in memories:
+            node = KGNode(
+                user_id=user_id,
+                name=memory.title,
+                entity_type="memory",
+                description=memory.content,
+                properties_={
+                    "memory_type": memory.memory_type,
+                    "origin_source_types": memory.origin_source_types or [],
+                },
+                importance=min(max(memory.importance / 10, 0.1), 1.0),
             )
-            node = result.scalar_one_or_none()
-            if node:
-                existing_nodes[name] = node
+            self.db.add(node)
+            await self.db.flush()
+            node_map[f"memory:{memory.id}"] = node
 
-        # 插入新节点
-        entity_map = {}
-        for entity_data in entities:
-            name = entity_data["name"]
-            if name in existing_nodes:
-                entity_map[name] = existing_nodes[name].id
-            else:
-                node = KGNode(
-                    user_id=user_id,
-                    name=name,
-                    entity_type=entity_data["type"],
-                    description=entity_data.get("description", ""),
-                    source_document_id=chunk.document_id,
-                )
-                self.db.add(node)
-                await self.db.flush()
-                entity_map[name] = node.id
-
-        # 插入关系（去重）
-        for rel in relations:
-            src, tgt = rel["source"], rel["target"]
-            if src not in entity_map or tgt not in entity_map:
-                continue
-
-            # 检查关系是否已存在
-            result = await self.db.execute(
-                select(KGEdge).where(
-                    KGEdge.source_id == entity_map[src],
-                    KGEdge.target_id == entity_map[tgt],
-                    KGEdge.relation_type == rel["relation_type"],
-                )
+        for tag in tags:
+            node = KGNode(
+                user_id=user_id,
+                name=tag.name,
+                entity_type="tag",
+                description=f"{tag.category} / {tag.priority}",
+                properties_={
+                    "category": tag.category,
+                    "priority": tag.priority,
+                    "score": tag.score,
+                },
+                importance=min(max(tag.score / 10, 0.1), 1.0),
             )
-            existing = result.scalar_one_or_none()
-            if not existing:
-                edge = KGEdge(
-                    source_id=entity_map[src],
-                    target_id=entity_map[tgt],
-                    relation_type=rel["relation_type"],
-                )
-                self.db.add(edge)
+            self.db.add(node)
+            await self.db.flush()
+            node_map[f"tag:{tag.id}"] = node
+
+        for memory in memories:
+            memory_node = node_map.get(f"memory:{memory.id}")
+            if not memory_node:
+                continue
+            memory_text = f"{memory.title} {memory.content}".lower()
+            for tag in tags:
+                if tag.name.lower() in memory_text:
+                    tag_node = node_map.get(f"tag:{tag.id}")
+                    if not tag_node:
+                        continue
+                    self.db.add(KGEdge(
+                        source_id=memory_node.id,
+                        target_id=tag_node.id,
+                        relation_type="tagged_with",
+                        weight=min(max(tag.score / 10, 0.1), 1.0),
+                    ))
+
+        memory_nodes = [node_map[f"memory:{memory.id}"] for memory in memories if f"memory:{memory.id}" in node_map]
+        for index, source_node in enumerate(memory_nodes):
+            for target_node in memory_nodes[index + 1:]:
+                self.db.add(KGEdge(
+                    source_id=source_node.id,
+                    target_id=target_node.id,
+                    relation_type="related_to",
+                    weight=0.5,
+                ))
 
         await self.db.commit()
+        logger.info("[GraphService] brain 图谱投影完成")
 
     async def get_graph_summary(self, user_id: str) -> str:
         """获取用户图谱的整体摘要"""

backend/app/services/llm_service.py

@@ -5,11 +5,14 @@ OpenAI / Claude / Ollama / DeepSeek / 任意 OpenAI 兼容接口
 
 from abc import ABC, abstractmethod
 from typing import AsyncIterator
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
 from langchain_core.messages import BaseMessage, AIMessage
 from langchain_openai import ChatOpenAI
 from langchain_anthropic import ChatAnthropic
 from langchain_ollama import ChatOllama
 from app.config import settings
+from app.models.user import User
 import httpx
 import os
 

backend/app/services/settings_service.py

@@ -1,9 +1,11 @@
+import copy
 import logging
 from typing import Optional
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select
 from app.models.user import User
 from app.services.auth_service import verify_password, get_password_hash
+from app.logging_utils import summarize_llm_config
 
 logger = logging.getLogger(__name__)
 
@@ -49,9 +51,7 @@ async def update_user_profile(
 
 async def update_llm_config(user_id: str, config: dict, db: AsyncSession) -> dict:
     """更新 LLM 配置"""
-    import copy
-    logger.info(f"update_llm_config called with config keys: {list(config.keys())}")
-    logger.info(f"chat config: {config.get('chat')}")
+    logger.info("update_llm_config called", extra={"details": {"keys": list(config.keys())}})
     result = await db.execute(select(User).where(User.id == user_id))
     user = result.scalar_one_or_none()
     if not user:
@@ -59,7 +59,7 @@ async def update_llm_config(user_id: str, config: dict, db: AsyncSession) -> dic
 
     # 创建深拷贝，避免 SQLAlchemy 变更检测问题
     current = copy.deepcopy(user.llm_config) or {}
-    logger.info(f"current llm_config before update: {current}")
+    logger.info("llm_config before update", extra={"details": summarize_llm_config(current)})
     # 合并配置 - 直接替换整个类型配置列表
     for key, value in config.items():
         if value is not None:
@@ -74,11 +74,11 @@ async def update_llm_config(user_id: str, config: dict, db: AsyncSession) -> dic
                     current[key] = value
             else:
                 current[key] = value
-    logger.info(f"current llm_config after update: {current}")
+    logger.info("llm_config after update", extra={"details": summarize_llm_config(current)})
     user.llm_config = current
     await db.commit()
     await db.refresh(user)
-    logger.info(f"user.llm_config after refresh: {user.llm_config}")
+    logger.info("user.llm_config after refresh", extra={"details": summarize_llm_config(user.llm_config)})
     return current
 
 

backend/app/services/system_service.py

@@ -0,0 +1,27 @@
+from datetime import datetime, UTC
+
+try:
+    import psutil
+except ModuleNotFoundError:  # pragma: no cover - optional runtime dependency fallback
+    psutil = None
+
+
+class SystemService:
+    def get_status(self) -> dict:
+        if psutil is None:
+            return {
+                'cpu_percent': 0.0,
+                'memory_percent': 0.0,
+                'disk_percent': 0.0,
+                'timestamp': datetime.now(UTC).isoformat(),
+            }
+
+        cpu_percent = psutil.cpu_percent(interval=None)
+        memory = psutil.virtual_memory()
+        disk = psutil.disk_usage('/')
+        return {
+            'cpu_percent': round(cpu_percent, 1),
+            'memory_percent': round(memory.percent, 1),
+            'disk_percent': round(disk.percent, 1),
+            'timestamp': datetime.now(UTC).isoformat(),
+        }