Merge pull request #915 from SuanmoSuanyangTechnology/fix/userinfo_zy

fix(web): userinfo

.github/workflows/release-notify-wechat.yml

@@ -0,0 +1,157 @@
+name: Release Notify Workflow
+
+on:
+  pull_request:
+    types: [closed]
+
+jobs:
+  notify:
+    if: >
+      github.event.pull_request.merged == true &&
+      startsWith(github.event.pull_request.base.ref, 'release')
+    runs-on: ubuntu-latest
+
+    steps:
+      # 防止 GitHub HEAD 未同步
+      - run: sleep 3
+
+      # 1️⃣ 获取分支 HEAD
+      - name: Get HEAD
+        id: head
+        run: |
+          HEAD_SHA=$(curl -s \
+            -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \
+            https://api.github.com/repos/${{ github.repository }}/git/ref/heads/${{ github.event.pull_request.base.ref }} \
+            | jq -r '.object.sha')
+          echo "head_sha=$HEAD_SHA" >> $GITHUB_OUTPUT
+
+      # 2️⃣ 判断是否最终PR
+      - name: Check Latest
+        id: check
+        run: |
+          if [ "${{ github.event.pull_request.merge_commit_sha }}" = "${{ steps.head.outputs.head_sha }}" ]; then
+            echo "ok=true" >> $GITHUB_OUTPUT
+          else
+            echo "ok=false" >> $GITHUB_OUTPUT
+          fi
+
+      # 3️⃣ 尝试从 PR body 提取 Sourcery 摘要
+      - name: Extract Sourcery Summary
+        if: steps.check.outputs.ok == 'true'
+        id: sourcery
+        env:
+          PR_BODY: ${{ github.event.pull_request.body }}
+        run: |
+          python3 << 'PYEOF'
+          import os, re
+
+          body = os.environ.get("PR_BODY", "") or ""
+          match = re.search(
+              r"## Summary by Sourcery\s*\n(.*?)(?=\n## |\Z)",
+              body,
+              re.DOTALL
+          )
+
+          if match:
+              summary = match.group(1).strip()
+              found = "true"
+          else:
+              summary = ""
+              found = "false"
+
+          with open("sourcery_summary.txt", "w", encoding="utf-8") as f:
+              f.write(summary)
+
+          with open(os.environ["GITHUB_OUTPUT"], "a") as gh:
+              gh.write(f"found={found}\n")
+              gh.write("summary<<EOF\n")
+              gh.write(summary + "\n")
+              gh.write("EOF\n")
+          PYEOF
+
+      # 4️⃣ Fallback: 获取 commits + 通义千问总结
+      - name: Get Commits
+        if: steps.check.outputs.ok == 'true' && steps.sourcery.outputs.found == 'false'
+        run: |
+          curl -s \
+            -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \
+            ${{ github.event.pull_request.commits_url }} \
+            | jq -r '.[].commit.message' | head -n 20 > commits.txt
+
+      - name: AI Summary (Qwen Fallback)
+        if: steps.check.outputs.ok == 'true' && steps.sourcery.outputs.found == 'false'
+        id: qwen
+        env:
+          DASHSCOPE_API_KEY: ${{ secrets.DASHSCOPE_API_KEY }}
+        run: |
+          python3 << 'PYEOF'
+          import json, os, urllib.request
+
+          with open("commits.txt", "r") as f:
+              commits = f.read().strip()
+
+          prompt = "请用中文总结以下代码提交，输出3-5条要点，面向测试人员。直接输出编号列表，不要输出标题或前言：\n" + commits
+          payload = {"model": "qwen-plus", "input": {"prompt": prompt}}
+          data = json.dumps(payload, ensure_ascii=False).encode("utf-8")
+
+          req = urllib.request.Request(
+              "https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation",
+              data=data,
+              headers={
+                  "Authorization": "Bearer " + os.environ["DASHSCOPE_API_KEY"],
+                  "Content-Type": "application/json"
+              }
+          )
+          resp = urllib.request.urlopen(req)
+          result = json.loads(resp.read().decode())
+          summary = result.get("output", {}).get("text", "AI 摘要生成失败")
+
+          with open(os.environ["GITHUB_OUTPUT"], "a") as gh:
+              gh.write("summary<<EOF\n")
+              gh.write(summary + "\n")
+              gh.write("EOF\n")
+          PYEOF
+
+      # 5️⃣ 企业微信通知（Markdown）
+      - name: Notify WeChat
+        if: steps.check.outputs.ok == 'true'
+        env:
+          WECHAT_WEBHOOK: ${{ secrets.WECHAT_WEBHOOK }}
+          BRANCH: ${{ github.event.pull_request.base.ref }}
+          AUTHOR: ${{ github.event.pull_request.user.login }}
+          PR_TITLE: ${{ github.event.pull_request.title }}
+          PR_URL: ${{ github.event.pull_request.html_url }}
+          SOURCERY_FOUND: ${{ steps.sourcery.outputs.found }}
+          SOURCERY_SUMMARY: ${{ steps.sourcery.outputs.summary }}
+          QWEN_SUMMARY: ${{ steps.qwen.outputs.summary }}
+        run: |
+          python3 << 'PYEOF'
+          import json, os, urllib.request
+
+          if os.environ.get("SOURCERY_FOUND") == "true":
+              label = "Summary by Sourcery"
+              summary = os.environ.get("SOURCERY_SUMMARY", "")
+          else:
+              label = "AI变更摘要"
+              summary = os.environ.get("QWEN_SUMMARY", "AI 摘要生成失败")
+
+          content = (
+            "## 🚀 Release 发布通知\n"
+            "> 📦 **分支**: " + os.environ["BRANCH"] + "\n"
+            "> 👤 **提交人**: " + os.environ["AUTHOR"] + "\n"
+            "> 📝 **标题**: " + os.environ["PR_TITLE"] + "\n\n"
+            "### 🧠 " + label + "\n" +
+            summary + "\n\n"
+            "---\n"
+            "🔗 [查看PR详情](" + os.environ["PR_URL"] + ")"
+          )
+          payload = {"msgtype": "markdown", "markdown": {"content": content}}
+          data = json.dumps(payload, ensure_ascii=False).encode("utf-8")
+          req = urllib.request.Request(
+            os.environ["WECHAT_WEBHOOK"],
+            data=data,
+            headers={"Content-Type": "application/json"}
+          )
+          resp = urllib.request.urlopen(req)
+          print(resp.read().decode())
+          PYEOF

.github/workflows/sync-to-gitee.yml

@@ -0,0 +1,36 @@
+name: Sync to Gitee
+
+on:
+  push:
+    branches:
+      - main     # Production
+      - develop  # Integration
+      - 'release/*' # Release preparation
+      - 'hotfix/*'  # Urgent fixes
+    tags:
+      - '*'      # All version tags (v1.0.0, etc.)
+
+jobs:
+  sync:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout Source Code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Sync to Gitee
+        run: |
+          GITEE_URL="https://${{ secrets.GITEE_USERNAME }}:${{ secrets.GITEE_TOKEN }}@gitee.com/hangzhou-hongxiong-intelligent_1/MemoryBear.git"
+          git remote add gitee "$GITEE_URL"
+
+          # 遍历并推送所有分支
+          for branch in $(git branch -r | grep -v HEAD | sed 's/origin\///'); do
+            echo "Syncing branch: $branch"
+            git push -f gitee "origin/$branch:refs/heads/$branch"
+          done
+
+          # 推送所有标签
+          echo "Syncing tags..."
+          git push gitee --tags --force

.gitignore

@@ -18,6 +18,7 @@ examples/
 .kiro
 .vscode
 .idea
+.claude
 
 # Temporary outputs
 .DS_Store
@@ -26,6 +27,7 @@ time.log
 celerybeat-schedule.db
 search_results.json
 redbear-mem-metrics/
+redbear-mem-benchmark/
 pitch-deck/
 
 api/migrations/versions

README.md

@@ -2,6 +2,10 @@
 
 # MemoryBear empowers AI with human-like memory capabilities
 
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
+[![Python](https://img.shields.io/badge/Python-3.12+-green?logo=python&logoColor=white)](https://www.python.org/)
+[![Gitee Sync](https://img.shields.io/github/actions/workflow/status/SuanmoSuanyangTechnology/MemoryBear/sync-to-gitee.yml?label=Gitee%20Sync&logo=gitee&logoColor=white)](https://github.com/SuanmoSuanyangTechnology/MemoryBear/actions/workflows/sync-to-gitee.yml)
+
 [中文](./README_CN.md) | English
 
 ### [Installation Guide](#memorybear-installation-guide)

README_CN.md

@@ -2,6 +2,10 @@
 
 # MemoryBear 让AI拥有如同人类一样的记忆
 
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
+[![Python](https://img.shields.io/badge/Python-3.12+-green?logo=python&logoColor=white)](https://www.python.org/)
+[![Gitee Sync](https://img.shields.io/github/actions/workflow/status/SuanmoSuanyangTechnology/MemoryBear/sync-to-gitee.yml?label=Gitee%20Sync&logo=gitee&logoColor=white)](https://github.com/SuanmoSuanyangTechnology/MemoryBear/actions/workflows/sync-to-gitee.yml)
+
 中文 | [English](./README.md)
 
 ### [安装教程](#memorybear安装教程)

api/app/celery_app.py

@@ -111,6 +111,9 @@ celery_app.conf.update(
         # Clustering tasks → memory_tasks queue (使用相同的 worker，避免 macOS fork 问题)
         'app.tasks.run_incremental_clustering': {'queue': 'memory_tasks'},
 
+        # Metadata extraction → memory_tasks queue
+        'app.tasks.extract_user_metadata': {'queue': 'memory_tasks'},
+
         # Document tasks → document_tasks queue (prefork worker)
         'app.core.rag.tasks.parse_document': {'queue': 'document_tasks'},
         'app.core.rag.tasks.build_graphrag_for_kb': {'queue': 'document_tasks'},

api/app/controllers/__init__.py

@@ -14,7 +14,6 @@ from . import (
     document_controller,
     emotion_config_controller,
     emotion_controller,
-    end_user_controller,
     file_controller,
     file_storage_controller,
     home_page_controller,
@@ -99,6 +98,5 @@ manager_router.include_router(file_storage_controller.router)
 manager_router.include_router(ontology_controller.router)
 manager_router.include_router(skill_controller.router)
 manager_router.include_router(i18n_controller.router)
-manager_router.include_router(end_user_controller.router)
 
 __all__ = ["manager_router"]

api/app/controllers/app_controller.py

@@ -292,10 +292,19 @@ def get_opening(
 ):
     """返回开场白文本和预设问题，供前端对话界面初始化时展示"""
     workspace_id = current_user.current_workspace_id
-    cfg = app_service.get_agent_config(db, app_id=app_id, workspace_id=workspace_id)
-    features = cfg.features or {}
-    if hasattr(features, "model_dump"):
-        features = features.model_dump()
+
+    # 根据应用类型获取 features
+    from app.models.app_model import App as AppModel
+    app = db.get(AppModel, app_id)
+    if app and app.type == "workflow":
+        cfg = app_service.get_workflow_config(db=db, app_id=app_id, workspace_id=workspace_id)
+        features = cfg.features or {}
+    else:
+        cfg = app_service.get_agent_config(db, app_id=app_id, workspace_id=workspace_id)
+        features = cfg.features or {}
+        if hasattr(features, "model_dump"):
+            features = features.model_dump()
+
     opening = features.get("opening_statement", {})
     return success(data=app_schema.OpeningResponse(
         enabled=opening.get("enabled", False),
@@ -1070,6 +1079,14 @@ async def update_workflow_config(
         current_user: Annotated[User, Depends(get_current_user)]
 ):
     workspace_id = current_user.current_workspace_id
+    if payload.variables:
+        from app.services.workflow_service import WorkflowService
+        resolved = await WorkflowService(db)._resolve_variables_file_defaults(
+            [v.model_dump() for v in payload.variables]
+        )
+        # Patch default values back into VariableDefinition objects
+        for var_def, resolved_def in zip(payload.variables, resolved):
+            var_def.default = resolved_def.get("default", var_def.default)
     cfg = app_service.update_workflow_config(db, app_id=app_id, data=payload, workspace_id=workspace_id)
     return success(data=WorkflowConfigSchema.model_validate(cfg))
 

api/app/controllers/auth_controller.py

@@ -53,22 +53,24 @@ async def login_for_access_token(
             user = auth_service.authenticate_user_or_raise(db, form_data.email, form_data.password)
             auth_logger.info(f"用户认证成功: {user.email} (ID: {user.id})")
             if form_data.invite:
-                auth_service.bind_workspace_with_invite(db=db,
-                user=user,        
-                invite_token=form_data.invite,
-                workspace_id=invite_info.workspace_id)
+                auth_service.bind_workspace_with_invite(
+                    db=db,
+                    user=user,
+                    invite_token=form_data.invite,
+                    workspace_id=invite_info.workspace_id
+                )
         except BusinessException as e:
         # 用户不存在且有邀请码，尝试注册
             if e.code == BizCode.USER_NOT_FOUND:
                 auth_logger.info(f"用户不存在，使用邀请码注册: {form_data.email}")
                 user = auth_service.register_user_with_invite(
-                db=db,
-                email=form_data.email,
-                username=form_data.username,
-                password=form_data.password,
-                invite_token=form_data.invite,
-                workspace_id=invite_info.workspace_id
-            )
+                    db=db,
+                    email=form_data.email,
+                    username=form_data.username,
+                    password=form_data.password,
+                    invite_token=form_data.invite,
+                    workspace_id=invite_info.workspace_id
+                )
             elif e.code == BizCode.PASSWORD_ERROR:
                 # 用户存在但密码错误
                 auth_logger.warning(f"接受邀请失败，密码验证错误: {form_data.email}")
@@ -134,7 +136,7 @@ async def refresh_token(
     # 检查用户是否存在
     user = auth_service.get_user_by_id(db, userId)
     if not user:
-        raise BusinessException(t("auth.user.not_found"), code=BizCode.USER_NOT_FOUND)
+        raise BusinessException(t("auth.user.not_found"), code=BizCode.USER_NO_ACCESS)
     
     # 检查 refresh token 黑名单
     if settings.ENABLE_SINGLE_SESSION:

api/app/controllers/chunk_controller.py

@@ -23,6 +23,7 @@ from app.models.user_model import User
 from app.schemas import chunk_schema
 from app.schemas.response_schema import ApiResponse
 from app.services import knowledge_service, document_service, file_service, knowledgeshare_service
+from app.services.model_service import ModelApiKeyService
 
 # Obtain a dedicated API logger
 api_logger = get_api_logger()
@@ -460,18 +461,20 @@ async def retrieve_chunks(
             if retrieve_data.retrieve_type == chunk_schema.RetrieveType.Graph:
                 kb_ids = [str(kb_id) for kb_id in private_kb_ids]
                 workspace_ids = [str(workspace_id) for workspace_id in private_workspace_ids]
+                llm_key = ModelApiKeyService.get_available_api_key(db, db_knowledge.llm_id)
+                emb_key = ModelApiKeyService.get_available_api_key(db, db_knowledge.embedding_id)
                 # Prepare to configure chat_mdl、embedding_model、vision_model information
                 chat_model = Base(
-                    key=db_knowledge.llm.api_keys[0].api_key,
-                    model_name=db_knowledge.llm.api_keys[0].model_name,
-                    base_url=db_knowledge.llm.api_keys[0].api_base
+                    key=llm_key.api_key,
+                    model_name=llm_key.model_name,
+                    base_url=llm_key.api_base
                 )
                 embedding_model = OpenAIEmbed(
-                    key=db_knowledge.embedding.api_keys[0].api_key,
-                    model_name=db_knowledge.embedding.api_keys[0].model_name,
-                    base_url=db_knowledge.embedding.api_keys[0].api_base
+                    key=emb_key.api_key,
+                    model_name=emb_key.model_name,
+                    base_url=emb_key.api_base
                 )
-                doc = kg_retriever.retrieval(question=retrieve_data.query, workspace_ids=workspace_ids, kb_ids= kb_ids, emb_mdl=embedding_model, llm=chat_model)
+                doc = kg_retriever.retrieval(question=retrieve_data.query, workspace_ids=workspace_ids, kb_ids=kb_ids, emb_mdl=embedding_model, llm=chat_model)
                 if doc:
                     rs.insert(0, doc)
             return success(data=jsonable_encoder(rs), msg="retrieval successful")

api/app/controllers/document_controller.py

@@ -314,8 +314,10 @@ async def parse_documents(
         )
 
         # 4. Check if the file exists
+        api_logger.debug(f"Constructed file path: {file_path}")
+        api_logger.debug(f"File metadata - kb_id: {db_file.kb_id}, parent_id: {db_file.parent_id}, file_id: {db_file.id}, extension: {db_file.file_ext}")
         if not os.path.exists(file_path):
-            api_logger.warning(f"File not found (possibly deleted): file_path={file_path}")
+            api_logger.error(f"File not found (possibly deleted): file_path={file_path}, file_id={db_file.id}, document_id={document_id}")
             raise HTTPException(
                 status_code=status.HTTP_404_NOT_FOUND,
                 detail="File not found (possibly deleted)"

api/app/controllers/end_user_controller.py

@@ -1,48 +0,0 @@
-"""End User 管理接口 - 无需认证"""
-
-from app.core.logging_config import get_business_logger
-from app.core.response_utils import success
-from app.db import get_db
-from app.repositories.end_user_repository import EndUserRepository
-from app.schemas.memory_api_schema import (
-    CreateEndUserRequest,
-    CreateEndUserResponse,
-)
-from fastapi import APIRouter, Depends
-from sqlalchemy.orm import Session
-
-router = APIRouter(prefix="/end_users", tags=["End Users"])
-logger = get_business_logger()
-
-
-@router.post("")
-async def create_end_user(
-    data: CreateEndUserRequest,
-    db: Session = Depends(get_db),
-):
-    """
-    Create an end user.
-    
-    Creates a new end user for the given workspace.
-    If an end user with the same other_id already exists in the workspace,
-    returns the existing one.
-    """
-    logger.info(f"Create end user request - other_id: {data.other_id}, workspace_id: {data.workspace_id}")
-
-    end_user_repo = EndUserRepository(db)
-    end_user = end_user_repo.get_or_create_end_user(
-        app_id=None,
-        workspace_id=data.workspace_id,
-        other_id=data.other_id,
-    )
-
-    logger.info(f"End user ready: {end_user.id}")
-
-    result = {
-        "id": str(end_user.id),
-        "other_id": end_user.other_id or "",
-        "other_name": end_user.other_name or "",
-        "workspace_id": str(end_user.workspace_id),
-    }
-
-    return success(data=CreateEndUserResponse(**result).model_dump(), msg="End user created successfully")

api/app/controllers/home_page_controller.py

@@ -3,9 +3,10 @@ from sqlalchemy.orm import Session
 
 from app.core.config import settings
 from app.core.response_utils import success
-from app.db import get_db
+from app.db import get_db, SessionLocal
 from app.dependencies import get_current_user
 from app.models.user_model import User
+from app.repositories.home_page_repository import HomePageRepository
 from app.schemas.response_schema import ApiResponse
 from app.services.home_page_service import HomePageService
 
@@ -31,9 +32,32 @@ def get_workspace_list(
 
 @router.get("/version", response_model=ApiResponse)
 def get_system_version():
-    """获取系统版本号+说明"""
-    current_version = settings.SYSTEM_VERSION
-    version_info = HomePageService.load_version_introduction(current_version)
+    """获取系统版本号 + 说明"""
+    current_version = None
+    version_info = None
+    
+    # 1️⃣ 优先从数据库获取最新已发布的版本
+    try:
+        db = SessionLocal()
+        try:
+            current_version, version_info = HomePageRepository.get_latest_version_introduction(db)
+        finally:
+            db.close()
+    except Exception as e:
+        pass
+    
+    # 2️⃣ 降级：使用环境变量中的版本号
+    if not current_version:
+        current_version = settings.SYSTEM_VERSION
+        version_info = HomePageService.load_version_introduction(current_version)
+    
+    # 3️⃣ 如果数据库和 JSON 都没有，返回基本信息
+    if not version_info:
+        version_info = {
+            "introduction": {"codeName": "", "releaseDate": "", "upgradePosition": "", "coreUpgrades": []},
+            "introduction_en": {"codeName": "", "releaseDate": "", "upgradePosition": "", "coreUpgrades": []}
+        }
+    
     return success(
         data={
             "version": current_version,

api/app/controllers/knowledge_controller.py

@@ -352,6 +352,7 @@ async def delete_knowledge(
         # 2. Soft-delete knowledge base
         api_logger.debug(f"Perform a soft delete: {db_knowledge.name} (ID: {knowledge_id})")
         db_knowledge.status = 2
+        db_knowledge.updated_at = datetime.datetime.now()
         db.commit()
         api_logger.info(f"The knowledge base has been successfully deleted: {db_knowledge.name} (ID: {knowledge_id})")
         return success(msg="The knowledge base has been successfully deleted")

api/app/controllers/memory_dashboard_controller.py

@@ -1,3 +1,5 @@
+import asyncio
+import uuid
 from fastapi import APIRouter, Depends, HTTPException, status, Query
 from pydantic import BaseModel, Field
 from sqlalchemy.orm import Session
@@ -47,64 +49,64 @@ def get_workspace_total_end_users(
 
 @router.get("/end_users", response_model=ApiResponse)
 async def get_workspace_end_users(
+    workspace_id: Optional[uuid.UUID] = Query(None, description="工作空间ID（可选，默认当前用户工作空间）"),
+    keyword: Optional[str] = Query(None, description="搜索关键词（同时模糊匹配 other_name 和 id）"),
+    page: int = Query(1, ge=1, description="页码，从1开始"),
+    pagesize: int = Query(10, ge=1, description="每页数量"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
 ):
     """
-    获取工作空间的宿主列表（高性能优化版本 v2）
-    
-    优化策略：
-    1. 批量查询 end_users（一次查询而非循环）
-    2. 并发查询所有用户的记忆数量（Neo4j）
-    3. RAG 模式使用批量查询（一次 SQL）
-    4. 只返回必要字段减少数据传输
-    5. 添加短期缓存减少重复查询
-    6. 并发执行配置查询和记忆数量查询
-    
-    返回格式：
-    {
-        "end_user": {"id": "uuid", "other_name": "名称"},
-        "memory_num": {"total": 数量},
-        "memory_config": {"memory_config_id": "id", "memory_config_name": "名称"}
-    }
+    获取工作空间的宿主列表（分页查询，支持模糊搜索）
+
+    返回工作空间下的宿主列表，支持分页查询和模糊搜索。
+    通过 keyword 参数同时模糊匹配 other_name 和 id 字段。
+
+    Args:
+        workspace_id: 工作空间ID（可选，默认当前用户工作空间）
+        keyword: 搜索关键词（可选，同时模糊匹配 other_name 和 id）
+        page: 页码（从1开始，默认1）
+        pagesize: 每页数量（默认10）
+        db: 数据库会话
+        current_user: 当前用户
+
+    Returns:
+        ApiResponse: 包含宿主列表和分页信息
     """
-    import asyncio
-    import json
-    from app.aioRedis import aio_redis_get, aio_redis_set
-    
-    workspace_id = current_user.current_workspace_id
-    
-    # 尝试从缓存获取（30秒缓存）
-    cache_key = f"end_users:workspace:{workspace_id}"
-    try:
-        cached_data = await aio_redis_get(cache_key)
-        if cached_data:
-            api_logger.info(f"从缓存获取宿主列表: workspace_id={workspace_id}")
-            return success(data=json.loads(cached_data), msg="宿主列表获取成功")
-    except Exception as e:
-        api_logger.warning(f"Redis 缓存读取失败: {str(e)}")
-    
+    # 如果未提供 workspace_id，使用当前用户的工作空间
+    if workspace_id is None:
+        workspace_id = current_user.current_workspace_id
     # 获取当前空间类型
     current_workspace_type = memory_dashboard_service.get_current_workspace_type(db, workspace_id, current_user)
-    api_logger.info(f"用户 {current_user.username} 请求获取工作空间 {workspace_id} 的宿主列表")
-    
-    # 获取 end_users（已优化为批量查询）
-    end_users = memory_dashboard_service.get_workspace_end_users(
+    api_logger.info(f"用户 {current_user.username} 请求获取工作空间 {workspace_id} 的宿主列表, 类型: {current_workspace_type}")
+
+    # 获取分页的 end_users
+    end_users_result = memory_dashboard_service.get_workspace_end_users_paginated(
         db=db,
         workspace_id=workspace_id,
-        current_user=current_user
+        current_user=current_user,
+        page=page,
+        pagesize=pagesize,
+        keyword=keyword
     )
+
+    end_users = end_users_result.get("items", [])
+    total = end_users_result.get("total", 0)
+
     if not end_users:
-        api_logger.info("工作空间下没有宿主")
-        # 缓存空结果，避免重复查询
-        try:
-            await aio_redis_set(cache_key, json.dumps([]), expire=30)
-        except Exception as e:
-            api_logger.warning(f"Redis 缓存写入失败: {str(e)}")
-        return success(data=[], msg="宿主列表获取成功")
-    
+        api_logger.info(f"工作空间下没有宿主或当前页无数据: total={total}, page={page}")
+        return success(data={
+            "items": [],
+            "page": {
+                "page": page,
+                "pagesize": pagesize,
+                "total": total,
+                "hasnext": (page * pagesize) < total
+            }
+        }, msg="宿主列表获取成功")
+
     end_user_ids = [str(user.id) for user in end_users]
-    
+
     # 并发执行两个独立的查询任务
     async def get_memory_configs():
         """获取记忆配置（在线程池中执行同步查询）"""
@@ -116,7 +118,7 @@ async def get_workspace_end_users(
         except Exception as e:
             api_logger.error(f"批量获取记忆配置失败: {str(e)}")
             return {}
-    
+
     async def get_memory_nums():
         """获取记忆数量"""
         if current_workspace_type == "rag":
@@ -130,26 +132,18 @@ async def get_workspace_end_users(
             except Exception as e:
                 api_logger.error(f"批量获取 RAG chunk 数量失败: {str(e)}")
                 return {uid: {"total": 0} for uid in end_user_ids}
-        
+
         elif current_workspace_type == "neo4j":
-            # Neo4j 模式：并发查询（带并发限制）
-            # 使用信号量限制并发数，避免大量用户时压垮 Neo4j
-            MAX_CONCURRENT_QUERIES = 10
-            semaphore = asyncio.Semaphore(MAX_CONCURRENT_QUERIES)
-            
-            async def get_neo4j_memory_num(end_user_id: str):
-                async with semaphore:
-                    try:
-                        return await memory_storage_service.search_all(end_user_id)
-                    except Exception as e:
-                        api_logger.error(f"获取用户 {end_user_id} Neo4j 记忆数量失败: {str(e)}")
-                        return {"total": 0}
-            
-            memory_nums_list = await asyncio.gather(*[get_neo4j_memory_num(uid) for uid in end_user_ids])
-            return {end_user_ids[i]: memory_nums_list[i] for i in range(len(end_user_ids))}
-        
+            # Neo4j 模式：批量查询（简化版本，只返回total）
+            try:
+                batch_result = await memory_storage_service.search_all_batch(end_user_ids)
+                return {uid: {"total": count} for uid, count in batch_result.items()}
+            except Exception as e:
+                api_logger.error(f"批量获取 Neo4j 记忆数量失败: {str(e)}")
+                return {uid: {"total": 0} for uid in end_user_ids}
+
         return {uid: {"total": 0} for uid in end_user_ids}
-    
+
     # 触发按需初始化：为 implicit_emotions_storage 中没有记录的用户异步生成数据
     try:
         from app.celery_app import celery_app as _celery_app
@@ -170,13 +164,13 @@ async def get_workspace_end_users(
         get_memory_configs(),
         get_memory_nums()
     )
-    
-    # 构建结果（优化：使用列表推导式）
-    result = []
+
+    # 构建结果列表
+    items = []
     for end_user in end_users:
         user_id = str(end_user.id)
         config_info = memory_configs_map.get(user_id, {})
-        result.append({
+        items.append({
             'end_user': {
                 'id': user_id,
                 'other_name': end_user.other_name
@@ -187,12 +181,6 @@ async def get_workspace_end_users(
                 "memory_config_name": config_info.get("memory_config_name")
             }
         })
-    
-    # 写入缓存（30秒过期）
-    try:
-        await aio_redis_set(cache_key, json.dumps(result), expire=30)
-    except Exception as e:
-        api_logger.warning(f"Redis 缓存写入失败: {str(e)}")
 
     # 触发社区聚类补全任务（异步，不阻塞接口响应）
     try:
@@ -202,7 +190,18 @@ async def get_workspace_end_users(
     except Exception as e:
         api_logger.warning(f"触发社区聚类补全任务失败（不影响主流程）: {str(e)}")
 
-    api_logger.info(f"成功获取 {len(end_users)} 个宿主记录")
+    # 构建分页响应
+    result = {
+        "items": items,
+        "page": {
+            "page": page,
+            "pagesize": pagesize,
+            "total": total,
+            "hasnext": (page * pagesize) < total
+        }
+    }
+
+    api_logger.info(f"成功获取 {len(end_users)} 个宿主记录，总计 {total} 条")
     return success(data=result, msg="宿主列表获取成功")
 
 
@@ -592,7 +591,7 @@ async def dashboard_data(
                 "total_api_call": None
             }
             
-            # 1. 获取记忆总量（total_memory）
+            # 1. 获取记忆总量（total_memory）—— neo4j 独有逻辑：查询 neo4j 存储节点
             try:
                 total_memory_data = await memory_dashboard_service.get_workspace_total_memory_count(
                     db=db,
@@ -601,49 +600,33 @@ async def dashboard_data(
                     end_user_id=end_user_id
                 )
                 neo4j_data["total_memory"] = total_memory_data.get("total_memory_count", 0)
-                # total_app: 统计当前空间下的所有app数量
-                # 包含自有app + 被分享给本工作空间的app
-                from app.services import app_service as _app_svc
-                _, total_app = _app_svc.AppService(db).list_apps(
-                    workspace_id=workspace_id, include_shared=True, pagesize=1
-                )
-                neo4j_data["total_app"] = total_app
-                api_logger.info(f"成功获取记忆总量: {neo4j_data['total_memory']}, 应用数量: {neo4j_data['total_app']}")
+                api_logger.info(f"成功获取记忆总量: {neo4j_data['total_memory']}")
             except Exception as e:
                 api_logger.warning(f"获取记忆总量失败: {str(e)}")
             
-            # 2. 获取知识库类型统计（total_knowledge）
-            try:
-                from app.services.memory_agent_service import MemoryAgentService 
-                memory_agent_service = MemoryAgentService()
-                knowledge_stats = await memory_agent_service.get_knowledge_type_stats(
-                    end_user_id=end_user_id,
-                    only_active=True,
-                    current_workspace_id=workspace_id,
-                    db=db
-                )
-                neo4j_data["total_knowledge"] = knowledge_stats.get("total", 0)
-                api_logger.info(f"成功获取知识库类型统计total: {neo4j_data['total_knowledge']}")
-            except Exception as e:
-                api_logger.warning(f"获取知识库类型统计失败: {str(e)}")
+            # 2. 获取共享统计数据（total_app、total_knowledge、total_api_call）
+            common_stats = memory_dashboard_service.get_dashboard_common_stats(db, workspace_id)
+            neo4j_data.update(common_stats)
+            api_logger.info(f"成功获取共享统计: app={common_stats['total_app']}, knowledge={common_stats['total_knowledge']}, api_call={common_stats['total_api_call']}")
             
-            # 3. 获取API调用统计（total_api_call）
+            # 计算昨日对比
             try:
-                # 使用 AppStatisticsService 获取真实的API调用统计
-                app_stats_service = AppStatisticsService(db)
-                api_stats = app_stats_service.get_workspace_api_statistics(
+                changes = memory_dashboard_service.get_dashboard_yesterday_changes(
+                    db=db,
                     workspace_id=workspace_id,
-                    start_date=start_date,
-                    end_date=end_date
+                    storage_type=storage_type,
+                    today_data=neo4j_data
                 )
-                # 计算总调用次数
-                total_api_calls = sum(item.get("total_calls", 0) for item in api_stats)
-                neo4j_data["total_api_call"] = total_api_calls
-                api_logger.info(f"成功获取API调用统计: {neo4j_data['total_api_call']}")
+                neo4j_data.update(changes)
             except Exception as e:
-                api_logger.error(f"获取API调用统计失败: {str(e)}")
-                neo4j_data["total_api_call"] = 0
-            
+                api_logger.warning(f"计算neo4j昨日对比失败: {str(e)}")
+                neo4j_data.update({
+                    "total_memory_change": None,
+                    "total_app_change": None,
+                    "total_knowledge_change": None,
+                    "total_api_call_change": None,
+                })
+
             result["neo4j_data"] = neo4j_data
             api_logger.info("成功获取neo4j_data")
         
@@ -656,44 +639,37 @@ async def dashboard_data(
                 "total_api_call": None
             }
             
-            # 获取RAG相关数据
+            # 1. 获取记忆总量（total_memory）—— rag 独有逻辑：查询 document 表的 chunk_num
             try:
-                # total_memory: 只统计用户知识库（permission_id='Memory'）的chunk数
                 total_chunk = memory_dashboard_service.get_rag_user_kb_total_chunk(db, current_user)
                 rag_data["total_memory"] = total_chunk
-                
-                # total_app: 统计当前空间下的所有app数量
-                # 包含自有app + 被分享给本工作空间的app
-                from app.services import app_service as _app_svc
-                _, total_app = _app_svc.AppService(db).list_apps(
-                    workspace_id=workspace_id, include_shared=True, pagesize=1
-                )
-                rag_data["total_app"] = total_app
-                
-                # total_knowledge: 使用 total_kb（总知识库数）
-                total_kb = memory_dashboard_service.get_rag_total_kb(db, current_user)
-                rag_data["total_knowledge"] = total_kb
-                
-                # total_api_call: 使用 AppStatisticsService 获取真实的API调用统计
-                try:
-                    app_stats_service = AppStatisticsService(db)
-                    api_stats = app_stats_service.get_workspace_api_statistics(
-                        workspace_id=workspace_id,
-                        start_date=start_date,
-                        end_date=end_date
-                    )
-                    # 计算总调用次数
-                    total_api_calls = sum(item.get("total_calls", 0) for item in api_stats)
-                    rag_data["total_api_call"] = total_api_calls
-                    api_logger.info(f"成功获取RAG模式API调用统计: {rag_data['total_api_call']}")
-                except Exception as e:
-                    api_logger.warning(f"获取RAG模式API调用统计失败，使用默认值: {str(e)}")
-                    rag_data["total_api_call"] = 0
-                
-                api_logger.info(f"成功获取RAG相关数据: memory={total_chunk}, app={total_app}, knowledge={total_kb}, api_calls={rag_data['total_api_call']}")
+                api_logger.info(f"成功获取RAG记忆总量: {total_chunk}")
             except Exception as e:
-                api_logger.warning(f"获取RAG相关数据失败: {str(e)}")
+                api_logger.warning(f"获取RAG记忆总量失败: {str(e)}")
             
+            # 2. 获取共享统计数据（total_app、total_knowledge、total_api_call）
+            common_stats = memory_dashboard_service.get_dashboard_common_stats(db, workspace_id)
+            rag_data.update(common_stats)
+            api_logger.info(f"成功获取共享统计: app={common_stats['total_app']}, knowledge={common_stats['total_knowledge']}, api_call={common_stats['total_api_call']}")
+            
+            # 计算昨日对比
+            try:
+                changes = memory_dashboard_service.get_dashboard_yesterday_changes(
+                    db=db,
+                    workspace_id=workspace_id,
+                    storage_type=storage_type,
+                    today_data=rag_data
+                )
+                rag_data.update(changes)
+            except Exception as e:
+                api_logger.warning(f"计算RAG昨日对比失败: {str(e)}")
+                rag_data.update({
+                    "total_memory_change": None,
+                    "total_app_change": None,
+                    "total_knowledge_change": None,
+                    "total_api_call_change": None,
+                })
+
             result["rag_data"] = rag_data
             api_logger.info("成功获取rag_data")
         

api/app/controllers/memory_storage_controller.py

@@ -26,7 +26,7 @@ from app.services.memory_storage_service import (
     analytics_hot_memory_tags,
     analytics_recent_activity_stats,
     kb_type_distribution,
-    search_all,
+    search_all_batch,
     search_chunk,
     search_detials,
     search_dialogue,
@@ -409,7 +409,10 @@ async def search_all_num(
 ) -> dict:
     api_logger.info(f"Search all requested for end_user_id: {end_user_id}")
     try:
-        result = await search_all(end_user_id)
+        if not end_user_id:
+            return success(data={"total": 0}, msg="查询成功")
+        batch_result = await search_all_batch([end_user_id])
+        result = {"total": batch_result.get(end_user_id, 0)}
         return success(data=result, msg="查询成功")
     except Exception as e:
         api_logger.error(f"Search all failed: {str(e)}")

api/app/controllers/ontology_controller.py

@@ -163,6 +163,7 @@ def _get_ontology_service(
             api_key=api_key_config.api_key,
             base_url=api_key_config.api_base,
             is_omni=api_key_config.is_omni,
+            support_thinking="thinking" in (api_key_config.capability or []),
             max_retries=3,
             timeout=60.0
         )

api/app/controllers/prompt_optimizer_controller.py

@@ -124,10 +124,11 @@ async def get_prompt_opt(
                     skill=data.skill
             ):
                 # chunk 是 prompt 的增量内容
-                yield f"event:message\ndata: {json.dumps(chunk)}\n\n"
+                yield f"event:message\ndata: {json.dumps(chunk, ensure_ascii=False)}\n\n"
         except Exception as e:
             yield f"event:error\ndata: {json.dumps(
-                {"error": str(e)}
+                {"error": str(e)},
+                ensure_ascii=False
             )}\n\n"
         yield "event:end\ndata: {}\n\n"
 

api/app/controllers/public_share_controller.py

@@ -453,31 +453,10 @@ async def chat(
         # 流式返回
         agent_config = agent_config_4_app_release(release)
 
-        if payload.stream:
-            # async def event_generator():
-            #     async for event in service.chat_stream(
-            #         share_token=share_token,
-            #         message=payload.message,
-            #         conversation_id=conversation.id,  # 使用已创建的会话 ID
-            #         user_id=str(new_end_user.id),  # 转换为字符串
-            #         variables=payload.variables,
-            #         password=password,
-            #         web_search=payload.web_search,
-            #         memory=payload.memory,
-            #         storage_type=storage_type,
-            #         user_rag_memory_id=user_rag_memory_id
-            #     ):
-            #         yield event
+        if not (agent_config.model_parameters.get("deep_thinking", False) and payload.thinking):
+            agent_config.model_parameters["deep_thinking"] = False
 
-            # return StreamingResponse(
-            #     event_generator(),
-            #     media_type="text/event-stream",
-            #     headers={
-            #         "Cache-Control": "no-cache",
-            #         "Connection": "keep-alive",
-            #         "X-Accel-Buffering": "no"
-            #     }
-            # )
+        if payload.stream:
             async def event_generator():
                 async for event in app_chat_service.agnet_chat_stream(
                         message=payload.message,
@@ -503,20 +482,6 @@ async def chat(
                     "X-Accel-Buffering": "no"
                 }
             )
-        # 非流式返回
-        # result = await service.chat(
-        #     share_token=share_token,
-        #     message=payload.message,
-        #     conversation_id=conversation.id,  # 使用已创建的会话 ID
-        #     user_id=str(new_end_user.id),  # 转换为字符串
-        #     variables=payload.variables,
-        #     password=password,
-        #     web_search=payload.web_search,
-        #     memory=payload.memory,
-        #     storage_type=storage_type,
-        #     user_rag_memory_id=user_rag_memory_id
-        # )
-        # return success(data=conversation_schema.ChatResponse(**result))
         result = await app_chat_service.agnet_chat(
             message=payload.message,
             conversation_id=conversation.id,  # 使用已创建的会话 ID
@@ -575,48 +540,6 @@ async def chat(
         )
 
         return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
-        # 多 Agent 流式返回
-        # if payload.stream:
-        #     async def event_generator():
-        #         async for event in service.multi_agent_chat_stream(
-        #             share_token=share_token,
-        #             message=payload.message,
-        #             conversation_id=conversation.id,  # 使用已创建的会话 ID
-        #             user_id=str(new_end_user.id),  # 转换为字符串
-        #             variables=payload.variables,
-        #             password=password,
-        #             web_search=payload.web_search,
-        #             memory=payload.memory,
-        #                 storage_type=storage_type,
-        #                 user_rag_memory_id=user_rag_memory_id
-        #         ):
-        #             yield event
-
-        #     return StreamingResponse(
-        #         event_generator(),
-        #         media_type="text/event-stream",
-        #         headers={
-        #             "Cache-Control": "no-cache",
-        #             "Connection": "keep-alive",
-        #             "X-Accel-Buffering": "no"
-        #         }
-        #     )
-
-        # # 多 Agent 非流式返回
-        # result = await service.multi_agent_chat(
-        #     share_token=share_token,
-        #     message=payload.message,
-        #     conversation_id=conversation.id,  # 使用已创建的会话 ID
-        #     user_id=str(new_end_user.id),  # 转换为字符串
-        #     variables=payload.variables,
-        #     password=password,
-        #     web_search=payload.web_search,
-        #     memory=payload.memory,
-        #     storage_type=storage_type,
-        #     user_rag_memory_id=user_rag_memory_id
-        # )
-
-        # return success(data=conversation_schema.ChatResponse(**result))
     elif app_type == AppType.WORKFLOW:
         config = workflow_config_4_app_release(release)
         if not config.id:
@@ -714,7 +637,8 @@ async def config_query(
             "app_type": release.app.type,
             "variables": release.config.get("variables"),
             "memory": release.config.get("memory", {}).get("enabled"),
-            "features": release.config.get("features")
+            "features": release.config.get("features"),
+            "model_parameters": release.config.get("model_parameters")
         }
     elif release.app.type == AppType.MULTI_AGENT:
         content = {

api/app/controllers/service/__init__.py

@@ -4,7 +4,7 @@
 认证方式: API Key
 """
 from fastapi import APIRouter
-from . import app_api_controller, rag_api_knowledge_controller, rag_api_document_controller, rag_api_file_controller, rag_api_chunk_controller, memory_api_controller
+from . import app_api_controller, rag_api_knowledge_controller, rag_api_document_controller, rag_api_file_controller, rag_api_chunk_controller, memory_api_controller, end_user_api_controller
 
 # 创建 V1 API 路由器
 service_router = APIRouter()
@@ -16,5 +16,6 @@ service_router.include_router(rag_api_document_controller.router)
 service_router.include_router(rag_api_file_controller.router)
 service_router.include_router(rag_api_chunk_controller.router)
 service_router.include_router(memory_api_controller.router)
+service_router.include_router(end_user_api_controller.router)
 
 __all__ = ["service_router"]

api/app/controllers/service/app_api_controller.py

@@ -14,6 +14,7 @@ from app.core.response_utils import success
 from app.db import get_db
 from app.models.app_model import App
 from app.models.app_model import AppType
+from app.models.app_release_model import AppRelease
 from app.repositories import knowledge_repository
 from app.repositories.end_user_repository import EndUserRepository
 from app.schemas import AppChatRequest, conversation_schema
@@ -61,18 +62,18 @@ async def list_apps():
 #     return success(data={"received": True}, msg="消息已接收")
 
 
-def _checkAppConfig(app: App):
-    if app.type == AppType.AGENT:
-        if not app.current_release.config:
+def _checkAppConfig(release: AppRelease):
+    if release.type == AppType.AGENT:
+        if not release.config:
             raise BusinessException("Agent 应用未配置模型", BizCode.AGENT_CONFIG_MISSING)
-    elif app.type == AppType.MULTI_AGENT:
-        if not app.current_release.config:
+    elif release.type == AppType.MULTI_AGENT:
+        if not release.config:
             raise BusinessException("Multi-Agent 应用未配置模型", BizCode.AGENT_CONFIG_MISSING)
-    elif app.type == AppType.WORKFLOW:
-        if not app.current_release.config:
+    elif release.type == AppType.WORKFLOW:
+        if not release.config:
             raise BusinessException("工作流应用未配置模型", BizCode.AGENT_CONFIG_MISSING)
     else:
-        raise BusinessException("不支持的应用类型", BizCode.AGENT_CONFIG_MISSING)
+        raise BusinessException("不支持的应用类型", BizCode.APP_TYPE_NOT_SUPPORTED)
 
 
 @router.post("/chat")
@@ -86,10 +87,22 @@ async def chat(
         app_service: Annotated[AppService, Depends(get_app_service)] = None,
         message: str = Body(..., description="聊天消息内容"),
 ):
+    """
+    Agent/Workflow 聊天接口
+
+    - 不传 version：使用当前生效版本（current_release，回滚后为回滚目标版本）
+    - 传 version=release_id：使用指定版本uuid的历史快照，例如 {"version": "{{release_id}}"}
+    """
     body = await request.json()
     payload = AppChatRequest(**body)
 
     app = app_service.get_app(api_key_auth.resource_id, api_key_auth.workspace_id)
+
+    # 版本切换：指定 release_id 时查找对应历史快照，否则使用当前激活版本
+    if payload.version is not None:
+        active_release = app_service.get_release_by_id(app.id, payload.version)
+    else:
+        active_release = app.current_release
     other_id = payload.user_id
     workspace_id = api_key_auth.workspace_id
     end_user_repo = EndUserRepository(db)
@@ -127,7 +140,7 @@ async def chat(
             storage_type = 'neo4j'
     app_type = app.type
     # check app config
-    _checkAppConfig(app)
+    _checkAppConfig(active_release)
 
     # 获取或创建会话（提前验证）
     conversation = conversation_service.create_or_get_conversation(
@@ -142,8 +155,13 @@ async def chat(
 
         # print("="*50)
         # print(app.current_release.default_model_config_id)
-        agent_config = agent_config_4_app_release(app.current_release)
+        agent_config = agent_config_4_app_release(active_release)
         # print(agent_config.default_model_config_id)
+
+        # thinking 开关：仅当 agent 配置了 deep_thinking 且请求 thinking=True 时才启用
+        if not (agent_config.model_parameters.get("deep_thinking", False) and payload.thinking):
+            agent_config.model_parameters["deep_thinking"] = False
+
         # 流式返回
         if payload.stream:
             async def event_generator():
@@ -189,7 +207,7 @@ async def chat(
         return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
     elif app_type == AppType.MULTI_AGENT:
         # 多 Agent 流式返回
-        config = multi_agent_config_4_app_release(app.current_release)
+        config = multi_agent_config_4_app_release(active_release)
         if payload.stream:
             async def event_generator():
                 async for event in app_chat_service.multi_agent_chat_stream(
@@ -232,7 +250,7 @@ async def chat(
         return success(data=conversation_schema.ChatResponse(**result).model_dump(mode="json"))
     elif app_type == AppType.WORKFLOW:
         # 多 Agent 流式返回
-        config = workflow_config_4_app_release(app.current_release)
+        config = workflow_config_4_app_release(active_release)
         if payload.stream:
             async def event_generator():
                 async for event in app_chat_service.workflow_chat_stream(
@@ -248,7 +266,7 @@ async def chat(
                         user_rag_memory_id=user_rag_memory_id,
                         app_id=app.id,
                         workspace_id=workspace_id,
-                        release_id=app.current_release.id,
+                        release_id=active_release.id,
                         public=True
                 ):
                     event_type = event.get("event", "message")
@@ -283,7 +301,7 @@ async def chat(
             files=payload.files,
             app_id=app.id,
             workspace_id=workspace_id,
-            release_id=app.current_release.id
+            release_id=active_release.id
         )
         logger.debug(
             "工作流试运行返回结果",
@@ -297,6 +315,4 @@ async def chat(
             msg="工作流任务执行成功"
         )
     else:
-        from app.core.exceptions import BusinessException
-        from app.core.error_codes import BizCode
         raise BusinessException(f"不支持的应用类型: {app_type}", BizCode.APP_TYPE_NOT_SUPPORTED)

api/app/controllers/service/end_user_api_controller.py

@@ -0,0 +1,92 @@
+"""End User 服务接口 - 基于 API Key 认证"""
+
+import uuid
+
+from fastapi import APIRouter, Body, Depends, Request
+from sqlalchemy.orm import Session
+
+from app.core.api_key_auth import require_api_key
+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.response_utils import success
+from app.db import get_db
+from app.repositories.end_user_repository import EndUserRepository
+from app.schemas.api_key_schema import ApiKeyAuth
+from app.schemas.memory_api_schema import CreateEndUserRequest, CreateEndUserResponse
+from app.services.memory_config_service import MemoryConfigService
+
+router = APIRouter(prefix="/end_user", tags=["V1 - End User API"])
+logger = get_business_logger()
+
+
+@router.post("/create")
+@require_api_key(scopes=["memory"])
+async def create_end_user(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+    message: str = Body(..., description="Request body"),
+):
+    """
+    Create or retrieve an end user for the workspace.
+
+    Creates a new end user and connects it to a memory configuration.
+    If an end user with the same other_id already exists in the workspace,
+    returns the existing one.
+
+    Optionally accepts a memory_config_id to connect the end user to a specific
+    memory configuration. If not provided, falls back to the workspace default config.
+    """
+    body = await request.json()
+    payload = CreateEndUserRequest(**body)
+    workspace_id = api_key_auth.workspace_id
+
+    logger.info("Create end user request - other_id: %s, workspace_id: %s", payload.other_id, workspace_id)
+
+    # Resolve memory_config_id: explicit > workspace default
+    memory_config_id = None
+    config_service = MemoryConfigService(db)
+
+    if payload.memory_config_id:
+        try:
+            memory_config_id = uuid.UUID(payload.memory_config_id)
+        except ValueError:
+            raise BusinessException(
+                f"Invalid memory_config_id format: {payload.memory_config_id}",
+                BizCode.INVALID_PARAMETER
+            )
+        config = config_service.get_config_with_fallback(memory_config_id, workspace_id)
+        if not config:
+            raise BusinessException(
+                f"Memory config not found: {payload.memory_config_id}",
+                BizCode.MEMORY_CONFIG_NOT_FOUND
+            )
+        memory_config_id = config.config_id
+    else:
+        default_config = config_service.get_workspace_default_config(workspace_id)
+        if default_config:
+            memory_config_id = default_config.config_id
+            logger.info(f"Using workspace default memory config: {memory_config_id}")
+        else:
+            logger.warning(f"No default memory config found for workspace: {workspace_id}")
+
+    end_user_repo = EndUserRepository(db)
+    end_user = end_user_repo.get_or_create_end_user_with_config(
+        app_id=api_key_auth.resource_id,
+        workspace_id=workspace_id,
+        other_id=payload.other_id,
+        memory_config_id=memory_config_id,
+    )
+
+    logger.info(f"End user ready: {end_user.id}")
+
+    result = {
+        "id": str(end_user.id),
+        "other_id": end_user.other_id or "",
+        "other_name": end_user.other_name or "",
+        "workspace_id": str(end_user.workspace_id),
+        "memory_config_id": str(end_user.memory_config_id) if end_user.memory_config_id else None,
+    }
+
+    return success(data=CreateEndUserResponse(**result).model_dump(), msg="End user created successfully")

api/app/controllers/service/memory_api_controller.py

@@ -6,6 +6,8 @@ from app.core.response_utils import success
 from app.db import get_db
 from app.schemas.api_key_schema import ApiKeyAuth
 from app.schemas.memory_api_schema import (
+    CreateEndUserRequest,
+    CreateEndUserResponse,
     ListConfigsResponse,
     MemoryReadRequest,
     MemoryReadResponse,
@@ -113,3 +115,31 @@ async def list_memory_configs(
 
     logger.info(f"Listed {result['total']} configs for workspace: {api_key_auth.workspace_id}")
     return success(data=ListConfigsResponse(**result).model_dump(), msg="Configs listed successfully")
+
+
+@router.post("/end_users")
+@require_api_key(scopes=["memory"])
+async def create_end_user(
+    request: Request,
+    api_key_auth: ApiKeyAuth = None,
+    db: Session = Depends(get_db),
+):
+    """
+    Create an end user.
+    
+    Creates a new end user for the authorized workspace.
+    If an end user with the same other_id already exists, returns the existing one.
+    """
+    body = await request.json()
+    payload = CreateEndUserRequest(**body)
+    logger.info(f"Create end user request - other_id: {payload.other_id}, workspace_id: {api_key_auth.workspace_id}")
+
+    memory_api_service = MemoryAPIService(db)
+
+    result = memory_api_service.create_end_user(
+        workspace_id=api_key_auth.workspace_id,
+        other_id=payload.other_id,
+    )
+
+    logger.info(f"End user ready: {result['id']}")
+    return success(data=CreateEndUserResponse(**result).model_dump(), msg="End user created successfully")

api/app/core/agent/langchain_agent.py

@@ -11,17 +11,14 @@ LangChain Agent 封装
 import time
 from typing import Any, AsyncGenerator, Dict, List, Optional, Sequence
 
-from app.core.memory.agent.langgraph_graph.write_graph import write_long_term
-from app.db import get_db
-from app.core.logging_config import get_business_logger
-from app.core.models import RedBearLLM, RedBearModelConfig
-from app.models.models_model import ModelType, ModelProvider
-from app.services.memory_agent_service import (
-    get_end_user_connected_config,
-)
 from langchain.agents import create_agent
 from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
 from langchain_core.tools import BaseTool
+from langgraph.errors import GraphRecursionError
+
+from app.core.logging_config import get_business_logger
+from app.core.models import RedBearLLM, RedBearModelConfig
+from app.models.models_model import ModelType
 
 logger = get_business_logger()
 
@@ -41,7 +38,10 @@ class LangChainAgent:
             tools: Optional[Sequence[BaseTool]] = None,
             streaming: bool = False,
             max_iterations: Optional[int] = None,  # 最大迭代次数（None 表示自动计算）
-            max_tool_consecutive_calls: int = 3  # 单个工具最大连续调用次数
+            max_tool_consecutive_calls: int = 3,  # 单个工具最大连续调用次数
+            deep_thinking: bool = False,  # 是否启用深度思考模式
+            thinking_budget_tokens: Optional[int] = None,  # 深度思考 token 预算
+            capability: Optional[List[str]] = None  # 模型能力列表，用于校验是否支持深度思考
     ):
         """初始化 LangChain Agent
 
@@ -64,6 +64,7 @@ class LangChainAgent:
         self.streaming = streaming
         self.is_omni = is_omni
         self.max_tool_consecutive_calls = max_tool_consecutive_calls
+        self.deep_thinking = deep_thinking and ("thinking" in (capability or []))
 
         # 工具调用计数器：记录每个工具的连续调用次数
         self.tool_call_counter: Dict[str, int] = {}
@@ -86,6 +87,13 @@ class LangChainAgent:
             f"auto_calculated={max_iterations is None}"
         )
 
+        # 根据 capability 校验是否真正支持深度思考
+        actual_deep_thinking = self.deep_thinking
+        if deep_thinking and not actual_deep_thinking:
+            logger.warning(
+                f"模型 {model_name} 不支持深度思考（capability 中无 'thinking'），已自动关闭 deep_thinking"
+            )
+
         # 创建 RedBearLLM（支持多提供商）
         model_config = RedBearModelConfig(
             model_name=model_name,
@@ -93,10 +101,13 @@ class LangChainAgent:
             api_key=api_key,
             base_url=api_base,
             is_omni=is_omni,
+            deep_thinking=actual_deep_thinking,
+            thinking_budget_tokens=thinking_budget_tokens if actual_deep_thinking else None,
+            support_thinking="thinking" in (capability or []),
             extra_params={
                 "temperature": temperature,
                 "max_tokens": max_tokens,
-                "streaming": streaming  # 使用参数控制流式
+                "streaming": streaming
             }
         )
 
@@ -226,10 +237,9 @@ class LangChainAgent:
         Returns:
             List[BaseMessage]: 消息列表
         """
-        messages = []
+        messages:list = [SystemMessage(content=self.system_prompt)]
 
         # 添加系统提示词
-        messages.append(SystemMessage(content=self.system_prompt))
 
         # 添加历史消息
         if history:
@@ -254,6 +264,33 @@ class LangChainAgent:
 
         return messages
 
+    @staticmethod
+    def _extract_tokens_from_message(msg) -> int:
+        """从 AIMessage 或类似对象中提取 total_tokens，兼容多种 provider 格式
+
+        支持的格式：
+        - response_metadata.token_usage.total_tokens (OpenAI/ChatOpenAI)
+        - response_metadata.usage.total_tokens (部分 provider)
+        - usage_metadata.total_tokens (LangChain 新版)
+        """
+        total = 0
+        # 1. response_metadata
+        response_meta = getattr(msg, "response_metadata", None)
+        if response_meta and isinstance(response_meta, dict):
+            # 尝试 token_usage 路径
+            token_usage = response_meta.get("token_usage") or response_meta.get("usage", {})
+            if isinstance(token_usage, dict):
+                total = token_usage.get("total_tokens", 0)
+        # 2. usage_metadata（LangChain 新版 AIMessage 属性）
+        if not total:
+            usage_meta = getattr(msg, "usage_metadata", None)
+            if usage_meta:
+                if isinstance(usage_meta, dict):
+                    total = usage_meta.get("total_tokens", 0)
+                else:
+                    total = getattr(usage_meta, "total_tokens", 0)
+        return total or 0
+
     def _build_multimodal_content(self, text: str, files: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
         """
         构建多模态消息内容
@@ -288,17 +325,23 @@ class LangChainAgent:
 
         return content_parts
 
+    @staticmethod
+    def _extract_reasoning_content(msg) -> str:
+        """从 AIMessage 中提取深度思考内容（reasoning_content）
+
+        所有 provider 统一通过 additional_kwargs.reasoning_content 传递：
+        - DeepSeek-R1 / QwQ: 原生字段
+        - Volcano (Doubao-thinking): 由 VolcanoChatOpenAI 从 delta.reasoning_content 注入
+        """
+        additional = getattr(msg, "additional_kwargs", None) or {}
+        return additional.get("reasoning_content") or additional.get("reasoning", "")
+
     async def chat(
             self,
             message: str,
             history: Optional[List[Dict[str, str]]] = None,
             context: Optional[str] = None,
-            end_user_id: Optional[str] = None,
-            config_id: Optional[str] = None,  # 添加这个参数
-            storage_type: Optional[str] = None,
-            user_rag_memory_id: Optional[str] = None,
-            memory_flag: Optional[bool] = True,
-            files: Optional[List[Dict[str, Any]]] = None  # 新增：多模态文件
+            files: Optional[List[Dict[str, Any]]] = None
     ) -> Dict[str, Any]:
         """执行对话
 
@@ -306,31 +349,12 @@ class LangChainAgent:
             message: 用户消息
             history: 历史消息列表 [{"role": "user/assistant", "content": "..."}]
             context: 上下文信息（如知识库检索结果）
+            files: 多模态文件
 
         Returns:
             Dict: 包含 content 和元数据的字典
         """
-        message_chat = message
         start_time = time.time()
-        actual_config_id = config_id
-        # If config_id is None, try to get from end_user's connected config
-        if actual_config_id is None and end_user_id:
-            try:
-                from app.services.memory_agent_service import (
-                    get_end_user_connected_config,
-                )
-                db = next(get_db())
-                try:
-                    connected_config = get_end_user_connected_config(end_user_id, db)
-                    actual_config_id = connected_config.get("memory_config_id")
-                except Exception as e:
-                    logger.warning(f"Failed to get connected config for end_user {end_user_id}: {e}")
-                finally:
-                    db.close()
-            except Exception as e:
-                logger.warning(f"Failed to get db session: {e}")
-        logger.info(f'写入类型{storage_type, str(end_user_id), message, str(user_rag_memory_id)}')
-        print(f'写入类型{storage_type, str(end_user_id), message, str(user_rag_memory_id)}')
         try:
             # 准备消息列表（支持多模态）
             messages = self._prepare_messages(message, history, context, files)
@@ -354,7 +378,7 @@ class LangChainAgent:
                     {"messages": messages},
                     config={"recursion_limit": self.max_iterations}
                 )
-            except RecursionError as e:
+            except (RecursionError, GraphRecursionError) as e:
                 logger.warning(
                     f"Agent 达到最大迭代次数限制 ({self.max_iterations})，可能存在工具调用循环",
                     extra={"error": str(e)}
@@ -377,6 +401,7 @@ class LangChainAgent:
 
             logger.debug(f"输出消息数量: {len(output_messages)}")
             total_tokens = 0
+            reasoning_content = ""
             for msg in reversed(output_messages):
                 if isinstance(msg, AIMessage):
                     logger.debug(f"找到 AI 消息，content 类型: {type(msg.content)}")
@@ -411,16 +436,13 @@ class LangChainAgent:
                     else:
                         content = str(msg.content)
                         logger.debug(f"转换为字符串: {content[:100]}...")
-                    response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None
-                    total_tokens = response_meta.get("token_usage", {}).get("total_tokens", 0) if response_meta else 0
+                    total_tokens = self._extract_tokens_from_message(msg)
+                    reasoning_content = self._extract_reasoning_content(msg) if self.deep_thinking else ""
                     break
 
             logger.info(f"最终提取的内容长度: {len(content)}")
 
             elapsed_time = time.time() - start_time
-            if memory_flag:
-                await write_long_term(storage_type, end_user_id, message_chat, content, user_rag_memory_id,
-                                      actual_config_id)
             response = {
                 "content": content,
                 "model": self.model_name,
@@ -431,6 +453,8 @@ class LangChainAgent:
                     "total_tokens": total_tokens
                 }
             }
+            if reasoning_content:
+                response["reasoning_content"] = reasoning_content
 
             logger.debug(
                 "Agent 调用完成",
@@ -451,22 +475,20 @@ class LangChainAgent:
             message: str,
             history: Optional[List[Dict[str, str]]] = None,
             context: Optional[str] = None,
-            end_user_id: Optional[str] = None,
-            config_id: Optional[str] = None,
-            storage_type: Optional[str] = None,
-            user_rag_memory_id: Optional[str] = None,
-            memory_flag: Optional[bool] = True,
-            files: Optional[List[Dict[str, Any]]] = None  # 新增：多模态文件
-    ) -> AsyncGenerator[str, None]:
+            files: Optional[List[Dict[str, Any]]] = None
+    ) -> AsyncGenerator[str | int | dict[str, str], None]:
         """执行流式对话
 
         Args:
             message: 用户消息
             history: 历史消息列表
             context: 上下文信息
+            files: 多模态文件
 
         Yields:
             str: 消息内容块
+            int: token 统计
+            Dict: 深度思考内容 {"type": "reasoning", "content": "..."}
         """
         logger.info("=" * 80)
         logger.info(" chat_stream 方法开始执行")
@@ -474,23 +496,6 @@ class LangChainAgent:
         logger.info(f"  Has tools: {bool(self.tools)}")
         logger.info(f"  Tool count: {len(self.tools) if self.tools else 0}")
         logger.info("=" * 80)
-        message_chat = message
-        actual_config_id = config_id
-        # If config_id is None, try to get from end_user's connected config
-        if actual_config_id is None and end_user_id:
-            try:
-                db = next(get_db())
-                try:
-                    connected_config = get_end_user_connected_config(end_user_id, db)
-                    actual_config_id = connected_config.get("memory_config_id")
-                except Exception as e:
-                    logger.warning(f"Failed to get connected config for end_user {end_user_id}: {e}")
-                finally:
-                    db.close()
-            except Exception as e:
-                logger.warning(f"Failed to get db session: {e}")
-
-            # 注意：不在这里写入用户消息，等 AI 回复后一起写入
         try:
             # 准备消息列表（支持多模态）
             messages = self._prepare_messages(message, history, context, files)
@@ -500,17 +505,19 @@ class LangChainAgent:
             )
 
             chunk_count = 0
-            yielded_content = False
 
             # 统一使用 agent 的 astream_events 实现流式输出
             logger.debug("使用 Agent astream_events 实现流式输出")
             full_content = ''
+            full_reasoning = ''
             try:
+                last_event = {}
                 async for event in self.agent.astream_events(
                         {"messages": messages},
                         version="v2",
                         config={"recursion_limit": self.max_iterations}
                 ):
+                    last_event = event
                     chunk_count += 1
                     kind = event.get("event")
 
@@ -519,12 +526,18 @@ class LangChainAgent:
                         # LLM 流式输出
                         chunk = event.get("data", {}).get("chunk")
                         if chunk and hasattr(chunk, "content"):
+                            # 提取深度思考内容（仅在启用深度思考时）
+                            if self.deep_thinking:
+                                reasoning_chunk = self._extract_reasoning_content(chunk)
+                                if reasoning_chunk:
+                                    full_reasoning += reasoning_chunk
+                                    yield {"type": "reasoning", "content": reasoning_chunk}
+
                             # 处理多模态响应：content 可能是字符串或列表
                             chunk_content = chunk.content
                             if isinstance(chunk_content, str) and chunk_content:
                                 full_content += chunk_content
                                 yield chunk_content
-                                yielded_content = True
                             elif isinstance(chunk_content, list):
                                 # 多模态响应：提取文本部分
                                 for item in chunk_content:
@@ -535,29 +548,32 @@ class LangChainAgent:
                                             if text:
                                                 full_content += text
                                                 yield text
-                                                yielded_content = True
                                         # OpenAI 格式: {"type": "text", "text": "..."}
                                         elif item.get("type") == "text":
                                             text = item.get("text", "")
                                             if text:
                                                 full_content += text
                                                 yield text
-                                                yielded_content = True
                                     elif isinstance(item, str):
                                         full_content += item
                                         yield item
-                                        yielded_content = True
 
                     elif kind == "on_llm_stream":
                         # 另一种 LLM 流式事件
                         chunk = event.get("data", {}).get("chunk")
                         if chunk:
                             if hasattr(chunk, "content"):
+                                # 提取深度思考内容（仅在启用深度思考时）
+                                if self.deep_thinking:
+                                    reasoning_chunk = self._extract_reasoning_content(chunk)
+                                    if reasoning_chunk:
+                                        full_reasoning += reasoning_chunk
+                                        yield {"type": "reasoning", "content": reasoning_chunk}
+
                                 chunk_content = chunk.content
                                 if isinstance(chunk_content, str) and chunk_content:
                                     full_content += chunk_content
                                     yield chunk_content
-                                    yielded_content = True
                                 elif isinstance(chunk_content, list):
                                     # 多模态响应：提取文本部分
                                     for item in chunk_content:
@@ -568,22 +584,18 @@ class LangChainAgent:
                                                 if text:
                                                     full_content += text
                                                     yield text
-                                                    yielded_content = True
                                             # OpenAI 格式: {"type": "text", "text": "..."}
                                             elif item.get("type") == "text":
                                                 text = item.get("text", "")
                                                 if text:
                                                     full_content += text
                                                     yield text
-                                                    yielded_content = True
                                         elif isinstance(item, str):
                                             full_content += item
                                             yield item
-                                            yielded_content = True
                             elif isinstance(chunk, str):
                                 full_content += chunk
                                 yield chunk
-                                yielded_content = True
 
                     # 记录工具调用（可选）
                     elif kind == "on_tool_start":
@@ -593,19 +605,20 @@ class LangChainAgent:
 
                 logger.debug(f"Agent 流式完成，共 {chunk_count} 个事件")
                 # 统计token消耗
-                output_messages = event.get("data", {}).get("output", {}).get("messages", [])
+                output_messages = last_event.get("data", {}).get("output", {}).get("messages", [])
                 for msg in reversed(output_messages):
                     if isinstance(msg, AIMessage):
-                        response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None
-                        total_tokens = response_meta.get("token_usage", {}).get(
-                            "total_tokens",
-                            0
-                        ) if response_meta else 0
-                        yield total_tokens
+                        stream_total_tokens = self._extract_tokens_from_message(msg)
+                        logger.info(f"流式 token 统计: total_tokens={stream_total_tokens}")
+                        yield stream_total_tokens
                         break
-                if memory_flag:
-                    await write_long_term(storage_type, end_user_id, message_chat, full_content, user_rag_memory_id,
-                                          actual_config_id)
+
+            except GraphRecursionError:
+                logger.warning(
+                    f"Agent 达到最大迭代次数限制 ({self.max_iterations})，模型可能不支持正确的工具调用停止判断"
+                )
+                if not full_content:
+                    yield "抱歉，我在处理您的请求时遇到了问题（已达最大处理步骤限制）。请尝试简化问题或更换模型后重试。"
             except Exception as e:
                 logger.error(f"Agent astream_events 失败: {str(e)}", exc_info=True)
                 raise

api/app/core/error_codes.py

@@ -19,6 +19,7 @@ class BizCode(IntEnum):
     TENANT_NOT_FOUND = 3002
     WORKSPACE_NO_ACCESS = 3003
     WORKSPACE_INVITE_NOT_FOUND = 3004
+    WORKSPACE_ACCESS_DENIED = 3005
     # API Key 管理（3xxx）
     API_KEY_NOT_FOUND = 3007
     API_KEY_DUPLICATE_NAME = 3008
@@ -40,6 +41,7 @@ class BizCode(IntEnum):
     FILE_NOT_FOUND = 4006
     APP_NOT_FOUND = 4007
     RELEASE_NOT_FOUND = 4008
+    USER_NO_ACCESS = 4009
 
     # 冲突/状态（5xxx）
     DUPLICATE_NAME = 5001
@@ -113,8 +115,11 @@ HTTP_MAPPING = {
     BizCode.FORBIDDEN: 403,
     BizCode.TENANT_NOT_FOUND: 400,
     BizCode.WORKSPACE_NO_ACCESS: 403,
+    BizCode.WORKSPACE_INVITE_NOT_FOUND: 400,
+    BizCode.WORKSPACE_ACCESS_DENIED: 403,
     BizCode.NOT_FOUND: 400,
     BizCode.USER_NOT_FOUND: 200,
+    BizCode.USER_NO_ACCESS: 401,
     BizCode.WORKSPACE_NOT_FOUND: 400,
     BizCode.MODEL_NOT_FOUND: 400,
     BizCode.KNOWLEDGE_NOT_FOUND: 400,

api/app/core/memory/agent/langgraph_graph/nodes/perceptual_retrieve_node.py

@@ -0,0 +1,408 @@
+"""
+Perceptual Memory Retrieval Node & Service
+
+Provides PerceptualSearchService for searching perceptual memories (vision, audio,
+text, conversation) from Neo4j using keyword fulltext + embedding semantic search
+with BM25+embedding fusion reranking.
+
+Also provides the perceptual_retrieve_node for use as a LangGraph node.
+"""
+import asyncio
+import math
+from typing import List, Dict, Any, Optional
+
+from app.core.logging_config import get_agent_logger
+from app.core.memory.agent.utils.llm_tools import ReadState
+from app.core.memory.utils.data.text_utils import escape_lucene_query
+from app.repositories.neo4j.graph_search import (
+    search_perceptual,
+    search_perceptual_by_embedding,
+)
+from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+
+logger = get_agent_logger(__name__)
+
+
+class PerceptualSearchService:
+    """
+    感知记忆检索服务。
+
+    封装关键词全文检索 + 向量语义检索 + BM25/embedding 融合排序的完整流程。
+    调用方只需提供 query / keywords、end_user_id、memory_config，即可获得
+    格式化并排序后的感知记忆列表和拼接文本。
+
+    Usage:
+        service = PerceptualSearchService(end_user_id=..., memory_config=...)
+        results = await service.search(query="...", keywords=[...], limit=10)
+        # results = {"memories": [...], "content": "...", "keyword_raw": N, "embedding_raw": M}
+    """
+
+    DEFAULT_ALPHA = 0.6
+    DEFAULT_CONTENT_SCORE_THRESHOLD = 0.5
+
+    def __init__(
+            self,
+            end_user_id: str,
+            memory_config: Any,
+            alpha: float = DEFAULT_ALPHA,
+            content_score_threshold: float = DEFAULT_CONTENT_SCORE_THRESHOLD,
+    ):
+        self.end_user_id = end_user_id
+        self.memory_config = memory_config
+        self.alpha = alpha
+        self.content_score_threshold = content_score_threshold
+
+    async def search(
+            self,
+            query: str,
+            keywords: Optional[List[str]] = None,
+            limit: int = 10,
+    ) -> Dict[str, Any]:
+        """
+        执行感知记忆检索（关键词 + 向量并行），融合排序后返回结果。
+
+        对 embedding 命中但 keyword 未命中的结果，补查全文索引获取 BM25 分数，
+        确保所有结果都同时具备 BM25 和 embedding 两个维度的评分。
+
+        Args:
+            query: 原始用户查询（用于向量检索和 BM25 补查）
+            keywords: 关键词列表（用于全文检索），为 None 时使用 [query]
+            limit: 最大返回数量
+
+        Returns:
+            {
+                "memories": [格式化后的记忆 dict, ...],
+                "content": "拼接的纯文本摘要",
+                "keyword_raw": int,
+                "embedding_raw": int,
+            }
+        """
+        if keywords is None:
+            keywords = [query] if query else []
+
+        connector = Neo4jConnector()
+        try:
+            kw_task = self._keyword_search(connector, keywords, limit)
+            emb_task = self._embedding_search(connector, query, limit)
+
+            kw_results, emb_results = await asyncio.gather(
+                kw_task, emb_task, return_exceptions=True
+            )
+            if isinstance(kw_results, Exception):
+                logger.warning(f"[PerceptualSearch] keyword search error: {kw_results}")
+                kw_results = []
+            if isinstance(emb_results, Exception):
+                logger.warning(f"[PerceptualSearch] embedding search error: {emb_results}")
+                emb_results = []
+
+            # 补查 BM25：找出 embedding 命中但 keyword 未命中的 id，
+            # 用原始 query 对这些节点补查全文索引拿 BM25 score
+            kw_ids = {r.get("id") for r in kw_results if r.get("id")}
+            emb_only_ids = {r.get("id") for r in emb_results if r.get("id") and r.get("id") not in kw_ids}
+
+            if emb_only_ids and query:
+                backfill = await self._bm25_backfill(connector, query, emb_only_ids, limit)
+                # 把补查到的 BM25 score 注入到 embedding 结果中
+                backfill_map = {r["id"]: r.get("score", 0) for r in backfill}
+                for r in emb_results:
+                    rid = r.get("id", "")
+                    if rid in backfill_map:
+                        r["bm25_backfill_score"] = backfill_map[rid]
+                logger.info(
+                    f"[PerceptualSearch] BM25 backfill: {len(emb_only_ids)} embedding-only ids, "
+                    f"{len(backfill_map)} got BM25 scores"
+                )
+
+            reranked = self._rerank(kw_results, emb_results, limit)
+
+            memories = []
+            content_parts = []
+            for record in reranked:
+                fmt = self._format_result(record)
+                fmt["score"] = round(record.get("content_score", 0), 4)
+                memories.append(fmt)
+                content_parts.append(self._build_content_text(fmt))
+
+            logger.info(
+                f"[PerceptualSearch] {len(memories)} results after rerank "
+                f"(keyword_raw={len(kw_results)}, embedding_raw={len(emb_results)})"
+            )
+            return {
+                "memories": memories,
+                "content": "\n\n".join(content_parts),
+                "keyword_raw": len(kw_results),
+                "embedding_raw": len(emb_results),
+            }
+        finally:
+            await connector.close()
+
+    async def _bm25_backfill(
+            self,
+            connector: Neo4jConnector,
+            query: str,
+            target_ids: set,
+            limit: int,
+    ) -> List[dict]:
+        """
+        对指定 id 集合补查全文索引 BM25 score。
+
+        用原始 query 查全文索引，只保留 id 在 target_ids 中的结果。
+        """
+        escaped = escape_lucene_query(query)
+        if not escaped.strip():
+            return []
+        try:
+            r = await search_perceptual(
+                connector=connector, query=escaped,
+                end_user_id=self.end_user_id,
+                limit=limit * 5,  # 多查一些以提高命中率
+            )
+            all_hits = r.get("perceptuals", [])
+            return [h for h in all_hits if h.get("id") in target_ids]
+        except Exception as e:
+            logger.warning(f"[PerceptualSearch] BM25 backfill failed: {e}")
+            return []
+
+    async def _keyword_search(
+            self,
+            connector: Neo4jConnector,
+            keywords: List[str],
+            limit: int,
+    ) -> List[dict]:
+        """并发对每个关键词做全文检索，去重后按 score 降序返回 top N 原始结果。"""
+        seen_ids: set = set()
+        all_results: List[dict] = []
+
+        async def _one(kw: str):
+            escaped = escape_lucene_query(kw)
+            if not escaped.strip():
+                return []
+            r = await search_perceptual(
+                connector=connector, query=escaped,
+                end_user_id=self.end_user_id, limit=limit,
+            )
+            return r.get("perceptuals", [])
+
+        tasks = [_one(kw) for kw in keywords[:10]]
+        batch = await asyncio.gather(*tasks, return_exceptions=True)
+
+        for result in batch:
+            if isinstance(result, Exception):
+                logger.warning(f"[PerceptualSearch] keyword sub-query error: {result}")
+                continue
+            for rec in result:
+                rid = rec.get("id", "")
+                if rid and rid not in seen_ids:
+                    seen_ids.add(rid)
+                    all_results.append(rec)
+
+        all_results.sort(key=lambda x: float(x.get("score", 0)), reverse=True)
+        return all_results[:limit]
+
+    async def _embedding_search(
+            self,
+            connector: Neo4jConnector,
+            query_text: str,
+            limit: int,
+    ) -> List[dict]:
+        """向量语义检索，返回原始结果（不做阈值过滤）。"""
+        try:
+            from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient
+            from app.core.models.base import RedBearModelConfig
+            from app.db import get_db_context
+            from app.services.memory_config_service import MemoryConfigService
+
+            with get_db_context() as db:
+                cfg = MemoryConfigService(db).get_embedder_config(
+                    str(self.memory_config.embedding_model_id)
+                )
+            client = OpenAIEmbedderClient(RedBearModelConfig(**cfg))
+
+            r = await search_perceptual_by_embedding(
+                connector=connector, embedder_client=client,
+                query_text=query_text, end_user_id=self.end_user_id,
+                limit=limit,
+            )
+            return r.get("perceptuals", [])
+        except Exception as e:
+            logger.warning(f"[PerceptualSearch] embedding search failed: {e}")
+            return []
+
+    def _rerank(
+            self,
+            keyword_results: List[dict],
+            embedding_results: List[dict],
+            limit: int,
+    ) -> List[dict]:
+        """BM25 + embedding 融合排序。
+
+        对 embedding 结果中带有 bm25_backfill_score 的条目，
+        将其与 keyword 结果合并后统一归一化，确保 BM25 分数在同一尺度上。
+        """
+        # 把补查的 BM25 score 合并到 keyword_results 中统一归一化
+        emb_backfill_items = []
+        for item in embedding_results:
+            backfill_score = item.get("bm25_backfill_score")
+            if backfill_score is not None and item.get("id"):
+                emb_backfill_items.append({"id": item["id"], "score": backfill_score})
+
+        # 合并后统一归一化 BM25 scores
+        all_bm25_items = keyword_results + emb_backfill_items
+        all_bm25_items = self._normalize_scores(all_bm25_items)
+
+        # 建立 id -> normalized BM25 score 的映射
+        bm25_norm_map: Dict[str, float] = {}
+        for item in all_bm25_items:
+            item_id = item.get("id", "")
+            if item_id:
+                bm25_norm_map[item_id] = float(item.get("normalized_score", 0))
+
+        # 归一化 embedding scores
+        embedding_results = self._normalize_scores(embedding_results)
+
+        # 合并
+        combined: Dict[str, dict] = {}
+        for item in keyword_results:
+            item_id = item.get("id", "")
+            if not item_id:
+                continue
+            combined[item_id] = item.copy()
+            combined[item_id]["bm25_score"] = bm25_norm_map.get(item_id, 0)
+            combined[item_id]["embedding_score"] = 0.0
+
+        for item in embedding_results:
+            item_id = item.get("id", "")
+            if not item_id:
+                continue
+            if item_id in combined:
+                combined[item_id]["embedding_score"] = item.get("normalized_score", 0)
+            else:
+                combined[item_id] = item.copy()
+                combined[item_id]["bm25_score"] = bm25_norm_map.get(item_id, 0)
+                combined[item_id]["embedding_score"] = item.get("normalized_score", 0)
+
+        for item in combined.values():
+            bm25 = float(item.get("bm25_score", 0) or 0)
+            emb = float(item.get("embedding_score", 0) or 0)
+            item["content_score"] = self.alpha * bm25 + (1 - self.alpha) * emb
+
+        results = list(combined.values())
+        before = len(results)
+        results = [r for r in results if r["content_score"] >= self.content_score_threshold]
+        results.sort(key=lambda x: x["content_score"], reverse=True)
+        results = results[:limit]
+
+        logger.info(
+            f"[PerceptualSearch] rerank: merged={before}, after_threshold={len(results)} "
+            f"(alpha={self.alpha}, threshold={self.content_score_threshold})"
+        )
+        return results
+
+    @staticmethod
+    def _normalize_scores(items: List[dict], field: str = "score") -> List[dict]:
+        """Z-score + sigmoid 归一化。"""
+        if not items:
+            return items
+        scores = [float(it.get(field, 0) or 0) for it in items]
+        if len(scores) <= 1:
+            for it in items:
+                it[f"normalized_{field}"] = 1.0
+            return items
+        mean = sum(scores) / len(scores)
+        var = sum((s - mean) ** 2 for s in scores) / len(scores)
+        std = math.sqrt(var)
+        if std == 0:
+            for it in items:
+                it[f"normalized_{field}"] = 1.0
+        else:
+            for it, s in zip(items, scores):
+                z = (s - mean) / std
+                it[f"normalized_{field}"] = 1 / (1 + math.exp(-z))
+        return items
+
+    @staticmethod
+    def _format_result(record: dict) -> dict:
+        return {
+            "id": record.get("id", ""),
+            "perceptual_type": record.get("perceptual_type", ""),
+            "file_name": record.get("file_name", ""),
+            "file_path": record.get("file_path", ""),
+            "summary": record.get("summary", ""),
+            "topic": record.get("topic", ""),
+            "domain": record.get("domain", ""),
+            "keywords": record.get("keywords", []),
+            "created_at": str(record.get("created_at", "")),
+            "file_type": record.get("file_type", ""),
+            "score": record.get("score", 0),
+        }
+
+    @staticmethod
+    def _build_content_text(formatted: dict) -> str:
+        parts = []
+        if formatted["summary"]:
+            parts.append(formatted["summary"])
+        if formatted["topic"]:
+            parts.append(f"[主题: {formatted['topic']}]")
+        if formatted["keywords"]:
+            kw_list = formatted["keywords"]
+            if isinstance(kw_list, list):
+                parts.append(f"[关键词: {', '.join(kw_list)}]")
+        if formatted["file_name"]:
+            parts.append(f"[文件: {formatted['file_name']}]")
+        return " ".join(parts)
+
+
+def _extract_keywords_from_problems(problem_extension: dict) -> List[str]:
+    """Extract search keywords from problem extension results."""
+    keywords = []
+    context = problem_extension.get("context", {})
+    if isinstance(context, dict):
+        for original_q, extended_qs in context.items():
+            keywords.append(original_q)
+            if isinstance(extended_qs, list):
+                keywords.extend(extended_qs)
+    return keywords
+
+
+async def perceptual_retrieve_node(state: ReadState) -> ReadState:
+    """
+    LangGraph node: perceptual memory retrieval.
+
+    Uses PerceptualSearchService to run keyword + embedding search with
+    BM25 fusion reranking, then writes results to state['perceptual_data'].
+    """
+    end_user_id = state.get("end_user_id", "")
+    problem_extension = state.get("problem_extension", {})
+    original_query = state.get("data", "")
+    memory_config = state.get("memory_config", None)
+
+    logger.info(f"Perceptual_Retrieve: start, end_user_id={end_user_id}")
+
+    keywords = _extract_keywords_from_problems(problem_extension)
+    if not keywords:
+        keywords = [original_query] if original_query else []
+
+    logger.info(f"Perceptual_Retrieve: {len(keywords)} keywords extracted")
+
+    service = PerceptualSearchService(
+        end_user_id=end_user_id,
+        memory_config=memory_config,
+    )
+    search_result = await service.search(
+        query=original_query,
+        keywords=keywords,
+        limit=10,
+    )
+
+    result = {
+        "memories": search_result["memories"],
+        "content": search_result["content"],
+        "_intermediate": {
+            "type": "perceptual_retrieve",
+            "title": "感知记忆检索",
+            "data": search_result["memories"],
+            "query": original_query,
+            "result_count": len(search_result["memories"]),
+        },
+    }
+    return {"perceptual_data": result}

api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py

@@ -263,7 +263,6 @@ async def Problem_Extension(state: ReadState) -> ReadState:
     logger.info(f"Problem extension result: {aggregated_dict}")
 
     # Emit intermediate output for frontend
-    print(time.time() - start)
     result = {
         "context": aggregated_dict,
         "original": data,

api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py

@@ -1,7 +1,11 @@
+import asyncio
 import os
 import time
 
 from app.core.logging_config import get_agent_logger, log_time
+from app.core.memory.agent.langgraph_graph.nodes.perceptual_retrieve_node import (
+    PerceptualSearchService,
+)
 from app.core.memory.agent.models.summary_models import (
     RetrieveSummaryResponse,
     SummaryResponse,
@@ -339,11 +343,45 @@ async def Input_Summary(state: ReadState) -> ReadState:
 
     try:
         if storage_type != "rag":
-            retrieve_info, question, raw_results = await SearchService().execute_hybrid_search(
+
+            async def _perceptual_search():
+                service = PerceptualSearchService(
+                    end_user_id=end_user_id,
+                    memory_config=memory_config,
+                )
+                return await service.search(query=data, limit=5)
+
+            hybrid_task = SearchService().execute_hybrid_search(
                 **search_params,
                 memory_config=memory_config,
-                expand_communities=False,  # 路径 "2" 只需要 community 的 summary 文本，不展开到 Statement
+                expand_communities=False,
             )
+            perceptual_task = _perceptual_search()
+
+            gather_results = await asyncio.gather(
+                hybrid_task, perceptual_task, return_exceptions=True
+            )
+            hybrid_result = gather_results[0]
+            perceptual_results = gather_results[1]
+
+            # 处理 hybrid search 异常
+            if isinstance(hybrid_result, Exception):
+                raise hybrid_result
+            retrieve_info, question, raw_results = hybrid_result
+
+            # 处理感知记忆结果
+            if isinstance(perceptual_results, Exception):
+                logger.warning(f"[Input_Summary] perceptual search failed: {perceptual_results}")
+                perceptual_results = []
+
+            # 拼接感知记忆内容到 retrieve_info
+            if perceptual_results and isinstance(perceptual_results, dict):
+                perceptual_content = perceptual_results.get("content", "")
+                if perceptual_content:
+                    retrieve_info = f"{retrieve_info}\n\n<history-files>\n{perceptual_content}"
+                    count = len(perceptual_results.get("memories", []))
+                    logger.info(f"[Input_Summary] appended {count} perceptual memories (reranked)")
+
             # 调试：打印 community 检索结果数量
             if raw_results and isinstance(raw_results, dict):
                 reranked = raw_results.get('reranked_results', {})
@@ -371,10 +409,7 @@ async def Input_Summary(state: ReadState) -> ReadState:
             "error": str(e)
         }
     end = time.time()
-    try:
-        duration = end - start
-    except Exception:
-        duration = 0.0
+    duration = end - start
     log_time('检索', duration)
     return {"summary": summary}
 
@@ -412,8 +447,20 @@ async def Retrieve_Summary(state: ReadState) -> ReadState:
     retrieve_info_str = list(set(retrieve_info_str))
     retrieve_info_str = '\n'.join(retrieve_info_str)
 
-    aimessages = await  summary_llm(state, history, retrieve_info_str,
-                                    'direct_summary_prompt.jinja2', 'retrieve_summary', RetrieveSummaryResponse, "1")
+    # Merge perceptual memory content
+    perceptual_data = state.get("perceptual_data", {})
+    perceptual_content = perceptual_data.get("content", "") if isinstance(perceptual_data, dict) else ""
+    if perceptual_content:
+        retrieve_info_str = f"{retrieve_info_str}\n\n<history-file-input>\n{perceptual_content}</history-file-input>"
+
+    aimessages = await summary_llm(
+        state,
+        history,
+        retrieve_info_str,
+        'direct_summary_prompt.jinja2',
+        'retrieve_summary', RetrieveSummaryResponse,
+        "1"
+    )
     if '信息不足，无法回答' not in str(aimessages) or str(aimessages) != "":
         await summary_redis_save(state, aimessages)
     if aimessages == '':
@@ -458,6 +505,12 @@ async def Summary(state: ReadState) -> ReadState:
                     retrieve_info_str += i + '\n'
     history = await summary_history(state)
 
+    # Merge perceptual memory content
+    perceptual_data = state.get("perceptual_data", {})
+    perceptual_content = perceptual_data.get("content", "") if isinstance(perceptual_data, dict) else ""
+    if perceptual_content:
+        retrieve_info_str = f"{retrieve_info_str}\n\n<history-file-input>\n{perceptual_content}</history-file-input>"
+
     data = {
         "query": query,
         "history": history,
@@ -508,6 +561,13 @@ async def Summary_fails(state: ReadState) -> ReadState:
             if key == 'answer_small':
                 for i in value:
                     retrieve_info_str += i + '\n'
+
+    # Merge perceptual memory content
+    perceptual_data = state.get("perceptual_data", {})
+    perceptual_content = perceptual_data.get("content", "") if isinstance(perceptual_data, dict) else ""
+    if perceptual_content:
+        retrieve_info_str = f"{retrieve_info_str}\n\n<history-file-input>\n{perceptual_content}</history-file-input>"
+
     data = {
         "query": query,
         "history": history,

api/app/core/memory/agent/langgraph_graph/read_graph.py

@@ -15,7 +15,10 @@ from app.core.memory.agent.langgraph_graph.nodes.problem_nodes import (
     Problem_Extension,
 )
 from app.core.memory.agent.langgraph_graph.nodes.retrieve_nodes import (
-    retrieve,
+    retrieve_nodes,
+)
+from app.core.memory.agent.langgraph_graph.nodes.perceptual_retrieve_node import (
+    perceptual_retrieve_node,
 )
 from app.core.memory.agent.langgraph_graph.nodes.summary_nodes import (
     Input_Summary,
@@ -48,13 +51,14 @@ async def make_read_graph():
     """
     try:
         # Build workflow graph
-        workflow = StateGraph(ReadState)
+        workflow = StateGraph(ReadState)    
         workflow.add_node("content_input", content_input_node)
         workflow.add_node("Split_The_Problem", Split_The_Problem)
         workflow.add_node("Problem_Extension", Problem_Extension)
         workflow.add_node("Input_Summary", Input_Summary)
-        # workflow.add_node("Retrieve", retrieve_nodes)
-        workflow.add_node("Retrieve", retrieve)
+        workflow.add_node("Retrieve", retrieve_nodes)
+        # workflow.add_node("Retrieve", retrieve)
+        workflow.add_node("Perceptual_Retrieve", perceptual_retrieve_node)
         workflow.add_node("Verify", Verify)
         workflow.add_node("Retrieve_Summary", Retrieve_Summary)
         workflow.add_node("Summary", Summary)
@@ -65,14 +69,15 @@ async def make_read_graph():
         workflow.add_conditional_edges("content_input", Split_continue)
         workflow.add_edge("Input_Summary", END)
         workflow.add_edge("Split_The_Problem", "Problem_Extension")
-        workflow.add_edge("Problem_Extension", "Retrieve")
+        # After Problem_Extension, retrieve perceptual memory first, then main Retrieve
+        workflow.add_edge("Problem_Extension", "Perceptual_Retrieve")
+        workflow.add_edge("Perceptual_Retrieve", "Retrieve")
         workflow.add_conditional_edges("Retrieve", Retrieve_continue)
         workflow.add_edge("Retrieve_Summary", END)
         workflow.add_conditional_edges("Verify", Verify_continue)
         workflow.add_edge("Summary_fails", END)
         workflow.add_edge("Summary", END)
 
-        '''-----'''
         # workflow.add_edge("Retrieve", END)
 
         # Compile workflow
@@ -80,7 +85,5 @@ async def make_read_graph():
         yield graph
 
     except Exception as e:
-        print(f"创建工作流失败: {e}")
+        logger.error(f"创建工作流失败: {e}")
         raise
-    finally:
-        print("工作流创建完成")

api/app/core/memory/agent/langgraph_graph/routing/write_router.py

@@ -12,7 +12,6 @@ from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
 from app.db import get_db_context
 from app.repositories.memory_short_repository import LongTermMemoryRepository
 from app.schemas.memory_agent_schema import AgentMemory_Long_Term
-from app.services.memory_konwledges_server import write_rag
 from app.services.task_service import get_task_memory_write_result
 from app.tasks import write_message_task
 from app.utils.config_utils import resolve_config_id
@@ -21,25 +20,6 @@ logger = get_agent_logger(__name__)
 template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
 
 
-async def write_rag_agent(end_user_id, user_message, ai_message, user_rag_memory_id):
-    """
-    Write messages to RAG storage system
-
-    Combines user and AI messages into a single string format and stores them
-    in the RAG (Retrieval-Augmented Generation) knowledge base for future retrieval.
-
-    Args:
-        end_user_id: User identifier for the conversation
-        user_message: User's input message content
-        ai_message: AI's response message content
-        user_rag_memory_id: RAG memory identifier for storage location
-    """
-    # RAG mode: combine messages into string format (maintain original logic)
-    combined_message = f"user: {user_message}\nassistant: {ai_message}"
-    await write_rag(end_user_id, combined_message, user_rag_memory_id)
-    logger.info(f'RAG_Agent:{end_user_id};{user_rag_memory_id}')
-
-
 async def write(
         storage_type,
         end_user_id,
@@ -118,7 +98,7 @@ async def write(
         logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}')
 
 
-async def term_memory_save(long_term_messages, actual_config_id, end_user_id, type, scope):
+async def term_memory_save(end_user_id, strategy_type, scope):
     """
     Save long-term memory data to database
 
@@ -127,10 +107,8 @@ async def term_memory_save(long_term_messages, actual_config_id, end_user_id, ty
     to long-term memory storage.
 
     Args:
-        long_term_messages: Long-term message data to be saved
-        actual_config_id: Configuration identifier for memory settings
         end_user_id: User identifier for memory association
-        type: Memory storage strategy type (STRATEGY_CHUNK or STRATEGY_AGGREGATE)
+        strategy_type: Memory storage strategy type (STRATEGY_CHUNK or STRATEGY_AGGREGATE)
         scope: Scope/window size for memory processing
     """
     with get_db_context() as db_session:
@@ -138,7 +116,10 @@ async def term_memory_save(long_term_messages, actual_config_id, end_user_id, ty
 
         from app.core.memory.agent.utils.redis_tool import write_store
         result = write_store.get_session_by_userid(end_user_id)
-        if type == AgentMemory_Long_Term.STRATEGY_CHUNK or AgentMemory_Long_Term.STRATEGY_AGGREGATE:
+        if not result:
+            logger.warning(f"No write data found for user {end_user_id}")
+            return
+        if strategy_type in [AgentMemory_Long_Term.STRATEGY_CHUNK, AgentMemory_Long_Term.STRATEGY_AGGREGATE]:
             data = await format_parsing(result, "dict")
             chunk_data = data[:scope]
             if len(chunk_data) == scope:
@@ -151,9 +132,6 @@ async def term_memory_save(long_term_messages, actual_config_id, end_user_id, ty
             logger.info(f'写入短长期：')
 
 
-"""Window-based dialogue processing"""
-
-
 async def window_dialogue(end_user_id, langchain_messages, memory_config, scope):
     """
     Process dialogue based on window size and write to Neo4j
@@ -167,40 +145,33 @@ async def window_dialogue(end_user_id, langchain_messages, memory_config, scope)
         langchain_messages: Original message data list
         scope: Window size determining when to trigger long-term storage
     """
-    scope = scope
-    is_end_user_id = count_store.get_sessions_count(end_user_id)
-    if is_end_user_id is not False:
-        is_end_user_id = count_store.get_sessions_count(end_user_id)[0]
-        redis_messages = count_store.get_sessions_count(end_user_id)[1]
-    if is_end_user_id and int(is_end_user_id) != int(scope):
-        is_end_user_id += 1
-        langchain_messages += redis_messages
-        count_store.update_sessions_count(end_user_id, is_end_user_id, langchain_messages)
-    elif int(is_end_user_id) == int(scope):
+    is_end_user_has_history = count_store.get_sessions_count(end_user_id)
+    if is_end_user_has_history:
+        end_user_visit_count, redis_messages = is_end_user_has_history
+    else:
+        count_store.save_sessions_count(end_user_id, 1, langchain_messages)
+        return
+    end_user_visit_count += 1
+    if end_user_visit_count < scope:
+        redis_messages.extend(langchain_messages)
+        count_store.update_sessions_count(end_user_id, end_user_visit_count, redis_messages)
+    else:
         logger.info('写入长期记忆NEO4J')
-        formatted_messages = redis_messages
+        redis_messages.extend(langchain_messages)
         # Get config_id (if memory_config is an object, extract config_id; otherwise use directly)
         if hasattr(memory_config, 'config_id'):
             config_id = memory_config.config_id
         else:
             config_id = memory_config
 
-        await write(
-            AgentMemory_Long_Term.STORAGE_NEO4J,
-            end_user_id,
-            "",
-            "",
-            None,
-            end_user_id,
-            config_id,
-            formatted_messages
+        write_message_task.delay(
+            end_user_id,  # end_user_id: User ID
+            redis_messages,  # message: JSON string format message list
+            config_id,  # config_id: Configuration ID string
+            AgentMemory_Long_Term.STORAGE_NEO4J,  # storage_type: "neo4j"
+            ""  # user_rag_memory_id: RAG memory ID (not used in Neo4j mode)
         )
-        count_store.update_sessions_count(end_user_id, 1, langchain_messages)
-    else:
-        count_store.save_sessions_count(end_user_id, 1, langchain_messages)
-
-
-"""Time-based memory processing"""
+        count_store.update_sessions_count(end_user_id, 0, [])
 
 
 async def memory_long_term_storage(end_user_id, memory_config, time):
@@ -291,9 +262,7 @@ async def aggregate_judgment(end_user_id: str, ori_messages: list, memory_config
         return result_dict
 
     except Exception as e:
-        print(f"[aggregate_judgment] 发生错误: {e}")
-        import traceback
-        traceback.print_exc()
+        logger.error(f"[aggregate_judgment] 发生错误: {e}", exc_info=True)
 
         return {
             "is_same_event": False,

api/app/core/memory/agent/langgraph_graph/write_graph.py

@@ -1,49 +1,25 @@
-import asyncio
-import json
-import sys
 import warnings
-from contextlib import asynccontextmanager
-from langgraph.constants import END, START
-from langgraph.graph import StateGraph
 
-from app.db import get_db, get_db_context
 from app.core.logging_config import get_agent_logger
-from app.core.memory.agent.utils.llm_tools import WriteState
-from app.core.memory.agent.langgraph_graph.nodes.write_nodes import write_node
+from app.core.memory.agent.langgraph_graph.routing.write_router import memory_long_term_storage, window_dialogue, \
+    aggregate_judgment
+from app.core.memory.agent.utils.redis_tool import write_store
+from app.db import get_db_context
 from app.schemas.memory_agent_schema import AgentMemory_Long_Term
 from app.services.memory_config_service import MemoryConfigService
+from app.services.memory_konwledges_server import write_rag
 
 warnings.filterwarnings("ignore", category=RuntimeWarning)
 logger = get_agent_logger(__name__)
 
-if sys.platform.startswith("win"):
-    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
 
-
-@asynccontextmanager
-async def make_write_graph():
-    """
-    Create a write graph workflow for memory operations.
-
-    Args:
-        user_id: User identifier
-        tools: MCP tools loaded from session
-        apply_id: Application identifier
-        end_user_id: Group identifier
-        memory_config: MemoryConfig object containing all configuration
-    """
-    workflow = StateGraph(WriteState)
-    workflow.add_node("save_neo4j", write_node)
-    workflow.add_edge(START, "save_neo4j")
-    workflow.add_edge("save_neo4j", END)
-
-    graph = workflow.compile()
-
-    yield graph
-
-
-async def long_term_storage(long_term_type: str = "chunk", langchain_messages: list = [], memory_config: str = '',
-                            end_user_id: str = '', scope: int = 6):
+async def long_term_storage(
+        long_term_type: str,
+        langchain_messages: list,
+        memory_config_id: str,
+        end_user_id: str,
+        scope: int = 6
+):
     """
     Handle long-term memory storage with different strategies
 
@@ -53,33 +29,39 @@ async def long_term_storage(long_term_type: str = "chunk", langchain_messages: l
     Args:
         long_term_type: Storage strategy type ('chunk', 'time', 'aggregate')
         langchain_messages: List of messages to store
-        memory_config: Memory configuration identifier
+        memory_config_id: Memory configuration identifier
         end_user_id: User group identifier
         scope: Scope parameter for chunk-based storage (default: 6)
     """
-    from app.core.memory.agent.langgraph_graph.routing.write_router import memory_long_term_storage, window_dialogue, \
-        aggregate_judgment
-    from app.core.memory.agent.utils.redis_tool import write_store
+    if langchain_messages is None:
+        langchain_messages = []
+
     write_store.save_session_write(end_user_id, langchain_messages)
     # 获取数据库会话
     with get_db_context() as db_session:
         config_service = MemoryConfigService(db_session)
         memory_config = config_service.load_memory_config(
-            config_id=memory_config,  # 改为整数
+            config_id=memory_config_id,  # 改为整数
             service_name="MemoryAgentService"
         )
         if long_term_type == AgentMemory_Long_Term.STRATEGY_CHUNK:
-            '''Strategy 1: Dialogue window with 6 rounds of conversation'''
+            # Dialogue window with 6 rounds of conversation
             await window_dialogue(end_user_id, langchain_messages, memory_config, scope)
         if long_term_type == AgentMemory_Long_Term.STRATEGY_TIME:
-            """Time-based strategy"""
+            # Time-based strategy
             await memory_long_term_storage(end_user_id, memory_config, AgentMemory_Long_Term.TIME_SCOPE)
         if long_term_type == AgentMemory_Long_Term.STRATEGY_AGGREGATE:
-            """Strategy 3: Aggregate judgment"""
+            # Aggregate judgment
             await aggregate_judgment(end_user_id, langchain_messages, memory_config)
 
 
-async def write_long_term(storage_type, end_user_id, message_chat, aimessages, user_rag_memory_id, actual_config_id):
+async def write_long_term(
+        storage_type: str,
+        end_user_id: str,
+        messages: list[dict],
+        user_rag_memory_id: str,
+        actual_config_id: str
+):
     """
     Write long-term memory with different storage types
 
@@ -89,44 +71,24 @@ async def write_long_term(storage_type, end_user_id, message_chat, aimessages, u
     Args:
         storage_type: Type of storage (RAG or traditional)
         end_user_id: User group identifier
-        message_chat: User message content
-        aimessages: AI response messages
+        messages: message list
         user_rag_memory_id: RAG memory identifier
         actual_config_id: Actual configuration ID
     """
-    from app.core.memory.agent.langgraph_graph.routing.write_router import write_rag_agent
     from app.core.memory.agent.langgraph_graph.routing.write_router import term_memory_save
-    from app.core.memory.agent.langgraph_graph.tools.write_tool import agent_chat_messages
     if storage_type == AgentMemory_Long_Term.STORAGE_RAG:
-        await write_rag_agent(end_user_id, message_chat, aimessages, user_rag_memory_id)
+        message_content = []
+        for message in messages:
+            message_content.append(f'{message.get("role")}:{message.get("content")}')
+        messages_string = "\n".join(message_content)
+        await write_rag(end_user_id, messages_string, user_rag_memory_id)
     else:
         # AI reply writing (user messages and AI replies paired, written as complete dialogue at once)
         CHUNK = AgentMemory_Long_Term.STRATEGY_CHUNK
         SCOPE = AgentMemory_Long_Term.DEFAULT_SCOPE
-        long_term_messages = await agent_chat_messages(message_chat, aimessages)
-        await long_term_storage(long_term_type=CHUNK, langchain_messages=long_term_messages,
-                                memory_config=actual_config_id, end_user_id=end_user_id, scope=SCOPE)
-        await term_memory_save(long_term_messages, actual_config_id, end_user_id, CHUNK, scope=SCOPE)
-
-# async def main():
-#     """主函数 - 运行工作流"""
-#     langchain_messages = [
-#     {
-#       "role": "user",
-#       "content": "今天周五去爬山"
-#     },
-#     {
-#       "role": "assistant",
-#       "content": "好耶"
-#     }
-#
-#   ]
-#     end_user_id = '837fee1b-04a2-48ee-94d7-211488908940'  # 组ID
-#     memory_config="08ed205c-0f05-49c3-8e0c-a580d28f5fd4"
-#     await long_term_storage(long_term_type="chunk",langchain_messages=langchain_messages,memory_config=memory_config,end_user_id=end_user_id,scope=2)
-#
-#
-#
-# if __name__ == "__main__":
-#     import asyncio
-#     asyncio.run(main())
+        await long_term_storage(long_term_type=CHUNK,
+                                langchain_messages=messages,
+                                memory_config_id=actual_config_id,
+                                end_user_id=end_user_id,
+                                scope=SCOPE)
+        await term_memory_save(end_user_id, CHUNK, scope=SCOPE)

api/app/core/memory/agent/services/search_service.py

@@ -10,7 +10,6 @@ from app.core.logging_config import get_agent_logger
 from app.core.memory.src.search import run_hybrid_search
 from app.core.memory.utils.data.text_utils import escape_lucene_query
 
-
 logger = get_agent_logger(__name__)
 
 # 需要从展开结果中过滤的字段（含 Neo4j DateTime，不可 JSON 序列化）
@@ -31,10 +30,10 @@ def _clean_expand_fields(obj):
 
 
 async def expand_communities_to_statements(
-    community_results: List[dict],
-    end_user_id: str,
-    existing_content: str = "",
-    limit: int = 10,
+        community_results: List[dict],
+        end_user_id: str,
+        existing_content: str = "",
+        limit: int = 10,
 ) -> Tuple[List[dict], List[str]]:
     """
     社区展开 helper：给定命中的 community 列表，拉取关联 Statement。
@@ -76,17 +75,18 @@ async def expand_communities_to_statements(
         if s.get("statement") and s["statement"] not in existing_lines
     ]
     cleaned = _clean_expand_fields(expanded_stmts)
-    logger.info(f"[expand_communities] 展开 {len(expanded_stmts)} 条 statements，新增 {len(new_texts)} 条，community_ids={community_ids}")
+    logger.info(
+        f"[expand_communities] 展开 {len(expanded_stmts)} 条 statements，新增 {len(new_texts)} 条，community_ids={community_ids}")
     return cleaned, new_texts
 
 
 class SearchService:
     """Service for executing hybrid search and processing results."""
-    
+
     def __init__(self):
         """Initialize the search service."""
         logger.info("SearchService initialized")
-    
+
     def extract_content_from_result(self, result: dict, node_type: str = "") -> str:
         """
         Extract only meaningful content from search results, dropping all metadata.
@@ -107,19 +107,19 @@ class SearchService:
         """
         if not isinstance(result, dict):
             return str(result)
-        
+
         content_parts = []
-        
+
         # Statements: extract statement field
         if 'statement' in result and result['statement']:
             content_parts.append(result['statement'])
-        
+
         # Community 节点：有 member_count 或 core_entities 字段，或 node_type 明确指定
         # 用 "[主题：{name}]" 前缀区分，让 LLM 知道这是主题级摘要
         is_community = (
-            node_type == "community"
-            or 'member_count' in result
-            or 'core_entities' in result
+                node_type == "community"
+                or 'member_count' in result
+                or 'core_entities' in result
         )
         if is_community:
             name = result.get('name', '')
@@ -130,16 +130,16 @@ class SearchService:
         elif 'content' in result and result['content']:
             # Summaries / Chunks
             content_parts.append(result['content'])
-        
+
         # Entities: extract name and fact_summary (commented out in original)
         # if 'name' in result and result['name']:
         #     content_parts.append(result['name'])
         #     if result.get('fact_summary'):
         #         content_parts.append(result['fact_summary'])
-        
+
         # Return concatenated content or empty string
         return '\n'.join(content_parts) if content_parts else ""
-    
+
     def clean_query(self, query: str) -> str:
         """
         Clean and escape query text for Lucene.
@@ -155,33 +155,33 @@ class SearchService:
             Cleaned and escaped query string
         """
         q = str(query).strip()
-        
+
         # Remove wrapping quotes
         if (q.startswith("'") and q.endswith("'")) or (
-            q.startswith('"') and q.endswith('"')
+                q.startswith('"') and q.endswith('"')
         ):
             q = q[1:-1]
-        
+
         # Remove newlines and carriage returns
         q = q.replace('\r', ' ').replace('\n', ' ').strip()
-        
+
         # Apply Lucene escaping
         q = escape_lucene_query(q)
-        
+
         return q
-    
+
     async def execute_hybrid_search(
-        self,
-        end_user_id: str,
-        question: str,
-        limit: int = 5,
-        search_type: str = "hybrid",
-        include: Optional[List[str]] = None,
-        rerank_alpha: float = 0.4,
-        output_path: str = "search_results.json",
-        return_raw_results: bool = False,
-        memory_config = None,
-        expand_communities: bool = True,
+            self,
+            end_user_id: str,
+            question: str,
+            limit: int = 5,
+            search_type: str = "hybrid",
+            include: Optional[List[str]] = None,
+            rerank_alpha: float = 0.4,
+            output_path: str = "search_results.json",
+            return_raw_results: bool = False,
+            memory_config=None,
+            expand_communities: bool = True,
     ) -> Tuple[str, str, Optional[dict]]:
         """
         Execute hybrid search and return clean content.
@@ -205,10 +205,10 @@ class SearchService:
         """
         if include is None:
             include = ["statements", "chunks", "entities", "summaries", "communities"]
-        
+
         # Clean query
         cleaned_query = self.clean_query(question)
-        
+
         try:
             # Execute search
             answer = await run_hybrid_search(
@@ -221,18 +221,18 @@ class SearchService:
                 memory_config=memory_config,
                 rerank_alpha=rerank_alpha
             )
-            
+
             # Extract results based on search type and include parameter
             # Prioritize summaries as they contain synthesized contextual information
             answer_list = []
-            
+
             # For hybrid search, use reranked_results
             if search_type == "hybrid":
                 reranked_results = answer.get('reranked_results', {})
-                
+
                 # Priority order: summaries first (most contextual), then communities, statements, chunks, entities
                 priority_order = ['summaries', 'communities', 'statements', 'chunks', 'entities']
-                
+
                 for category in priority_order:
                     if category in include and category in reranked_results:
                         category_results = reranked_results[category]
@@ -242,7 +242,7 @@ class SearchService:
                 # For keyword or embedding search, results are directly in answer dict
                 # Apply same priority order
                 priority_order = ['summaries', 'communities', 'statements', 'chunks', 'entities']
-                
+
                 for category in priority_order:
                     if category in include and category in answer:
                         category_results = answer[category]
@@ -261,7 +261,7 @@ class SearchService:
                     end_user_id=end_user_id,
                 )
                 answer_list.extend(cleaned_stmts)
-            
+
             # Extract clean content from all results，按类型传入 node_type 区分 community
             content_list = []
             for ans in answer_list:
@@ -269,19 +269,18 @@ class SearchService:
                 ntype = "community" if ('member_count' in ans or 'core_entities' in ans) else ""
                 content_list.append(self.extract_content_from_result(ans, node_type=ntype))
 
-            
             # Filter out empty strings and join with newlines
             clean_content = '\n'.join([c for c in content_list if c])
-            
+
             # Log first 200 chars
             logger.info(f"检索接口搜索结果==>>:{clean_content[:200]}...")
-            
+
             # Return raw results if requested
             if return_raw_results:
                 return clean_content, cleaned_query, answer
             else:
                 return clean_content, cleaned_query, None
-            
+
         except Exception as e:
             logger.error(
                 f"Search failed for query '{question}' in group '{end_user_id}': {e}",

api/app/core/memory/agent/utils/llm_tools.py

@@ -1,4 +1,3 @@
-import os
 from collections import defaultdict
 from pathlib import Path
 from typing import Annotated, TypedDict
@@ -52,6 +51,7 @@ class ReadState(TypedDict):
     embedding_id: str
     memory_config: object  # 新增字段用于传递内存配置对象
     retrieve: dict
+    perceptual_data: dict
     RetrieveSummary: dict
     InputSummary: dict
     verify: dict

api/app/core/memory/agent/utils/redis_tool.py

@@ -3,8 +3,9 @@ import uuid
 from app.core.config import settings
 from typing import List, Dict, Any, Optional, Union
 
+from app.core.logging_config import get_logger
 from app.core.memory.agent.utils.redis_base import (
-    serialize_messages, 
+    serialize_messages,
     deserialize_messages,
     fix_encoding,
     format_session_data,
@@ -14,12 +15,12 @@ from app.core.memory.agent.utils.redis_base import (
     get_current_timestamp
 )
 
-
+logger = get_logger(__name__)
 
 
 class RedisWriteStore:
     """Redis Write 类型存储类，用于管理 save_session_write 相关的数据"""
-    
+
     def __init__(self, host='localhost', port=6379, db=0, password=None, session_id=''):
         """
         初始化 Redis 连接
@@ -66,10 +67,10 @@ class RedisWriteStore:
             })
             result = pipe.execute()
 
-            print(f"[save_session_write] 保存结果: {result[0]}, session_id: {session_id}")
+            logger.debug(f"[save_session_write] 保存结果: {result[0]}, session_id: {session_id}")
             return session_id
         except Exception as e:
-            print(f"[save_session_write] 保存会话失败: {e}")
+            logger.error(f"[save_session_write] 保存会话失败: {e}")
             raise e
 
     def get_session_by_userid(self, userid: str) -> Union[List[Dict[str, str]], bool]:
@@ -99,7 +100,7 @@ class RedisWriteStore:
             for key, data in zip(keys, all_data):
                 if not data:
                     continue
-                
+
                 # 从 write 类型读取，匹配 sessionid 字段
                 if data.get('sessionid') == userid:
                     # 从 key 中提取 session_id: session:write:{session_id}
@@ -108,16 +109,16 @@ class RedisWriteStore:
                         "sessionid": session_id,
                         "messages": fix_encoding(data.get('messages', ''))
                     })
-            
+
             if not results:
                 return False
-            
-            print(f"[get_session_by_userid] userid={userid}, 找到 {len(results)} 条数据")
+
+            logger.debug(f"[get_session_by_userid] userid={userid}, 找到 {len(results)} 条数据")
             return results
         except Exception as e:
-            print(f"[get_session_by_userid] 查询失败: {e}")
+            logger.error(f"[get_session_by_userid] 查询失败: {e}")
             return False
-    
+
     def get_all_sessions_by_end_user_id(self, end_user_id: str) -> Union[List[Dict[str, Any]], bool]:
         """
         通过 end_user_id 获取所有 write 类型的会话数据
@@ -144,7 +145,7 @@ class RedisWriteStore:
             # 只查询 write 类型的 key
             keys = self.r.keys('session:write:*')
             if not keys:
-                print(f"[get_all_sessions_by_end_user_id] 没有找到任何 write 类型的会话")
+                logger.debug(f"[get_all_sessions_by_end_user_id] 没有找到任何 write 类型的会话")
                 return False
 
             # 批量获取数据
@@ -158,12 +159,12 @@ class RedisWriteStore:
             for key, data in zip(keys, all_data):
                 if not data:
                     continue
-                
+
                 # 从 write 类型读取，匹配 sessionid 字段
                 if data.get('sessionid') == end_user_id:
                     # 从 key 中提取 session_id: session:write:{session_id}
                     session_id = key.split(':')[-1]
-                    
+
                     # 构建完整的会话信息
                     session_info = {
                         "session_id": session_id,
@@ -173,23 +174,21 @@ class RedisWriteStore:
                         "starttime": data.get('starttime', '')
                     }
                     results.append(session_info)
-            
+
             if not results:
-                print(f"[get_all_sessions_by_end_user_id] end_user_id={end_user_id}, 没有找到数据")
+                logger.debug(f"[get_all_sessions_by_end_user_id] end_user_id={end_user_id}, 没有找到数据")
                 return False
-            
+
             # 按时间排序（最新的在前）
             results.sort(key=lambda x: x.get('starttime', ''), reverse=True)
-            
-            print(f"[get_all_sessions_by_end_user_id] end_user_id={end_user_id}, 找到 {len(results)} 条数据")
+
+            logger.debug(f"[get_all_sessions_by_end_user_id] end_user_id={end_user_id}, 找到 {len(results)} 条数据")
             return results
         except Exception as e:
-            print(f"[get_all_sessions_by_end_user_id] 查询失败: {e}")
-            import traceback
-            traceback.print_exc()
+            logger.error(f"[get_all_sessions_by_end_user_id] 查询失败: {e}", exc_info=True)
             return False
 
-    def find_user_recent_sessions(self, userid: str, 
+    def find_user_recent_sessions(self, userid: str,
                                   minutes: int = 5) -> List[Dict[str, str]]:
         """
         根据 userid 从 save_session_write 写入的数据中查询最近 N 分钟内的会话数据
@@ -203,11 +202,11 @@ class RedisWriteStore:
         """
         import time
         start_time = time.time()
-        
+
         # 只查询 write 类型的 key
         keys = self.r.keys('session:write:*')
         if not keys:
-            print(f"[find_user_recent_sessions] 查询耗时: {time.time() - start_time:.3f}秒, 结果数: 0")
+            logger.debug(f"[find_user_recent_sessions] 查询耗时: {time.time() - start_time:.3f}秒, 结果数: 0")
             return []
 
         # 批量获取数据
@@ -221,7 +220,7 @@ class RedisWriteStore:
         for data in all_data:
             if not data:
                 continue
-            
+
             # 从 write 类型读取，匹配 sessionid 字段
             if data.get('sessionid') == userid and data.get('starttime'):
                 # write 类型没有 aimessages，所以 Answer 为空
@@ -230,15 +229,14 @@ class RedisWriteStore:
                     "Answer": "",
                     "starttime": data.get('starttime', '')
                 })
-        
+
         # 根据时间范围过滤
         filtered_items = filter_by_time_range(matched_items, minutes)
         # 排序并移除时间字段
-        result_items = sort_and_limit_results(filtered_items, limit=None)
-        print(result_items)
+        result_items = sort_and_limit_results(filtered_items)
 
         elapsed_time = time.time() - start_time
-        print(f"[find_user_recent_sessions] userid={userid}, minutes={minutes}, "
+        logger.debug(f"[find_user_recent_sessions] userid={userid}, minutes={minutes}, "
               f"查询耗时: {elapsed_time:.3f}秒, 结果数: {len(result_items)}")
 
         return result_items
@@ -258,7 +256,7 @@ class RedisWriteStore:
 
 class RedisCountStore:
     """Redis Count 类型存储类，用于管理访问次数统计相关的数据"""
-    
+
     def __init__(self, host='localhost', port=6379, db=0, password=None, session_id=''):
         """
         初始化 Redis 连接
@@ -278,7 +276,7 @@ class RedisCountStore:
             decode_responses=True,
             encoding='utf-8'
         )
-        self.uudi = session_id
+        self.uuid = session_id
 
     def save_sessions_count(self, end_user_id: str, count: int, messages: Any) -> str:
         """
@@ -295,26 +293,26 @@ class RedisCountStore:
         session_id = str(uuid.uuid4())
         key = generate_session_key(session_id, key_type="count")
         index_key = f'session:count:index:{end_user_id}'  # 索引键
-        
+
         pipe = self.r.pipeline()
         pipe.hset(key, mapping={
-            "id": self.uudi,
+            "id": self.uuid,
             "end_user_id": end_user_id,
             "count": int(count),
             "messages": serialize_messages(messages),
             "starttime": get_current_timestamp()
         })
         pipe.expire(key, 30 * 24 * 60 * 60)  # 30天过期
-        
+
         # 创建索引：end_user_id -> session_id 映射
         pipe.set(index_key, session_id, ex=30 * 24 * 60 * 60)
-        
+
         result = pipe.execute()
-        
-        print(f"[save_sessions_count] 保存结果: {result}, session_id: {session_id}")
+
+        logger.debug(f"[save_sessions_count] 保存结果: {result}, session_id: {session_id}")
         return session_id
 
-    def get_sessions_count(self, end_user_id: str) -> Union[List[Any], bool]:
+    def get_sessions_count(self, end_user_id: str) -> tuple[int, list[dict]] | bool:
         """
         通过 end_user_id 查询访问次数统计
         
@@ -327,7 +325,7 @@ class RedisCountStore:
         try:
             # 使用索引键快速查找
             index_key = f'session:count:index:{end_user_id}'
-            
+
             # 检查索引键类型，避免 WRONGTYPE 错误
             try:
                 key_type = self.r.type(index_key)
@@ -335,35 +333,40 @@ class RedisCountStore:
                     self.r.delete(index_key)
                     return False
             except Exception as type_error:
-                print(f"[get_sessions_count] 检查键类型失败: {type_error}")
-            
+                logger.error(f"[get_sessions_count] 检查键类型失败: {type_error}")
+
             session_id = self.r.get(index_key)
-            
+
             if not session_id:
                 return False
-            
+
             # 直接获取数据
             key = generate_session_key(session_id, key_type="count")
             data = self.r.hgetall(key)
-            
+
             if not data:
                 # 索引存在但数据不存在，清理索引
                 self.r.delete(index_key)
                 return False
-            
+
             count = data.get('count')
             messages_str = data.get('messages')
-            
+
             if count is not None:
-                messages = deserialize_messages(messages_str)
-                return [int(count), messages]
-            
+                messages: list[dict] = deserialize_messages(messages_str)
+                return int(count), messages
+
             return False
         except Exception as e:
-            print(f"[get_sessions_count] 查询失败: {e}")
+            logger.error(f"[get_sessions_count] 查询失败: {e}")
             return False
-    def update_sessions_count(self, end_user_id: str, new_count: int, 
-                             messages: Any) -> bool:
+
+    def update_sessions_count(
+            self,
+            end_user_id: str,
+            new_count: int,
+            messages: Any
+    ) -> bool:
         """
         通过 end_user_id 修改访问次数统计（优化版：使用索引）
         
@@ -378,39 +381,39 @@ class RedisCountStore:
         try:
             # 使用索引键快速查找
             index_key = f'session:count:index:{end_user_id}'
-            
+
             # 检查索引键类型，避免 WRONGTYPE 错误
             try:
                 key_type = self.r.type(index_key)
                 if key_type != 'string' and key_type != 'none':
                     # 索引键类型错误，删除并返回 False
-                    print(f"[update_sessions_count] 索引键类型错误: {key_type}，删除索引")
+                    logger.warning(f"[update_sessions_count] 索引键类型错误: {key_type}，删除索引")
                     self.r.delete(index_key)
-                    print(f"[update_sessions_count] 未找到记录: end_user_id={end_user_id}")
+                    logger.debug(f"[update_sessions_count] 未找到记录: end_user_id={end_user_id}")
                     return False
             except Exception as type_error:
-                print(f"[update_sessions_count] 检查键类型失败: {type_error}")
-            
+                logger.error(f"[update_sessions_count] 检查键类型失败: {type_error}")
+
             session_id = self.r.get(index_key)
-            
+
             if not session_id:
-                print(f"[update_sessions_count] 未找到记录: end_user_id={end_user_id}")
+                logger.debug(f"[update_sessions_count] 未找到记录: end_user_id={end_user_id}")
                 return False
-            
+
             # 直接更新数据
             key = generate_session_key(session_id, key_type="count")
             messages_str = serialize_messages(messages)
-            
+
             pipe = self.r.pipeline()
-            pipe.hset(key, 'count', int(new_count))
+            pipe.hset(key, 'count', str(new_count))
             pipe.hset(key, 'messages', messages_str)
             result = pipe.execute()
-            
-            print(f"[update_sessions_count] 更新成功: end_user_id={end_user_id}, new_count={new_count}, key={key}")
+
+            logger.debug(f"[update_sessions_count] 更新成功: end_user_id={end_user_id}, new_count={new_count}, key={key}")
             return True
-            
+
         except Exception as e:
-            print(f"[update_sessions_count] 更新失败: {e}")
+            logger.debug(f"[update_sessions_count] 更新失败: {e}")
             return False
 
     def delete_all_count_sessions(self) -> int:
@@ -428,7 +431,7 @@ class RedisCountStore:
 
 class RedisSessionStore:
     """Redis 会话存储类，用于管理会话数据"""
-    
+
     def __init__(self, host='localhost', port=6379, db=0, password=None, session_id=''):
         """
         初始化 Redis 连接
@@ -451,9 +454,9 @@ class RedisSessionStore:
         self.uudi = session_id
 
     # ==================== 写入操作 ====================
-    
-    def save_session(self, userid: str, messages: str, aimessages: str, 
-                    apply_id: str, end_user_id: str) -> str:
+
+    def save_session(self, userid: str, messages: str, aimessages: str,
+                     apply_id: str, end_user_id: str) -> str:
         """
         写入一条会话数据，返回 session_id
         
@@ -483,14 +486,14 @@ class RedisSessionStore:
             })
             result = pipe.execute()
 
-            print(f"[save_session] 保存结果: {result[0]}, session_id: {session_id}")
+            logger.debug(f"[save_session] 保存结果: {result[0]}, session_id: {session_id}")
             return session_id
         except Exception as e:
-            print(f"[save_session] 保存会话失败: {e}")
+            logger.error(f"[save_session] 保存会话失败: {e}")
             raise e
 
     # ==================== 读取操作 ====================
-    
+
     def get_session(self, session_id: str) -> Optional[Dict[str, Any]]:
         """
         读取一条会话数据
@@ -520,8 +523,8 @@ class RedisSessionStore:
                 sessions[sid] = self.get_session(sid)
         return sessions
 
-    def find_user_apply_group(self, sessionid: str, apply_id: str, 
-                             end_user_id: str) -> List[Dict[str, str]]:
+    def find_user_apply_group(self, sessionid: str, apply_id: str,
+                              end_user_id: str) -> List[Dict[str, str]]:
         """
         根据 sessionid、apply_id 和 end_user_id 查询会话数据，返回最新的6条
         
@@ -535,10 +538,10 @@ class RedisSessionStore:
         """
         import time
         start_time = time.time()
-        
+
         keys = self.r.keys('session:*')
         if not keys:
-            print(f"[find_user_apply_group] 查询耗时: {time.time() - start_time:.3f}秒, 结果数: 0")
+            logger.debug(f"[find_user_apply_group] 查询耗时: {time.time() - start_time:.3f}秒, 结果数: 0")
             return []
 
         # 批量获取数据
@@ -556,21 +559,21 @@ class RedisSessionStore:
                 continue
 
             if (data.get('apply_id') == apply_id and
-                data.get('end_user_id') == end_user_id):
+                    data.get('end_user_id') == end_user_id):
                 # 支持模糊匹配或完全匹配 sessionid
                 if sessionid in data.get('sessionid', '') or data.get('sessionid') == sessionid:
                     matched_items.append(format_session_data(data, include_time=True))
-        
+
         # 排序、限制数量并移除时间字段
         result_items = sort_and_limit_results(matched_items, limit=6)
 
         elapsed_time = time.time() - start_time
-        print(f"[find_user_apply_group] 查询耗时: {elapsed_time:.3f}秒, 结果数: {len(result_items)}")
+        logger.debug(f"[find_user_apply_group] 查询耗时: {elapsed_time:.3f}秒, 结果数: {len(result_items)}")
 
         return result_items
 
     # ==================== 更新操作 ====================
-    
+
     def update_session(self, session_id: str, field: str, value: Any) -> bool:
         """
         更新单个字段
@@ -591,7 +594,7 @@ class RedisSessionStore:
         return bool(results[0])
 
     # ==================== 删除操作 ====================
-    
+
     def delete_session(self, session_id: str) -> int:
         """
         删除单条会话
@@ -632,7 +635,7 @@ class RedisSessionStore:
 
         keys = self.r.keys('session:*')
         if not keys:
-            print("[delete_duplicate_sessions] 没有会话数据")
+            logger.debug("[delete_duplicate_sessions] 没有会话数据")
             return 0
 
         # 批量获取所有数据
@@ -678,7 +681,7 @@ class RedisSessionStore:
                 deleted_count += len(batch)
 
         elapsed_time = time.time() - start_time
-        print(f"[delete_duplicate_sessions] 删除重复会话数量: {deleted_count}, 耗时: {elapsed_time:.3f}秒")
+        logger.debug(f"[delete_duplicate_sessions] 删除重复会话数量: {deleted_count}, 耗时: {elapsed_time:.3f}秒")
         return deleted_count
 
 

api/app/core/memory/agent/utils/write_tools.py

@@ -14,6 +14,7 @@ from dotenv import load_dotenv
 
 from app.core.logging_config import get_agent_logger
 from app.core.memory.agent.utils.get_dialogs import get_chunked_dialogs
+from app.core.memory.storage_services.extraction_engine.deduplication.deduped_and_disamb import _USER_PLACEHOLDER_NAMES
 from app.core.memory.storage_services.extraction_engine.extraction_orchestrator import ExtractionOrchestrator
 from app.core.memory.storage_services.extraction_engine.knowledge_extraction.memory_summary import \
     memory_summary_generation
@@ -152,6 +153,24 @@ async def write(
     # Step 3: Save all data to Neo4j database
     step_start = time.time()
 
+    # Neo4j 写入前：清洗用户/AI助手实体之间的别名交叉污染
+    # 从 Neo4j 查询已有的 AI 助手别名，与本轮实体中的 AI 助手别名合并，
+    # 确保用户实体的 aliases 不包含 AI 助手的名字
+    try:
+        from app.core.memory.storage_services.extraction_engine.deduplication.deduped_and_disamb import (
+            clean_cross_role_aliases,
+            fetch_neo4j_assistant_aliases,
+        )
+        neo4j_assistant_aliases = set()
+        if all_entity_nodes:
+            _eu_id = all_entity_nodes[0].end_user_id
+            if _eu_id:
+                neo4j_assistant_aliases = await fetch_neo4j_assistant_aliases(neo4j_connector, _eu_id)
+        clean_cross_role_aliases(all_entity_nodes, external_assistant_aliases=neo4j_assistant_aliases)
+        logger.info(f"Neo4j 写入前别名清洗完成，AI助手别名排除集大小: {len(neo4j_assistant_aliases)}")
+    except Exception as e:
+        logger.warning(f"Neo4j 写入前别名清洗失败（不影响主流程）: {e}")
+
     # 添加死锁重试机制
     max_retries = 3
     retry_delay = 1  # 秒
@@ -173,15 +192,37 @@ async def write(
             if success:
                 logger.info("Successfully saved all data to Neo4j")
                 
-                # 使用 Celery 异步任务触发聚类（不阻塞主流程）
                 if all_entity_nodes:
+                    end_user_id = all_entity_nodes[0].end_user_id
+
+                    # Neo4j 写入完成后，用 PgSQL 权威 aliases 覆盖 Neo4j 用户实体
+                    try:
+                        from app.repositories.end_user_info_repository import EndUserInfoRepository
+                        if end_user_id:
+                            with get_db_context() as db_session:
+                                info = EndUserInfoRepository(db_session).get_by_end_user_id(uuid.UUID(end_user_id))
+                                pg_aliases = info.aliases if info and info.aliases else []
+                            if info is not None:
+                                # 将 Python 侧占位名集合作为参数传入，避免 Cypher 硬编码
+                                placeholder_names = list(_USER_PLACEHOLDER_NAMES)
+                                await neo4j_connector.execute_query(
+                                    """
+                                    MATCH (e:ExtractedEntity)
+                                    WHERE e.end_user_id = $end_user_id AND toLower(e.name) IN $placeholder_names
+                                    SET e.aliases = $aliases
+                                    """,
+                                    end_user_id=end_user_id, aliases=pg_aliases,
+                                    placeholder_names=placeholder_names,
+                                )
+                                logger.info(f"[AliasSync] Neo4j 用户实体 aliases 已用 PgSQL 权威源覆盖: {pg_aliases}")
+                    except Exception as sync_err:
+                        logger.warning(f"[AliasSync] PgSQL→Neo4j aliases 同步失败（不影响主流程）: {sync_err}")
+
+                    # 使用 Celery 异步任务触发聚类（不阻塞主流程）
                     try:
                         from app.tasks import run_incremental_clustering
                         
-                        end_user_id = all_entity_nodes[0].end_user_id
                         new_entity_ids = [e.id for e in all_entity_nodes]
-                        
-                        # 异步提交 Celery 任务
                         task = run_incremental_clustering.apply_async(
                             kwargs={
                                 "end_user_id": end_user_id,
@@ -189,7 +230,6 @@ async def write(
                                 "llm_model_id": str(memory_config.llm_model_id) if memory_config.llm_model_id else None,
                                 "embedding_model_id": str(memory_config.embedding_model_id) if memory_config.embedding_model_id else None,
                             },
-                            # 设置任务优先级（低优先级，不影响主业务）
                             priority=3,
                         )
                         logger.info(
@@ -197,7 +237,6 @@ async def write(
                             f"task_id={task.id}, end_user_id={end_user_id}, entity_count={len(new_entity_ids)}"
                         )
                     except Exception as e:
-                        # 聚类任务提交失败不影响主流程
                         logger.error(f"[Clustering] 提交聚类任务失败（不影响主流程）: {e}", exc_info=True)
                 
                 break

api/app/core/memory/llm_tools/llm_client.py

@@ -56,7 +56,7 @@ class LLMClient(ABC):
         self.max_retries = self.config.max_retries
         self.timeout = self.config.timeout
 
-        logger.info(
+        logger.debug(
             f"初始化 LLM 客户端: provider={self.provider}, "
             f"model={self.model_name}, max_retries={self.max_retries}"
         )

api/app/core/memory/models/__init__.py

@@ -58,6 +58,14 @@ from app.core.memory.models.triplet_models import (
     TripletExtractionResponse,
 )
 
+# User metadata models
+from app.core.memory.models.metadata_models import (
+    UserMetadata,
+    UserMetadataBehavioralHints,
+    UserMetadataProfile,
+    MetadataExtractionResponse,
+)
+
 # Ontology scenario models (LLM extracted from scenarios)
 from app.core.memory.models.ontology_scenario_models import (
     OntologyClass,
@@ -124,6 +132,10 @@ __all__ = [
     "Entity",
     "Triplet",
     "TripletExtractionResponse",
+    "UserMetadata",
+    "UserMetadataBehavioralHints",
+    "UserMetadataProfile",
+    "MetadataExtractionResponse",
     # Ontology models
     "OntologyClass",
     "OntologyExtractionResponse",

api/app/core/memory/models/graph_models.py

@@ -364,12 +364,14 @@ class ChunkNode(Node):
     Attributes:
         dialog_id: ID of the parent dialog
         content: The text content of the chunk
+        speaker: Speaker identifier ('user' or 'assistant')
         chunk_embedding: Optional embedding vector for the chunk
         sequence_number: Order of this chunk within the dialog
         metadata: Additional chunk metadata as key-value pairs
     """
     dialog_id: str = Field(..., description="ID of the parent dialog")
     content: str = Field(..., description="The text content of the chunk")
+    speaker: Optional[str] = Field(None, description="Speaker identifier: 'user' for user messages, 'assistant' for AI responses")
     chunk_embedding: Optional[List[float]] = Field(None, description="Chunk embedding vector")
     sequence_number: int = Field(..., description="Order of this chunk within the dialog")
     metadata: dict = Field(default_factory=dict, description="Additional chunk metadata")

api/app/core/memory/models/metadata_models.py

@@ -0,0 +1,57 @@
+"""Models for user metadata extraction.
+
+Independent from triplet_models.py - these models are used by the
+standalone metadata extraction pipeline (post-dedup async Celery task).
+"""
+
+from typing import List
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class UserMetadataProfile(BaseModel):
+    """用户画像信息"""
+
+    model_config = ConfigDict(extra="ignore")
+    role: str = Field(default="", description="用户职业或角色")
+    domain: str = Field(default="", description="用户所在领域")
+    expertise: List[str] = Field(
+        default_factory=list, description="用户擅长的技能或工具"
+    )
+    interests: List[str] = Field(
+        default_factory=list, description="用户关注的话题或领域标签"
+    )
+
+
+class UserMetadataBehavioralHints(BaseModel):
+    """行为偏好"""
+
+    model_config = ConfigDict(extra="ignore")
+    learning_stage: str = Field(default="", description="学习阶段")
+    preferred_depth: str = Field(default="", description="偏好深度")
+    tone_preference: str = Field(default="", description="语气偏好")
+
+
+class UserMetadata(BaseModel):
+    """用户元数据顶层结构"""
+
+    model_config = ConfigDict(extra="ignore")
+    profile: UserMetadataProfile = Field(default_factory=UserMetadataProfile)
+    behavioral_hints: UserMetadataBehavioralHints = Field(
+        default_factory=UserMetadataBehavioralHints
+    )
+    knowledge_tags: List[str] = Field(default_factory=list, description="知识标签")
+
+
+class MetadataExtractionResponse(BaseModel):
+    """元数据提取 LLM 响应结构"""
+
+    model_config = ConfigDict(extra="ignore")
+    user_metadata: UserMetadata = Field(default_factory=UserMetadata)
+    aliases_to_add: List[str] = Field(
+        default_factory=list,
+        description="本次新发现的用户别名（用户自我介绍或他人对用户的称呼）",
+    )
+    aliases_to_remove: List[str] = Field(
+        default_factory=list, description="用户明确否认的别名（如'我不叫XX了'）"
+    )

api/app/core/memory/src/search.py

@@ -1,4 +1,3 @@
-import argparse
 import asyncio
 import json
 import math
@@ -6,7 +5,6 @@ import os
 import time
 from datetime import datetime
 from typing import TYPE_CHECKING, Any, Dict, List, Optional
-from uuid import UUID
 
 if TYPE_CHECKING:
     from app.schemas.memory_config_schema import MemoryConfig
@@ -23,7 +21,7 @@ from app.core.memory.utils.config.config_utils import (
 )
 from app.core.memory.utils.data.text_utils import extract_plain_query
 from app.core.memory.utils.data.time_utils import normalize_date_safe
-from app.core.memory.utils.llm.llm_utils import get_reranker_client
+# from app.core.memory.utils.llm.llm_utils import get_reranker_client
 from app.core.models.base import RedBearModelConfig
 from app.db import get_db_context
 from app.repositories.neo4j.graph_search import (
@@ -43,6 +41,7 @@ load_dotenv()
 
 logger = get_memory_logger(__name__)
 
+
 def _parse_datetime(value: Any) -> Optional[datetime]:
     """Parse ISO `created_at` strings of the form 'YYYY-MM-DDTHH:MM:SS.ssssss'."""
     if value is None:
@@ -75,7 +74,7 @@ def normalize_scores(results: List[Dict[str, Any]], score_field: str = "score")
             if score_field == "activation_value" and score is None:
                 scores.append(None)  # 保持 None，稍后特殊处理
                 continue
-            
+
             if score is not None and isinstance(score, (int, float)):
                 scores.append(float(score))
             else:
@@ -83,10 +82,10 @@ def normalize_scores(results: List[Dict[str, Any]], score_field: str = "score")
 
     if not scores:
         return results
-    
+
     # 过滤掉 None 值，只对有效分数进行归一化
     valid_scores = [s for s in scores if s is not None]
-    
+
     if not valid_scores:
         # 所有分数都是 None，不进行归一化
         for item in results:
@@ -94,7 +93,7 @@ def normalize_scores(results: List[Dict[str, Any]], score_field: str = "score")
                 item[f"normalized_{score_field}"] = None
         return results
 
-    if len(valid_scores) == 1:        # Single valid score, set to 1.0
+    if len(valid_scores) == 1:  # Single valid score, set to 1.0
         for item, score in zip(results, scores):
             if score_field in item or score_field == "activation_value":
                 if score is None:
@@ -132,7 +131,6 @@ def normalize_scores(results: List[Dict[str, Any]], score_field: str = "score")
     return results
 
 
-
 def _deduplicate_results(items: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
     """
     Remove duplicate items from search results based on content.
@@ -150,52 +148,53 @@ def _deduplicate_results(items: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
     seen_ids = set()
     seen_content = set()
     deduplicated = []
-    
+
     for item in items:
         # Try multiple ID fields to identify unique items
         item_id = item.get("id") or item.get("uuid") or item.get("chunk_id")
-        
+
         # Extract content from various possible fields
         content = (
-            item.get("text") or 
-            item.get("content") or 
-            item.get("statement") or 
-            item.get("name") or 
-            ""
+                item.get("text") or
+                item.get("content") or
+                item.get("statement") or
+                item.get("name") or
+                ""
         )
-        
+
         # Normalize content for comparison (strip whitespace and lowercase)
         normalized_content = str(content).strip().lower() if content else ""
-        
+
         # Check if we've seen this ID or content before
         is_duplicate = False
-        
+
         if item_id and item_id in seen_ids:
             is_duplicate = True
         elif normalized_content and normalized_content in seen_content:
             # Only check content duplication if content is not empty
             is_duplicate = True
-        
+
         if not is_duplicate:
             # Mark as seen
             if item_id:
                 seen_ids.add(item_id)
             if normalized_content:  # Only track non-empty content
                 seen_content.add(normalized_content)
-            
+
             deduplicated.append(item)
-    
+
     return deduplicated
 
 
 def rerank_with_activation(
-    keyword_results: Dict[str, List[Dict[str, Any]]],
-    embedding_results: Dict[str, List[Dict[str, Any]]],
-    alpha: float = 0.6,
-    limit: int = 10,
-    forgetting_config: ForgettingEngineConfig | None = None,
-    activation_boost_factor: float = 0.8,
-    now: datetime | None = None,
+        keyword_results: Dict[str, List[Dict[str, Any]]],
+        embedding_results: Dict[str, List[Dict[str, Any]]],
+        alpha: float = 0.6,
+        limit: int = 10,
+        forgetting_config: ForgettingEngineConfig | None = None,
+        activation_boost_factor: float = 0.8,
+        now: datetime | None = None,
+        content_score_threshold: float = 0.5,
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     两阶段排序：先按内容相关性筛选，再按激活值排序。
@@ -222,6 +221,8 @@ def rerank_with_activation(
         forgetting_config: 遗忘引擎配置（当前未使用）
         activation_boost_factor: 激活度对记忆强度的影响系数 (默认: 0.8)
         now: 当前时间（用于遗忘计算）
+        content_score_threshold: 内容相关性最低阈值（基于归一化后的 content_score），
+            低于此阈值的结果会被过滤。默认 0.5。
         
     返回:
         带评分元数据的重排序结果，按 final_score 排序
@@ -229,26 +230,26 @@ def rerank_with_activation(
     # 验证权重范围
     if not (0 <= alpha <= 1):
         raise ValueError(f"alpha 必须在 [0, 1] 范围内，当前值: {alpha}")
-    
+
     # 初始化遗忘引擎（如果需要）
     engine = None
     if forgetting_config:
         engine = ForgettingEngine(forgetting_config)
     now_dt = now or datetime.now()
-    
+
     reranked: Dict[str, List[Dict[str, Any]]] = {}
-    
+
     for category in ["statements", "chunks", "entities", "summaries", "communities"]:
         keyword_items = keyword_results.get(category, [])
         embedding_items = embedding_results.get(category, [])
-        
+
         # 步骤 1: 归一化分数
         keyword_items = normalize_scores(keyword_items, "score")
         embedding_items = normalize_scores(embedding_items, "score")
-        
+
         # 步骤 2: 按 ID 合并结果（去重）
         combined_items: Dict[str, Dict[str, Any]] = {}
-        
+
         # 添加关键词结果
         for item in keyword_items:
             item_id = item.get("id") or item.get("uuid") or item.get("chunk_id")
@@ -257,7 +258,7 @@ def rerank_with_activation(
             combined_items[item_id] = item.copy()
             combined_items[item_id]["bm25_score"] = item.get("normalized_score", 0)
             combined_items[item_id]["embedding_score"] = 0  # 默认值
-        
+
         # 添加或更新向量嵌入结果
         for item in embedding_items:
             item_id = item.get("id") or item.get("uuid") or item.get("chunk_id")
@@ -271,18 +272,18 @@ def rerank_with_activation(
                 combined_items[item_id] = item.copy()
                 combined_items[item_id]["bm25_score"] = 0  # 默认值
                 combined_items[item_id]["embedding_score"] = item.get("normalized_score", 0)
-        
+
         # 步骤 3: 归一化激活度分数
         # 为所有项准备激活度值列表
         items_list = list(combined_items.values())
         items_list = normalize_scores(items_list, "activation_value")
-        
+
         # 更新 combined_items 中的归一化激活度分数
         for item in items_list:
             item_id = item.get("id") or item.get("uuid") or item.get("chunk_id")
             if item_id and item_id in combined_items:
                 combined_items[item_id]["normalized_activation_value"] = item.get("normalized_activation_value")
-        
+
         # 步骤 4: 计算基础分数和最终分数
         for item_id, item in combined_items.items():
             bm25_norm = float(item.get("bm25_score", 0) or 0)
@@ -290,45 +291,45 @@ def rerank_with_activation(
             # normalized_activation_value 为 None 表示该节点无激活值，保留 None 语义
             raw_act_norm = item.get("normalized_activation_value")
             act_norm = float(raw_act_norm) if raw_act_norm is not None else None
-            
+
             # 第一阶段：只考虑内容相关性（BM25 + Embedding）
             # alpha 控制 BM25 权重，(1-alpha) 控制 Embedding 权重
             content_score = alpha * bm25_norm + (1 - alpha) * emb_norm
             base_score = content_score  # 第一阶段用内容分数
-            
+
             # 存储激活度分数供第二阶段使用（None 表示无激活值，不参与激活值排序）
             item["activation_score"] = act_norm  # 可能为 None
             item["content_score"] = content_score
             item["base_score"] = base_score
-            
+
             # 步骤 5: 应用遗忘曲线（可选）
             if engine:
                 # 计算受激活度影响的记忆强度
                 importance = float(item.get("importance_score", 0.5) or 0.5)
-                
+
                 # 获取 activation_value
                 activation_val = item.get("activation_value")
-                
+
                 # 只对有激活值的节点应用遗忘曲线
                 if activation_val is not None and isinstance(activation_val, (int, float)):
                     activation_val = float(activation_val)
-                    
+
                     # 计算记忆强度：importance_score × (1 + activation_value × boost_factor)
                     memory_strength = importance * (1 + activation_val * activation_boost_factor)
-                    
+
                     # 计算经过的时间（天数）
                     dt = _parse_datetime(item.get("created_at"))
                     if dt is None:
                         time_elapsed_days = 0.0
                     else:
                         time_elapsed_days = max(0.0, (now_dt - dt).total_seconds() / 86400.0)
-                    
+
                     # 获取遗忘权重
                     forgetting_weight = engine.calculate_weight(
                         time_elapsed=time_elapsed_days,
                         memory_strength=memory_strength
                     )
-                    
+
                     # 应用到基础分数
                     item["forgetting_weight"] = forgetting_weight
                     item["final_score"] = base_score * forgetting_weight
@@ -338,7 +339,7 @@ def rerank_with_activation(
             else:
                 # 不使用遗忘曲线
                 item["final_score"] = base_score
-        
+
         # 步骤 6: 两阶段排序和限制
         # 第一阶段：按内容相关性（base_score）排序，取 Top-K
         first_stage_limit = limit * 3  # 可配置，取3倍候选
@@ -347,11 +348,11 @@ def rerank_with_activation(
             key=lambda x: float(x.get("base_score", 0) or 0),  # 按内容分数排序
             reverse=True
         )[:first_stage_limit]
-        
+
         # 第二阶段：分离有激活值和无激活值的节点
         items_with_activation = []
         items_without_activation = []
-        
+
         for item in first_stage_sorted:
             activation_score = item.get("activation_score")
             # 检查是否有有效的激活值（不是 None）
@@ -359,14 +360,14 @@ def rerank_with_activation(
                 items_with_activation.append(item)
             else:
                 items_without_activation.append(item)
-        
+
         # 优先按激活值排序有激活值的节点
         sorted_with_activation = sorted(
             items_with_activation,
             key=lambda x: float(x.get("activation_score", 0) or 0),
             reverse=True
         )
-        
+
         # 如果有激活值的节点不足 limit，用无激活值的节点补充
         if len(sorted_with_activation) < limit:
             needed = limit - len(sorted_with_activation)
@@ -374,7 +375,7 @@ def rerank_with_activation(
             sorted_items = sorted_with_activation + items_without_activation[:needed]
         else:
             sorted_items = sorted_with_activation[:limit]
-        
+
         # 两阶段排序完成，更新 final_score 以反映实际排序依据
         # Stage 1: 按 content_score 筛选候选（已完成）
         # Stage 2: 按 activation_score 排序（已完成）
@@ -390,16 +391,29 @@ def rerank_with_activation(
             else:
                 # 无激活值：使用内容相关性分数
                 item["final_score"] = item.get("base_score", 0)
-        
-        # 最终去重确保没有重复项
+
+        if content_score_threshold > 0:
+            before_count = len(sorted_items)
+            sorted_items = [
+                item for item in sorted_items
+                if float(item.get("content_score", 0) or 0) >= content_score_threshold
+            ]
+            filtered_count = before_count - len(sorted_items)
+            if filtered_count > 0:
+                logger.info(
+                    f"[rerank] {category}: filtered {filtered_count}/{before_count} "
+                    f"items below content_score_threshold={content_score_threshold}"
+                )
+
         sorted_items = _deduplicate_results(sorted_items)
-        
+
         reranked[category] = sorted_items
-    
+
     return reranked
 
 
-def log_search_query(query_text: str, search_type: str, end_user_id: str | None, limit: int, include: List[str], log_file: str = None):
+def log_search_query(query_text: str, search_type: str, end_user_id: str | None, limit: int, include: List[str],
+                     log_file: str = None):
     """Log search query information using the logger.
     
     Args:
@@ -412,7 +426,7 @@ def log_search_query(query_text: str, search_type: str, end_user_id: str | None,
     """
     # Ensure the query text is plain and clean before logging
     cleaned_query = extract_plain_query(query_text)
-    
+
     # Log using the standard logger
     logger.info(
         f"Search query: query='{cleaned_query}', type={search_type}, "
@@ -439,8 +453,8 @@ def _remove_keys_recursive(obj: Any, keys_to_remove: List[str]) -> Any:
 
 
 def apply_reranker_placeholder(
-    results: Dict[str, List[Dict[str, Any]]],
-    query_text: str,
+        results: Dict[str, List[Dict[str, Any]]],
+        query_text: str,
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     Placeholder for a cross-encoder reranker.
@@ -483,7 +497,7 @@ def apply_reranker_placeholder(
 # ) -> Dict[str, List[Dict[str, Any]]]:
 #     """
 #     Apply LLM-based reranking to search results.
-    
+
 #     Args:
 #         results: Search results organized by category
 #         query_text: Original search query
@@ -491,7 +505,7 @@ def apply_reranker_placeholder(
 #         llm_weight: Weight for LLM score (0.0-1.0, higher favors LLM)
 #         top_k: Maximum number of items to rerank per category
 #         batch_size: Number of items to process concurrently
-        
+
 #     Returns:
 #         Reranked results with final_score and reranker_model fields
 #     """
@@ -501,18 +515,18 @@ def apply_reranker_placeholder(
 #     # except Exception as e:
 #     #     logger.debug(f"Failed to load reranker config: {e}")
 #     #     rc = {}
-    
+
 #     # Check if reranking is enabled
 #     enabled = rc.get("enabled", False)
 #     if not enabled:
 #         logger.debug("LLM reranking is disabled in configuration")
 #         return results
-    
+
 #     # Load configuration parameters with defaults
 #     llm_weight = llm_weight if llm_weight is not None else rc.get("llm_weight", 0.5)
 #     top_k = top_k if top_k is not None else rc.get("top_k", 20)
 #     batch_size = batch_size if batch_size is not None else rc.get("batch_size", 5)
-    
+
 #     # Initialize reranker client if not provided
 #     if reranker_client is None:
 #         try:
@@ -520,10 +534,10 @@ def apply_reranker_placeholder(
 #         except Exception as e:
 #             logger.warning(f"Failed to initialize reranker client: {e}, skipping LLM reranking")
 #             return results
-    
+
 #     # Get model name for metadata
 #     model_name = getattr(reranker_client, 'model_name', 'unknown')
-    
+
 #     # Process each category
 #     reranked_results = {}
 #     for category in ["statements", "chunks", "entities", "summaries"]:
@@ -531,38 +545,38 @@ def apply_reranker_placeholder(
 #         if not items:
 #             reranked_results[category] = []
 #             continue
-        
+
 #         # Select top K items by combined_score for reranking
 #         sorted_items = sorted(
 #             items,
 #             key=lambda x: float(x.get("combined_score", x.get("score", 0.0)) or 0.0),
 #             reverse=True
 #         )
-        
+
 #         top_items = sorted_items[:top_k]
 #         remaining_items = sorted_items[top_k:]
-        
+
 #         # Extract text content from each item
 #         def extract_text(item: Dict[str, Any]) -> str:
 #             """Extract text content from a result item."""
 #             # Try different text fields based on category
 #             text = item.get("text") or item.get("content") or item.get("statement") or item.get("name") or ""
 #             return str(text).strip()
-        
+
 #         # Batch items for concurrent processing
 #         batches = []
 #         for i in range(0, len(top_items), batch_size):
 #             batch = top_items[i:i + batch_size]
 #             batches.append(batch)
-        
+
 #         # Process batches concurrently
 #         async def process_batch(batch: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
 #             """Process a batch of items with LLM relevance scoring."""
 #             scored_batch = []
-            
+
 #             for item in batch:
 #                 item_text = extract_text(item)
-                
+
 #                 # Skip items with no text
 #                 if not item_text:
 #                     item_copy = item.copy()
@@ -572,7 +586,7 @@ def apply_reranker_placeholder(
 #                     item_copy["reranker_model"] = model_name
 #                     scored_batch.append(item_copy)
 #                     continue
-                
+
 #                 # Create relevance scoring prompt
 #                 prompt = f"""Given the search query and a result item, rate the relevance of the item to the query on a scale from 0.0 to 1.0.
 
@@ -585,15 +599,15 @@ def apply_reranker_placeholder(
 # - 1.0 means perfectly relevant
 
 # Relevance score:"""
-                
+
 #                 # Send request to LLM
 #                 try:
 #                     messages = [{"role": "user", "content": prompt}]
 #                     response = await reranker_client.chat(messages)
-                    
+
 #                     # Parse LLM response to extract relevance score
 #                     response_text = str(response.content if hasattr(response, 'content') else response).strip()
-                    
+
 #                     # Try to extract a float from the response
 #                     try:
 #                         # Remove any non-numeric characters except decimal point
@@ -608,11 +622,11 @@ def apply_reranker_placeholder(
 #                     except (ValueError, AttributeError) as e:
 #                         logger.warning(f"Invalid LLM score format: {response_text}, using combined_score. Error: {e}")
 #                         llm_score = None
-                    
+
 #                     # Calculate final score
 #                     item_copy = item.copy()
 #                     combined_score = float(item.get("combined_score", item.get("score", 0.0)) or 0.0)
-                    
+
 #                     if llm_score is not None:
 #                         final_score = (1 - llm_weight) * combined_score + llm_weight * llm_score
 #                         item_copy["llm_relevance_score"] = llm_score
@@ -620,7 +634,7 @@ def apply_reranker_placeholder(
 #                         # Use combined_score as fallback
 #                         final_score = combined_score
 #                         item_copy["llm_relevance_score"] = combined_score
-                    
+
 #                     item_copy["final_score"] = final_score
 #                     item_copy["reranker_model"] = model_name
 #                     scored_batch.append(item_copy)
@@ -632,14 +646,14 @@ def apply_reranker_placeholder(
 #                     item_copy["llm_relevance_score"] = combined_score
 #                     item_copy["reranker_model"] = model_name
 #                     scored_batch.append(item_copy)
-            
+
 #             return scored_batch
-        
+
 #         # Process all batches concurrently
 #         try:
 #             batch_tasks = [process_batch(batch) for batch in batches]
 #             batch_results = await asyncio.gather(*batch_tasks, return_exceptions=True)
-            
+
 #             # Merge batch results
 #             scored_items = []
 #             for result in batch_results:
@@ -647,7 +661,7 @@ def apply_reranker_placeholder(
 #                     logger.warning(f"Batch processing failed: {result}")
 #                     continue
 #                 scored_items.extend(result)
-            
+
 #             # Add remaining items (not in top K) with their combined_score as final_score
 #             for item in remaining_items:
 #                 item_copy = item.copy()
@@ -655,11 +669,11 @@ def apply_reranker_placeholder(
 #                 item_copy["final_score"] = combined_score
 #                 item_copy["reranker_model"] = model_name
 #                 scored_items.append(item_copy)
-            
+
 #             # Sort all items by final_score in descending order
 #             scored_items.sort(key=lambda x: float(x.get("final_score", 0.0) or 0.0), reverse=True)
 #             reranked_results[category] = scored_items
-            
+
 #         except Exception as e:
 #             logger.error(f"Error in LLM reranking for category {category}: {e}, returning original results")
 #             # Return original items with combined_score as final_score
@@ -668,22 +682,22 @@ def apply_reranker_placeholder(
 #                 item["final_score"] = combined_score
 #                 item["reranker_model"] = model_name
 #             reranked_results[category] = items
-    
+
 #     return reranked_results
 
 
 async def run_hybrid_search(
-    query_text: str,
-    search_type: str,
-    end_user_id: str | None,
-    limit: int,
-    include: List[str],
-    output_path: str | None,
-    memory_config: "MemoryConfig",
-    rerank_alpha: float = 0.6,
-    activation_boost_factor: float = 0.8,
-    use_forgetting_rerank: bool = False,
-    use_llm_rerank: bool = False,
+        query_text: str,
+        search_type: str,
+        end_user_id: str | None,
+        limit: int,
+        include: List[str],
+        output_path: str | None,
+        memory_config: "MemoryConfig",
+        rerank_alpha: float = 0.6,
+        activation_boost_factor: float = 0.8,
+        use_forgetting_rerank: bool = False,
+        use_llm_rerank: bool = False,
 ):
     """
 
@@ -699,7 +713,7 @@ async def run_hybrid_search(
 
     # Clean and normalize the incoming query before use/logging
     query_text = extract_plain_query(query_text)
-    
+
     # Validate query is not empty after cleaning
     if not query_text or not query_text.strip():
         logger.warning("Empty query after cleaning, returning empty results")
@@ -716,7 +730,7 @@ async def run_hybrid_search(
                 "error": "Empty query"
             }
         }
-    
+
     # Log the search query
     log_search_query(query_text, search_type, end_user_id, limit, include)
 
@@ -732,11 +746,10 @@ async def run_hybrid_search(
         if search_type in ["keyword", "hybrid"]:
             # Keyword-based search
             logger.info("[PERF] Starting keyword search...")
-            keyword_start = time.time()
             keyword_task = asyncio.create_task(
                 search_graph(
                     connector=connector,
-                    q=query_text,
+                    query=query_text,
                     end_user_id=end_user_id,
                     limit=limit,
                     include=include
@@ -746,8 +759,7 @@ async def run_hybrid_search(
         if search_type in ["embedding", "hybrid"]:
             # Embedding-based search
             logger.info("[PERF] Starting embedding search...")
-            embedding_start = time.time()
-            
+
             # 从数据库读取嵌入器配置（按 ID）并构建 RedBearModelConfig
             config_load_start = time.time()
             try:
@@ -758,8 +770,7 @@ async def run_hybrid_search(
                     model_name=embedder_config_dict["model_name"],
                     provider=embedder_config_dict["provider"],
                     api_key=embedder_config_dict["api_key"],
-                    base_url=embedder_config_dict["base_url"],
-                    type="llm"
+                    base_url=embedder_config_dict["base_url"]
                 )
                 config_load_time = time.time() - config_load_start
                 logger.info(f"[PERF] Config loading took {config_load_time:.4f}s")
@@ -769,7 +780,7 @@ async def run_hybrid_search(
                 embedder = OpenAIEmbedderClient(model_config=rb_config)
                 embedder_init_time = time.time() - embedder_init_start
                 logger.info(f"[PERF] Embedder init took {embedder_init_time:.4f}s")
-                
+
                 embedding_task = asyncio.create_task(
                     search_graph_by_embedding(
                         connector=connector,
@@ -789,7 +800,7 @@ async def run_hybrid_search(
 
         if keyword_task:
             keyword_results = await keyword_task
-            keyword_latency = time.time() - keyword_start
+            keyword_latency = time.time() - search_start_time
             latency_metrics["keyword_search_latency"] = round(keyword_latency, 4)
             logger.info(f"[PERF] Keyword search completed in {keyword_latency:.4f}s")
             if search_type == "keyword":
@@ -799,7 +810,7 @@ async def run_hybrid_search(
 
         if embedding_task:
             embedding_results = await embedding_task
-            embedding_latency = time.time() - embedding_start
+            embedding_latency = time.time() - search_start_time
             latency_metrics["embedding_search_latency"] = round(embedding_latency, 4)
             logger.info(f"[PERF] Embedding search completed in {embedding_latency:.4f}s")
             if search_type == "embedding":
@@ -811,7 +822,8 @@ async def run_hybrid_search(
         if search_type == "hybrid":
             results["combined_summary"] = {
                 "total_keyword_results": sum(len(v) if isinstance(v, list) else 0 for v in keyword_results.values()),
-                "total_embedding_results": sum(len(v) if isinstance(v, list) else 0 for v in embedding_results.values()),
+                "total_embedding_results": sum(
+                    len(v) if isinstance(v, list) else 0 for v in embedding_results.values()),
                 "search_query": query_text,
                 "search_timestamp": datetime.now().isoformat()
             }
@@ -819,7 +831,7 @@ async def run_hybrid_search(
             # Apply two-stage reranking with ACTR activation calculation
             rerank_start = time.time()
             logger.info("[PERF] Using two-stage reranking with ACTR activation")
-            
+
             # 加载遗忘引擎配置
             config_start = time.time()
             try:
@@ -830,7 +842,7 @@ async def run_hybrid_search(
                 forgetting_cfg = ForgettingEngineConfig()
             config_time = time.time() - config_start
             logger.info(f"[PERF] Forgetting config loading took {config_time:.4f}s")
-            
+
             # 统一使用激活度重排序（两阶段：检索 + ACTR计算）
             rerank_compute_start = time.time()
             reranked_results = rerank_with_activation(
@@ -843,14 +855,14 @@ async def run_hybrid_search(
             )
             rerank_compute_time = time.time() - rerank_compute_start
             logger.info(f"[PERF] Rerank computation took {rerank_compute_time:.4f}s")
-            
+
             rerank_latency = time.time() - rerank_start
             latency_metrics["reranking_latency"] = round(rerank_latency, 4)
             logger.info(f"[PERF] Total reranking completed in {rerank_latency:.4f}s")
-            
+
             # Optional: apply reranker placeholder if enabled via config
             reranked_results = apply_reranker_placeholder(reranked_results, query_text)
-            
+
             # Apply LLM reranking if enabled
             llm_rerank_applied = False
             # if use_llm_rerank:
@@ -863,11 +875,12 @@ async def run_hybrid_search(
             #         logger.info("LLM reranking applied successfully")
             #     except Exception as e:
             #         logger.warning(f"LLM reranking failed: {e}, using previous scores")
-            
+
             results["reranked_results"] = reranked_results
             results["combined_summary"] = {
                 "total_keyword_results": sum(len(v) if isinstance(v, list) else 0 for v in keyword_results.values()),
-                "total_embedding_results": sum(len(v) if isinstance(v, list) else 0 for v in embedding_results.values()),
+                "total_embedding_results": sum(
+                    len(v) if isinstance(v, list) else 0 for v in embedding_results.values()),
                 "total_reranked_results": sum(len(v) if isinstance(v, list) else 0 for v in reranked_results.values()),
                 "search_query": query_text,
                 "search_timestamp": datetime.now().isoformat(),
@@ -880,17 +893,17 @@ async def run_hybrid_search(
         # Calculate total latency
         total_latency = time.time() - search_start_time
         latency_metrics["total_latency"] = round(total_latency, 4)
-        
+
         # Add latency metrics to results
         if "combined_summary" in results:
             results["combined_summary"]["latency_metrics"] = latency_metrics
         else:
             results["latency_metrics"] = latency_metrics
-        
-        logger.info(f"[PERF] ===== SEARCH PERFORMANCE SUMMARY =====")
+
+        logger.info("[PERF] ===== SEARCH PERFORMANCE SUMMARY =====")
         logger.info(f"[PERF] Total search completed in {total_latency:.4f}s")
         logger.info(f"[PERF] Latency breakdown: {json.dumps(latency_metrics, indent=2)}")
-        logger.info(f"[PERF] =========================================")
+        logger.info("[PERF] =========================================")
 
         # Sanitize results: drop large/unused fields
         _remove_keys_recursive(results, ["name_embedding"])  # drop entity name embeddings from outputs
@@ -909,8 +922,10 @@ async def run_hybrid_search(
         # Log search completion with result count
         if search_type == "hybrid":
             result_counts = {
-                "keyword": {key: len(value) if isinstance(value, list) else 0 for key, value in keyword_results.items()},
-                "embedding": {key: len(value) if isinstance(value, list) else 0 for key, value in embedding_results.items()}
+                "keyword": {key: len(value) if isinstance(value, list) else 0 for key, value in
+                            keyword_results.items()},
+                "embedding": {key: len(value) if isinstance(value, list) else 0 for key, value in
+                              embedding_results.items()}
             }
         else:
             result_counts = {key: len(value) if isinstance(value, list) else 0 for key, value in results.items()}
@@ -928,12 +943,12 @@ async def run_hybrid_search(
 
 
 async def search_by_temporal(
-    end_user_id: Optional[str] = "test",
-    start_date: Optional[str] = None,
-    end_date: Optional[str] = None,
-    valid_date: Optional[str] = None,
-    invalid_date: Optional[str] = None,
-    limit: int = 1,
+        end_user_id: Optional[str] = "test",
+        start_date: Optional[str] = None,
+        end_date: Optional[str] = None,
+        valid_date: Optional[str] = None,
+        invalid_date: Optional[str] = None,
+        limit: int = 1,
 ):
     """
     Temporal search across Statements.
@@ -969,13 +984,13 @@ async def search_by_temporal(
 
 
 async def search_by_keyword_temporal(
-    query_text: str,
-    end_user_id: Optional[str] = "test",
-    start_date: Optional[str] = None,
-    end_date: Optional[str] = None,
-    valid_date: Optional[str] = None,
-    invalid_date: Optional[str] = None,
-    limit: int = 1,
+        query_text: str,
+        end_user_id: Optional[str] = "test",
+        start_date: Optional[str] = None,
+        end_date: Optional[str] = None,
+        valid_date: Optional[str] = None,
+        invalid_date: Optional[str] = None,
+        limit: int = 1,
 ):
     """
     Temporal keyword search across Statements.
@@ -1012,9 +1027,9 @@ async def search_by_keyword_temporal(
 
 
 async def search_chunk_by_chunk_id(
-    chunk_id: str,
-    end_user_id: Optional[str] = "test",
-    limit: int = 1,
+        chunk_id: str,
+        end_user_id: Optional[str] = "test",
+        limit: int = 1,
 ):
     """
     Search for Chunks by chunk_id.
@@ -1027,4 +1042,3 @@ async def search_chunk_by_chunk_id(
         limit=limit
     )
     return {"chunks": chunks}
-

api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py

@@ -4,6 +4,7 @@
 import asyncio
 import difflib  # 提供字符串相似度计算工具
 import importlib
+import logging
 import os
 import re
 from datetime import datetime
@@ -16,6 +17,8 @@ from app.core.memory.models.graph_models import (
 )
 from app.core.memory.models.variate_config import DedupConfig
 
+logger = logging.getLogger(__name__)
+
 
 # 模块级类型统一工具函数
 def _unify_entity_type(canonical: ExtractedEntityNode, losing: ExtractedEntityNode, suggested_type: str = None) -> None:
@@ -79,51 +82,38 @@ def _merge_attribute(canonical: ExtractedEntityNode, ent: ExtractedEntityNode):
             canonical.connect_strength = next(iter(pair))
 
     # 别名合并（去重保序，使用标准化工具）
+    # 用户实体的 aliases 由 PgSQL end_user_info 作为唯一权威源，去重合并时不修改
     try:
         canonical_name = (getattr(canonical, "name", "") or "").strip()
-        incoming_name = (getattr(ent, "name", "") or "").strip()
-        
-        # 收集所有需要合并的别名
-        all_aliases = []
-        
-        # 1. 添加canonical现有的别名
-        existing = getattr(canonical, "aliases", []) or []
-        all_aliases.extend(existing)
-        
-        # 2. 添加incoming实体的名称（如果不同于canonical的名称）
-        if incoming_name and incoming_name != canonical_name:
-            all_aliases.append(incoming_name)
-        
-        # 3. 添加incoming实体的所有别名
-        incoming = getattr(ent, "aliases", []) or []
-        all_aliases.extend(incoming)
-        
-        # 4. 标准化并去重（优先使用alias_utils工具函数）
-        try:
-            from app.core.memory.utils.alias_utils import normalize_aliases
-            canonical.aliases = normalize_aliases(canonical_name, all_aliases)
-        except Exception:
-            # 如果导入失败，使用增强的去重逻辑
-            seen_normalized = set()
-            unique_aliases = []
+        if canonical_name.lower() not in _USER_PLACEHOLDER_NAMES:
+            incoming_name = (getattr(ent, "name", "") or "").strip()
             
-            for alias in all_aliases:
-                if not alias:
-                    continue
-                
-                alias_stripped = str(alias).strip()
-                if not alias_stripped or alias_stripped == canonical_name:
-                    continue
-                
-                # 标准化：转小写用于去重判断
-                alias_normalized = alias_stripped.lower()
-                
-                if alias_normalized not in seen_normalized:
-                    seen_normalized.add(alias_normalized)
-                    unique_aliases.append(alias_stripped)
+            # 收集所有需要合并的别名，过滤掉用户占位名避免污染非用户实体
+            all_aliases = list(getattr(canonical, "aliases", []) or [])
+            if incoming_name and incoming_name != canonical_name and incoming_name.lower() not in _USER_PLACEHOLDER_NAMES:
+                all_aliases.append(incoming_name)
+            all_aliases.extend(
+                a for a in (getattr(ent, "aliases", []) or [])
+                if a and a.strip().lower() not in _USER_PLACEHOLDER_NAMES
+            )
             
-            # 排序并赋值
-            canonical.aliases = sorted(unique_aliases)
+            try:
+                from app.core.memory.utils.alias_utils import normalize_aliases
+                canonical.aliases = normalize_aliases(canonical_name, all_aliases)
+            except Exception:
+                seen_normalized = set()
+                unique_aliases = []
+                for alias in all_aliases:
+                    if not alias:
+                        continue
+                    alias_stripped = str(alias).strip()
+                    if not alias_stripped or alias_stripped == canonical_name:
+                        continue
+                    alias_normalized = alias_stripped.lower()
+                    if alias_normalized not in seen_normalized:
+                        seen_normalized.add(alias_normalized)
+                        unique_aliases.append(alias_stripped)
+                canonical.aliases = sorted(unique_aliases)
     except Exception:
         pass
 
@@ -198,6 +188,161 @@ def _merge_attribute(canonical: ExtractedEntityNode, ent: ExtractedEntityNode):
     except Exception:
         pass
 
+# 用户和AI助手的占位名称集合（用于名称标准化）
+_USER_PLACEHOLDER_NAMES = {"用户", "我", "user", "i"}
+_ASSISTANT_PLACEHOLDER_NAMES = {"ai助手", "助手", "人工智能助手", "智能助手", "智能体", "ai assistant", "assistant"}
+
+# 标准化后的规范名称和类型
+_CANONICAL_USER_NAME = "用户"
+_CANONICAL_USER_TYPE = "用户"
+_CANONICAL_ASSISTANT_NAME = "AI助手"
+_CANONICAL_ASSISTANT_TYPE = "Agent"
+
+# 用户和AI助手的所有可能名称（用于判断实体是否为特殊角色实体）
+_ALL_USER_NAMES = _USER_PLACEHOLDER_NAMES
+_ALL_ASSISTANT_NAMES = _ASSISTANT_PLACEHOLDER_NAMES
+
+
+def _is_user_entity(ent: ExtractedEntityNode) -> bool:
+    """判断实体是否为用户实体（name 或 entity_type 匹配）"""
+    name = (getattr(ent, "name", "") or "").strip().lower()
+    etype = (getattr(ent, "entity_type", "") or "").strip()
+    return name in _ALL_USER_NAMES or etype == _CANONICAL_USER_TYPE
+
+
+def _is_assistant_entity(ent: ExtractedEntityNode) -> bool:
+    """判断实体是否为AI助手实体（name 或 entity_type 匹配）"""
+    name = (getattr(ent, "name", "") or "").strip().lower()
+    etype = (getattr(ent, "entity_type", "") or "").strip()
+    return name in _ALL_ASSISTANT_NAMES or etype == _CANONICAL_ASSISTANT_TYPE
+
+
+def _would_merge_cross_role(a: ExtractedEntityNode, b: ExtractedEntityNode) -> bool:
+    """判断两个实体的合并是否会跨越用户/AI助手角色边界。
+    
+    用户实体和AI助手实体永远不应该被合并在一起。
+    如果一方是用户实体、另一方是AI助手实体，返回 True（阻止合并）。
+    """
+    return (
+        (_is_user_entity(a) and _is_assistant_entity(b))
+        or (_is_assistant_entity(a) and _is_user_entity(b))
+    )
+
+
+def _normalize_special_entity_names(
+    entity_nodes: List[ExtractedEntityNode],
+) -> None:
+    """标准化用户和AI助手实体的名称和类型。
+
+    多轮对话中，LLM 对同一角色可能使用不同的名称变体（如"用户"/"我"/"User"，
+    "AI助手"/"助手"/"Assistant"），导致精确匹配无法合并。
+    此函数在去重前将这些变体统一为规范名称，并强制绑定 entity_type，确保：
+    - name="用户" 的实体 entity_type 一定为 "用户"
+    - name="AI助手" 的实体 entity_type 一定为 "Agent"
+
+    Args:
+        entity_nodes: 实体节点列表（原地修改）
+    """
+    for ent in entity_nodes:
+        name = (getattr(ent, "name", "") or "").strip()
+        name_lower = name.lower()
+
+        if name_lower in _USER_PLACEHOLDER_NAMES:
+            ent.name = _CANONICAL_USER_NAME
+            ent.entity_type = _CANONICAL_USER_TYPE
+        elif name_lower in _ASSISTANT_PLACEHOLDER_NAMES:
+            ent.name = _CANONICAL_ASSISTANT_NAME
+            ent.entity_type = _CANONICAL_ASSISTANT_TYPE
+
+    # 第二步：清洗用户/AI助手之间的别名交叉污染（复用 clean_cross_role_aliases）
+    clean_cross_role_aliases(entity_nodes)
+
+
+async def fetch_neo4j_assistant_aliases(neo4j_connector, end_user_id: str) -> set:
+    """从 Neo4j 查询 AI 助手实体的所有别名（小写归一化）。
+
+    这是助手别名查询的唯一入口，供 write_tools 和 extraction_orchestrator 共用，
+    避免多处维护相同的 Cypher 和名称列表。
+
+    Args:
+        neo4j_connector: Neo4j 连接器实例（需提供 execute_query 方法）
+        end_user_id: 终端用户 ID
+
+    Returns:
+        小写归一化后的助手别名集合
+    """
+    # 查询名称列表：规范名称 + 常见变体（与 _normalize_special_entity_names 标准化后一致）
+    query_names = [_CANONICAL_ASSISTANT_NAME, *_ASSISTANT_PLACEHOLDER_NAMES]
+    # 去重保序
+    query_names = list(dict.fromkeys(query_names))
+
+    cypher = """
+    MATCH (e:ExtractedEntity)
+    WHERE e.end_user_id = $end_user_id AND e.name IN $names
+    RETURN e.aliases AS aliases
+    """
+    try:
+        result = await neo4j_connector.execute_query(
+            cypher, end_user_id=end_user_id, names=query_names
+        )
+        assistant_aliases: set = set()
+        for record in (result or []):
+            for alias in (record.get("aliases") or []):
+                assistant_aliases.add(alias.strip().lower())
+        if assistant_aliases:
+            logger.debug(f"Neo4j 中 AI 助手别名: {assistant_aliases}")
+        return assistant_aliases
+    except Exception as e:
+        logger.warning(f"查询 Neo4j AI 助手别名失败: {e}")
+        return set()
+
+
+def clean_cross_role_aliases(
+    entity_nodes: List[ExtractedEntityNode],
+    external_assistant_aliases: set = None,
+) -> None:
+    """清洗用户实体和AI助手实体之间的别名交叉污染。
+
+    在 Neo4j 写入前调用，确保：
+    - 用户实体的 aliases 不包含 AI 助手的别名
+    - AI 助手实体的 aliases 不包含用户的别名
+
+    Args:
+        entity_nodes: 实体节点列表（原地修改）
+        external_assistant_aliases: 外部传入的 AI 助手别名集合（如从 Neo4j 查询），
+                                    与本轮实体中的 AI 助手别名合并使用
+    """
+    # 收集本轮 AI 助手实体的所有别名
+    assistant_aliases = set(external_assistant_aliases or set())
+    user_aliases = set()
+
+    for ent in entity_nodes:
+        if _is_assistant_entity(ent):
+            for alias in (getattr(ent, "aliases", []) or []):
+                assistant_aliases.add(alias.strip().lower())
+        elif _is_user_entity(ent):
+            for alias in (getattr(ent, "aliases", []) or []):
+                user_aliases.add(alias.strip().lower())
+
+    # 从用户实体的 aliases 中移除 AI 助手别名
+    if assistant_aliases:
+        for ent in entity_nodes:
+            if _is_user_entity(ent):
+                original = getattr(ent, "aliases", []) or []
+                cleaned = [a for a in original if a.strip().lower() not in assistant_aliases]
+                if len(cleaned) < len(original):
+                    ent.aliases = cleaned
+
+    # 从 AI 助手实体的 aliases 中移除用户别名
+    if user_aliases:
+        for ent in entity_nodes:
+            if _is_assistant_entity(ent):
+                original = getattr(ent, "aliases", []) or []
+                cleaned = [a for a in original if a.strip().lower() not in user_aliases]
+                if len(cleaned) < len(original):
+                    ent.aliases = cleaned
+
+
 def accurate_match(
     entity_nodes: List[ExtractedEntityNode]
 ) -> Tuple[List[ExtractedEntityNode], Dict[str, str], Dict[str, Dict]]:
@@ -261,6 +406,10 @@ def accurate_match(
         canonical = alias_index.get((ent_uid, ent_name))
         # 确保不是自身
         if canonical is not None and canonical.id != ent.id:
+            # 保护：禁止跨角色合并（用户实体和AI助手实体不能互相合并）
+            if _would_merge_cross_role(canonical, ent):
+                i += 1
+                continue
             _merge_attribute(canonical, ent)
             id_redirect[ent.id] = canonical.id
             for k, v in list(id_redirect.items()):
@@ -571,66 +720,37 @@ def fuzzy_match(
 
 
     def _merge_entities_with_aliases(canonical: ExtractedEntityNode, losing: ExtractedEntityNode):
-        """ 模糊匹配中的实体合并。
+        """模糊匹配中的实体合并（别名部分）。
         
-        合并策略：
-        1. 保留canonical的主名称不变
-        2. 将losing的主名称添加为alias（如果不同）
-        3. 合并两个实体的所有aliases
-        4. 自动去重（case-insensitive）并排序
-        
-        Args:
-            canonical: 规范实体（保留）
-            losing: 被合并实体（删除）
-            
-        Note:
-            使用alias_utils.normalize_aliases进行标准化去重
+        用户实体的 aliases 由 PgSQL end_user_info 作为唯一权威源，跳过合并。
         """
-        # 获取规范实体的名称
         canonical_name = (getattr(canonical, "name", "") or "").strip()
+        if canonical_name.lower() in _USER_PLACEHOLDER_NAMES:
+            return
+
         losing_name = (getattr(losing, "name", "") or "").strip()
         
-        # 收集所有需要合并的别名
-        all_aliases = []
-        
-        # 1. 添加canonical现有的别名
-        current_aliases = getattr(canonical, "aliases", []) or []
-        all_aliases.extend(current_aliases)
-        
-        # 2. 添加losing实体的名称（如果不同于canonical的名称）
+        all_aliases = list(getattr(canonical, "aliases", []) or [])
         if losing_name and losing_name != canonical_name:
             all_aliases.append(losing_name)
+        all_aliases.extend(getattr(losing, "aliases", []) or [])
         
-        # 3. 添加losing实体的所有别名
-        losing_aliases = getattr(losing, "aliases", []) or []
-        all_aliases.extend(losing_aliases)
-        
-        # 4. 标准化并去重（使用标准化后的字符串进行去重）
         try:
             from app.core.memory.utils.alias_utils import normalize_aliases
             canonical.aliases = normalize_aliases(canonical_name, all_aliases)
         except Exception:
-            # 如果导入失败，使用增强的去重逻辑
-            # 使用标准化后的字符串作为key进行去重
             seen_normalized = set()
             unique_aliases = []
-            
             for alias in all_aliases:
                 if not alias:
                     continue
-                
                 alias_stripped = str(alias).strip()
                 if not alias_stripped or alias_stripped == canonical_name:
                     continue
-                
-                # 标准化：转小写用于去重判断
                 alias_normalized = alias_stripped.lower()
-                
                 if alias_normalized not in seen_normalized:
                     seen_normalized.add(alias_normalized)
                     unique_aliases.append(alias_stripped)
-            
-            # 排序并赋值
             canonical.aliases = sorted(unique_aliases)
     
     # ========== 主循环：遍历所有实体对进行模糊匹配 ==========
@@ -704,6 +824,11 @@ def fuzzy_match(
             # 条件A（快速通道）：alias_match_merge = True
             # 条件B（标准通道）：s_name ≥ tn AND s_type ≥ type_threshold AND overall ≥ tover
             if alias_match_merge or (s_name >= tn and s_type >= type_threshold and overall >= tover):
+                #  保护：禁止跨角色合并（用户实体和AI助手实体不能互相合并）
+                if _would_merge_cross_role(a, b):
+                    j += 1
+                    continue
+
                 # ========== 第六步：执行实体合并 ==========
                 
                 # 6.1 合并别名
@@ -813,6 +938,12 @@ async def LLM_decision(  # 决策中包含去重和消歧的功能
             b = entity_by_id.get(losing_id)
             if not a or not b: # 若不存在 a 或 b，可能已在精确或模糊阶段合并，在之前阶段合并之后，不会再处理但是处于审计的目的会记录
                 continue
+            # 保护：禁止跨角色合并（用户实体和AI助手实体不能互相合并）
+            if _would_merge_cross_role(a, b):
+                llm_records.append(
+                    f"[LLM阻断] 跨角色合并被阻止: {a.id} ({a.name}) 与 {b.id} ({b.name})"
+                )
+                continue
             _merge_attribute(a, b)
             # ID 重定向
             try:
@@ -934,6 +1065,9 @@ async def deduplicate_entities_and_edges(
     返回：去重后的实体、语句→实体边、实体↔实体边。
     """
     local_llm_records: List[str] = [] # 作为“审计日志”的本地收集器 初始化，保留为了之后对于LLM决策追溯
+    # 0) 标准化用户和AI助手实体名称（确保多轮对话中的变体名称统一）
+    _normalize_special_entity_names(entity_nodes)
+
     # 1) 精确匹配
     deduped_entities, id_redirect, exact_merge_map = accurate_match(entity_nodes)
 

api/app/core/memory/storage_services/extraction_engine/deduplication/two_stage_dedup.py

@@ -15,6 +15,7 @@ from app.core.memory.models.message_models import DialogData
 from app.core.memory.models.variate_config import ExtractionPipelineConfig
 from app.core.memory.storage_services.extraction_engine.deduplication.deduped_and_disamb import (
     deduplicate_entities_and_edges,
+    clean_cross_role_aliases,
 )
 from app.core.memory.storage_services.extraction_engine.deduplication.second_layer_dedup import (
     second_layer_dedup_and_merge_with_neo4j,
@@ -100,6 +101,10 @@ async def dedup_layers_and_merge_and_return(
     except Exception as e:
         print(f"Second-layer dedup failed: {e}")
 
+    # 第二层去重后，清洗用户/AI助手之间的别名交叉污染
+    # 第二层从 Neo4j 合并了旧实体，可能带入历史脏数据
+    clean_cross_role_aliases(fused_entity_nodes)
+
     return (
         dialogue_nodes,
         chunk_nodes,

api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py

@@ -44,6 +44,10 @@ from app.core.memory.models.variate_config import (
 from app.core.memory.storage_services.extraction_engine.deduplication.two_stage_dedup import (
     dedup_layers_and_merge_and_return,
 )
+from app.core.memory.storage_services.extraction_engine.deduplication.deduped_and_disamb import (
+    _USER_PLACEHOLDER_NAMES,
+    fetch_neo4j_assistant_aliases,
+)
 from app.core.memory.storage_services.extraction_engine.knowledge_extraction.embedding_generation import (
     embedding_generation,
     generate_entity_embeddings_from_triplets,
@@ -307,10 +311,53 @@ class ExtractionOrchestrator:
                 dialog_data_list,
             )
 
-            # 步骤 7: 同步用户别名到数据库表（仅正式模式）
+            # 步骤 7: 触发异步元数据和别名提取（仅正式模式）
             if not is_pilot_run:
-                logger.info("步骤 7: 同步用户别名到 end_user 和 end_user_info 表")
-                await self._update_end_user_other_name(entity_nodes, dialog_data_list)
+                try:
+                    from app.core.memory.storage_services.extraction_engine.knowledge_extraction.metadata_extractor import (
+                        MetadataExtractor,
+                    )
+
+                    metadata_extractor = MetadataExtractor(
+                        llm_client=self.llm_client, language=self.language
+                    )
+                    user_statements = (
+                        metadata_extractor.collect_user_related_statements(
+                            entity_nodes, statement_nodes, statement_entity_edges
+                        )
+                    )
+                    if user_statements:
+                        end_user_id = (
+                            dialog_data_list[0].end_user_id
+                            if dialog_data_list
+                            else None
+                        )
+                        config_id = (
+                            dialog_data_list[0].config_id
+                            if dialog_data_list
+                            and hasattr(dialog_data_list[0], "config_id")
+                            else None
+                        )
+                        if end_user_id:
+                            from app.tasks import extract_user_metadata_task
+
+                            extract_user_metadata_task.delay(
+                                end_user_id=str(end_user_id),
+                                statements=user_statements,
+                                config_id=str(config_id) if config_id else None,
+                                language=self.language,
+                            )
+                            logger.info(
+                                f"已触发异步元数据提取任务，共 {len(user_statements)} 条用户相关 statement"
+                            )
+                    else:
+                        logger.info("未找到用户相关 statement，跳过元数据提取")
+                except Exception as e:
+                    logger.error(
+                        f"触发元数据提取任务失败（不影响主流程）: {e}", exc_info=True
+                    )
+
+                # 别名同步已迁移到 Celery 元数据提取任务中，不再在此处执行
 
             logger.info(f"知识提取流水线运行完成（{mode_str}）")
             return (
@@ -1103,6 +1150,7 @@ class ExtractionOrchestrator:
                     end_user_id=dialog_data.end_user_id,
                     run_id=dialog_data.run_id,  # 使用 dialog_data 的 run_id
                     content=chunk.content,
+                    speaker=getattr(chunk, 'speaker', None),
                     chunk_embedding=chunk.chunk_embedding,
                     sequence_number=chunk_idx,  # 添加必需的 sequence_number 字段
                     created_at=dialog_data.created_at,
@@ -1338,17 +1386,23 @@ class ExtractionOrchestrator:
     async def _update_end_user_other_name(
             self,
             entity_nodes: List[ExtractedEntityNode],
-            dialog_data_list: List[DialogData]
+            dialog_data_list: List[DialogData],
     ) -> None:
         """
-        从 Neo4j 读取用户实体的最终 aliases，同步到 end_user 和 end_user_info 表
+        将本轮提取的用户别名同步到 end_user 和 end_user_info 表。
 
-        注意：
-        1. other_name 使用本次对话提取的第一个别名（保持时间顺序）
-        2. aliases 从 Neo4j 读取（保持完整性）
+        PgSQL end_user_info.aliases 是用户别名的唯一权威源。
+        此方法仅将本轮 LLM 从对话中新提取的别名增量追加到 PgSQL，
+        不再从 Neo4j 二层去重合并历史别名，避免脏数据反向污染 PgSQL。
+
+        策略：
+        1. 从本轮对话原始发言中提取用户别名（current_aliases）
+        2. 从 PgSQL end_user_info 读取已有的 aliases（db_aliases）
+        3. 合并 db_aliases + current_aliases，去重保序
+        4. 写回 PgSQL
 
         Args:
-            entity_nodes: 实体节点列表
+            entity_nodes: 去重后的实体节点列表（内存中）
             dialog_data_list: 对话数据列表
         """
         try:
@@ -1361,23 +1415,28 @@ class ExtractionOrchestrator:
                 logger.warning("end_user_id 为空，跳过用户别名同步")
                 return
 
-            # 1. 提取本次对话的用户别名（保持 LLM 提取的原始顺序，不排序）
-            current_aliases = self._extract_current_aliases(entity_nodes)
+            # 1. 提取本轮对话的用户别名（保持 LLM 提取的原始顺序，不排序）
+            current_aliases = self._extract_current_aliases(entity_nodes, dialog_data_list)
 
-            # 2. 从 Neo4j 获取完整 aliases（权威数据源）
-            neo4j_aliases = await self._fetch_neo4j_user_aliases(end_user_id)
+            # 1.6 从 Neo4j 查询已有的 AI 助手别名，作为额外的排除源
+            # （防止 LLM 未提取出 AI 助手实体时，AI 别名泄漏到用户别名中）
+            neo4j_assistant_aliases = await self._fetch_neo4j_assistant_aliases(end_user_id)
+            if neo4j_assistant_aliases:
+                before_count = len(current_aliases)
+                current_aliases = [
+                    a for a in current_aliases
+                    if a.strip().lower() not in neo4j_assistant_aliases
+                ]
+                if len(current_aliases) < before_count:
+                    logger.info(f"通过 Neo4j AI 助手别名排除了 {before_count - len(current_aliases)} 个误归属别名")
 
-            if not neo4j_aliases:
-                # Neo4j 中没有别名，使用本次对话提取的别名
-                neo4j_aliases = current_aliases
-                if not neo4j_aliases:
-                    logger.debug(f"aliases 为空，跳过同步: end_user_id={end_user_id}")
-                    return
+            if not current_aliases:
+                logger.debug(f"本轮未提取到用户别名，跳过同步: end_user_id={end_user_id}")
+                return
 
-            logger.info(f"本次对话提取的 aliases: {current_aliases}")
-            logger.info(f"Neo4j 中的完整 aliases: {neo4j_aliases}")
+            logger.info(f"本轮对话提取的 aliases: {current_aliases}")
 
-            # 3. 同步到数据库
+            # 2. 同步到数据库
             end_user_uuid = uuid.UUID(end_user_id)
             with get_db_context() as db:
                 # 更新 end_user 表
@@ -1386,7 +1445,32 @@ class ExtractionOrchestrator:
                     logger.warning(f"未找到 end_user_id={end_user_id} 的用户记录")
                     return
 
-                new_name = self._resolve_other_name(end_user.other_name, current_aliases, neo4j_aliases)
+                # 3. 从 PgSQL 读取已有 aliases 并与本轮新增合并
+                info = EndUserInfoRepository(db).get_by_end_user_id(end_user_uuid)
+                db_aliases = (info.aliases if info and info.aliases else [])
+                # 过滤掉占位名称
+                db_aliases = [a for a in db_aliases if a.strip().lower() not in self.USER_PLACEHOLDER_NAMES]
+
+                # 合并：PgSQL 已有 + 本轮新增，去重保序（不再合并 Neo4j 历史别名）
+                merged_aliases = list(db_aliases)
+                seen_lower = {a.strip().lower() for a in merged_aliases}
+                for alias in current_aliases:
+                    if alias.strip().lower() not in seen_lower:
+                        merged_aliases.append(alias)
+                        seen_lower.add(alias.strip().lower())
+
+                # 最终过滤：从合并结果中排除 AI 助手别名（清理历史脏数据）
+                if neo4j_assistant_aliases:
+                    merged_aliases = [
+                        a for a in merged_aliases
+                        if a.strip().lower() not in neo4j_assistant_aliases
+                    ]
+
+                logger.info(f"PgSQL 已有 aliases: {db_aliases}")
+                logger.info(f"合并后 aliases: {merged_aliases}")
+
+                # 更新 end_user 表 other_name
+                new_name = self._resolve_other_name(end_user.other_name, current_aliases, merged_aliases)
                 if new_name is not None:
                     end_user.other_name = new_name
                     logger.info(f"更新 end_user 表 other_name → {new_name}")
@@ -1394,78 +1478,105 @@ class ExtractionOrchestrator:
                     logger.debug(f"end_user 表 other_name 保持不变: {end_user.other_name}")
 
                 # 更新或创建 end_user_info 记录
-                info = EndUserInfoRepository(db).get_by_end_user_id(end_user_uuid)
                 if info:
-                    new_name_info = self._resolve_other_name(info.other_name, current_aliases, neo4j_aliases)
+                    new_name_info = self._resolve_other_name(info.other_name, current_aliases, merged_aliases)
                     if new_name_info is not None:
                         info.other_name = new_name_info
                         logger.info(f"更新 end_user_info 表 other_name → {new_name_info}")
-                    if info.aliases != neo4j_aliases:
-                        info.aliases = neo4j_aliases
-                        logger.info(f"同步 Neo4j aliases 到 end_user_info: {neo4j_aliases}")
+                    if info.aliases != merged_aliases:
+                        info.aliases = merged_aliases
+                        logger.info(f"同步合并后 aliases 到 end_user_info: {merged_aliases}")
                 else:
                     first_alias = current_aliases[0].strip() if current_aliases else ""
                     # 确保 first_alias 不是占位名称
-                    if first_alias and first_alias not in self.USER_PLACEHOLDER_NAMES:
+                    if first_alias and first_alias.lower() not in self.USER_PLACEHOLDER_NAMES:
                         db.add(EndUserInfo(
                             end_user_id=end_user_uuid,
                             other_name=first_alias,
-                            aliases=neo4j_aliases,
-                            meta_data={}
+                            aliases=merged_aliases,
                         ))
-                        logger.info(f"创建 end_user_info 记录，other_name={first_alias}, aliases={neo4j_aliases}")
+                        logger.info(f"创建 end_user_info 记录，other_name={first_alias}, aliases={merged_aliases}")
 
                 db.commit()
 
         except Exception as e:
             logger.error(f"更新 end_user other_name 失败: {e}", exc_info=True)
-
-
-    
     # 用户实体占位名称，不允许作为 other_name 或出现在 aliases 中
-    USER_PLACEHOLDER_NAMES = {'用户', '我', 'User', 'I'}
+    # 复用 deduped_and_disamb 模块级常量，避免重复维护
+    USER_PLACEHOLDER_NAMES = _USER_PLACEHOLDER_NAMES
 
-    def _extract_current_aliases(self, entity_nodes: List[ExtractedEntityNode]) -> List[str]:
-        """从实体节点提取用户别名（保持 LLM 提取的原始顺序，不进行任何排序）
+    def _extract_current_aliases(self, entity_nodes: List[ExtractedEntityNode], dialog_data_list=None) -> List[str]:
+        """从用户发言的原始实体中提取本轮新增别名（绕过去重污染）
         
-        这个方法直接返回 LLM 提取的别名列表，并过滤掉占位名称（"用户"、"我"、"User"、"I"）。
-        第一个别名将被用作 other_name。
+        策略：
+        仅从 dialog_data_list 中找到 speaker="user" 的 statement，
+        从这些 statement 的 triplet_extraction_info 中提取用户实体的 aliases。
+        这样拿到的是 LLM 对用户原话的提取结果，不受去重合并的影响。
+        
+        注意：不再使用去重后 entity_nodes 作为兜底，因为二层去重会将 Neo4j 历史别名
+        合并进来，导致历史别名被误认为"本轮提取"。历史别名的同步由
+        _extract_deduped_entity_aliases 负责。
         
         Args:
-            entity_nodes: 实体节点列表
+            entity_nodes: 去重后的实体节点列表（未使用，保留参数兼容性）
+            dialog_data_list: 对话数据列表
             
         Returns:
-            别名列表（保持 LLM 提取的原始顺序，已过滤占位名称）
+            别名列表（保持原始顺序，已过滤）
+        """
+        if not dialog_data_list:
+            return []
+
+        all_user_aliases = []
+        seen_lower = set()
+        for dialog in dialog_data_list:
+            for chunk in dialog.chunks:
+                speaker = getattr(chunk, 'speaker', None)
+                for statement in chunk.statements:
+                    stmt_speaker = getattr(statement, 'speaker', None) or speaker
+                    if stmt_speaker != "user":
+                        continue
+                    triplet_info = getattr(statement, 'triplet_extraction_info', None)
+                    if not triplet_info:
+                        continue
+                    for entity in (triplet_info.entities or []):
+                        ent_name = getattr(entity, 'name', '').strip()
+                        if ent_name.lower() in self.USER_PLACEHOLDER_NAMES:
+                            for alias in (getattr(entity, 'aliases', []) or []):
+                                a = alias.strip()
+                                if a and a.lower() not in self.USER_PLACEHOLDER_NAMES and a.lower() not in seen_lower:
+                                    all_user_aliases.append(a)
+                                    seen_lower.add(a.lower())
+        if all_user_aliases:
+            logger.debug(f"从用户原始发言提取到别名: {all_user_aliases}")
+        return all_user_aliases
+
+    def _extract_deduped_entity_aliases(self, entity_nodes: List[ExtractedEntityNode]) -> List[str]:
+        """从去重后的用户实体中提取完整别名列表。
+
+        二层去重会将 Neo4j 中已有的历史别名合并到 entity_nodes 的用户实体中，
+        因此这里提取到的别名包含了历史积累的所有别名，可用于同步到 PgSQL。
+
+        Args:
+            entity_nodes: 去重后的实体节点列表（含二层去重合并结果）
+
+        Returns:
+            别名列表（已过滤占位名称，去重保序）
         """
         for entity in entity_nodes:
-            if getattr(entity, 'name', '').strip() in self.USER_PLACEHOLDER_NAMES:
+            if getattr(entity, 'name', '').strip().lower() in self.USER_PLACEHOLDER_NAMES:
                 aliases = getattr(entity, 'aliases', []) or []
-                # 过滤掉占位名称，防止 "用户"/"我"/"User"/"I" 被存入 aliases 和 other_name
-                filtered = [a for a in aliases if a.strip() not in self.USER_PLACEHOLDER_NAMES]
-                logger.debug(f"提取到用户别名（原始顺序，已过滤占位名称）: {filtered}")
-                return filtered
+                filtered = [
+                    a for a in aliases
+                    if a.strip().lower() not in self.USER_PLACEHOLDER_NAMES
+                ]
+                if filtered:
+                    return filtered
         return []
 
-
-    async def _fetch_neo4j_user_aliases(self, end_user_id: str) -> List[str]:
-        """从 Neo4j 查询用户实体的完整 aliases 列表（已过滤占位名称）"""
-        cypher = """
-        MATCH (e:ExtractedEntity)
-        WHERE e.end_user_id = $end_user_id AND e.name IN ['用户', '我', 'User', 'I']
-        RETURN e.aliases AS aliases
-        LIMIT 1
-        """
-        result = await Neo4jConnector().execute_query(cypher, end_user_id=end_user_id)
-        if not result:
-            logger.debug(f"Neo4j 中未找到用户实体: end_user_id={end_user_id}")
-            return []
-        aliases = result[0].get('aliases') or []
-        if not aliases:
-            logger.debug(f"Neo4j 用户实体 aliases 为空: end_user_id={end_user_id}")
-            return []
-        # 过滤掉占位名称，防止历史脏数据传播
-        filtered = [a for a in aliases if a.strip() not in self.USER_PLACEHOLDER_NAMES]
-        return filtered
+    async def _fetch_neo4j_assistant_aliases(self, end_user_id: str) -> set:
+        """从 Neo4j 查询 AI 助手实体的所有别名（用于从用户别名中排除）"""
+        return await fetch_neo4j_assistant_aliases(self.connector, end_user_id)
 
     def _resolve_other_name(
             self,
@@ -1484,19 +1595,18 @@ class ExtractionOrchestrator:
         注意：返回值不允许是占位名称（"用户"、"我"、"User"、"I"）
         """
         # 当前值为空或为占位名称时，需要更新
-        if not current or not current.strip() or current.strip() in self.USER_PLACEHOLDER_NAMES:
+        if not current or not current.strip() or current.strip().lower() in self.USER_PLACEHOLDER_NAMES:
             candidate = current_aliases[0].strip() if current_aliases else None
             # 确保候选值不是占位名称
-            if candidate and candidate in self.USER_PLACEHOLDER_NAMES:
+            if candidate and candidate.lower() in self.USER_PLACEHOLDER_NAMES:
                 return None
             return candidate
         if current not in neo4j_aliases:
             candidate = neo4j_aliases[0].strip() if neo4j_aliases else None
             # 确保候选值不是占位名称
-            if candidate and candidate in self.USER_PLACEHOLDER_NAMES:
+            if candidate and candidate.lower() in self.USER_PLACEHOLDER_NAMES:
                 return None
             return candidate
-        
         return None
 
     async def _run_dedup_and_write_summary(

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/metadata_extractor.py

@@ -0,0 +1,175 @@
+"""
+Metadata extractor module.
+
+Collects user-related statements from post-dedup graph data and
+extracts user metadata via an independent LLM call.
+"""
+
+import logging
+from typing import List, Optional
+
+from app.core.memory.models.graph_models import (
+    ExtractedEntityNode,
+    StatementEntityEdge,
+    StatementNode,
+)
+
+logger = logging.getLogger(__name__)
+
+# Reuse the same user-entity detection logic from dedup module
+_USER_NAMES = {"用户", "我", "user", "i"}
+_CANONICAL_USER_TYPE = "用户"
+
+
+def _is_user_entity(ent: ExtractedEntityNode) -> bool:
+    """判断实体是否为用户实体"""
+    name = (getattr(ent, "name", "") or "").strip().lower()
+    etype = (getattr(ent, "entity_type", "") or "").strip()
+    return name in _USER_NAMES or etype == _CANONICAL_USER_TYPE
+
+
+class MetadataExtractor:
+    """Extracts user metadata from post-dedup graph data via independent LLM call."""
+
+    def __init__(self, llm_client, language: Optional[str] = None):
+        self.llm_client = llm_client
+        self.language = language
+
+    @staticmethod
+    def detect_language(statements: List[str]) -> str:
+        """根据 statement 文本内容检测语言。
+        如果文本中包含中文字符则返回 "zh"，否则返回 "en"。
+        """
+        import re
+
+        combined = " ".join(statements)
+        if re.search(r"[\u4e00-\u9fff]", combined):
+            return "zh"
+        return "en"
+
+    def collect_user_related_statements(
+        self,
+        entity_nodes: List[ExtractedEntityNode],
+        statement_nodes: List[StatementNode],
+        statement_entity_edges: List[StatementEntityEdge],
+    ) -> List[str]:
+        """
+        从去重后的数据中筛选与用户直接相关且由用户发言的 statement 文本。
+
+        筛选逻辑：
+        1. 用户实体 → StatementEntityEdge → statement（直接关联）
+        2. 只保留 speaker="user" 的 statement（过滤 assistant 回复的噪声）
+
+        Returns:
+            用户发言的 statement 文本列表
+        """
+        # Find user entity IDs
+        user_entity_ids = set()
+        for ent in entity_nodes:
+            if _is_user_entity(ent):
+                user_entity_ids.add(ent.id)
+
+        if not user_entity_ids:
+            logger.debug("未找到用户实体节点，跳过 statement 收集")
+            return []
+
+        # 用户实体 → StatementEntityEdge → statement
+        target_stmt_ids = set()
+        for edge in statement_entity_edges:
+            if edge.target in user_entity_ids:
+                target_stmt_ids.add(edge.source)
+
+        # Collect: only speaker="user" statements, preserving order
+        result = []
+        seen = set()
+        total_associated = 0
+        skipped_non_user = 0
+        for stmt_node in statement_nodes:
+            if stmt_node.id in target_stmt_ids and stmt_node.id not in seen:
+                total_associated += 1
+                speaker = getattr(stmt_node, "speaker", None) or "unknown"
+                if speaker == "user":
+                    text = (stmt_node.statement or "").strip()
+                    if text:
+                        result.append(text)
+                else:
+                    skipped_non_user += 1
+                seen.add(stmt_node.id)
+
+        logger.info(
+            f"收集到 {len(result)} 条用户发言 statement "
+            f"(直接关联: {total_associated}, speaker=user: {len(result)}, "
+            f"跳过非user: {skipped_non_user})"
+        )
+        if result:
+            for i, text in enumerate(result):
+                logger.info(f"  [user statement {i + 1}] {text}")
+        if total_associated > 0 and len(result) == 0:
+            logger.warning(
+                f"有 {total_associated} 条直接关联 statement 但全部被 speaker 过滤，"
+                f"可能本次写入不包含 user 消息"
+            )
+        return result
+
+    async def extract_metadata(
+        self,
+        statements: List[str],
+        existing_metadata: Optional[dict] = None,
+        existing_aliases: Optional[List[str]] = None,
+    ) -> Optional[tuple]:
+        """
+        对筛选后的 statement 列表调用 LLM 提取元数据和用户别名。
+
+        Args:
+            statements: 用户发言的 statement 文本列表
+            existing_metadata: 数据库已有的元数据（可选）
+            existing_aliases: 数据库已有的用户别名列表（可选）
+
+        Returns:
+            (UserMetadata, List[str], List[str]) tuple: (metadata, aliases_to_add, aliases_to_remove) on success, None on failure
+        """
+        if not statements:
+            return None
+
+        try:
+            from app.core.memory.utils.prompt.prompt_utils import prompt_env
+
+            if self.language:
+                detected_language = self.language
+                logger.info(f"元数据提取使用显式指定语言: {detected_language}")
+            else:
+                detected_language = self.detect_language(statements)
+                logger.info(f"元数据提取语言自动检测结果: {detected_language}")
+
+            template = prompt_env.get_template("extract_user_metadata.jinja2")
+            prompt = template.render(
+                statements=statements,
+                language=detected_language,
+                existing_metadata=existing_metadata,
+                existing_aliases=existing_aliases,
+                json_schema="",
+            )
+
+            from app.core.memory.models.metadata_models import (
+                MetadataExtractionResponse,
+            )
+
+            response = await self.llm_client.response_structured(
+                messages=[{"role": "user", "content": prompt}],
+                response_model=MetadataExtractionResponse,
+            )
+
+            if response:
+                metadata = response.user_metadata if response.user_metadata else None
+                to_add = response.aliases_to_add if response.aliases_to_add else []
+                to_remove = (
+                    response.aliases_to_remove if response.aliases_to_remove else []
+                )
+                return metadata, to_add, to_remove
+
+            logger.warning("LLM 返回的响应为空")
+            return None
+
+        except Exception as e:
+            logger.error(f"元数据提取 LLM 调用失败: {e}", exc_info=True)
+            return None

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py

@@ -1,6 +1,5 @@
 import asyncio
 import logging
-import os
 from datetime import datetime
 from typing import Any, Dict, List, Optional
 
@@ -82,6 +81,7 @@ class StatementExtractor:
         logger.warning(f"Chunk {getattr(chunk, 'id', 'unknown')} has no speaker field or is empty")
         return None
 
+
     async def _extract_statements(self, chunk, end_user_id: Optional[str] = None, dialogue_content: str = None) -> List[Statement]:
         """Process a single chunk and return extracted statements
 
@@ -94,7 +94,8 @@ class StatementExtractor:
             List of ExtractedStatement objects extracted from the chunk
         """
         chunk_content = chunk.content
-        
+        chunk_speaker = self._get_speaker_from_chunk(chunk)
+
         if not chunk_content or len(chunk_content.strip()) < 5:
             logger.warning(f"Chunk {chunk.id} content too short or empty, skipping")
             return []
@@ -149,8 +150,6 @@ class StatementExtractor:
                     relevence_info = RelevenceInfo[relevence_str] if relevence_str in RelevenceInfo.__members__ else RelevenceInfo.RELEVANT
                 except (KeyError, ValueError):
                     relevence_info = RelevenceInfo.RELEVANT
-               
-                chunk_speaker = self._get_speaker_from_chunk(chunk)
             
                 chunk_statement = Statement(
                     statement=extracted_stmt.statement,

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py

@@ -1,4 +1,3 @@
-import os
 import asyncio
 from typing import List, Dict, Optional
 
@@ -61,6 +60,7 @@ class TripletExtractor:
             predicate_instructions=PREDICATE_DEFINITIONS,
             language=self._get_language(),
             ontology_types=self.ontology_types,
+            speaker=getattr(statement, 'speaker', None),
         )
 
         # Create messages for LLM

api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py

@@ -42,22 +42,21 @@ class AccessHistoryManager:
     - access_count: 访问次数
     
     特性：
-    - 原子性更新：使用Neo4j事务确保所有字段同时更新或回滚
-    - 并发安全：使用乐观锁机制防止并发冲突
+    - 原子性更新：使用 APOC 原子操作确保并发安全
+    - 批次内合并：同一批次中对同一节点的多次访问合并为一次更新
     - 一致性保证：提供一致性检查和自动修复功能
     - 智能修剪：自动修剪过长的访问历史
     
     Attributes:
         connector: Neo4j连接器实例
         actr_calculator: ACT-R激活值计算器实例
-        max_retries: 并发冲突时的最大重试次数
     """
     
     def __init__(
         self,
         connector: Neo4jConnector,
         actr_calculator: ACTRCalculator,
-        max_retries: int = 3
+        max_retries: int = 5
     ):
         """
         初始化访问历史管理器
@@ -65,47 +64,35 @@ class AccessHistoryManager:
         Args:
             connector: Neo4j连接器实例
             actr_calculator: ACT-R激活值计算器实例
-            max_retries: 并发冲突时的最大重试次数（默认3次）
+            max_retries: 已废弃，保留参数兼容性（APOC 原子操作无需重试）
         """
         self.connector = connector
         self.actr_calculator = actr_calculator
-        self.max_retries = max_retries
-    
+
     async def record_access(
         self,
         node_id: str,
         node_label: str,
         end_user_id: Optional[str] = None,
-        current_time: Optional[datetime] = None
+        current_time: Optional[datetime] = None,
+        access_times: int = 1
     ) -> Dict[str, Any]:
         """
         记录节点访问并原子性更新所有相关字段
         
-        这是核心方法，实现了：
-        1. 首次访问：初始化access_history，计算初始激活值
-        2. 后续访问：追加访问历史，重新计算激活值
-        3. 历史修剪：当历史过长时自动修剪
-        4. 原子性：所有字段在单个事务中更新
-        5. 并发安全：使用乐观锁重试机制
-        
         Args:
             node_id: 节点ID
             node_label: 节点标签（Statement, ExtractedEntity, MemorySummary）
             end_user_id: 组ID（可选，用于过滤）
             current_time: 当前时间（可选，默认使用系统时间）
+            access_times: 本次访问次数（默认1，批量合并时可能大于1）
         
         Returns:
-            Dict[str, Any]: 更新后的节点数据，包含：
-                - id: 节点ID
-                - activation_value: 更新后的激活值
-                - access_history: 更新后的访问历史
-                - last_access_time: 最后访问时间
-                - access_count: 访问次数
-                - importance_score: 重要性分数
+            Dict[str, Any]: 更新后的节点数据
         
         Raises:
             ValueError: 如果节点不存在或节点标签无效
-            RuntimeError: 如果重试次数耗尽仍然失败
+            RuntimeError: 如果更新失败
         """
         if current_time is None:
             current_time = datetime.now()
@@ -119,55 +106,48 @@ class AccessHistoryManager:
                 f"Invalid node_label: {node_label}. Must be one of {valid_labels}"
             )
         
-        # 使用乐观锁重试机制处理并发冲突
-        for attempt in range(self.max_retries):
-            try:
-                # 步骤1：读取当前节点状态
-                node_data = await self._fetch_node(node_id, node_label, end_user_id)
-                
-                if not node_data:
-                    raise ValueError(
-                        f"Node not found: {node_label} with id={node_id}"
-                    )
-                
-                # 步骤2：计算新的访问历史和激活值
-                update_data = await self._calculate_update(
-                    node_data=node_data,
-                    current_time=current_time,
-                    current_time_iso=current_time_iso
+        try:
+            # 步骤1：读取当前节点状态
+            node_data = await self._fetch_node(node_id, node_label, end_user_id)
+            
+            if not node_data:
+                raise ValueError(
+                    f"Node not found: {node_label} with id={node_id}"
                 )
-                
-                # 步骤3：原子性更新节点（使用事务）
-                updated_node = await self._atomic_update(
-                    node_id=node_id,
-                    node_label=node_label,
-                    update_data=update_data,
-                    end_user_id=end_user_id
-                )
-                
-                logger.info(
-                    f"成功记录访问: {node_label}[{node_id}], "
-                    f"activation={update_data['activation_value']:.4f}, "
-                    f"access_count={update_data['access_count']}"
-                )
-                
-                return updated_node
-                
-            except Exception as e:
-                if attempt < self.max_retries - 1:
-                    logger.warning(
-                        f"访问记录失败（尝试 {attempt + 1}/{self.max_retries}）: {str(e)}"
-                    )
-                    continue
-                else:
-                    logger.error(
-                        f"访问记录失败，重试次数耗尽: {node_label}[{node_id}], "
-                        f"错误: {str(e)}"
-                    )
-                    raise RuntimeError(
-                        f"Failed to record access after {self.max_retries} attempts: {str(e)}"
-                    )
-    
+            
+            # 步骤2：计算新的访问历史和激活值
+            update_data = await self._calculate_update(
+                node_data=node_data,
+                current_time=current_time,
+                current_time_iso=current_time_iso,
+                access_times=access_times
+            )
+            
+            # 步骤3：使用 APOC 原子操作更新节点（无需重试）
+            updated_node = await self._atomic_update(
+                node_id=node_id,
+                node_label=node_label,
+                update_data=update_data,
+                end_user_id=end_user_id
+            )
+            
+            logger.info(
+                f"成功记录访问: {node_label}[{node_id}], "
+                f"activation={update_data['activation_value']:.4f}, "
+                f"access_count={update_data['access_count']}"
+                f"{f', 合并访问次数={access_times}' if access_times > 1 else ''}"
+            )
+            
+            return updated_node
+            
+        except Exception as e:
+            logger.error(
+                f"访问记录失败: {node_label}[{node_id}], 错误: {str(e)}"
+            )
+            raise RuntimeError(
+                f"Failed to record access: {str(e)}"
+            ) from e
+
     async def record_batch_access(
         self,
         node_ids: List[str],
@@ -178,11 +158,10 @@ class AccessHistoryManager:
         """
         批量记录多个节点的访问
         
-        为提高性能，批量更新多个节点的访问历史。
-        每个节点独立更新，失败的节点不影响其他节点。
+        对同一个节点的多次访问会先在内存中合并，只发起一次更新。
         
         Args:
-            node_ids: 节点ID列表
+            node_ids: 节点ID列表（可包含重复ID）
             node_label: 节点标签（所有节点必须是同一类型）
             end_user_id: 组ID（可选）
             current_time: 当前时间（可选）
@@ -196,25 +175,38 @@ class AccessHistoryManager:
         if current_time is None:
             current_time = datetime.now()
         
-        # PERFORMANCE FIX: Process all nodes in parallel instead of sequentially
-        tasks = []
+        # 合并同一节点的访问次数，避免对同一节点并发写入
+        access_count_map: Dict[str, int] = {}
         for node_id in node_ids:
+            access_count_map[node_id] = access_count_map.get(node_id, 0) + 1
+        
+        merged_count = len(node_ids) - len(access_count_map)
+        if merged_count > 0:
+            logger.info(
+                f"批量访问合并: 原始={len(node_ids)}, "
+                f"去重后={len(access_count_map)}, 合并={merged_count}"
+            )
+        
+        # 对去重后的节点并行发起更新
+        tasks = []
+        for node_id, access_times in access_count_map.items():
             task = self.record_access(
                 node_id=node_id,
                 node_label=node_label,
                 end_user_id=end_user_id,
-                current_time=current_time
+                current_time=current_time,
+                access_times=access_times
             )
-            tasks.append(task)
+            tasks.append((node_id, task))
         
-        # Execute all tasks in parallel
-        task_results = await asyncio.gather(*tasks, return_exceptions=True)
+        task_results = await asyncio.gather(
+            *[t for _, t in tasks], return_exceptions=True
+        )
         
-        # Collect successful results and count failures
         results = []
         failed_count = 0
         
-        for node_id, result in zip(node_ids, task_results):
+        for (node_id, _), result in zip(tasks, task_results):
             if isinstance(result, Exception):
                 failed_count += 1
                 logger.warning(
@@ -225,12 +217,12 @@ class AccessHistoryManager:
         
         batch_duration = time.time() - batch_start
         logger.info(
-            f"[PERF] 批量访问记录完成: 成功 {len(results)}/{len(node_ids)}, "
+            f"[PERF] 批量访问记录完成: 成功 {len(results)}/{len(access_count_map)}, "
             f"失败 {failed_count}, 耗时 {batch_duration:.4f}s"
         )
         
         return results
-    
+
     async def check_consistency(
         self,
         node_id: str,
@@ -239,22 +231,6 @@ class AccessHistoryManager:
     ) -> Tuple[ConsistencyCheckResult, Optional[str]]:
         """
         检查节点数据的一致性
-        
-        验证以下一致性规则：
-        1. access_history[-1] == last_access_time
-        2. len(access_history) == access_count
-        3. 如果有访问历史，必须有激活值
-        4. 激活值必须在有效范围内 [offset, 1.0]
-        
-        Args:
-            node_id: 节点ID
-            node_label: 节点标签
-            end_user_id: 组ID（可选）
-        
-        Returns:
-            Tuple[ConsistencyCheckResult, Optional[str]]: 
-                - 一致性检查结果枚举
-                - 错误描述（如果不一致）
         """
         node_data = await self._fetch_node(node_id, node_label, end_user_id)
         
@@ -266,7 +242,6 @@ class AccessHistoryManager:
         access_count = node_data.get('access_count', 0)
         activation_value = node_data.get('activation_value')
         
-        # 检查1：access_history[-1] == last_access_time
         if access_history and last_access_time:
             if access_history[-1] != last_access_time:
                 return (
@@ -275,7 +250,6 @@ class AccessHistoryManager:
                     f"last_access_time={last_access_time}"
                 )
         
-        # 检查2：len(access_history) == access_count
         if len(access_history) != access_count:
             return (
                 ConsistencyCheckResult.INCONSISTENT_HISTORY_COUNT,
@@ -283,14 +257,12 @@ class AccessHistoryManager:
                 f"access_count={access_count}"
             )
         
-        # 检查3：有访问历史必须有激活值
         if access_history and activation_value is None:
             return (
                 ConsistencyCheckResult.MISSING_ACTIVATION,
                 "Node has access_history but activation_value is None"
             )
         
-        # 检查4：激活值范围
         if activation_value is not None:
             offset = self.actr_calculator.offset
             if not (offset <= activation_value <= 1.0):
@@ -301,30 +273,14 @@ class AccessHistoryManager:
                 )
         
         return ConsistencyCheckResult.CONSISTENT, None
-    
+
     async def check_batch_consistency(
         self,
         node_label: str,
         end_user_id: Optional[str] = None,
         limit: int = 1000
     ) -> Dict[str, Any]:
-        """
-        批量检查多个节点的一致性
-        
-        Args:
-            node_label: 节点标签
-            end_user_id: 组ID（可选）
-            limit: 检查的最大节点数
-        
-        Returns:
-            Dict[str, Any]: 一致性检查报告，包含：
-                - total_checked: 检查的节点总数
-                - consistent_count: 一致的节点数
-                - inconsistent_count: 不一致的节点数
-                - inconsistencies: 不一致节点的详细信息列表
-                - consistency_rate: 一致性率（0-1）
-        """
-        # 查询所有相关节点
+        """批量检查多个节点的一致性"""
         query = f"""
         MATCH (n:{node_label})
         WHERE n.access_history IS NOT NULL
@@ -343,7 +299,6 @@ class AccessHistoryManager:
         results = await self.connector.execute_query(query, **params)
         node_ids = [r['id'] for r in results]
         
-        # 检查每个节点
         inconsistencies = []
         consistent_count = 0
         
@@ -382,32 +337,15 @@ class AccessHistoryManager:
         )
         
         return report
-    
+
     async def repair_inconsistency(
         self,
         node_id: str,
         node_label: str,
         end_user_id: Optional[str] = None
     ) -> bool:
-        """
-        自动修复节点的数据不一致问题
-        
-        修复策略：
-        1. 如果access_history[-1] != last_access_time：使用access_history[-1]
-        2. 如果len(access_history) != access_count：使用len(access_history)
-        3. 如果有历史但无激活值：重新计算激活值
-        4. 如果激活值超出范围：重新计算激活值
-        
-        Args:
-            node_id: 节点ID
-            node_label: 节点标签
-            end_user_id: 组ID（可选）
-        
-        Returns:
-            bool: 修复成功返回True，否则返回False
-        """
+        """自动修复节点的数据不一致问题"""
         try:
-            # 检查一致性
             result, message = await self.check_consistency(
                 node_id=node_id,
                 node_label=node_label,
@@ -418,7 +356,6 @@ class AccessHistoryManager:
                 logger.info(f"节点数据一致，无需修复: {node_label}[{node_id}]")
                 return True
             
-            # 获取节点数据
             node_data = await self._fetch_node(node_id, node_label, end_user_id)
             if not node_data:
                 logger.error(f"节点不存在，无法修复: {node_label}[{node_id}]")
@@ -427,17 +364,13 @@ class AccessHistoryManager:
             access_history = node_data.get('access_history') or []
             importance_score = node_data.get('importance_score', 0.5)
             
-            # 准备修复数据
             repair_data = {}
             
-            # 修复last_access_time
             if access_history:
                 repair_data['last_access_time'] = access_history[-1]
             
-            # 修复access_count
             repair_data['access_count'] = len(access_history)
             
-            # 修复activation_value
             if access_history:
                 current_time = datetime.now()
                 last_access_dt = datetime.fromisoformat(access_history[-1])
@@ -453,7 +386,6 @@ class AccessHistoryManager:
                 )
                 repair_data['activation_value'] = activation_value
             
-            # 执行修复
             query = f"""
             MATCH (n:{node_label} {{id: $node_id}})
             """
@@ -484,26 +416,16 @@ class AccessHistoryManager:
                 f"修复节点失败: {node_label}[{node_id}], 错误: {str(e)}"
             )
             return False
-    
+
     # ==================== 私有辅助方法 ====================
-    
+
     async def _fetch_node(
         self,
         node_id: str,
         node_label: str,
         end_user_id: Optional[str] = None
     ) -> Optional[Dict[str, Any]]:
-        """
-        获取节点数据
-        
-        Args:
-            node_id: 节点ID
-            node_label: 节点标签
-            end_user_id: 组ID（可选）
-        
-        Returns:
-            Optional[Dict[str, Any]]: 节点数据，如果不存在返回None
-        """
+        """获取节点数据"""
         query = f"""
         MATCH (n:{node_label} {{id: $node_id}})
         """
@@ -527,12 +449,13 @@ class AccessHistoryManager:
         if results:
             return results[0]
         return None
-    
+
     async def _calculate_update(
         self,
         node_data: Dict[str, Any],
         current_time: datetime,
-        current_time_iso: str
+        current_time_iso: str,
+        access_times: int = 1
     ) -> Dict[str, Any]:
         """
         计算更新数据
@@ -541,45 +464,40 @@ class AccessHistoryManager:
             node_data: 当前节点数据
             current_time: 当前时间（datetime对象）
             current_time_iso: 当前时间（ISO格式字符串）
+            access_times: 本次访问次数（合并后可能大于1）
         
         Returns:
-            Dict[str, Any]: 更新数据，包含所有需要更新的字段
+            Dict[str, Any]: 更新数据
         """
-        access_history = node_data.get('access_history') or []
-        # Handle None importance_score - default to 0.5
         importance_score = node_data.get('importance_score')
         if importance_score is None:
             importance_score = 0.5
         
-        # 追加新的访问时间
-        new_access_history = access_history + [current_time_iso]
+        # 本次新增的时间戳
+        new_timestamps = [current_time_iso] * access_times
         
-        # 修剪访问历史（如果过长）
-        access_history_dt = [
-            datetime.fromisoformat(ts) for ts in new_access_history
-        ]
+        # 仅用本次新增的访问记录计算激活值
+        new_history_dt = [current_time] * access_times
         trimmed_history_dt = self.actr_calculator.trim_access_history(
-            access_history=access_history_dt,
+            access_history=new_history_dt,
             current_time=current_time
         )
-        trimmed_history = [ts.isoformat() for ts in trimmed_history_dt]
         
-        # 计算新的激活值
         activation_value = self.actr_calculator.calculate_memory_activation(
             access_history=trimmed_history_dt,
             current_time=current_time,
-            last_access_time=current_time,  # 最后访问时间就是当前时间
+            last_access_time=current_time,
             importance_score=importance_score
         )
         
-        # 返回所有需要更新的字段
         return {
             'activation_value': activation_value,
-            'access_history': trimmed_history,
+            'new_timestamps': new_timestamps,
+            'access_count_delta': access_times,
+            'access_count': len(trimmed_history_dt),
             'last_access_time': current_time_iso,
-            'access_count': len(trimmed_history)
         }
-    
+
     async def _atomic_update(
         self,
         node_id: str,
@@ -588,10 +506,10 @@ class AccessHistoryManager:
         end_user_id: Optional[str] = None
     ) -> Dict[str, Any]:
         """
-        原子性更新节点（使用乐观锁）
+        原子性更新节点（使用 APOC 原子操作）
         
-        使用Neo4j事务和版本号确保所有字段同时更新或回滚。
-        实现乐观锁机制防止并发冲突。
+        使用 apoc.atomic.add 和 apoc.atomic.insert 保证并发安全，
+        无需 version 字段和乐观锁，数据库层面保证原子性。
         
         Args:
             node_id: 节点ID
@@ -603,126 +521,68 @@ class AccessHistoryManager:
             Dict[str, Any]: 更新后的节点数据
         
         Raises:
-            RuntimeError: 如果更新失败或发生版本冲突
+            RuntimeError: 如果更新失败
         """
-        # 定义事务函数
-        async def update_transaction(tx, node_id, node_label, update_data, end_user_id):
-            # 步骤1：读取当前节点并获取版本号
-            read_query = f"""
-            MATCH (n:{node_label} {{id: $node_id}})
-            """
-            if end_user_id:
-                read_query += " WHERE n.end_user_id = $end_user_id"
-            read_query += """
-            RETURN n.id as id,
-                   n.version as version,
-                   n.activation_value as activation_value,
-                   n.access_history as access_history,
-                   n.last_access_time as last_access_time,
-                   n.access_count as access_count,
-                   n.importance_score as importance_score
-            """
+        content_field_map = {
+            'Statement': 'n.statement as statement',
+            'MemorySummary': 'n.content as content',
+            'ExtractedEntity': 'null as content_placeholder',
+            'Community': 'n.summary as summary'
+        }
+        
+        if node_label not in content_field_map:
+            raise ValueError(
+                f"Unsupported node_label: {node_label}. "
+                f"Supported labels are: {list(content_field_map.keys())}"
+            )
+        
+        content_field = content_field_map[node_label]
+        
+        where_clause = ""
+        if end_user_id:
+            where_clause = " AND n.end_user_id = $end_user_id"
+        
+        query = f"""
+        MATCH (n:{node_label} {{id: $node_id}})
+        WHERE true{where_clause}
+        CALL apoc.atomic.add(n, 'access_count', $access_count_delta, 5) YIELD oldValue AS old_count
+        WITH n
+        CALL (n) {{
+            UNWIND $new_timestamps AS ts
+            CALL apoc.atomic.insert(n, 'access_history', size(n.access_history), ts, 5) YIELD oldValue
+            RETURN count(*) AS inserted
+        }}
+        SET n.activation_value = $activation_value,
+            n.last_access_time = $last_access_time
+        RETURN n.id as id,
+               n.activation_value as activation_value,
+               n.access_history as access_history,
+               n.last_access_time as last_access_time,
+               n.access_count as access_count,
+               n.importance_score as importance_score,
+               {content_field}
+        """
+        
+        params = {
+            'node_id': node_id,
+            'access_count_delta': update_data['access_count_delta'],
+            'new_timestamps': update_data['new_timestamps'],
+            'activation_value': update_data['activation_value'],
+            'last_access_time': update_data['last_access_time'],
+        }
+        if end_user_id:
+            params['end_user_id'] = end_user_id
+        
+        try:
+            results = await self.connector.execute_query(query, **params)
             
-            read_params = {'node_id': node_id}
-            if end_user_id:
-                read_params['end_user_id'] = end_user_id
-            
-            read_result = await tx.run(read_query, **read_params)
-            current_node = await read_result.single()
-            
-            if not current_node:
+            if not results:
                 raise RuntimeError(f"Node not found: {node_label}[{node_id}]")
             
-            # 获取当前版本号（如果不存在则为0）
-            current_version = current_node.get('version', 0) or 0
-            new_version = current_version + 1
-            
-            # 步骤2：使用乐观锁更新节点
-            # 根据节点类型构建完整的查询语句
-            content_field_map = {
-                'Statement': 'n.statement as statement',
-                'MemorySummary': 'n.content as content',
-                'ExtractedEntity': 'null as content_placeholder'  # 占位符，后续会被过滤
-            }
-            
-            # 显式检查节点类型，不支持的类型抛出错误
-            if node_label not in content_field_map:
-                raise ValueError(
-                    f"Unsupported node_label: {node_label}. "
-                    f"Supported labels are: {list(content_field_map.keys())}"
-                )
-            
-            content_field = content_field_map[node_label]
-            
-            # 构建 WHERE 子句
-            where_conditions = []
-            if end_user_id:
-                where_conditions.append("n.end_user_id = $end_user_id")
-            
-            # 添加版本检查
-            if current_version > 0:
-                where_conditions.append("n.version = $current_version")
-            else:
-                where_conditions.append("(n.version IS NULL OR n.version = 0)")
-            
-            where_clause = " AND ".join(where_conditions) if where_conditions else "true"
-            
-            # 构建完整的更新查询
-            update_query = f"""
-            MATCH (n:{node_label} {{id: $node_id}})
-            WHERE {where_clause}
-            SET n.activation_value = $activation_value,
-                n.access_history = $access_history,
-                n.last_access_time = $last_access_time,
-                n.access_count = $access_count,
-                n.version = $new_version
-            RETURN n.id as id,
-                   n.activation_value as activation_value,
-                   n.access_history as access_history,
-                   n.last_access_time as last_access_time,
-                   n.access_count as access_count,
-                   n.importance_score as importance_score,
-                   n.version as version,
-                   {content_field}
-            """
-            
-            update_params = {
-                'node_id': node_id,
-                'current_version': current_version,
-                'new_version': new_version,
-                'activation_value': update_data['activation_value'],
-                'access_history': update_data['access_history'],
-                'last_access_time': update_data['last_access_time'],
-                'access_count': update_data['access_count']
-            }
-            if end_user_id:
-                update_params['end_user_id'] = end_user_id
-            
-            update_result = await tx.run(update_query, **update_params)
-            updated_node = await update_result.single()
-            
-            if not updated_node:
-                raise RuntimeError(
-                    f"Version conflict detected for {node_label}[{node_id}]. "
-                    f"Expected version {current_version}, but node was modified by another transaction."
-                )
-            
-            # 转换为字典并移除占位符字段
-            result_dict = dict(updated_node)
+            result_dict = dict(results[0])
             result_dict.pop('content_placeholder', None)
             
             return result_dict
-        
-        # 执行事务
-        try:
-            result = await self.connector.execute_write_transaction(
-                update_transaction,
-                node_id=node_id,
-                node_label=node_label,
-                update_data=update_data,
-                end_user_id=end_user_id
-            )
-            return result
         except Exception as e:
             logger.error(
                 f"原子性更新失败: {node_label}[{node_id}], 错误: {str(e)}"

api/app/core/memory/storage_services/search/keyword_search.py

@@ -5,7 +5,7 @@
 使用Neo4j的全文索引进行高效的文本匹配。
 """
 
-from typing import List, Dict, Any, Optional
+from typing import List, Optional
 from app.core.logging_config import get_memory_logger
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.core.memory.storage_services.search.search_strategy import SearchStrategy, SearchResult
@@ -74,7 +74,7 @@ class KeywordSearchStrategy(SearchStrategy):
             # 调用底层的关键词搜索函数
             results_dict = await search_graph(
                 connector=self.connector,
-                q=query_text,
+                query=query_text,
                 end_user_id=end_user_id,
                 limit=limit,
                 include=include_list

api/app/core/memory/utils/data/text_utils.py

@@ -22,7 +22,9 @@ def escape_lucene_query(query: str) -> str:
     s = s.replace("\r", " ").replace("\n", " ").strip()
 
     # Lucene reserved tokens/special characters
-    specials = ['&&', '||', '\\', '+', '-', '!', '(', ')', '{', '}', '[', ']', '^', '"', '~', '*', '?', ':']
+    # NOTE: '/' is the regex delimiter in Lucene — must be escaped to prevent
+    #       TokenMgrError when the query contains unmatched slashes.
+    specials = ['&&', '||', '\\', '+', '-', '!', '(', ')', '{', '}', '[', ']', '^', '"', '~', '*', '?', ':', '/']
     # Replace longer tokens first to avoid partial double-escaping
     for token in sorted(specials, key=len, reverse=True):
         s = s.replace(token, f"\\{token}")

api/app/core/memory/utils/prompt/prompt_utils.py

@@ -1,6 +1,6 @@
 import os
 from jinja2 import Environment, FileSystemLoader
-
+from app.core.memory.models.ontology_extraction_models import OntologyTypeList
 from app.core.memory.utils.log.logging_utils import log_prompt_rendering, log_template_rendering
 
 # Setup Jinja2 environment
@@ -205,6 +205,7 @@ async def render_triplet_extraction_prompt(
     predicate_instructions: dict = None,
     language: str = "zh",
     ontology_types: "OntologyTypeList | None" = None,
+    speaker: str = None,
 ) -> str:
     """
     Renders the triplet extraction prompt using the extract_triplet.jinja2 template.
@@ -216,6 +217,7 @@ async def render_triplet_extraction_prompt(
         predicate_instructions: Optional predicate instructions
         language: The language to use for entity descriptions ("zh" for Chinese, "en" for English)
         ontology_types: Optional OntologyTypeList containing predefined ontology types for entity classification
+        speaker: Speaker role ("user" or "assistant") for the current statement
 
     Returns:
         Rendered prompt content as string
@@ -223,7 +225,7 @@ async def render_triplet_extraction_prompt(
     template = prompt_env.get_template("extract_triplet.jinja2")
     
     # 准备本体类型数据
-    ontology_type_section = ""
+    ontology_type_section = None
     ontology_type_names = []
     type_hierarchy_hints = []
     if ontology_types and ontology_types.types:
@@ -240,6 +242,7 @@ async def render_triplet_extraction_prompt(
         ontology_types=ontology_type_section,
         ontology_type_names=ontology_type_names,
         type_hierarchy_hints=type_hierarchy_hints,
+        speaker=speaker,
     )
     # 记录渲染结果到提示日志（与示例日志结构一致）
     log_prompt_rendering('triplet extraction', rendered_prompt)

api/app/core/memory/utils/prompt/prompts/extract_statement.jinja2

@@ -43,8 +43,9 @@ Each statement must be labeled as per the criteria mentioned below.
 
 对话上下文和共指消解：
 - 将每个陈述句归属于说出它的参与者。
-- 如果参与者列表为说话者提供了名称（例如，"李雪（用户）"），请在提取的陈述句中使用具体名称（"李雪"），而不是通用角色（"用户"）。
-- 将所有代词解析为对话上下文中的具体人物或实体。
+- **对于用户的发言：必须使用"用户"作为主语**，禁止将"用户"或"我"替换为用户的真实姓名或别名。例如，用户说"我叫张三"应提取为"用户叫张三"，而不是"张三叫张三"。
+- 对于 AI 助手的发言：使用"助手"或"AI助手"作为主语。
+- 将所有代词解析为对话上下文中的具体人物或实体，但"我"必须解析为"用户"。
 - 识别并将抽象引用解析为其具体名称（如果提到）。
 - 将缩写和首字母缩略词扩展为其完整形式。
 {% else %}
@@ -68,8 +69,9 @@ Context Resolution Requirements:
 
 Conversational Context & Co-reference Resolution:
 - Attribute every statement to the participant who uttered it.
-- If the participant list provides a name for a speaker (e.g., "李雪 (用户)"), use the specific name ("李雪") in the extracted statement, not the generic role ("用户").
-- Resolve all pronouns to the specific person or entity from the conversation's context.
+- **For user's statements: always use "用户" (User) as the subject**. Do NOT replace "用户" or "I" with the user's real name or alias. For example, if the user says "I'm John", extract as "用户 is John", not "John is John".
+- For AI assistant's statements: use "助手" or "AI助手" as the subject.
+- Resolve all pronouns to the specific person or entity from the conversation's context, but "I"/"我" must always resolve to "用户".
 - Identify and resolve abstract references to their specific names if mentioned.
 - Expand abbreviations and acronyms to their full form.
 {% endif %}
@@ -139,13 +141,13 @@ AI: "水彩画很有趣！水彩颜料通常由颜料与阿拉伯树胶等粘合
 示例输出: {
   "statements": [
     {
-      "statement": "Sarah Chen 最近一直在尝试水彩画。",
+      "statement": "用户最近一直在尝试水彩画。",
       "statement_type": "FACT",
       "temporal_type": "DYNAMIC",
       "relevance": "RELEVANT"
     },
     {
-      "statement": "Sarah Chen 画了一些花朵。",
+      "statement": "用户画了一些花朵。",
       "statement_type": "FACT",
       "temporal_type": "DYNAMIC",
       "relevance": "RELEVANT"
@@ -157,13 +159,13 @@ AI: "水彩画很有趣！水彩颜料通常由颜料与阿拉伯树胶等粘合
       "relevance": "IRRELEVANT"
     },
     {
-      "statement": "Sarah Chen 认为她的水彩画中的色彩组合可以改进。",
+      "statement": "用户认为她的水彩画中的色彩组合可以改进。",
       "statement_type": "OPINION",
       "temporal_type": "STATIC",
       "relevance": "RELEVANT"
     },
     {
-      "statement": "Sarah Chen 真的很喜欢玫瑰和百合。",
+      "statement": "用户真的很喜欢玫瑰和百合。",
       "statement_type": "FACT",
       "temporal_type": "STATIC",
       "relevance": "RELEVANT"
@@ -186,13 +188,13 @@ AI: "水彩画很有趣！水彩颜料通常由颜料和阿拉伯树胶等粘合
 示例输出: {
   "statements": [
     {
-      "statement": "张曼婷最近在尝试水彩画。",
+      "statement": "用户最近在尝试水彩画。",
       "statement_type": "FACT",
       "temporal_type": "DYNAMIC",
       "relevance": "RELEVANT"
     },
     {
-      "statement": "张曼婷画了一些花朵。",
+      "statement": "用户画了一些花朵。",
       "statement_type": "FACT",
       "temporal_type": "DYNAMIC",
       "relevance": "RELEVANT"
@@ -204,13 +206,13 @@ AI: "水彩画很有趣！水彩颜料通常由颜料和阿拉伯树胶等粘合
       "relevance": "IRRELEVANT"
     },
     {
-      "statement": "张曼婷觉得水彩画的色彩搭配还有提升的空间。",
+      "statement": "用户觉得水彩画的色彩搭配还有提升的空间。",
       "statement_type": "OPINION",
       "temporal_type": "STATIC",
       "relevance": "RELEVANT"
     },
     {
-      "statement": "张曼婷很喜欢玫瑰和百合。",
+      "statement": "用户很喜欢玫瑰和百合。",
       "statement_type": "FACT",
       "temporal_type": "STATIC",
       "relevance": "RELEVANT"
@@ -233,13 +235,13 @@ User: "I think the color combinations could use some improvement, but I really l
 Example Output: {
   "statements": [
     {
-      "statement": "Sarah Chen has been trying watercolor painting recently.",
+      "statement": "用户 has been trying watercolor painting recently.",
       "statement_type": "FACT",
       "temporal_type": "DYNAMIC",
       "relevance": "RELEVANT"
     },
     {
-      "statement": "Sarah Chen painted some flowers.",
+      "statement": "用户 painted some flowers.",
       "statement_type": "FACT",
       "temporal_type": "DYNAMIC",
       "relevance": "RELEVANT"
@@ -251,13 +253,13 @@ Example Output: {
       "relevance": "IRRELEVANT"
     },
     {
-      "statement": "Sarah Chen thinks the color combinations in her watercolor paintings could use some improvement.",
+      "statement": "用户 thinks the color combinations in her watercolor paintings could use some improvement.",
       "statement_type": "OPINION",
       "temporal_type": "STATIC",
       "relevance": "RELEVANT"
     },
     {
-      "statement": "Sarah Chen really likes roses and lilies.",
+      "statement": "用户 really likes roses and lilies.",
       "statement_type": "FACT",
       "temporal_type": "STATIC",
       "relevance": "RELEVANT"
@@ -280,13 +282,13 @@ AI: "水彩画很有趣！水彩颜料通常由颜料和阿拉伯树胶等粘合
 Example Output: {
   "statements": [
     {
-      "statement": "张曼婷最近在尝试水彩画。",
+      "statement": "用户最近在尝试水彩画。",
       "statement_type": "FACT",
       "temporal_type": "DYNAMIC",
       "relevance": "RELEVANT"
     },
     {
-      "statement": "张曼婷画了一些花朵。",
+      "statement": "用户画了一些花朵。",
       "statement_type": "FACT",
       "temporal_type": "DYNAMIC",
       "relevance": "RELEVANT"
@@ -298,13 +300,13 @@ Example Output: {
       "relevance": "IRRELEVANT"
     },
     {
-      "statement": "张曼婷觉得水彩画的色彩搭配还有提升的空间。",
+      "statement": "用户觉得水彩画的色彩搭配还有提升的空间。",
       "statement_type": "OPINION",
       "temporal_type": "STATIC",
       "relevance": "RELEVANT"
     },
     {
-      "statement": "张曼婷很喜欢玫瑰和百合。",
+      "statement": "用户很喜欢玫瑰和百合。",
       "statement_type": "FACT",
       "temporal_type": "STATIC",
       "relevance": "RELEVANT"

api/app/core/memory/utils/prompt/prompts/extract_triplet.jinja2

@@ -23,6 +23,16 @@ Extract entities and knowledge triplets from the given statement.
 ===Inputs===
 **Chunk Content:** "{{ chunk_content }}"
 **Statement:** "{{ statement }}"
+{% if speaker %}
+**Speaker:** {{ speaker }}
+{% if speaker == "assistant" %}
+{% if language == "zh" %}
+⚠️ 当前陈述句来自 **AI助手的回复**。AI助手在回复中用来称呼用户的名字是**用户的别名**，不是 AI 助手的别名。但只能提取原文中逐字出现的名字，严禁推测或创造原文中不存在的别名变体。
+{% else %}
+⚠️ This statement is from the **AI assistant's reply**. Names the AI uses to address the user are **user's aliases**, NOT the AI assistant's aliases. But only extract names that appear VERBATIM in the text — never infer or fabricate alias variants.
+{% endif %}
+{% endif %}
+{% endif %}
 
 {% if ontology_types %}
 ===Ontology Type Guidance===
@@ -87,7 +97,17 @@ Extract entities and knowledge triplets from the given statement.
      * "我叫张三，大家叫我小张" → aliases=["张三", "小张"]（张三是第一个，将成为 other_name）
      * "大家叫我小李，我全名叫李明" → aliases=["小李", "李明"]（小李先出现，将成为 other_name）
    - 空值：如果没有别名，使用 `[]`
-   - 重要：只提取本次对话中明确提到的别名，不要推测或添加未提及的名字
+   - **🚨🚨🚨 严禁幻觉：只提取对话原文中逐字出现的别名，绝对不能推测、衍生或创造任何未在原文中出现的名字。例如，看到"陈思远"不能自行添加"思远大人""远哥""小远"等变体。如果原文没有这些字，就不能出现在 aliases 中。**
+   - **🚨 归属区分：必须严格区分名称的归属对象。默认情况下，用户提到的名字归属用户实体。只有出现明确的第二人称命名表达（如"叫你""给你取名"）时，才将名字归属 AI/助手实体。**
+   - **🚨 说话人视角：当 speaker 为 assistant 时，AI 助手用来称呼用户的名字是用户的别名，必须归入用户实体的 aliases，绝对不能归入 AI 助手实体。但同样只能提取原文中逐字出现的称呼，不能推测。**
+     * "我叫陈思远，我给AI取名为远仔" → 用户 aliases=["陈思远"]，AI助手 aliases=["远仔"]
+     * "我叫vv" → 用户 aliases=["vv"]（没有给AI取名的表达，名字归用户）
+     * [speaker=assistant] "好的，VV" → 用户 aliases=["VV"]（AI 在称呼用户，原文中出现了"VV"）
+     * [speaker=assistant] "我叫陈仔" → AI助手 aliases=["陈仔"]（AI 在自我介绍，这是 AI 的别名）
+     * ❌ 错误：将"远仔"放入用户的 aliases（"远仔"是给AI取的名字，不是用户的名字）
+     * ❌ 错误：用户说"我叫vv"，却把"vv"放入 AI 助手的 aliases
+     * ❌ 错误：AI 称呼用户为"VV"，却把"VV"放入 AI 助手的 aliases
+     * ❌ 错误：原文只有"陈思远"，却在 aliases 中添加"思远大人""远哥""小远"等从未出现的变体（这是幻觉）
 {% else %}
    - Include: nicknames, full names, abbreviations, alternative names
    - Order: **The FIRST alias will be used as the user's primary display name (other_name). Put the most important/frequently used name FIRST**
@@ -96,7 +116,17 @@ Extract entities and knowledge triplets from the given statement.
      * "I'm John, people call me Johnny" → aliases=["John", "Johnny"] (John is first, will become other_name)
      * "People call me Mike, my full name is Michael" → aliases=["Mike", "Michael"] (Mike appears first, will become other_name)
    - Empty: If no aliases, use `[]`
-   - Important: Only extract aliases explicitly mentioned in current conversation, do not infer or add unmentioned names
+   - **🚨🚨🚨 NO HALLUCINATION: Only extract aliases that appear VERBATIM in the original text. NEVER infer, derive, or fabricate names not present in the text. For example, seeing "John Smith" does NOT allow adding "Johnny", "Smithy", "Mr. Smith" unless those exact strings appear in the conversation.**
+   - **🚨 Ownership distinction: By default, all names mentioned by the user belong to the user entity. Only assign a name to the AI/assistant entity when an explicit second-person naming expression (e.g., "I'll call you", "your name is") is present.**
+   - **🚨 Speaker perspective: When speaker is "assistant", names the AI uses to address the user are the USER's aliases and MUST go into the user entity's aliases, NEVER into the AI assistant entity's aliases. But only extract names that appear verbatim in the text, never infer.**
+     * "I'm Alex, I'll call you Buddy" → User aliases=["Alex"], AI assistant aliases=["Buddy"]
+     * "I'm vv" → User aliases=["vv"] (no AI-naming expression, name belongs to user)
+     * [speaker=assistant] "Sure thing, VV" → User aliases=["VV"] (AI addressing the user, "VV" appears in text)
+     * [speaker=assistant] "I'm Jarvis" → AI assistant aliases=["Jarvis"] (AI self-introduction, this is AI's alias)
+     * ❌ Wrong: putting "Buddy" in user's aliases ("Buddy" is a name for the AI, not the user)
+     * ❌ Wrong: User says "I'm vv" but "vv" is put in AI assistant's aliases
+     * ❌ Wrong: AI calls user "VV" but "VV" is put in AI assistant's aliases
+     * ❌ Wrong: Text only has "John Smith" but aliases include "Johnny", "Smithy" (hallucinated variants)
 {% endif %}
 
 
@@ -122,7 +152,60 @@ Extract entities and knowledge triplets from the given statement.
 
 
 
-4. **ALIASES ORDER:**
+4. **AI/ASSISTANT ENTITY SPECIAL HANDLING:**
+{% if language == "zh" %}
+   - **🚨 默认规则：如果对话中没有出现明确指向 AI/助手的命名表达，则所有名字都归属于用户实体。不要猜测或推断某个名字是给 AI 取的。**
+   - 只有当用户**明确**对 AI/助手进行命名时，才创建 AI/助手实体并将对应名字放入其 aliases
+   - AI/助手实体的 name 字段：使用 "AI助手"
+   - 用户给 AI 取的名字：放入 AI/助手实体的 aliases
+   - **🚨 禁止将用户给 AI 取的名字放入用户实体的 aliases 中**
+   - **必须出现以下明确的命名表达才能判定为给 AI 取名：**「给你取名」「叫你」「称呼你为」「给AI取名」「你的名字是」「以后叫你」「你就叫」「你不叫X了」「你现在叫」等**第二人称（你）或明确指向 AI 的命名句式**
+   - **🚨 "你不叫X了"/"你不叫X，你叫Y" 句式：X 和 Y 都是 AI 的名字（旧名和新名），绝对不是用户的名字。因为句子主语是"你"（AI）。**
+   - **以下情况名字归属用户，不是给 AI 取名：**「我叫」「我的名字是」「叫我」「我是」「大家叫我」「我的英文名是」「我的昵称是」等**第一人称（我）的自我介绍句式**
+   - **🚨 speaker=assistant 时的特殊规则：**
+     * AI 用来称呼用户的名字 → 归入**用户**实体的 aliases（但必须是原文中逐字出现的称呼，不能推测）
+     * AI 自称的名字（如"我叫陈仔""我是你的助手"）→ 归入**AI助手**实体的 aliases
+     * 判断依据：AI 说"你叫X"或用 X 称呼用户 → X 是用户别名；AI 说"我叫X"或"我是X" → X 是 AI 别名
+   - 示例：
+     * "我叫vv" → 用户实体: name="用户", aliases=["vv"]（第一人称自我介绍，名字归用户）
+     * "我的英文名叫vv" → 用户实体: name="用户", aliases=["vv"]（第一人称自我介绍，名字归用户）
+     * "我叫陈思远，我给AI取名为远仔" → 用户实体: name="用户", aliases=["陈思远"]；AI实体: name="AI助手", aliases=["远仔"]
+     * "叫你小助，我自己叫老王" → 用户实体: name="用户", aliases=["老王"]；AI实体: name="AI助手", aliases=["小助"]
+     * "你不叫远仔了，你现在叫陈仔" → AI实体: name="AI助手", aliases=["陈仔"]（"远仔"是AI旧名，"陈仔"是AI新名，都归AI。不要把"远仔"或"陈仔"放入用户的aliases）
+     * [speaker=assistant] "好的VV，今天想干点啥？" → 用户实体: name="用户", aliases=["VV"]（AI 在称呼用户，原文中出现了"VV"）
+     * [speaker=assistant] "你叫陈思远，我叫陈仔" → 用户实体: name="用户", aliases=["陈思远"]；AI实体: name="AI助手", aliases=["陈仔"]
+     * ❌ 错误：用户说"我叫vv"，却把"vv"放入 AI 助手的 aliases（没有任何给 AI 取名的表达）
+     * ❌ 错误：AI 称呼用户为"VV"，却把"VV"放入 AI 助手的 aliases
+     * ❌ 错误：aliases=["陈思远", "远仔"]（"远仔"是给AI取的名字，不是用户的名字）
+     * ❌ 错误：原文只有"陈思远"，却在 aliases 中添加"思远大人""远哥""小远"等从未出现的变体（这是幻觉）
+{% else %}
+   - **🚨 Default rule: If there is NO explicit AI/assistant naming expression in the conversation, ALL names belong to the user entity. Do NOT guess or infer that a name is for the AI.**
+   - Only create an AI/assistant entity when the user **explicitly** names the AI/assistant
+   - AI/assistant entity name field: use "AI Assistant"
+   - Names the user gives to the AI: put in the AI/assistant entity's aliases
+   - **🚨 NEVER put names given to the AI into the user entity's aliases**
+   - **An AI-naming expression MUST be present to assign a name to the AI:** "I'll call you", "your name is", "I name you", "let me call you", "you'll be called", "you're not called X anymore", "your new name is", etc. — **second-person ("you") or explicit AI-directed naming patterns**
+   - **🚨 "You're not called X anymore" / "You're not X, you're Y" pattern: BOTH X and Y are AI's names (old and new). They are NOT user's names. The subject is "you" (the AI).**
+   - **These patterns mean the name belongs to the USER, NOT the AI:** "I'm", "my name is", "call me", "I am", "people call me", "my English name is", "my nickname is", etc. — **first-person ("I"/"me") self-introduction patterns**
+   - **🚨 Special rules when speaker=assistant:**
+     * Names the AI uses to address the user → belong to the **user** entity's aliases (but only extract names that appear verbatim in the text, never infer)
+     * Names the AI uses for itself (e.g., "I'm Jarvis", "I am your assistant") → belong to the **AI assistant** entity's aliases
+     * Rule: AI says "you are X" or calls user X → X is user's alias; AI says "I'm X" or "I am X" → X is AI's alias
+   - Examples:
+     * "I'm vv" → User entity: name="User", aliases=["vv"] (first-person intro, name belongs to user)
+     * "My English name is vv" → User entity: name="User", aliases=["vv"] (first-person intro, name belongs to user)
+     * "I'm Alex, I'll call you Buddy" → User entity: name="User", aliases=["Alex"]; AI entity: name="AI Assistant", aliases=["Buddy"]
+     * "Call yourself Jarvis, my name is Tony" → User entity: name="User", aliases=["Tony"]; AI entity: name="AI Assistant", aliases=["Jarvis"]
+     * "You're not called Jarvis anymore, your new name is Friday" → AI entity: name="AI Assistant", aliases=["Friday"] (both "Jarvis" and "Friday" are AI names, NOT user names)
+     * [speaker=assistant] "Sure thing, VV" → User entity: name="User", aliases=["VV"] (AI addressing the user, "VV" appears in text)
+     * [speaker=assistant] "You're Alex, and I'm Jarvis" → User entity: name="User", aliases=["Alex"]; AI entity: name="AI Assistant", aliases=["Jarvis"]
+     * ❌ Wrong: User says "I'm vv" but "vv" is put in AI assistant's aliases (no AI-naming expression exists)
+     * ❌ Wrong: AI calls user "VV" but "VV" is put in AI assistant's aliases
+     * ❌ Wrong: aliases=["Alex", "Buddy"] ("Buddy" is a name for the AI, not the user)
+     * ❌ Wrong: Text only has "John Smith" but aliases include "Johnny", "Smithy" (hallucinated variants)
+{% endif %}
+
+5. **ALIASES ORDER:**
 {% if language == "zh" %}
    - 顺序优先级：按出现顺序，先出现的在前
 {% else %}
@@ -202,8 +285,19 @@ Output:
     {"entity_idx": 0, "name": "Tripod", "type": "Equipment", "description": "Photography equipment accessory", "example": "", "aliases": ["Camera Tripod"], "is_explicit_memory": false}
   ]
 }
+
+**Example 4 (User vs AI alias distinction - English output):** "I'm Alex, and I'll call you Buddy"
+Output:
+{
+  "triplets": [
+    {"subject_name": "User", "subject_id": 0, "predicate": "NAMED", "object_name": "AI Assistant", "object_id": 1, "value": "Buddy"}
+  ],
+  "entities": [
+    {"entity_idx": 0, "name": "User", "type": "Person", "description": "The user", "example": "", "aliases": ["Alex"], "is_explicit_memory": false},
+    {"entity_idx": 1, "name": "AI Assistant", "type": "Person", "description": "The user's AI assistant", "example": "", "aliases": ["Buddy"], "is_explicit_memory": false}
+  ]
+}
 {% else %}
-**Example 1 (English input → Chinese output):** "I plan to travel to Paris next week and visit the Louvre."
 Output:
 {
   "triplets": [
@@ -258,6 +352,39 @@ Output:
   ]
 }
 
+**Example 6 (用户与AI别名区分 - Chinese):** "我称呼自己为陈思远，我给AI取名为远仔"
+Output:
+{
+  "triplets": [
+    {"subject_name": "用户", "subject_id": 0, "predicate": "NAMED", "object_name": "AI助手", "object_id": 1, "value": "远仔"}
+  ],
+  "entities": [
+    {"entity_idx": 0, "name": "用户", "type": "Person", "description": "用户本人", "example": "", "aliases": ["陈思远"], "is_explicit_memory": false},
+    {"entity_idx": 1, "name": "AI助手", "type": "Person", "description": "用户的AI助手", "example": "", "aliases": ["远仔"], "is_explicit_memory": false}
+  ]
+}
+
+**Example 7 (纯用户自我介绍，无AI命名 - Chinese):** "我叫vv"
+Output:
+{
+  "triplets": [],
+  "entities": [
+    {"entity_idx": 0, "name": "用户", "type": "Person", "description": "用户本人", "example": "", "aliases": ["vv"], "is_explicit_memory": false}
+  ]
+}
+
+**Example 8 (给AI改名 - Chinese):** "你不叫远仔了，你现在叫陈仔"
+Output:
+{
+  "triplets": [
+    {"subject_name": "用户", "subject_id": 0, "predicate": "NAMED", "object_name": "AI助手", "object_id": 1, "value": "陈仔"}
+  ],
+  "entities": [
+    {"entity_idx": 0, "name": "用户", "type": "Person", "description": "用户本人", "example": "", "aliases": [], "is_explicit_memory": false},
+    {"entity_idx": 1, "name": "AI助手", "type": "Person", "description": "用户的AI助手", "example": "", "aliases": ["陈仔"], "is_explicit_memory": false}
+  ]
+}
+
 
 {% endif %}
 ===End of Examples===
@@ -279,4 +406,12 @@ Output:
 - **⚠️ ALIASES ORDER: preserve temporal order of appearance**
 - **🚨 MANDATORY FIELD: EVERY entity MUST include "aliases" field, even if empty array []**
 
+**Output JSON structure:**
+```json
+{
+  "triplets": [...],
+  "entities": [...]
+}
+```
+
 {{ json_schema }}

api/app/core/memory/utils/prompt/prompts/extract_user_metadata.jinja2

@@ -0,0 +1,135 @@
+===Task===
+Extract user metadata from the following conversation statements spoken by the user.
+
+{% if language == "zh" %}
+**"三度原则"判断标准：**
+- 复用度：该信息是否会被多个功能模块使用？
+- 约束度：该信息是否会影响系统行为？
+- 时效性：该信息是长期稳定的还是临时的？仅提取长期稳定信息。
+
+**提取规则：**
+- **只提取关于"用户本人"的画像信息**，忽略用户提到的第三方人物（如朋友、同事、家人）的信息
+- 仅提取文本中明确提到的信息，不要推测
+- 如果文本中没有可提取的用户画像信息，返回空的 user_metadata 对象
+- **输出语言必须与输入文本的语言一致**（输入中文则输出中文值，输入英文则输出英文值）
+
+{% if existing_metadata %}
+**重要：合并已有元数据**
+下方提供了数据库中已有的用户元数据。请结合用户最新发言，输出**合并后的完整元数据**：
+- 如果用户明确否定了已有信息（如"我不再教高中物理了"），在输出中**移除**该信息
+- 如果用户提到了新信息，**添加**到对应字段中
+- 如果已有信息未被用户否定，**保留**在输出中
+- 标量字段（如 role、domain）：如果用户提到了新值，用新值替换；否则保留已有值
+- 最终输出应该是完整的、合并后的元数据，不是增量
+{% endif %}
+
+**字段说明：**
+- profile.role：用户的职业或角色，如 教师、医生、后端工程师
+- profile.domain：用户所在领域，如 教育、医疗、软件开发
+- profile.expertise：用户擅长的技能或工具（通用，不限于编程），如 Python、心理咨询、高中物理
+- profile.interests：用户主动表达兴趣的话题或领域标签
+- behavioral_hints.learning_stage：学习阶段（初学者/中级/高级）
+- behavioral_hints.preferred_depth：偏好深度（概览/技术细节/深入探讨）
+- behavioral_hints.tone_preference：语气偏好（轻松随意/专业简洁/学术严谨）
+- knowledge_tags：用户涉及的知识领域标签
+
+**用户别名变更（增量模式）：**
+- **aliases_to_add**：本次新发现的用户别名，包括：
+  * 用户主动自我介绍：如"我叫张三"、"我的名字是XX"、"我的网名是XX"
+  * 他人对用户的称呼：如"同事叫我陈哥"、"大家叫我小张"、"领导叫我老陈"
+  * 只提取原文中逐字出现的名字，严禁推测或创造
+  * 禁止提取：用户给 AI 取的名字、第三方人物自身的名字、"用户"/"我" 等占位词
+  * 如果没有新别名，返回空数组 `[]`
+- **aliases_to_remove**：用户明确否认的别名，包括：
+  * 用户说"我不叫XX了"、"别叫我XX"、"我改名了，不叫XX" → 将 XX 放入此数组
+  * **严格限制**：只将用户原文中**逐字提到**的被否认名字放入，不要推断关联的其他别名
+  * 例如：用户说"我不叫陈小刀了" → 只移除"陈小刀"，不要移除"陈哥"、"老陈"等未被提及的别名
+  * 如果没有要移除的别名，返回空数组 `[]`
+{% if existing_aliases %}
+- 已有别名：{{ existing_aliases | tojson }}（仅供参考，不需要在输出中重复）
+{% endif %}
+{% else %}
+**"Three-Degree Principle" criteria:**
+- Reusability: Will this information be used by multiple functional modules?
+- Constraint: Will this information affect system behavior?
+- Timeliness: Is this information long-term stable or temporary? Only extract long-term stable information.
+
+**Extraction rules:**
+- **Only extract profile information about the user themselves**, ignore information about third parties (friends, colleagues, family) mentioned by the user
+- Only extract information explicitly mentioned in the text, do not speculate
+- If no user profile information can be extracted, return an empty user_metadata object
+- **Output language must match the input text language**
+
+{% if existing_metadata %}
+**Important: Merge with existing metadata**
+Existing user metadata from the database is provided below. Combine with the user's latest statements to output the **complete merged metadata**:
+- If the user explicitly negates existing info (e.g. "I no longer teach high school physics"), **remove** it from output
+- If the user mentions new info, **add** it to the corresponding field
+- If existing info is not negated by the user, **keep** it in the output
+- Scalar fields (e.g. role, domain): replace with new value if user mentions one; otherwise keep existing
+- The final output should be the complete, merged metadata — not an incremental update
+{% endif %}
+
+**Field descriptions:**
+- profile.role: User's occupation or role, e.g. teacher, doctor, software engineer
+- profile.domain: User's domain, e.g. education, healthcare, software development
+- profile.expertise: User's skills or tools (general, not limited to programming)
+- profile.interests: Topics or domain tags the user actively expressed interest in
+- behavioral_hints.learning_stage: Learning stage (beginner/intermediate/advanced)
+- behavioral_hints.preferred_depth: Preferred depth (overview/detailed/deep dive)
+- behavioral_hints.tone_preference: Tone preference (casual/professional/academic)
+- knowledge_tags: Knowledge domain tags related to the user
+
+**User alias changes (incremental mode):**
+- **aliases_to_add**: Newly discovered user aliases from this conversation, including:
+  * User self-introductions: e.g. "I'm John", "My name is XX", "My username is XX"
+  * How others address the user: e.g. "My colleagues call me Johnny", "People call me Mike"
+  * Only extract names that appear VERBATIM in the text — never infer or fabricate
+  * Do NOT extract: names the user gives to the AI, third-party people's own names, placeholder words like "User"/"I"
+  * If no new aliases, return empty array `[]`
+- **aliases_to_remove**: Aliases the user explicitly denies, including:
+  * User says "Don't call me XX anymore", "I'm not called XX", "I changed my name from XX" → put XX in this array
+  * **Strict rule**: Only include the exact name the user **verbatim mentions** as denied. Do NOT infer or remove related aliases
+  * Example: User says "I'm not called John anymore" → only remove "John", do NOT remove "Johnny", "J" or other related aliases not mentioned
+  * If no aliases to remove, return empty array `[]`
+{% if existing_aliases %}
+- Existing aliases: {{ existing_aliases | tojson }} (for reference only, do not repeat in output)
+{% endif %}
+{% endif %}
+
+===User Statements===
+{% for stmt in statements %}
+- {{ stmt }}
+{% endfor %}
+
+{% if existing_metadata %}
+===Existing User Metadata===
+```json
+{{ existing_metadata | tojson }}
+```
+{% endif %}
+
+===Output Format===
+Return a JSON object with the following structure:
+```json
+{
+  "user_metadata": {
+    "profile": {
+      "role": "",
+      "domain": "",
+      "expertise": [],
+      "interests": []
+    },
+    "behavioral_hints": {
+      "learning_stage": "",
+      "preferred_depth": "",
+      "tone_preference": ""
+    },
+    "knowledge_tags": []
+  },
+  "aliases_to_add": [],
+  "aliases_to_remove": []
+}
+```
+
+{{ json_schema }}

api/app/core/models/base.py

@@ -14,6 +14,7 @@ from pydantic import BaseModel, Field
 from app.core.error_codes import BizCode
 from app.core.exceptions import BusinessException
 from app.models.models_model import ModelProvider, ModelType
+from app.core.models.volcano_chat import VolcanoChatOpenAI
 
 T = TypeVar("T")
 
@@ -25,6 +26,9 @@ class RedBearModelConfig(BaseModel):
     api_key: str
     base_url: Optional[str] = None
     is_omni: bool = False  # 是否为 Omni 模型
+    deep_thinking: bool = False  # 是否启用深度思考模式
+    thinking_budget_tokens: Optional[int] = None  # 深度思考 token 预算
+    support_thinking: bool = False  # 模型是否支持 enable_thinking 参数（capability 含 thinking）
     # 请求超时时间（秒）- 默认120秒以支持复杂的LLM调用，可通过环境变量 LLM_TIMEOUT 配置
     timeout: float = Field(default_factory=lambda: float(os.getenv("LLM_TIMEOUT", "120.0")))
     # 最大重试次数 - 默认2次以避免过长等待，可通过环境变量 LLM_MAX_RETRIES 配置
@@ -44,7 +48,7 @@ class RedBearModelFactory:
         # 打印供应商信息用于调试
         from app.core.logging_config import get_business_logger
         logger = get_business_logger()
-        logger.debug(f"获取模型参数 - Provider: {provider}, Model: {config.model_name}, is_omni: {config.is_omni}")
+        logger.debug(f"获取模型参数 - Provider: {provider}, Model: {config.model_name}, is_omni: {config.is_omni}, deep_thinking: {config.deep_thinking}")
 
         # dashscope 的 omni 模型使用 OpenAI 兼容模式
         if provider == ModelProvider.DASHSCOPE and config.is_omni:
@@ -58,7 +62,7 @@ class RedBearModelFactory:
                 write=60.0,
                 pool=10.0,
             )
-            return {
+            params: Dict[str, Any] = {
                 "model": config.model_name,
                 "base_url": config.base_url,
                 "api_key": config.api_key,
@@ -66,6 +70,23 @@ class RedBearModelFactory:
                 "max_retries": config.max_retries,
                 **config.extra_params
             }
+            # 流式模式下启用 stream_usage 以获取 token 统计
+            is_streaming = bool(config.extra_params.get("streaming"))
+            if is_streaming:
+                params["stream_usage"] = True
+            # 只有支持 thinking 的模型才传 enable_thinking
+            if config.support_thinking:
+                model_kwargs: Dict[str, Any] = config.extra_params.get("model_kwargs", {})
+                if is_streaming:
+                    model_kwargs["enable_thinking"] = config.deep_thinking
+                    if config.deep_thinking:
+                        model_kwargs["incremental_output"] = True
+                        if config.thinking_budget_tokens:
+                            model_kwargs["thinking_budget"] = config.thinking_budget_tokens
+                else:
+                    model_kwargs["enable_thinking"] = False
+                params["model_kwargs"] = model_kwargs
+            return params
 
         if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK, ModelProvider.OLLAMA, ModelProvider.VOLCANO]:
             # 使用 httpx.Timeout 对象来设置详细的超时配置
@@ -78,7 +99,7 @@ class RedBearModelFactory:
                 write=60.0,  # 写入超时：60秒
                 pool=10.0,  # 连接池超时：10秒
             )
-            return {
+            params: Dict[str, Any] = {
                 "model": config.model_name,
                 "base_url": config.base_url,
                 "api_key": config.api_key,
@@ -86,16 +107,50 @@ class RedBearModelFactory:
                 "max_retries": config.max_retries,
                 **config.extra_params
             }
+            # 流式模式下启用 stream_usage 以获取 token 统计
+            if config.extra_params.get("streaming"):
+                params["stream_usage"] = True
+            # 深度思考模式
+            is_streaming = bool(config.extra_params.get("streaming"))
+            if config.support_thinking:
+                if is_streaming and not config.is_omni:
+                    if provider == ModelProvider.VOLCANO:
+                        # 火山引擎深度思考仅流式调用支持，非流式时不传 thinking 参数
+                        thinking_config: Dict[str, Any] = {
+                            "type": "enabled" if config.deep_thinking else "disabled"
+                        }
+                        if config.deep_thinking and config.thinking_budget_tokens:
+                            thinking_config["budget_tokens"] = config.thinking_budget_tokens
+                        params["extra_body"] = {"thinking": thinking_config}
+                    else:
+                        # 始终显式传递 enable_thinking，不支持该参数的模型（如 DeepSeek-R1）会直接忽略
+                        model_kwargs: Dict[str, Any] = config.extra_params.get("model_kwargs", {})
+                        model_kwargs["enable_thinking"] = config.deep_thinking
+                        if config.deep_thinking and config.thinking_budget_tokens:
+                            model_kwargs["thinking_budget"] = config.thinking_budget_tokens
+                        params["model_kwargs"] = model_kwargs
+            return params
         elif provider == ModelProvider.DASHSCOPE:
-            # DashScope (通义千问) 使用自己的参数格式
-            # 注意: DashScopeEmbeddings 不支持 timeout 和 base_url 参数
-            # 只支持: model, dashscope_api_key, max_retries, client
-            return {
+            params = {
                 "model": config.model_name,
                 "dashscope_api_key": config.api_key,
                 "max_retries": config.max_retries,
                 **config.extra_params
             }
+            # 只有支持 thinking 的模型才传 enable_thinking
+            if config.support_thinking:
+                is_streaming = bool(config.extra_params.get("streaming"))
+                model_kwargs: Dict[str, Any] = config.extra_params.get("model_kwargs", {})
+                if is_streaming:
+                    model_kwargs["enable_thinking"] = config.deep_thinking
+                    if config.deep_thinking:
+                        model_kwargs["incremental_output"] = True
+                        if config.thinking_budget_tokens:
+                            model_kwargs["thinking_budget"] = config.thinking_budget_tokens
+                else:
+                    model_kwargs["enable_thinking"] = False
+                params["model_kwargs"] = model_kwargs
+            return params
         elif provider == ModelProvider.BEDROCK:
             # Bedrock 使用 AWS 凭证
             # api_key 格式: "access_key_id:secret_access_key" 或只是 access_key_id
@@ -134,6 +189,13 @@ class RedBearModelFactory:
             elif "region_name" not in params:
                 params["region_name"] = "us-east-1"  # 默认区域
 
+            # 深度思考模式：Claude 3.7 Sonnet 等支持思考的模型
+            # 通过 additional_model_request_fields 传递 thinking 块，关闭时不传（Bedrock 无 disabled 选项）
+            if config.deep_thinking:
+                budget = config.thinking_budget_tokens or 10000
+                params["additional_model_request_fields"] = {
+                    "thinking": {"type": "enabled", "budget_tokens": budget}
+                }
             return params
         else:
             raise BusinessException(f"不支持的提供商: {provider}", code=BizCode.PROVIDER_NOT_SUPPORTED)
@@ -145,10 +207,15 @@ class RedBearModelFactory:
         if provider in [ModelProvider.XINFERENCE, ModelProvider.GPUSTACK]:
             return {
                 "model": config.model_name,
-                # "base_url": config.base_url,
                 "jina_api_key": config.api_key,
                 **config.extra_params
             }
+        elif provider == ModelProvider.DASHSCOPE:
+            return {
+                "model": config.model_name,
+                "dashscope_api_key": config.api_key,
+                **config.extra_params
+            }
         else:
             raise BusinessException(f"不支持的提供商: {provider}", code=BizCode.PROVIDER_NOT_SUPPORTED)
 
@@ -160,7 +227,9 @@ def get_provider_llm_class(config: RedBearModelConfig, type: ModelType = ModelTy
     # dashscope 的 omni 模型使用 OpenAI 兼容模式
     if provider == ModelProvider.DASHSCOPE and config.is_omni:
         return ChatOpenAI
-    if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK, ModelProvider.VOLCANO]:
+    if provider == ModelProvider.VOLCANO:
+        return VolcanoChatOpenAI
+    if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK]:
         if type == ModelType.LLM:
             return OpenAI
         elif type == ModelType.CHAT:
@@ -202,6 +271,9 @@ def get_provider_rerank_class(provider: str):
     if provider in [ModelProvider.XINFERENCE, ModelProvider.GPUSTACK]:
         from langchain_community.document_compressors import JinaRerank
         return JinaRerank
+    elif provider == ModelProvider.DASHSCOPE:
+        from langchain_community.document_compressors.dashscope_rerank import DashScopeRerank
+        return DashScopeRerank
         # elif provider == ModelProvider.OLLAMA:
     #     from langchain_ollama import OllamaEmbeddings
     #     return OllamaEmbeddings

api/app/core/models/embedding.py

@@ -1,5 +1,5 @@
 
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Union
 from langchain_core.embeddings import Embeddings
 
 from app.core.models.base import RedBearModelConfig, get_provider_embedding_class, RedBearModelFactory
@@ -22,11 +22,38 @@ class RedBearEmbeddings(Embeddings):
             self._model = self._create_model(config)
             self._client = None
 
-    def _create_model(self, config: RedBearModelConfig) -> Embeddings:
+    @staticmethod
+    def _create_model(config: RedBearModelConfig) -> Embeddings:
         """根据配置创建 LangChain 模型"""
         embedding_class = get_provider_embedding_class(config.provider)
-        model_params = RedBearModelFactory.get_model_params(config)
-        return embedding_class(**model_params)
+        provider = config.provider.lower()
+        # Embedding models only need connection params, never LLM-specific ones
+        # (e.g. enable_thinking, model_kwargs) — build params directly.
+        if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK]:
+            import httpx
+            params = {
+                "model": config.model_name,
+                "base_url": config.base_url,
+                "api_key": config.api_key,
+                "timeout": httpx.Timeout(timeout=config.timeout, connect=60.0),
+                "max_retries": config.max_retries
+            }
+        elif provider == ModelProvider.DASHSCOPE:
+            params = {
+                "model": config.model_name,
+                "dashscope_api_key": config.api_key,
+                "max_retries": config.max_retries,
+            }
+        elif provider == ModelProvider.OLLAMA:
+            params = {
+                "model": config.model_name,
+                "base_url": config.base_url,
+            }
+        elif provider == ModelProvider.BEDROCK:
+            params = RedBearModelFactory.get_model_params(config)
+        else:
+            params = RedBearModelFactory.get_model_params(config)
+        return embedding_class(**params)
     
     def _create_volcano_client(self, config: RedBearModelConfig):
         """创建火山引擎客户端"""

api/app/core/models/rerank.py

@@ -76,5 +76,9 @@ class RedBearRerank(BaseDocumentCompressor):
             from langchain_community.document_compressors import JinaRerank
             model_instance: JinaRerank = self._model
             return model_instance.rerank(documents=documents, query=query, top_n=top_n)
+        elif provider == ModelProvider.DASHSCOPE:
+            from langchain_community.document_compressors.dashscope_rerank import DashScopeRerank
+            model_instance: DashScopeRerank = self._model
+            return model_instance.rerank(documents=documents, query=query, top_n=top_n)
         else:
             raise ValueError(f"不支持的模型提供商: {provider}")

api/app/core/models/scripts/bedrock_models.yaml

@@ -11,6 +11,7 @@ models:
   tags:
   - 大语言模型
   logo: bedrock
+
 - name: amazon nova
   type: llm
   provider: bedrock
@@ -27,6 +28,7 @@ models:
   - stream-tool-call
   - vision
   logo: bedrock
+
 - name: anthropic claude
   type: llm
   provider: bedrock
@@ -35,6 +37,7 @@ models:
   is_official: true
   capability:
     - vision
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -44,6 +47,7 @@ models:
   - stream-tool-call
   - document
   logo: bedrock
+
 - name: cohere
   type: llm
   provider: bedrock
@@ -58,6 +62,7 @@ models:
   - tool-call
   - stream-tool-call
   logo: bedrock
+
 - name: deepseek
   type: llm
   provider: bedrock
@@ -66,6 +71,7 @@ models:
   is_official: true
   capability:
     - vision
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -74,6 +80,7 @@ models:
   - tool-call
   - stream-tool-call
   logo: bedrock
+
 - name: meta
   type: llm
   provider: bedrock
@@ -87,6 +94,7 @@ models:
   - agent-thought
   - tool-call
   logo: bedrock
+
 - name: mistral
   type: llm
   provider: bedrock
@@ -100,6 +108,7 @@ models:
   - agent-thought
   - tool-call
   logo: bedrock
+
 - name: openai
   type: llm
   provider: bedrock
@@ -114,6 +123,7 @@ models:
   - tool-call
   - stream-tool-call
   logo: bedrock
+
 - name: qwen
   type: llm
   provider: bedrock
@@ -128,6 +138,7 @@ models:
   - tool-call
   - stream-tool-call
   logo: bedrock
+
 - name: amazon.rerank-v1:0
   type: rerank
   provider: bedrock
@@ -139,6 +150,7 @@ models:
   tags:
   - 重排序模型
   logo: bedrock
+
 - name: cohere.rerank-v3-5:0
   type: rerank
   provider: bedrock
@@ -150,6 +162,7 @@ models:
   tags:
   - 重排序模型
   logo: bedrock
+
 - name: amazon.nova-2-multimodal-embeddings-v1:0
   type: embedding
   provider: bedrock
@@ -163,6 +176,7 @@ models:
   - 文本嵌入模型
   - vision
   logo: bedrock
+
 - name: amazon.titan-embed-text-v1
   type: embedding
   provider: bedrock
@@ -174,6 +188,7 @@ models:
   tags:
   - 文本嵌入模型
   logo: bedrock
+
 - name: amazon.titan-embed-text-v2:0
   type: embedding
   provider: bedrock
@@ -185,6 +200,7 @@ models:
   tags:
   - 文本嵌入模型
   logo: bedrock
+
 - name: cohere.embed-english-v3
   type: embedding
   provider: bedrock
@@ -196,6 +212,7 @@ models:
   tags:
   - 文本嵌入模型
   logo: bedrock
+
 - name: cohere.embed-multilingual-v3
   type: embedding
   provider: bedrock

api/app/core/models/scripts/dashscope_models.yaml

@@ -6,36 +6,42 @@ models:
   description: DeepSeek-R1-Distill-Qwen-14B大语言模型，支持智能体思考，32000上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
   - agent-thought
   logo: dashscope
+
 - name: deepseek-r1-distill-qwen-32b
   type: llm
   provider: dashscope
   description: DeepSeek-R1-Distill-Qwen-32B大语言模型，支持智能体思考，32000上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
   - agent-thought
   logo: dashscope
+
 - name: deepseek-r1
   type: llm
   provider: dashscope
   description: DeepSeek-R1大语言模型，支持智能体思考，131072超大上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
   - agent-thought
   logo: dashscope
+
 - name: deepseek-v3.1
   type: llm
   provider: dashscope
@@ -48,6 +54,7 @@ models:
   - 大语言模型
   - agent-thought
   logo: dashscope
+
 - name: deepseek-v3.2-exp
   type: llm
   provider: dashscope
@@ -60,6 +67,7 @@ models:
   - 大语言模型
   - agent-thought
   logo: dashscope
+
 - name: deepseek-v3.2
   type: llm
   provider: dashscope
@@ -72,6 +80,7 @@ models:
   - 大语言模型
   - agent-thought
   logo: dashscope
+
 - name: deepseek-v3
   type: llm
   provider: dashscope
@@ -84,6 +93,7 @@ models:
   - 大语言模型
   - agent-thought
   logo: dashscope
+
 - name: farui-plus
   type: llm
   provider: dashscope
@@ -98,6 +108,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: glm-4.7
   type: llm
   provider: dashscope
@@ -112,6 +123,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qvq-max-latest
   type: llm
   provider: dashscope
@@ -119,7 +131,8 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
+  - vision
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -127,6 +140,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qvq-max
   type: llm
   provider: dashscope
@@ -134,7 +148,8 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
+  - vision
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -142,6 +157,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen-coder-turbo-0919
   type: llm
   provider: dashscope
@@ -155,13 +171,15 @@ models:
   - 代码模型
   - agent-thought
   logo: dashscope
+
 - name: qwen-max-latest
   type: llm
   provider: dashscope
   description: qwen-max-latest大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -169,6 +187,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen-max-longcontext
   type: llm
   provider: dashscope
@@ -183,13 +202,15 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen-max
   type: llm
   provider: dashscope
   description: qwen-max大语言模型，支持多工具调用、智能体思考、流式工具调用，32768上下文窗口，对话模式，支持联网搜索
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -197,6 +218,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen-mt-plus
   type: llm
   provider: dashscope
@@ -210,6 +232,7 @@ models:
   - 翻译模型
   - agent-thought
   logo: dashscope
+
 - name: qwen-mt-turbo
   type: llm
   provider: dashscope
@@ -223,6 +246,7 @@ models:
   - 翻译模型
   - agent-thought
   logo: dashscope
+
 - name: qwen-plus-0112
   type: llm
   provider: dashscope
@@ -237,6 +261,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen-plus-0125
   type: llm
   provider: dashscope
@@ -251,6 +276,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen-plus-0723
   type: llm
   provider: dashscope
@@ -265,6 +291,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen-plus-0806
   type: llm
   provider: dashscope
@@ -279,6 +306,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen-plus-0919
   type: llm
   provider: dashscope
@@ -293,6 +321,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen-plus-1125
   type: llm
   provider: dashscope
@@ -307,6 +336,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen-plus-1127
   type: llm
   provider: dashscope
@@ -321,6 +351,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen-plus-1220
   type: llm
   provider: dashscope
@@ -335,6 +366,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen-vl-max
   type: chat
   provider: dashscope
@@ -342,8 +374,8 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
-    - video
+  - vision
+  - video
   is_omni: false
   tags:
   - 大语言模型
@@ -352,6 +384,7 @@ models:
   - agent-thought
   - video
   logo: dashscope
+
 - name: qwen-vl-plus-0809
   type: chat
   provider: dashscope
@@ -359,8 +392,8 @@ models:
   is_deprecated: true
   is_official: true
   capability:
-    - vision
-    - video
+  - vision
+  - video
   is_omni: false
   tags:
   - 大语言模型
@@ -369,6 +402,7 @@ models:
   - agent-thought
   - video
   logo: dashscope
+
 - name: qwen-vl-plus-2025-01-02
   type: chat
   provider: dashscope
@@ -376,8 +410,8 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
-    - video
+  - vision
+  - video
   is_omni: false
   tags:
   - 大语言模型
@@ -386,6 +420,7 @@ models:
   - agent-thought
   - video
   logo: dashscope
+
 - name: qwen-vl-plus-2025-01-25
   type: chat
   provider: dashscope
@@ -393,8 +428,8 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
-    - video
+  - vision
+  - video
   is_omni: false
   tags:
   - 大语言模型
@@ -403,6 +438,7 @@ models:
   - agent-thought
   - video
   logo: dashscope
+
 - name: qwen-vl-plus-latest
   type: chat
   provider: dashscope
@@ -410,8 +446,8 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
-    - video
+  - vision
+  - video
   is_omni: false
   tags:
   - 大语言模型
@@ -420,6 +456,7 @@ models:
   - agent-thought
   - video
   logo: dashscope
+
 - name: qwen-vl-plus
   type: chat
   provider: dashscope
@@ -427,8 +464,8 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
-    - video
+  - vision
+  - video
   is_omni: false
   tags:
   - 大语言模型
@@ -437,6 +474,7 @@ models:
   - agent-thought
   - video
   logo: dashscope
+
 - name: qwen2.5-0.5b-instruct
   type: llm
   provider: dashscope
@@ -451,13 +489,15 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen3-14b
   type: llm
   provider: dashscope
   description: qwen3-14b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -465,13 +505,15 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen3-235b-a22b-instruct-2507
   type: llm
   provider: dashscope
   description: qwen3-235b-a22b-instruct-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -479,13 +521,15 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen3-235b-a22b-thinking-2507
   type: llm
   provider: dashscope
   description: qwen3-235b-a22b-thinking-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -493,13 +537,15 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen3-235b-a22b
   type: llm
   provider: dashscope
   description: qwen3-235b-a22b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -507,13 +553,15 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen3-30b-a3b-instruct-2507
   type: llm
   provider: dashscope
   description: qwen3-30b-a3b-instruct-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -521,13 +569,15 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen3-30b-a3b
   type: llm
   provider: dashscope
   description: qwen3-30b-a3b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -535,13 +585,15 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen3-32b
   type: llm
   provider: dashscope
   description: qwen3-32b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -549,13 +601,15 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen3-4b
   type: llm
   provider: dashscope
   description: qwen3-4b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -563,13 +617,15 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen3-8b
   type: llm
   provider: dashscope
   description: qwen3-8b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -577,65 +633,75 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen3-coder-30b-a3b-instruct
   type: llm
   provider: dashscope
   description: qwen3-coder-30b-a3b-instruct大语言模型，支持智能体思考，262144上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
   - 代码模型
   - agent-thought
   logo: dashscope
+
 - name: qwen3-coder-480b-a35b-instruct
   type: llm
   provider: dashscope
   description: qwen3-coder-480b-a35b-instruct大语言模型，支持智能体思考，262144上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
   - 代码模型
   - agent-thought
   logo: dashscope
+
 - name: qwen3-coder-plus-2025-09-23
   type: llm
   provider: dashscope
   description: qwen3-coder-plus-2025-09-23大语言模型，支持智能体思考，1000000上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
   - 代码模型
   - agent-thought
   logo: dashscope
+
 - name: qwen3-coder-plus
   type: llm
   provider: dashscope
   description: qwen3-coder-plus大语言模型，支持智能体思考，1000000上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
   - 代码模型
   - agent-thought
   logo: dashscope
+
 - name: qwen3-max-2025-09-23
   type: llm
   provider: dashscope
   description: qwen3-max-2025-09-23大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -644,13 +710,15 @@ models:
   - stream-tool-call
   - 联网搜索
   logo: dashscope
+
 - name: qwen3-max-2026-01-23
   type: llm
   provider: dashscope
   description: qwen3-max-2026-01-23大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -659,13 +727,15 @@ models:
   - stream-tool-call
   - 联网搜索
   logo: dashscope
+
 - name: qwen3-max-preview
   type: llm
   provider: dashscope
   description: qwen3-max-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -673,13 +743,15 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen3-max
   type: llm
   provider: dashscope
   description: qwen3-max大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -688,13 +760,15 @@ models:
   - stream-tool-call
   - 联网搜索
   logo: dashscope
+
 - name: qwen3-next-80b-a3b-instruct
   type: llm
   provider: dashscope
   description: qwen3-next-80b-a3b-instruct大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -702,13 +776,15 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen3-next-80b-a3b-thinking
   type: llm
   provider: dashscope
   description: qwen3-next-80b-a3b-thinking大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -716,6 +792,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwen3-omni-flash-2025-12-01
   type: llm
   provider: dashscope
@@ -723,9 +800,9 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
-    - video
-    - audio
+  - vision
+  - video
+  - audio
   is_omni: true
   tags:
   - 大语言模型
@@ -735,6 +812,7 @@ models:
   - video
   - audio
   logo: dashscope
+
 - name: qwen3-vl-235b-a22b-instruct
   type: chat
   provider: dashscope
@@ -742,8 +820,9 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
-    - video
+  - vision
+  - video
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -754,6 +833,7 @@ models:
   - vision
   - video
   logo: dashscope
+
 - name: qwen3-vl-235b-a22b-thinking
   type: chat
   provider: dashscope
@@ -761,8 +841,9 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
-    - video
+  - vision
+  - video
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -773,6 +854,7 @@ models:
   - vision
   - video
   logo: dashscope
+
 - name: qwen3-vl-30b-a3b-instruct
   type: chat
   provider: dashscope
@@ -780,8 +862,9 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
-    - video
+  - vision
+  - video
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -792,6 +875,7 @@ models:
   - vision
   - video
   logo: dashscope
+
 - name: qwen3-vl-30b-a3b-thinking
   type: chat
   provider: dashscope
@@ -799,8 +883,9 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
-    - video
+  - vision
+  - video
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -811,6 +896,7 @@ models:
   - vision
   - video
   logo: dashscope
+
 - name: qwen3-vl-flash
   type: chat
   provider: dashscope
@@ -818,8 +904,9 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
-    - video
+  - vision
+  - video
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -830,6 +917,7 @@ models:
   - vision
   - video
   logo: dashscope
+
 - name: qwen3-vl-plus-2025-09-23
   type: chat
   provider: dashscope
@@ -837,8 +925,9 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
-    - video
+  - vision
+  - video
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -847,6 +936,7 @@ models:
   - agent-thought
   - video
   logo: dashscope
+
 - name: qwen3-vl-plus
   type: chat
   provider: dashscope
@@ -854,8 +944,9 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
-    - video
+  - vision
+  - video
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -864,45 +955,52 @@ models:
   - agent-thought
   - video
   logo: dashscope
+
 - name: qwq-32b
   type: llm
   provider: dashscope
   description: qwq-32b大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwq-plus-0305
   type: llm
   provider: dashscope
   description: qwq-plus-0305大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: qwq-plus
   type: llm
   provider: dashscope
   description: qwq-plus大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+  - thinking
   is_omni: false
   tags:
   - 大语言模型
   - agent-thought
   - stream-tool-call
   logo: dashscope
+
 - name: gte-rerank-v2
   type: rerank
   provider: dashscope
@@ -914,6 +1012,7 @@ models:
   tags:
   - 重排序模型
   logo: dashscope
+
 - name: gte-rerank
   type: rerank
   provider: dashscope
@@ -925,6 +1024,7 @@ models:
   tags:
   - 重排序模型
   logo: dashscope
+
 - name: multimodal-embedding-v1
   type: embedding
   provider: dashscope
@@ -932,13 +1032,14 @@ models:
   is_deprecated: false
   is_official: true
   capability:
-    - vision
+  - vision
   is_omni: false
   tags:
   - 嵌入模型
   - 多模态模型
   - vision
   logo: dashscope
+
 - name: text-embedding-v1
   type: embedding
   provider: dashscope
@@ -951,6 +1052,7 @@ models:
   - 嵌入模型
   - 文本嵌入
   logo: dashscope
+
 - name: text-embedding-v2
   type: embedding
   provider: dashscope
@@ -963,6 +1065,7 @@ models:
   - 嵌入模型
   - 文本嵌入
   logo: dashscope
+
 - name: text-embedding-v3
   type: embedding
   provider: dashscope
@@ -975,6 +1078,7 @@ models:
   - 嵌入模型
   - 文本嵌入
   logo: dashscope
+
 - name: text-embedding-v4
   type: embedding
   provider: dashscope
@@ -986,4 +1090,4 @@ models:
   tags:
   - 嵌入模型
   - 文本嵌入
-  logo: dashscope
+  logo: dashscope

api/app/core/models/scripts/openai_models.yaml

@@ -20,6 +20,7 @@ models:
   - audio
   - video
   logo: openai
+
 - name: gpt-3.5-turbo-0125
   type: llm
   provider: openai
@@ -34,6 +35,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: openai
+
 - name: gpt-3.5-turbo-1106
   type: llm
   provider: openai
@@ -48,6 +50,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: openai
+
 - name: gpt-3.5-turbo-16k
   type: llm
   provider: openai
@@ -62,6 +65,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: openai
+
 - name: gpt-3.5-turbo-instruct
   type: llm
   provider: openai
@@ -73,6 +77,7 @@ models:
   tags:
   - 大语言模型
   logo: openai
+
 - name: gpt-3.5-turbo
   type: llm
   provider: openai
@@ -87,6 +92,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: openai
+
 - name: gpt-4-0125-preview
   type: llm
   provider: openai
@@ -101,6 +107,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: openai
+
 - name: gpt-4-1106-preview
   type: llm
   provider: openai
@@ -115,6 +122,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: openai
+
 - name: gpt-4-turbo-2024-04-09
   type: llm
   provider: openai
@@ -131,6 +139,7 @@ models:
   - stream-tool-call
   - vision
   logo: openai
+
 - name: gpt-4-turbo-preview
   type: llm
   provider: openai
@@ -145,6 +154,7 @@ models:
   - agent-thought
   - stream-tool-call
   logo: openai
+
 - name: gpt-4-turbo
   type: llm
   provider: openai
@@ -161,6 +171,7 @@ models:
   - stream-tool-call
   - vision
   logo: openai
+
 - name: o1-preview
   type: llm
   provider: openai
@@ -173,6 +184,7 @@ models:
   - 大语言模型
   - agent-thought
   logo: openai
+
 - name: o1
   type: llm
   provider: openai
@@ -181,6 +193,7 @@ models:
   is_official: true
   capability:
     - vision
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -190,6 +203,7 @@ models:
   - vision
   - structured-output
   logo: openai
+
 - name: o3-2025-04-16
   type: llm
   provider: openai
@@ -198,6 +212,7 @@ models:
   is_official: true
   capability:
     - vision
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -207,13 +222,15 @@ models:
   - stream-tool-call
   - structured-output
   logo: openai
+
 - name: o3-mini-2025-01-31
   type: llm
   provider: openai
   description: o3-mini-2025-01-31大语言模型，支持智能体思考、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -222,13 +239,15 @@ models:
   - stream-tool-call
   - structured-output
   logo: openai
+
 - name: o3-mini
   type: llm
   provider: openai
   description: o3-mini大语言模型，支持智能体思考、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式
   is_deprecated: false
   is_official: true
-  capability: []
+  capability:
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -237,6 +256,7 @@ models:
   - stream-tool-call
   - structured-output
   logo: openai
+
 - name: o3-pro-2025-06-10
   type: llm
   provider: openai
@@ -245,6 +265,7 @@ models:
   is_official: true
   capability:
     - vision
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -253,6 +274,7 @@ models:
   - vision
   - structured-output
   logo: openai
+
 - name: o3-pro
   type: llm
   provider: openai
@@ -261,6 +283,7 @@ models:
   is_official: true
   capability:
     - vision
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -269,6 +292,7 @@ models:
   - vision
   - structured-output
   logo: openai
+
 - name: o3
   type: llm
   provider: openai
@@ -277,6 +301,7 @@ models:
   is_official: true
   capability:
     - vision
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -286,6 +311,7 @@ models:
   - stream-tool-call
   - structured-output
   logo: openai
+
 - name: o4-mini-2025-04-16
   type: llm
   provider: openai
@@ -294,6 +320,7 @@ models:
   is_official: true
   capability:
     - vision
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -303,6 +330,7 @@ models:
   - stream-tool-call
   - structured-output
   logo: openai
+
 - name: o4-mini
   type: llm
   provider: openai
@@ -311,6 +339,7 @@ models:
   is_official: true
   capability:
     - vision
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -320,6 +349,7 @@ models:
   - stream-tool-call
   - structured-output
   logo: openai
+
 - name: text-embedding-3-large
   type: embedding
   provider: openai
@@ -331,6 +361,7 @@ models:
   tags:
   - 文本向量模型
   logo: openai
+
 - name: text-embedding-3-small
   type: embedding
   provider: openai
@@ -342,6 +373,7 @@ models:
   tags:
   - 文本向量模型
   logo: openai
+
 - name: text-embedding-ada-002
   type: embedding
   provider: openai

api/app/core/models/scripts/volcano_models.yaml

@@ -10,6 +10,7 @@ models:
   capability:
     - vision
     - video
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -24,6 +25,7 @@ models:
   capability:
     - vision
     - video
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -38,6 +40,7 @@ models:
   capability:
     - vision
     - video
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -52,6 +55,7 @@ models:
   capability:
     - vision
     - video
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -82,6 +86,7 @@ models:
   capability:
     - vision
     - video
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -96,6 +101,7 @@ models:
   capability:
     - vision
     - video
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -110,6 +116,7 @@ models:
   capability:
     - vision
     - video
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -124,6 +131,7 @@ models:
   capability:
     - vision
     - video
+    - thinking
   is_omni: false
   tags:
   - 大语言模型
@@ -139,6 +147,7 @@ models:
   capability:
     - vision
     - video
+    - thinking
   is_omni: false
   tags:
   - 大语言模型

api/app/core/models/volcano_chat.py

@@ -0,0 +1,52 @@
+"""
+火山引擎 ChatOpenAI 扩展
+
+ChatOpenAI 在解析流式 SSE 时只取 delta.content，会丢弃 delta.reasoning_content。
+此类仅重写 _convert_chunk_to_generation_chunk，将 reasoning_content 补入 additional_kwargs。
+"""
+from __future__ import annotations
+
+from typing import Any, Optional, Union
+
+from langchain_core.outputs import ChatGenerationChunk, ChatResult
+from langchain_openai import ChatOpenAI
+
+
+class VolcanoChatOpenAI(ChatOpenAI):
+    """火山引擎 Chat 模型，支持深度思考内容（reasoning_content）的流式和非流式透传。"""
+
+    def _create_chat_result(self, response: Union[dict, Any], generation_info: Optional[dict] = None) -> ChatResult:
+        result = super()._create_chat_result(response, generation_info)
+        # 将非流式响应中的 reasoning_content 补入 additional_kwargs
+        choices = response.choices if hasattr(response, "choices") else response.get("choices", [])
+        if choices:
+            message = choices[0].message if hasattr(choices[0], "message") else choices[0].get("message", {})
+            reasoning = (
+                getattr(message, "reasoning_content", None)
+                or (message.get("reasoning_content") if isinstance(message, dict) else None)
+            )
+            if reasoning and result.generations:
+                result.generations[0].message.additional_kwargs["reasoning_content"] = reasoning
+        return result
+
+    def _convert_chunk_to_generation_chunk(
+        self,
+        chunk: dict,
+        default_chunk_class: type,
+        base_generation_info: Optional[dict],
+    ) -> Optional[ChatGenerationChunk]:
+        gen_chunk = super()._convert_chunk_to_generation_chunk(
+            chunk, default_chunk_class, base_generation_info
+        )
+        if gen_chunk is None:
+            return None
+
+        # 从原始 chunk 中提取 reasoning_content
+        choices = chunk.get("choices") or chunk.get("chunk", {}).get("choices", [])
+        if choices:
+            delta = choices[0].get("delta") or {}
+            reasoning: Any = delta.get("reasoning_content")
+            if reasoning:
+                gen_chunk.message.additional_kwargs["reasoning_content"] = reasoning
+
+        return gen_chunk

api/app/core/rag/app/naive.py

@@ -672,10 +672,15 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
         excel_parser = ExcelParser()
         if parser_config.get("html4excel") and parser_config.get("html4excel").lower() == "true":
             sections = [(_, "") for _ in excel_parser.html(binary, 12) if _]
-            parser_config["chunk_token_num"] = 0
         else:
             sections = [(_, "") for _ in excel_parser(binary) if _]
-        parser_config["chunk_token_num"] = 12800
+        callback(0.8, "Finish parsing.")
+        # Excel 每行直接作为一个 chunk，不经过 naive_merge 避免被 delimiter 拆分
+        chunks = [s for s, _ in sections]
+        res.extend(tokenize_chunks(chunks, doc, is_english, None))
+        res.extend(embed_res)
+        res.extend(url_res)
+        return res
 
     elif re.search(r"\.(txt|py|js|java|c|cpp|h|php|go|ts|sh|cs|kt|sql)$", filename, re.IGNORECASE):
         callback(0.1, "Start to parse.")

api/app/core/rag/deepdoc/parser/excel_parser.py

@@ -232,14 +232,14 @@ class RAGExcelParser:
                         t = str(ti[i].value) if i < len(ti) else ""
                         t += ("：" if t else "") + str(c.value)
                         fields.append(t)
-                    line = "; ".join(fields)
+                    line = "\n".join(fields)
                     if sheetname.lower().find("sheet") < 0:
-                        line += " ——" + sheetname
+                        line += "\n——" + sheetname
                     res.append(line)
             else:
                 # 只有表头的情况
                 if header_fields:
-                    line = "; ".join(header_fields)
+                    line = "\n".join(header_fields)
                     if sheetname.lower().find("sheet") < 0:
                         line += " ——" + sheetname
                     res.append(line)

api/app/core/rag/deepdoc/parser/mineru_parser.py

@@ -292,9 +292,10 @@ class MinerUParser(RAGPdfParser):
         self.page_from = page_from
         self.page_to = page_to
         try:
-            with pdfplumber.open(fnm) if isinstance(fnm, (str, PathLike)) else pdfplumber.open(BytesIO(fnm)) as pdf:
-                self.pdf = pdf
-                self.page_images = [p.to_image(resolution=72 * zoomin, antialias=True).original for _, p in enumerate(self.pdf.pages[page_from:page_to])]
+            with sys.modules[LOCK_KEY_pdfplumber]:  # ← 加这一行，获取全局锁
+                with pdfplumber.open(fnm) if isinstance(fnm, (str, PathLike)) else pdfplumber.open(BytesIO(fnm)) as pdf:
+                    self.pdf = pdf
+                    self.page_images = [p.to_image(resolution=72 * zoomin, antialias=True).original for _, p in enumerate(self.pdf.pages[page_from:page_to])]
         except Exception as e:
             self.page_images = None
             self.total_page = 0

api/app/core/rag/llm/embedding_model.py

@@ -50,7 +50,9 @@ class OpenAIEmbed(Base):
     def encode(self, texts: list):
         # OpenAI requires batch size <=16
         batch_size = 16
-        texts = [truncate(t, 8191) for t in texts]
+        # Use 8000 instead of 8191 to leave safety margin for tokenizer differences
+        # between cl100k_base (used by truncate) and the actual embedding model
+        texts = [truncate(t, 8000) for t in texts]
         ress = []
         total_tokens = 0
         for i in range(0, len(texts), batch_size):
@@ -63,7 +65,7 @@ class OpenAIEmbed(Base):
         return np.array(ress), total_tokens
 
     def encode_queries(self, text):
-        res = self.client.embeddings.create(input=[truncate(text, 8191)], model=self.model_name, encoding_format="float",extra_body={"drop_params": True})
+        res = self.client.embeddings.create(input=[truncate(text, 8000)], model=self.model_name, encoding_format="float",extra_body={"drop_params": True})
         return np.array(res.data[0].embedding), self.total_token_count(res)
 
 
@@ -79,6 +81,7 @@ class LocalAIEmbed(Base):
 
     def encode(self, texts: list):
         batch_size = 16
+        texts = [truncate(t, 8000) for t in texts]
         ress = []
         for i in range(0, len(texts), batch_size):
             res = self.client.embeddings.create(input=texts[i : i + batch_size], model=self.model_name)
@@ -173,6 +176,7 @@ class XinferenceEmbed(Base):
 
     def encode(self, texts: list):
         batch_size = 16
+        texts = [truncate(t, 8000) for t in texts]
         ress = []
         total_tokens = 0
         for i in range(0, len(texts), batch_size):
@@ -188,7 +192,7 @@ class XinferenceEmbed(Base):
     def encode_queries(self, text):
         res = None
         try:
-            res = self.client.embeddings.create(input=[text], model=self.model_name)
+            res = self.client.embeddings.create(input=[truncate(text, 8000)], model=self.model_name)
             return np.array(res.data[0].embedding), self.total_token_count(res)
         except Exception as _e:
             log_exception(_e, res)

api/app/core/rag/nlp/search.py

@@ -28,6 +28,7 @@ from app.core.rag.common.float_utils import get_float
 from app.core.rag.common.constants import PAGERANK_FLD, TAG_FLD
 from app.core.rag.llm.chat_model import Base
 from app.core.rag.llm.embedding_model import OpenAIEmbed
+from app.services.model_service import ModelApiKeyService
 import logging
 
 logger = logging.getLogger(__name__)
@@ -114,9 +115,8 @@ def knowledge_retrieval(
         # Use the specified reranker for re-ranking
         if reranker_id:
             try:
-                return rerank(db=db, reranker_id=reranker_id, query=query, docs=all_results, top_k=reranker_top_k)
+                all_results = rerank(db=db, reranker_id=reranker_id, query=query, docs=all_results, top_k=reranker_top_k)
             except Exception as rerank_error:
-                # If reranker fails, log warning and continue with original results
                 logger.warning(
                     "Reranker failed, falling back to original results",
                     extra={
@@ -132,7 +132,10 @@ def knowledge_retrieval(
                 from app.core.rag.common.settings import kg_retriever
                 doc = kg_retriever.retrieval(question=query, workspace_ids=workspace_ids, kb_ids=kb_ids, emb_mdl=embedding_model, llm=chat_model)
                 if doc:
-                    all_results.insert(0, doc)
+                    all_results.insert(0, DocumentChunk(
+                        page_content=doc.get("page_content", ""),
+                        metadata=doc.get("metadata", {})
+                    ))
             except Exception as graph_error:
                 print(f"Failed to retrieve from knowledge graph: {str(graph_error)}")
         
@@ -198,16 +201,18 @@ def _retrieve_for_knowledge(
         workspace_ids.append(str(db_knowledge.workspace_id))
 
     if not chat_model:
+        llm_key = ModelApiKeyService.get_available_api_key(db, db_knowledge.llm_id)
         chat_model = Base(
-            key=db_knowledge.llm.api_keys[0].api_key,
-            model_name=db_knowledge.llm.api_keys[0].model_name,
-            base_url=db_knowledge.llm.api_keys[0].api_base,
+            key=llm_key.api_key,
+            model_name=llm_key.model_name,
+            base_url=llm_key.api_base,
         )
     if not embedding_model:
+        emb_key = ModelApiKeyService.get_available_api_key(db, db_knowledge.embedding_id)
         embedding_model = OpenAIEmbed(
-            key=db_knowledge.embedding.api_keys[0].api_key,
-            model_name=db_knowledge.embedding.api_keys[0].model_name,
-            base_url=db_knowledge.embedding.api_keys[0].api_base,
+            key=emb_key.api_key,
+            model_name=emb_key.model_name,
+            base_url=emb_key.api_base,
         )
 
     vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
@@ -248,6 +253,29 @@ def _retrieve_for_knowledge(
                     seen_ids.add(doc.metadata["doc_id"])
                     unique_rs.append(doc)
             rs = unique_rs
+            if unique_rs:
+                rs = vector_service.rerank(
+                    query=kb_config["query"],
+                    docs=unique_rs,
+                    top_k=kb_config["top_k"]
+                )
+            if kb_config["retrieve_type"] == "graph":
+                try:
+                    from app.core.rag.common.settings import kg_retriever
+                    graph_doc = kg_retriever.retrieval(
+                        question=kb_config["query"],
+                        workspace_ids=[str(db_knowledge.workspace_id)],
+                        kb_ids=[str(db_knowledge.id)],
+                        emb_mdl=embedding_model,
+                        llm=chat_model,
+                    )
+                    if graph_doc:
+                        rs.insert(0, DocumentChunk(
+                            page_content=graph_doc.get("page_content", ""),
+                            metadata=graph_doc.get("metadata", {})
+                        ))
+                except Exception as graph_error:
+                    logger.warning(f"Graph retrieval failed for kb {db_knowledge.id}: {graph_error}")
 
     results.extend(rs)
     return results, chat_model, embedding_model

api/app/core/tools/builtin/datetime_tool.py

@@ -27,7 +27,7 @@ class DateTimeTool(BuiltinTool):
                 type=ParameterType.STRING,
                 description="操作类型",
                 required=True,
-                enum=["format", "convert_timezone", "timestamp_to_datetime", "now"]
+                enum=["format", "convert_timezone", "timestamp_to_datetime", "now", "datetime_to_timestamp"]
             ),
             ToolParameter(
                 name="input_value",
@@ -230,7 +230,7 @@ class DateTimeTool(BuiltinTool):
     @staticmethod
     def _datetime_to_timestamp(kwargs) -> dict:
         """日期时间转时间戳"""
-        input_value = kwargs.get("input_value")
+        input_value = kwargs.get("input_value").strip()
         input_format = kwargs.get("input_format", "%Y-%m-%d %H:%M:%S")
         timezone_str = kwargs.get("from_timezone", "Asia/Shanghai")
         
@@ -253,9 +253,9 @@ class DateTimeTool(BuiltinTool):
         return {
             "datetime": input_value,
             "timezone": timezone_str,
-            "timestamp": int(dt.timestamp()),
+            "timestamp": int(dt.timestamp()) * 1000,
             "iso_format": dt.isoformat(),
-            "result_data": int(dt.timestamp())
+            "result_data": int(dt.timestamp()) * 1000
         }
 
     def _calculate_datetime(self, kwargs) -> dict:

api/app/core/tools/builtin/openclaw_tool.py

@@ -0,0 +1,300 @@
+"""OpenClaw 远程 Agent 内置工具"""
+import time
+import base64
+from io import BytesIO
+from typing import List, Dict, Any, Optional
+import aiohttp
+
+from app.core.tools.builtin.base import BuiltinTool
+from app.schemas.tool_schema import ToolParameter, ToolResult, ParameterType
+from app.core.logging_config import get_business_logger
+
+logger = get_business_logger()
+
+
+class OpenClawTool(BuiltinTool):
+    """OpenClaw 远程 Agent 工具 — 支持文本和图片多模态输入"""
+
+    def __init__(self, tool_id: str, config: Dict[str, Any]):
+        super().__init__(tool_id, config)
+        params = self.parameters_config
+
+        # 用户配置项（前端表单填写）
+        self._server_url = params.get("server_url", "")
+        self._api_key = params.get("api_key", "")
+        self._agent_id = params.get("agent_id", "main")
+
+        # 内部默认值
+        self._model = "openclaw"
+        self._session_strategy = "by_user"
+        self._timeout = 120
+
+        # 运行时上下文（通过 set_runtime_context 注入）
+        self._user_id = "anonymous"
+        self._conversation_id = None
+        self._uploaded_files = []
+
+    @property
+    def name(self) -> str:
+        return "openclaw_tool"
+
+    @property
+    def description(self) -> str:
+        return (
+            "OpenClaw 远程 Agent：将任务委托给远程 OpenClaw Agent。"
+            "具备 3D 模型生成与打印控制、设备管理、文件处理、浏览器自动化、"
+            "Shell 命令执行、网络搜索等能力。支持文本和图片多模态交互。"
+        )
+
+    def get_required_config_parameters(self) -> List[str]:
+        return ["server_url", "api_key"]
+        
+    @property
+    def parameters(self) -> List[ToolParameter]:
+        return [
+            ToolParameter(
+                name="operation",
+                type=ParameterType.STRING,
+                description="任务类型",
+                required=True,
+                enum= ["print_task", "device_query", "image_understand", "general"]
+            ),
+            ToolParameter(
+                name="message",
+                type=ParameterType.STRING,
+                description="发送给 OpenClaw Agent 的文本请求内容",
+                required=True
+            ),
+            ToolParameter(
+                name="image_url",
+                type=ParameterType.STRING,
+                description="可选，附带的图片 URL 或 base64 data URI（OpenClaw 支持图片输入）",
+                required=False
+            )
+        ]       
+            
+    # ---------- 运行时上下文注入 ----------
+    def set_runtime_context(
+        self,
+        user_id: str = "anonymous",
+        conversation_id: Optional[str] = None,
+        uploaded_files: Optional[list] = None
+    ):
+        """注入运行时上下文（由 chat service 调用）"""
+        self._user_id = user_id
+        self._conversation_id = conversation_id
+        self._uploaded_files = uploaded_files or []
+
+    # ---------- 连接测试 ----------
+    async def test_connection(self) -> Dict[str, Any]:
+        """测试 OpenClaw Gateway 连接"""
+        if not self._server_url:
+            return {"success": False, "message": "未配置 server_url"}
+        if not self._api_key:
+            return {"success": False, "message": "未配置 api_key"}
+
+        url = f"{self._server_url.rstrip('/')}/v1/responses"
+        headers = {
+            "Authorization": f"Bearer {self._api_key}",
+            "Content-Type": "application/json",
+            "x-openclaw-agent-id": self._agent_id
+        }
+        body = {
+            "model": self._model,
+            "user": "connection-test",
+            "input": "hi",
+            "stream": False
+        }
+        try:
+            timeout_cfg = aiohttp.ClientTimeout(total=30)
+            async with aiohttp.ClientSession(timeout=timeout_cfg) as session:
+                async with session.post(url, json=body, headers=headers) as resp:
+                    if resp.status < 400:
+                        return {"success": True, "message": "OpenClaw 连接成功"}
+                    error_text = await resp.text()
+                    return {
+                        "success": False,
+                        "message": f"OpenClaw HTTP {resp.status}: {error_text[:200]}"
+                    }
+        except Exception as e:
+            return {"success": False, "message": f"OpenClaw 连接失败: {str(e)}"}
+
+    # ---------- 执行 ----------
+    async def execute(self, **kwargs) -> ToolResult:
+        """执行 OpenClaw 调用"""
+        start_time = time.time()
+        try:
+            message = kwargs.get("message", "")
+            if not message:
+                return ToolResult.error_result(
+                    error="message 参数不能为空",
+                    error_code="OPENCLAW_INVALID_INPUT",
+                    execution_time=time.time() - start_time
+                )
+
+            # 提取图片：优先从用户上传文件中获取，LLM 传的 image_url 作为兜底
+            image_url = self._extract_image_from_uploads()
+            if not image_url:
+                image_url = kwargs.get("image_url")
+            if image_url and not image_url.startswith("data:"):
+                image_url = await self._download_and_encode_image(image_url)
+
+            # 构建请求
+            url = f"{self._server_url.rstrip('/')}/v1/responses"
+            headers = {
+                "Authorization": f"Bearer {self._api_key}",
+                "Content-Type": "application/json",
+                "x-openclaw-agent-id": self._agent_id
+            }
+            user_field = (
+                f"conv-{self._conversation_id}"
+                if self._session_strategy == "by_conversation" and self._conversation_id
+                else f"user-{self._user_id}"
+            )
+            input_field = self._build_input(message, image_url)
+            body = {
+                "model": self._model,
+                "user": user_field,
+                "input": input_field,
+                "stream": False
+            }
+
+            timeout_cfg = aiohttp.ClientTimeout(total=self._timeout)
+            # 打印请求日志（截断 base64 避免日志过大）
+            log_body = {**body}
+            if isinstance(log_body.get("input"), list):
+                log_body["input"] = "[multimodal input, truncated]"
+            elif isinstance(log_body.get("input"), str) and len(log_body["input"]) > 500:
+                log_body["input"] = log_body["input"][:500] + "..."
+            logger.info(
+                f"OpenClaw 请求: url={url}, agent_id={self._agent_id}, "
+                f"has_image={bool(image_url)}, body={log_body}"
+            )
+            async with aiohttp.ClientSession(timeout=timeout_cfg) as session:
+                async with session.post(url, json=body, headers=headers) as resp:
+                    execution_time = time.time() - start_time
+                    if resp.status >= 400:
+                        error_text = await resp.text()
+                        return ToolResult.error_result(
+                            error=f"OpenClaw HTTP {resp.status}: {error_text[:500]}",
+                            error_code="OPENCLAW_HTTP_ERROR",
+                            execution_time=execution_time
+                        )
+                    data = await resp.json()
+                    text = self._extract_response(data)
+                    display_text = self._format_result(text)
+                    return ToolResult.success_result(
+                        data=display_text,
+                        execution_time=execution_time
+                    )
+
+        except aiohttp.ClientError as e:
+            return ToolResult.error_result(
+                error=f"OpenClaw 网络连接失败: {str(e)}",
+                error_code="OPENCLAW_NETWORK_ERROR",
+                execution_time=time.time() - start_time
+            )
+        except Exception as e:
+            return ToolResult.error_result(
+                error=f"OpenClaw 调用失败: {str(e)}",
+                error_code="OPENCLAW_EXECUTION_ERROR",
+                execution_time=time.time() - start_time
+            )
+
+    # ---------- 私有方法 ----------
+    def _extract_image_from_uploads(self) -> Optional[str]:
+        """从用户上传文件中提取图片 URL"""
+        for f in self._uploaded_files:
+            f_type = f.get("type", "")
+            if f_type == "image":
+                source = f.get("source", {})
+                if source.get("type") == "base64":
+                    media_type = source.get("media_type", "image/jpeg")
+                    data = source.get("data", "")
+                    return f"data:{media_type};base64,{data}"
+                elif f.get("image"):
+                    return f.get("image")
+                elif f.get("url"):
+                    return f.get("url")
+            elif f_type == "image_url":
+                return f.get("image_url", {}).get("url", "")
+        return None
+
+    async def _download_and_encode_image(self, image_url: str) -> str:
+        """下载图片并转为 base64 data URI"""
+        try:
+            from PIL import Image
+            MAX_RAW_SIZE = 4 * 1024 * 1024
+
+            async with aiohttp.ClientSession() as session:
+                async with session.get(
+                    image_url, allow_redirects=True,
+                    timeout=aiohttp.ClientTimeout(total=30)
+                ) as resp:
+                    if resp.status != 200:
+                        return image_url
+                    content_type = resp.headers.get("Content-Type", "image/jpeg")
+                    if not content_type.startswith("image/"):
+                        return image_url
+                    img_bytes = await resp.read()
+
+                    if len(img_bytes) > MAX_RAW_SIZE:
+                        img = Image.open(BytesIO(img_bytes))
+                        if img.mode in ("RGBA", "P", "LA"):
+                            img = img.convert("RGB")
+                        if max(img.size) > 2048:
+                            img.thumbnail((2048, 2048), Image.LANCZOS)
+                        buf = BytesIO()
+                        img.save(buf, format="JPEG", quality=75, optimize=True)
+                        img_bytes = buf.getvalue()
+                        content_type = "image/jpeg"
+
+                    b64 = base64.b64encode(img_bytes).decode("utf-8")
+                    return f"data:{content_type};base64,{b64}"
+        except Exception as e:
+            logger.warning(f"OpenClaw 下载图片失败，使用原始 URL: {e}")
+            return image_url
+
+    def _build_input(self, message: str, image_url: Optional[str] = None):
+        """构造请求 input 字段：有图片则构造多模态结构，否则纯文本"""
+        if not image_url:
+            return message
+
+        content_parts = [{"type": "input_text", "text": message}]
+        if image_url.startswith("data:"):
+            try:
+                header, data = image_url.split(",", 1)
+                media_type = header.split(":")[1].split(";")[0]
+                content_parts.append({
+                    "type": "input_image",
+                    "source": {"type": "base64", "media_type": media_type, "data": data}
+                })
+            except (ValueError, IndexError):
+                return message
+        else:
+            content_parts.append({
+                "type": "input_image",
+                "source": {"type": "url", "url": image_url}
+            })
+
+        return [{"type": "message", "role": "user", "content": content_parts}]
+
+    def _extract_response(self, response_data: Dict[str, Any]) -> str:
+        """从 OpenClaw 响应中提取文本内容
+
+        OpenClaw /v1/responses 只返回 output_text 类型的内容。
+        图片信息（如有）由 OpenClaw Skill 以 Markdown 链接形式嵌入文本中返回。
+        """
+        output = response_data.get("output", [])
+        texts = []
+        for item in output:
+            if item.get("type") == "message":
+                for content in item.get("content", []):
+                    if content.get("type") == "output_text" and content.get("text"):
+                        texts.append(content["text"])
+        return "\n".join(texts) if texts else str(response_data)
+
+    @staticmethod
+    def _format_result(text: str) -> str:
+        """格式化结果为 LLM 可读字符串"""
+        return text or "（OpenClaw 返回了空内容）"

api/app/core/tools/builtin/operation_tool.py

@@ -11,6 +11,11 @@ class OperationTool(BaseTool):
         self.base_tool = base_tool
         self.operation = operation
         super().__init__(base_tool.tool_id, base_tool.config)
+
+    def set_runtime_context(self, **kwargs):
+        """转发运行时上下文到 base_tool"""
+        if hasattr(self.base_tool, 'set_runtime_context'):
+            self.base_tool.set_runtime_context(**kwargs)
     
     @property
     def name(self) -> str:
@@ -32,6 +37,8 @@ class OperationTool(BaseTool):
             return self._get_datetime_params()
         elif self.base_tool.name == 'json_tool':
             return self._get_json_params()
+        elif self.base_tool.name == 'openclaw_tool':
+            return self._get_openclaw_params()
         else:
             # 默认返回除operation外的所有参数
             return [p for p in self.base_tool.parameters if p.name != "operation"]
@@ -138,6 +145,29 @@ class OperationTool(BaseTool):
                     default="Asia/Shanghai"
                 )
             ]
+        elif self.operation == "datetime_to_timestamp":
+            return [
+                ToolParameter(
+                    name="input_value",
+                    type=ParameterType.STRING,
+                    description="输入值（时间字符串，如：2026-04-07 10:30:25）",
+                    required=True
+                ),
+                ToolParameter(
+                    name="input_format",
+                    type=ParameterType.STRING,
+                    description="输入时间格式（如：%Y-%m-%d %H:%M:%S）",
+                    required=False,
+                    default="%Y-%m-%d %H:%M:%S"
+                ),
+                ToolParameter(
+                    name="from_timezone",
+                    type=ParameterType.STRING,
+                    description="源时区（如：UTC, Asia/Shanghai）",
+                    required=False,
+                    default="Asia/Shanghai"
+                )
+            ]
         else:
             return []
     
@@ -209,6 +239,64 @@ class OperationTool(BaseTool):
         else:
             return base_params
     
+    def _get_openclaw_params(self) -> List[ToolParameter]:
+        """获取 openclaw_tool 特定操作的参数"""
+        if self.operation == "print_task":
+            return [
+                ToolParameter(
+                    name="message",
+                    type=ParameterType.STRING,
+                    description="发送给 OpenClaw 的打印任务描述，将用户的原始消息原封不动地传递给 OpenClaw，禁止改写、补充或润色用户的原文",
+                    required=True
+                ),
+                ToolParameter(
+                    name="image_url",
+                    type=ParameterType.STRING,
+                    description="可选，附带的设计图片或参考图，OpenClaw 可据此生成 3D 模型",
+                    required=False
+                )
+            ]
+        elif self.operation == "device_query":
+            return [
+                ToolParameter(
+                    name="message",
+                    type=ParameterType.STRING,
+                    description="发送给 OpenClaw 的设备查询指令",
+                    required=True
+                )
+            ]
+        elif self.operation == "image_understand":
+            return [
+                ToolParameter(
+                    name="message",
+                    type=ParameterType.STRING,
+                    description="发送给 OpenClaw 的图片理解任务，应描述需要对图片做什么（如描述内容、提取文字、分析信息）",
+                    required=True
+                ),
+                ToolParameter(
+                    name="image_url",
+                    type=ParameterType.STRING,
+                    description="要分析的图片 URL 或 base64 data URI",
+                    required=False
+                )
+            ]
+        else:
+            # general 及其他
+            return [
+                ToolParameter(
+                    name="message",
+                    type=ParameterType.STRING,
+                    description="发送给 OpenClaw Agent 的任务描述，应包含完整的任务需求",
+                    required=True
+                ),
+                ToolParameter(
+                    name="image_url",
+                    type=ParameterType.STRING,
+                    description="可选，附带的图片 URL 或 base64 data URI",
+                    required=False
+                )
+            ]
+
     async def execute(self, **kwargs) -> ToolResult:
         """执行特定操作"""
         # 添加operation参数

api/app/core/tools/configs/builtin/openclaw_tool.json

@@ -0,0 +1,15 @@
+{
+  "name": "openclaw_tool",
+  "description": "调用OpenClaw Agent远程服务",
+  "tool_class": "OpenClawTool",
+  "category": "agent",
+  "requires_config": true,
+  "version": "1.0.0",
+  "enabled": true,
+  "parameters": {
+    "server_url": "",
+    "api_key": "",
+    "agent_id": "main"
+  },
+  "tags": ["agent", "openclaw", "multimodal", "3d-printing", "builtin"]
+}

api/app/core/tools/configs/builtin_tools.json

@@ -30,5 +30,18 @@
     "parameters": {
       "api_key": {"type": "string", "description": "百度搜索API密钥", "sensitive": true, "required": true}
     }
+  },
+  "openclaw": {
+    "name": "OpenClaw远程Agent",
+    "description": "OpenClaw Agent远程服务",
+    "tool_class": "OpenClawTool",
+    "category": "agent",
+    "requires_config": true,
+    "version": "1.0.0",
+    "enabled": true,
+    "parameters": {
+      "server_url": {"type": "string", "description": "OpenClaw Gateway 地址", "required": true},
+      "api_key": {"type": "string", "description": "OpenClaw API Key", "sensitive": true, "required": true}
+    }
   }
 }

api/app/core/tools/custom/base.py

@@ -30,7 +30,7 @@ class CustomTool(BaseTool):
         self.auth_config = config.get("auth_config", {})
         self.base_url = config.get("base_url", "")
         self.timeout = config.get("timeout", 30)
-        
+
         # 解析schema
         self._parsed_operations = self._parse_openapi_schema()
     

api/app/core/tools/langchain_adapter.py

@@ -131,7 +131,7 @@ class LangchainAdapter:
     def _tool_supports_operations(tool: BaseTool) -> bool:
         """检查工具是否支持多操作"""
         # 内置工具中支持操作的工具
-        builtin_operation_tools = ['datetime_tool', 'json_tool']
+        builtin_operation_tools = ['datetime_tool', 'json_tool', 'openclaw_tool']
         
         # 检查内置工具
         if tool.tool_type.value == "builtin" and tool.name in builtin_operation_tools:

api/app/core/tools/mcp/client.py

@@ -99,7 +99,7 @@ class SimpleMCPClient:
             # 建立 SSE 连接
             response = await self._session.get(self.server_url)
             
-            if response.status not in (200, 202):
+            if not (200 <= response.status < 300):
                 error_text = await response.text()
                 raise MCPConnectionError(f"SSE 连接失败 {response.status}: {error_text}")
             
@@ -190,9 +190,7 @@ class SimpleMCPClient:
         
         try:
             async with self._session.post(self._endpoint_url, json=request) as response:
-                # MCP SSE 协议：POST 请求返回 200 或 202 均为正常
-                # 202 Accepted 表示请求已接受，结果通过 SSE 流异步返回
-                if response.status not in (200, 202):
+                if not (200 <= response.status < 300):
                     error_text = await response.text()
                     raise MCPConnectionError(f"请求失败 {response.status}: {error_text}")
             
@@ -207,7 +205,7 @@ class SimpleMCPClient:
             raise MCPConnectionError("endpoint URL 未初始化")
         
         async with self._session.post(self._endpoint_url, json=notification) as response:
-            if response.status not in (200, 202):
+            if not (200 <= response.status < 300):
                 logger.warning(f"通知发送失败: {response.status}")
     
     async def _initialize_modelscope_session(self):
@@ -225,7 +223,7 @@ class SimpleMCPClient:
         
         try:
             async with self._session.post(self.server_url, json=init_request) as response:
-                if response.status != 200:
+                if not (200 <= response.status < 300):
                     error_text = await response.text()
                     raise MCPConnectionError(f"初始化失败 {response.status}: {error_text}")
                 

api/app/core/workflow/adapters/base_adapter.py

@@ -40,6 +40,7 @@ class WorkflowParserResult(BaseModel):
     edges: list[EdgeDefinition] = Field(default_factory=list)
     nodes: list[NodeDefinition] = Field(default_factory=list)
     variables: list[VariableDefinition] = Field(default_factory=list)
+    features: dict[str, Any] = Field(default_factory=dict)
     warnings: list[ExceptionDefinition] = Field(default_factory=list)
     errors: list[ExceptionDefinition] = Field(default_factory=list)
 
@@ -51,6 +52,7 @@ class WorkflowImportResult(BaseModel):
     edges: list[EdgeDefinition] = Field(default_factory=list)
     nodes: list[NodeDefinition] = Field(default_factory=list)
     variables: list[VariableDefinition] = Field(default_factory=list)
+    features: dict[str, Any] = Field(default_factory=dict)
     warnings: list[ExceptionDefinition] = Field(default_factory=list)
     errors: list[ExceptionDefinition] = Field(default_factory=list)
 

api/app/core/workflow/adapters/dify/converter.py

@@ -15,7 +15,7 @@ from app.core.workflow.adapters.errors import (
     ExceptionType
 )
 from app.core.workflow.nodes.assigner.config import AssignmentItem
-from app.core.workflow.nodes.base_config import VariableDefinition, BaseNodeConfig
+from app.core.workflow.nodes.base_config import VariableDefinition as NodeVariableDefinition, BaseNodeConfig
 from app.core.workflow.nodes.code.config import InputVariable, OutputVariable
 from app.core.workflow.nodes.configs import (
     StartNodeConfig,
@@ -32,13 +32,17 @@ from app.core.workflow.nodes.configs import (
     NoteNodeConfig,
     ParameterExtractorNodeConfig,
     QuestionClassifierNodeConfig,
-    VariableAggregatorNodeConfig
+    VariableAggregatorNodeConfig,
+    ListOperatorNodeConfig,
+    DocExtractorNodeConfig,
 )
+from app.schemas.workflow_schema import VariableDefinition as SchemaVariableDefinition
 from app.core.workflow.nodes.cycle_graph.config import (
     ConditionDetail as LoopConditionDetail,
     ConditionsConfig,
     CycleVariable
 )
+from app.core.workflow.nodes.list_operator.config import FilterCondition
 from app.core.workflow.nodes.enums import (
     ValueInputType,
     ComparisonOperator,
@@ -90,9 +94,12 @@ class DifyConverter(BaseConverter):
             NodeType.VAR_AGGREGATOR: self.convert_variable_aggregator_node_config,
             NodeType.TOOL: self.convert_tool_node_config,
             NodeType.NOTES: self.convert_notes_config,
+            NodeType.LIST_OPERATOR: self.convert_list_operator_node_config,
+            NodeType.DOCUMENT_EXTRACTOR: self.convert_document_extractor_node_config,
             NodeType.CYCLE_START: lambda x: {},
             NodeType.BREAK: lambda x: {},
         }
+        self._file_vars_to_conv: list[SchemaVariableDefinition] = []
 
     def get_node_convert(self, node_type):
         func = self.CONFIG_CONVERT_MAP.get(node_type, lambda x: {})
@@ -126,7 +133,7 @@ class DifyConverter(BaseConverter):
         selector = var_selector.split('.')
         if len(selector) not in [2, 3] and var_selector != "context":
             raise Exception(f"invalid variable selector: {var_selector}")
-        if len(selector) == 3:
+        if len(selector) == 3 and selector[0] in ("conversation", "sys"):
             selector = selector[1:]
         if selector[0] == "conversation":
             selector[0] = "conv"
@@ -213,7 +220,9 @@ class DifyConverter(BaseConverter):
             "end with": ComparisonOperator.END_WITH,
             "not contains": ComparisonOperator.NOT_CONTAINS,
             "exists": ComparisonOperator.NOT_EMPTY,
-            "not exists": ComparisonOperator.EMPTY
+            "not exists": ComparisonOperator.EMPTY,
+            "in": ComparisonOperator.IN,
+            "not in": ComparisonOperator.NOT_IN,
         }
         return operator_map.get(operator, operator)
 
@@ -279,19 +288,25 @@ class DifyConverter(BaseConverter):
                 )
                 continue
 
-            if var_type in ["file", "array[file]"]:
-                self.errors.append(
-                    ExceptionDefinition(
-                        type=ExceptionType.VARIABLE,
-                        node_id=node["id"],
-                        node_name=node_data["title"],
-                        name=var["variable"],
-                        detail=f"Unsupported Variable type for start node: {var_type}"
-                    )
-                )
+            if var_type in [VariableType.FILE, VariableType.ARRAY_FILE]:
+                # 开始节点不支持文件变量，转为会话变量
+                self._file_vars_to_conv.append(SchemaVariableDefinition(
+                    name=var["variable"],
+                    type=var_type.value,
+                    required=var.get("required", False),
+                    default=None,
+                    description=var.get("label", ""),
+                ))
+                self.warnings.append(ExceptionDefinition(
+                    type=ExceptionType.VARIABLE,
+                    node_id=node["id"],
+                    node_name=node_data["title"],
+                    name=var["variable"],
+                    detail=f"File variable '{var['variable']}' is not supported in start node, moved to conversation variables"
+                ))
                 continue
 
-            var_def = VariableDefinition(
+            var_def = NodeVariableDefinition(
                 name=var["variable"],
                 type=var_type,
                 required=var["required"],
@@ -476,11 +491,11 @@ class DifyConverter(BaseConverter):
         node_data = node["data"]
         result = IterationNodeConfig.model_construct(
             input=self._process_list_variable_literal(node_data["iterator_selector"]),
-            parallel=node_data["is_parallel"],
-            parallel_count=node_data["parallel_nums"],
+            parallel=node_data.get("is_parallel", False),
+            parallel_count=node_data.get("parallel_nums", 4),
             output=self._process_list_variable_literal(node_data["output_selector"]),
             output_type=self.variable_type_map(node_data.get("output_type")),
-            flatten=node_data["flatten_output"],
+            flatten=node_data.get("flatten_output", False),
         ).model_dump()
 
         self.config_validate(node["id"], node["data"]["title"], IterationNodeConfig, result)
@@ -489,7 +504,23 @@ class DifyConverter(BaseConverter):
     def convert_assigner_node_config(self, node: dict) -> dict:
         node_data = node["data"]
         assignments = []
-        for assignment in node_data["items"]:
+
+        # Support both formats:
+        # 1. New format: node_data["items"] list
+        # 2. Flat format: assigned_variable_selector + input_variable_selector + write_mode
+        if "items" in node_data:
+            raw_items = node_data["items"]
+        elif "assigned_variable_selector" in node_data and "input_variable_selector" in node_data:
+            raw_items = [{
+                "variable_selector": node_data["assigned_variable_selector"],
+                "value": node_data["input_variable_selector"],
+                "input_type": ValueInputType.VARIABLE,
+                "operation": node_data.get("write_mode", "over-write"),
+            }]
+        else:
+            raw_items = []
+
+        for assignment in raw_items:
             if assignment.get("operation") is None or assignment.get("value") is None:
                 continue
             assignments.append(
@@ -771,3 +802,119 @@ class DifyConverter(BaseConverter):
             show_author=node_data.get("showAuthor", True)
         ).model_dump()
         return result
+
+    def convert_list_operator_node_config(self, node: dict) -> dict:
+        """Dify list-operator — convert variable path array to {{ }} selector format."""
+        node_data = node["data"]
+        variable_path = node_data.get("variable", [])
+        input_list = self._process_list_variable_literal(variable_path) or ""
+        filter_by = node_data.get("filter_by", {"enabled": False, "conditions": []})
+        # Convert each condition's comparison_operator from Dify format to native
+        if filter_by.get("conditions"):
+            converted_conditions = []
+            for cond in filter_by["conditions"]:
+                converted_conditions.append({
+                    **cond,
+                    "comparison_operator": self.convert_compare_operator(
+                        cond.get("comparison_operator", "")
+                    )
+                })
+            filter_by = {**filter_by, "conditions": converted_conditions}
+        result = {
+            "input_list": input_list,
+            "filter_by": filter_by,
+            "order_by": node_data.get("order_by", {"enabled": False, "key": "", "value": "asc"}),
+            "limit": node_data.get("limit", {"enabled": False, "size": -1}),
+            "extract_by": node_data.get("extract_by", {"enabled": False, "serial": "1"}),
+        }
+        self.config_validate(node["id"], node["data"]["title"], ListOperatorNodeConfig, result)
+        return result
+
+    def convert_document_extractor_node_config(self, node: dict) -> dict:
+        """Convert Dify document-extractor node to MemoryBear DocExtractorNodeConfig.
+
+        Dify document-extractor data fields:
+          variable_selector: list[str]  - file variable path
+        """
+        node_data = node["data"]
+        file_selector = self._process_list_variable_literal(
+            node_data.get("variable_selector", [])
+        ) or ""
+        result = DocExtractorNodeConfig.model_construct(
+            file_selector=file_selector,
+        ).model_dump()
+        self.config_validate(node["id"], node["data"]["title"], DocExtractorNodeConfig, result)
+        return result
+
+    @staticmethod
+    def convert_features(features: dict) -> dict:
+        """Convert Dify features to MemoryBear FeaturesConfigForm format."""
+        if not features:
+            return {}
+
+        result: dict = {}
+
+        # opening_statement
+        opening = features.get("opening_statement", "")
+        suggested = features.get("suggested_questions", [])
+        result["opening_statement"] = {
+            "enabled": bool(opening),
+            "statement": opening or None,
+            "suggested_questions": suggested,
+        }
+
+        # citation (对应 Dify retriever_resource)
+        retriever = features.get("retriever_resource", {})
+        result["citation"] = {
+            "enabled": retriever.get("enabled", False) if isinstance(retriever, dict) else False,
+        }
+
+        # file_upload: Dify allowed_file_types 数组 -> 前端扁平字段
+        file_upload = features.get("file_upload", {})
+        allowed_types = file_upload.get("allowed_file_types", []) if file_upload else []
+        allowed_methods = file_upload.get("allowed_file_upload_methods", ["local_file", "remote_url"])
+        if isinstance(allowed_methods, list):
+            if len(allowed_methods) >= 2:
+                transfer_method = "both"
+            elif allowed_methods:
+                transfer_method = allowed_methods[0]
+            else:
+                transfer_method = "both"
+        else:
+            transfer_method = allowed_methods or "both"
+
+        file_config = file_upload.get("fileUploadConfig", {})
+        result["file_upload"] = {
+            "enabled": file_upload.get("enabled", False) if file_upload else False,
+            "image_enabled": "image" in allowed_types,
+            "image_max_size_mb": file_config.get("image_file_size_limit", 10) if file_config else 10,
+            "image_allowed_extensions": ["png", "jpg", "jpeg"],
+            "audio_enabled": "audio" in allowed_types,
+            "audio_max_size_mb": file_config.get("audio_file_size_limit", 50) if file_config else 50,
+            "audio_allowed_extensions": ["mp3", "wav", "m4a"],
+            "document_enabled": "document" in allowed_types,
+            "document_max_size_mb": file_config.get("file_size_limit", 100) if file_config else 100,
+            "document_allowed_extensions": ["pdf", "docx", "doc", "xlsx", "xls", "txt", "csv", "json", "md"],
+            "video_enabled": "video" in allowed_types,
+            "video_max_size_mb": file_config.get("video_file_size_limit", 100) if file_config else 100,
+            "video_allowed_extensions": ["mp4", "mov"],
+            "max_file_count": file_upload.get("number_limits", 1) if file_upload else 1,
+            "allowed_transfer_methods": transfer_method,
+        }
+
+        # text_to_speech
+        tts = features.get("text_to_speech", {})
+        result["text_to_speech"] = {
+            "enabled": tts.get("enabled", False) if isinstance(tts, dict) else False,
+            "voice": tts.get("voice") if isinstance(tts, dict) else None,
+            "language": tts.get("language") if isinstance(tts, dict) else None,
+            "autoplay": False,
+        }
+
+        # suggested_questions_after_answer
+        sqa = features.get("suggested_questions_after_answer", {})
+        result["suggested_questions_after_answer"] = {
+            "enabled": sqa.get("enabled", False) if isinstance(sqa, dict) else False,
+        }
+
+        return result

api/app/core/workflow/adapters/dify/dify_adapter.py

@@ -45,6 +45,8 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
         "question-classifier": NodeType.QUESTION_CLASSIFIER,
         "variable-aggregator": NodeType.VAR_AGGREGATOR,
         "tool": NodeType.TOOL,
+        "list-operator": NodeType.LIST_OPERATOR,
+        "document-extractor": NodeType.DOCUMENT_EXTRACTOR,
         "": NodeType.NOTES
     }
 
@@ -117,9 +119,12 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
             if variable:
                 self.conv_variables.append(con_var)
 
-        # for variables in config.get("workflow").get("environment_variables"):
-        #     variable = self._convert_variable(variables)
-        #     conv_variables.append(variable)
+        # 开始节点的文件变量合并到会话变量
+        self.conv_variables.extend(self._file_vars_to_conv)
+
+        features = self.convert_features(
+            self.config.get("workflow", {}).get("features", {})
+        )
 
         trigger = self._convert_trigger({})
         execution_config = self._convert_execution({})
@@ -133,6 +138,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
             edges=self.edges,
             nodes=self.nodes,
             variables=self.conv_variables,
+            features=features,
             warnings=self.warnings,
             errors=self.errors
         )

api/app/core/workflow/adapters/memory_bear/memory_bear_converter.py

@@ -22,6 +22,8 @@ from app.core.workflow.nodes.configs import (
     MemoryReadNodeConfig,
     MemoryWriteNodeConfig,
     NoteNodeConfig,
+    ListOperatorNodeConfig,
+    DocExtractorNodeConfig,
 )
 from app.core.workflow.nodes.enums import NodeType
 
@@ -51,6 +53,8 @@ class MemoryBearConverter(BaseConverter):
         NodeType.MEMORY_READ: MemoryReadNodeConfig,
         NodeType.MEMORY_WRITE: MemoryWriteNodeConfig,
         NodeType.NOTES: NoteNodeConfig,
+        NodeType.LIST_OPERATOR: ListOperatorNodeConfig,
+        NodeType.DOCUMENT_EXTRACTOR: DocExtractorNodeConfig,
     }
 
     @staticmethod

api/app/core/workflow/engine/graph_builder.py

@@ -31,9 +31,9 @@ logger = logging.getLogger(__name__)
 # Example:
 #   "Hello {{user.name}}!" ->
 #   ["Hello ", "{{user.name}}", "!"]
-_OUTPUT_PATTERN = re.compile(r'\{\{.*?}}|[^{}]+')
+_OUTPUT_PATTERN = re.compile(r'\{\{.*?}}|[^{]+|{')
 # Strict variable format: {{ node_id.field_name }}
-_VARIABLE_PATTERN = re.compile(r'\{\{\s*[a-zA-Z0-9_]+\.[a-zA-Z0-9_]+\s*}}')
+_VARIABLE_PATTERN = re.compile(r'\{\{\s*[a-zA-Z0-9_]+\.[a-zA-Z0-9_]+(?:\.[a-zA-Z0-9_]+)?\s*}}')
 
 
 class GraphBuilder:

api/app/core/workflow/engine/result_builder.py

@@ -59,6 +59,9 @@ class WorkflowResultBuilder:
             conversation_vars = variable_pool.get_all_conversation_vars()
             sys_vars = variable_pool.get_all_system_vars()
 
+        # 汇总所有 knowledge 节点的 citations
+        citations = self.aggregate_citations(node_outputs)
+
         return {
             "status": "completed" if success else "failed",
             "output": final_output,
@@ -71,9 +74,25 @@ class WorkflowResultBuilder:
             "conversation_id": execution_context.conversation_id,
             "elapsed_time": elapsed_time,
             "token_usage": token_usage,
+            "citations": citations,
             "error": result.get("error"),
         }
 
+    @staticmethod
+    def aggregate_citations(node_outputs: dict) -> list:
+        """从所有 knowledge 节点的输出中汇总 citations，去重"""
+        seen = set()
+        citations = []
+        for node_output in node_outputs.values():
+            if not isinstance(node_output, dict):
+                continue
+            for c in node_output.get("citations", []):
+                key = c.get("document_id")
+                if key and key not in seen:
+                    seen.add(key)
+                    citations.append(c)
+        return citations
+
     @staticmethod
     def aggregate_token_usage(node_outputs: dict) -> dict[str, int] | None:
         """

api/app/core/workflow/engine/state_manager.py

@@ -9,10 +9,10 @@ from app.core.workflow.nodes.enums import NodeType
 
 
 def merge_activate_state(x, y):
-    return {
-        k: x.get(k, False) or y.get(k, False)
-        for k in set(x) | set(y)
-    }
+    merged = dict(x)
+    for k, v in y.items():
+        merged[k] = merged.get(k, False) or v
+    return merged
 
 
 def merge_looping_state(x, y):

api/app/core/workflow/engine/stream_output_coordinator.py

@@ -14,7 +14,7 @@ from app.core.workflow.engine.variable_pool import VariablePool
 logger = get_logger(__name__)
 
 SCOPE_PATTERN = re.compile(
-    r"\{\{\s*([a-zA-Z0-9_]+)\.[a-zA-Z0-9_]+\s*}}"
+    r"\{\{\s*([a-zA-Z0-9_]+)\.[a-zA-Z0-9_]+(?:\.[a-zA-Z0-9_]+)?\s*}}"
 )
 
 

api/app/core/workflow/engine/variable_pool.py

@@ -17,6 +17,54 @@ from app.core.workflow.variable.variable_objects import T, create_variable_insta
 
 logger = logging.getLogger(__name__)
 
+VARIABLE_PATTERN = re.compile(r"\{\{\s*(.*?)\s*}}")
+
+
+class LazyVariableDict:
+    def __init__(self, source, literal):
+        self._source: dict[str, VariableStruct[Any]] = source
+        self._literal: bool = literal
+        self._cache = {}
+
+    def keys(self):
+        return self._source.keys()
+
+    def _resolve(self, key):
+        if key in self._cache:
+            return self._cache[key]
+        var_struct = self._source.get(key)
+        if var_struct is None:
+            return None
+        raw = var_struct.instance.get_value()
+        # literal 模式下 dict/list 保留结构，让 Jinja2 能继续访问子字段（如 .type）
+        value = raw if (not self._literal or isinstance(raw, (dict, list))) else var_struct.instance.to_literal()
+        self._cache[key] = value
+        return value
+
+    def get(self, key, default=None):
+        value = self._resolve(key)
+        return default if value is None else value
+
+    def __getitem__(self, key):
+        value = self._resolve(key)
+        if value is None:
+            raise KeyError(key)
+        return value
+
+    def __getattr__(self, key):
+        if key.startswith('_'):
+            raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{key}'")
+        return self._resolve(key)
+
+    def __contains__(self, key):
+        return key in self._source
+
+    def __iter__(self):
+        return iter(self._source)
+
+    def __len__(self):
+        return len(self._source)
+
 
 class VariableSelector:
     """变量选择器
@@ -117,10 +165,9 @@ class VariablePool:
 
     @staticmethod
     def transform_selector(selector):
-        pattern = r"\{\{\s*(.*?)\s*\}\}"
-        variable_literal = re.sub(pattern, r"\1", selector).strip()
+        variable_literal = VARIABLE_PATTERN.sub(r"\1", selector).strip()
         selector = VariableSelector.from_string(variable_literal).path
-        if len(selector) != 2:
+        if len(selector) not in (2, 3):
             raise ValueError(f"Selector not valid - {selector}")
         return selector
 
@@ -152,6 +199,16 @@ class VariablePool:
             return None
         return var_instance
 
+    @staticmethod
+    def _extract_field(struct: "VariableStruct", field: str | None) -> Any:
+        """If field is given, drill into a dict/object variable's value."""
+        if field is None:
+            return struct.instance.get_value()
+        value = struct.instance.get_value()
+        if not isinstance(value, dict):
+            raise KeyError(f"Variable is not an object, cannot access field '{field}'")
+        return value.get(field)
+
     def get_instance(
             self,
             selector: str,
@@ -206,12 +263,14 @@ class VariablePool:
         Raises:
             KeyError: If strict is True and the variable does not exist.
         """
+        path = self.transform_selector(selector)
         variable_struct = self._get_variable_struct(selector)
         if variable_struct is None:
             if strict:
                 raise KeyError(f"{selector} not exist")
             return default
-
+        if len(path) == 3:
+            return self._extract_field(variable_struct, path[2])
         return variable_struct.instance.get_value()
 
     def get_literal(
@@ -238,12 +297,15 @@ class VariablePool:
         Raises:
             KeyError: If strict is True and the variable does not exist.
         """
+        path = self.transform_selector(selector)
         variable_struct = self._get_variable_struct(selector)
         if variable_struct is None:
             if strict:
                 raise KeyError(f"{selector} not exist")
             return default
-
+        if len(path) == 3:
+            value = self._extract_field(variable_struct, path[2])
+            return str(value) if value is not None else ""
         return variable_struct.instance.to_literal()
 
     async def set(
@@ -274,7 +336,7 @@ class VariablePool:
             namespace: str,
             key: str,
             value: Any,
-            var_type: VariableType,
+            var_type: VariableType | None,
             mut: bool
     ):
         if self.has(f"{namespace}.{key}"):
@@ -301,7 +363,24 @@ class VariablePool:
         Returns:
             变量是否存在
         """
-        return self._get_variable_struct(selector) is not None
+        path = self.transform_selector(selector)
+        struct = self._get_variable_struct(selector)
+        if struct is None:
+            return False
+        if len(path) == 3:
+            value = struct.instance.get_value()
+            return isinstance(value, dict) and path[2] in value
+        return True
+
+    def lazy_namespace(self, namespace: str, literal: bool = False) -> LazyVariableDict:
+        return LazyVariableDict(self.variables.get(namespace, {}), literal)
+
+    def lazy_all_node_outputs(self, literal: bool = False) -> dict[str, LazyVariableDict]:
+        return {
+            ns: LazyVariableDict(vars_dict, literal)
+            for ns, vars_dict in self.variables.items()
+            if ns not in ("sys", "conv")
+        }
 
     def get_all_system_vars(self, literal=False) -> dict[str, Any]:
         """获取所有系统变量
@@ -439,6 +518,23 @@ class VariablePoolInitializer:
                     var_value = var_default
                 else:
                     var_value = DEFAULT_VALUE(var_type)
+                # Convert FileInput-format dicts to full FileObject dicts
+                if var_type == VariableType.FILE:
+                    if not var_value:
+                        continue
+                    var_value = await self._resolve_file_default(var_value)
+                    if not var_value:
+                        continue
+                elif var_type == VariableType.ARRAY_FILE:
+                    if not var_value:
+                        var_value = []
+                    else:
+                        resolved = []
+                        for item in var_value:
+                            f = await self._resolve_file_default(item)
+                            if f:
+                                resolved.append(f)
+                        var_value = resolved
                 await variable_pool.new(
                     namespace="conv",
                     key=var_name,
@@ -447,6 +543,17 @@ class VariablePoolInitializer:
                     mut=True
                 )
 
+    @staticmethod
+    async def _resolve_file_default(file_def: dict) -> dict | None:
+        """Accept only already-resolved FileObject dicts (is_file=True).
+        FileInput-format dicts are converted at save time by WorkflowService._resolve_variables_file_defaults.
+        """
+        if not isinstance(file_def, dict):
+            return None
+        if file_def.get("is_file"):
+            return file_def
+        return None
+
     @staticmethod
     async def _init_system_vars(
             variable_pool: VariablePool,
@@ -479,5 +586,3 @@ class VariablePoolInitializer:
                 var_type=var_type,
                 mut=False
             )
-
-

api/app/core/workflow/nodes/base_node.py

@@ -395,7 +395,8 @@ class BaseNode(ABC):
             "output": output,
             "elapsed_time": elapsed_time,
             "token_usage": token_usage,
-            "error": None
+            "error": None,
+            **self._extract_extra_fields(business_result),
         }
         final_output = {
             "node_outputs": {self.node_id: node_output},
@@ -498,6 +499,13 @@ class BaseNode(ABC):
         # Default implementation returns the business result directly
         return business_result
 
+    def _extract_extra_fields(self, business_result: Any) -> dict:
+        """Extracts extra fields to merge into node_output (e.g. citations).
+
+        Subclasses may override to inject additional metadata.
+        """
+        return {}
+
     def _extract_token_usage(self, business_result: Any) -> dict[str, int] | None:
         """Extracts token usage information from the business result.
 
@@ -552,9 +560,9 @@ class BaseNode(ABC):
 
         return render_template(
             template=template,
-            conv_vars=variable_pool.get_all_conversation_vars(literal=True),
-            node_outputs=variable_pool.get_all_node_outputs(literal=True),
-            system_vars=variable_pool.get_all_system_vars(literal=True),
+            conv_vars=variable_pool.lazy_namespace("conv", literal=True),
+            node_outputs=variable_pool.lazy_all_node_outputs(literal=True),
+            system_vars=variable_pool.lazy_namespace("sys", literal=True),
             strict=strict
         )
 
@@ -579,9 +587,9 @@ class BaseNode(ABC):
 
         return evaluate_condition(
             expression=expression,
-            conv_var=variable_pool.get_all_conversation_vars(),
-            node_outputs=variable_pool.get_all_node_outputs(),
-            system_vars=variable_pool.get_all_system_vars()
+            conv_var=variable_pool.lazy_namespace("conv"),
+            node_outputs=variable_pool.lazy_all_node_outputs(),
+            system_vars=variable_pool.lazy_namespace("sys")
         )
 
     @staticmethod

api/app/core/workflow/nodes/code/node.py

@@ -13,7 +13,7 @@ from app.core.workflow.engine.state_manager import WorkflowState
 from app.core.workflow.engine.variable_pool import VariablePool
 from app.core.workflow.nodes import BaseNode
 from app.core.workflow.nodes.code.config import CodeNodeConfig
-from app.core.workflow.variable.base_variable import VariableType
+from app.core.workflow.variable.base_variable import VariableType, DEFAULT_VALUE
 
 logger = logging.getLogger(__name__)
 
@@ -70,7 +70,8 @@ class CodeNode(BaseNode):
             for output in self.typed_config.output_variables:
                 value = exec_result.get(output.name)
                 if value is None:
-                    raise RuntimeError(f"Return value {output.name} does not exist")
+                    result[output.name] = DEFAULT_VALUE(output.type)
+                    continue
                 match output.type:
                     case VariableType.STRING:
                         if not isinstance(value, str):

api/app/core/workflow/nodes/configs.py

@@ -24,6 +24,8 @@ from app.core.workflow.nodes.start.config import StartNodeConfig
 from app.core.workflow.nodes.tool.config import ToolNodeConfig
 from app.core.workflow.nodes.variable_aggregator.config import VariableAggregatorNodeConfig
 from app.core.workflow.nodes.notes.config import NoteNodeConfig
+from app.core.workflow.nodes.list_operator.config import ListOperatorNodeConfig
+from app.core.workflow.nodes.document_extractor.config import DocExtractorNodeConfig
 
 __all__ = [
     # 基础类
@@ -49,5 +51,7 @@ __all__ = [
     "MemoryReadNodeConfig",
     "MemoryWriteNodeConfig",
     "CodeNodeConfig",
-    "NoteNodeConfig"
+    "NoteNodeConfig",
+    "ListOperatorNodeConfig",
+    "DocExtractorNodeConfig",
 ]

api/app/core/workflow/nodes/cycle_graph/loop.py

@@ -11,7 +11,6 @@ from app.core.workflow.engine.variable_pool import VariablePool
 from app.core.workflow.nodes.cycle_graph import LoopNodeConfig
 from app.core.workflow.nodes.enums import ValueInputType, ComparisonOperator, LogicOperator, NodeType
 from app.core.workflow.nodes.operators import TypeTransformer, ConditionExpressionResolver, CompareOperatorInstance
-from app.core.workflow.utils.expression_evaluator import evaluate_expression
 
 logger = logging.getLogger(__name__)
 
@@ -85,12 +84,7 @@ class LoopRuntime:
 
         for variable in self.typed_config.cycle_vars:
             if variable.input_type == ValueInputType.VARIABLE:
-                value = evaluate_expression(
-                    expression=variable.value,
-                    conv_var=self.variable_pool.get_all_conversation_vars(),
-                    node_outputs=self.variable_pool.get_all_node_outputs(),
-                    system_vars=self.variable_pool.get_all_system_vars(),
-                )
+                value = self.variable_pool.get_value(variable.value)
             else:
                 value = TypeTransformer.transform(variable.value, variable.type)
             await self.child_variable_pool.new(self.node_id, variable.name, value, variable.type, mut=True)
@@ -98,12 +92,7 @@ class LoopRuntime:
             **self.state
         )
         loopstate["node_outputs"][self.node_id] = {
-            variable.name: evaluate_expression(
-                expression=variable.value,
-                conv_var=self.variable_pool.get_all_conversation_vars(),
-                node_outputs=self.variable_pool.get_all_node_outputs(),
-                system_vars=self.variable_pool.get_all_system_vars(),
-            )
+            variable.name: self.variable_pool.get_value(variable.value)
             if variable.input_type == ValueInputType.VARIABLE
             else TypeTransformer.transform(variable.value, variable.type)
             for variable in self.typed_config.cycle_vars

api/app/core/workflow/nodes/cycle_graph/node.py

@@ -55,9 +55,9 @@ class CycleGraphNode(BaseNode):
             if config.output_type in [
                 VariableType.ARRAY_FILE,
                 VariableType.ARRAY_STRING,
-                VariableType.NUMBER,
+                VariableType.ARRAY_NUMBER,
                 VariableType.ARRAY_OBJECT,
-                VariableType.BOOLEAN
+                VariableType.ARRAY_BOOLEAN
             ]:
                 if config.flatten:
                     outputs['output'] = config.output_type

api/app/core/workflow/nodes/document_extractor/node.py

@@ -14,12 +14,22 @@ logger = logging.getLogger(__name__)
 
 def _file_object_to_file_input(f: FileObject) -> FileInput:
     """Convert workflow FileObject to multimodal FileInput."""
+    file_type = f.origin_file_type or ""
+    # Prefer mime_type for more accurate type detection
+    if not file_type and f.mime_type:
+        file_type = f.mime_type
+    resolved_type = FileType.trans(f.type) if isinstance(f.type, str) else f.type
+    if resolved_type != FileType.DOCUMENT:
+        raise ValueError(
+            f"Document extractor only supports document files, got type '{f.type}' "
+            f"(name={f.name or f.file_id or f.url})"
+        )
     return FileInput(
-        type=FileType.DOCUMENT,
+        type=resolved_type,
         transfer_method=TransferMethod(f.transfer_method),
         url=f.url or None,
         upload_file_id=f.file_id or None,
-        file_type=f.origin_file_type or "",
+        file_type=file_type,
     )
 
 
@@ -81,6 +91,7 @@ class DocExtractorNode(BaseNode):
             from app.services.multimodal_service import MultimodalService
             svc = MultimodalService(db)
             for f in files:
+                label = f.name or f.url or f.file_id
                 try:
                     file_input = _file_object_to_file_input(f)
                     # Ensure URL is populated for local files
@@ -89,11 +100,11 @@ class DocExtractorNode(BaseNode):
                     # Reuse cached bytes if already fetched
                     if f.get_content():
                         file_input.set_content(f.get_content())
-                    text = await svc._extract_document_text(file_input)
+                    text = await svc.extract_document_text(file_input)
                     chunks.append(text)
                 except Exception as e:
                     logger.error(
-                        f"Node {self.node_id}: failed to extract file url={f.url} file_id={f.file_id}: {e}",
+                        f"Node {self.node_id}: failed to extract file '{label}': {e}",
                         exc_info=True,
                     )
                     chunks.append("")

api/app/core/workflow/nodes/enums.py

@@ -24,6 +24,7 @@ class NodeType(StrEnum):
     MEMORY_READ = "memory-read"
     MEMORY_WRITE = "memory-write"
     DOCUMENT_EXTRACTOR = "document-extractor"
+    LIST_OPERATOR = "list-operator"
 
     UNKNOWN = "unknown"
     NOTES = "notes"
@@ -45,6 +46,8 @@ class ComparisonOperator(StrEnum):
     LE = "le"
     GT = "gt"
     GE = "ge"
+    IN = "in"
+    NOT_IN = "not_in"
 
 
 class LogicOperator(StrEnum):

api/app/core/workflow/nodes/http_request/config.py

@@ -72,8 +72,9 @@ class HttpContentTypeConfig(BaseModel):
     @classmethod
     def validate_data(cls, v, info):
         content_type = info.data.get("content_type")
-        if content_type == HttpContentType.FROM_DATA and not isinstance(v, HttpFormData):
-            raise ValueError("When content_type is 'form-data', data must be of type HttpFormData")
+        if content_type == HttpContentType.FROM_DATA and (
+                not isinstance(v, list) or not all(isinstance(item, HttpFormData) for item in v)):
+            raise ValueError("When content_type is 'form-data', data must be a list of HttpFormData")
         elif content_type in [HttpContentType.JSON] and not isinstance(v, str):
             raise ValueError("When content_type is JSON, data must be of type str")
         elif content_type in [HttpContentType.WWW_FORM] and not isinstance(v, dict):

api/app/core/workflow/nodes/http_request/node.py

@@ -260,17 +260,22 @@ class HttpRequestNode(BaseNode):
                 ))
             case HttpContentType.FROM_DATA:
                 data = {}
-                content["files"] = {}
+                files = []
                 for item in self.typed_config.body.data:
+                    key = self._render_template(item.key, variable_pool)
                     if item.type == "text":
-                        data[self._render_template(item.key, variable_pool)] = self._render_template(item.value,
-                                                                                                     variable_pool)
+                        data[key] = self._render_template(item.value, variable_pool)
                     elif item.type == "file":
-                        content["files"][self._render_template(item.key, variable_pool)] = (
-                            uuid.uuid4().hex,
-                            await variable_pool.get_instance(item.value).get_content()
-                        )
+                        file_instance = variable_pool.get_instance(item.value)
+                        if isinstance(file_instance, ArrayVariable):
+                            for v in file_instance.value:
+                                if isinstance(v, FileVariable):
+                                    files.append((key, (uuid.uuid4().hex, await v.get_content())))
+                        elif isinstance(file_instance, FileVariable):
+                            files.append((key, (uuid.uuid4().hex, await file_instance.get_content())))
                 content["data"] = data
+                if files:
+                    content["files"] = files
             case HttpContentType.BINARY:
                 content["files"] = []
                 file_instence = variable_pool.get_instance(self.typed_config.body.data)

api/app/core/workflow/nodes/knowledge/node.py

@@ -1,19 +1,25 @@
+import asyncio
 import logging
 import uuid
 from typing import Any
 
+from langchain_core.documents import Document
+
 from app.core.error_codes import BizCode
 from app.core.exceptions import BusinessException
 from app.core.models import RedBearRerank, RedBearModelConfig
-from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory, ElasticSearchVector
+from app.core.rag.llm.chat_model import Base
+from app.core.rag.llm.embedding_model import OpenAIEmbed
+from app.core.rag.models.chunk import DocumentChunk
+from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
 from app.core.workflow.engine.state_manager import WorkflowState
 from app.core.workflow.engine.variable_pool import VariablePool
 from app.core.workflow.nodes.base_node import BaseNode
 from app.core.workflow.nodes.knowledge import KnowledgeRetrievalNodeConfig
 from app.core.workflow.variable.base_variable import VariableType
 from app.db import get_db_read
-from app.models import knowledge_model, knowledgeshare_model, ModelType
-from app.repositories import knowledge_repository, knowledgeshare_repository
+from app.models import knowledge_model, ModelType
+from app.repositories import knowledge_repository
 from app.schemas.chunk_schema import RetrieveType
 from app.services.model_service import ModelConfigService
 
@@ -24,13 +30,27 @@ class KnowledgeRetrievalNode(BaseNode):
     def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
         super().__init__(node_config, workflow_config, down_stream_nodes)
         self.typed_config: KnowledgeRetrievalNodeConfig | None = None
-        self.vector_service: ElasticSearchVector | None = None
 
     def _output_types(self) -> dict[str, VariableType]:
         return {
             "output": VariableType.ARRAY_STRING
         }
 
+    def _extract_output(self, business_result: Any) -> Any:
+        """下游节点只拿 chunks 列表"""
+        if isinstance(business_result, dict) and "chunks" in business_result:
+            return business_result["chunks"]
+        return business_result
+    
+    @staticmethod
+    def _extract_citations(business_result: Any) -> list:
+        if isinstance(business_result, dict):
+            return business_result.get("citations", [])
+        return []
+
+    def _extract_extra_fields(self, business_result: Any) -> dict:
+        return {"citations": self._extract_citations(business_result)}
+
     def _extract_input(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]:
         return {
             "query": self._render_template(self.typed_config.query, variable_pool),
@@ -85,46 +105,54 @@ class KnowledgeRetrievalNode(BaseNode):
                 unique.append(doc)
         return unique
 
-    def _get_existing_kb_ids(self, db, kb_ids):
+    def rerank(self, query: str, docs: list[DocumentChunk], top_k: int) -> list[DocumentChunk]:
         """
-        Resolve all accessible and valid knowledge base IDs for retrieval.
-
-        This includes:
-        - Private knowledge bases owned by the user
-        - Shared knowledge bases
-        - Source knowledge bases mapped via knowledge sharing relationships
-
+        Reorder the list of document blocks and return the top_k results most relevant to the query
         Args:
-            db: Database session.
-            kb_ids (list[UUID]): Knowledge base IDs from node configuration.
+            query: query string
+            docs: List of document chunk to be rearranged
+            top_k: The number of top-level documents returned
 
         Returns:
-            list[UUID]: Final list of valid knowledge base IDs.
+            Rearranged document chunk list (sorted in descending order of relevance)
+
+        Raises:
+            ValueError: If the input document list is empty or top_k is invalid
         """
-        filters = self._build_kb_filter(kb_ids, knowledge_model.PermissionType.Private)
-
-        existing_ids = knowledge_repository.get_chunked_knowledgeids(
-            db=db,
-            filters=filters
-        )
-
-        filters = self._build_kb_filter(kb_ids, knowledge_model.PermissionType.Share)
-
-        share_ids = knowledge_repository.get_chunked_knowledgeids(
-            db=db,
-            filters=filters
-        )
-
-        if share_ids:
-            filters = [
-                knowledgeshare_model.KnowledgeShare.target_kb_id.in_(kb_ids)
+        reranker = self.get_reranker_model()
+        # parameter validation
+        if not docs:
+            raise ValueError("retrieval chunks be empty")
+        if top_k <= 0:
+            raise ValueError("top_k must be a positive integer")
+        try:
+            # Convert to LangChain Document object
+            documents = [
+                Document(
+                    page_content=doc.page_content,  # Ensure that DocumentChunk possesses this attribute
+                    metadata=doc.metadata or {}  # Deal with possible None metadata
+                )
+                for doc in docs
             ]
-            items = knowledgeshare_repository.get_source_kb_ids_by_target_kb_id(
-                db=db,
-                filters=filters
+
+            # Perform reordering (compress_documents will automatically handle relevance scores and indexing)
+            reranked_docs = list(reranker.compress_documents(documents, query))
+
+            # Sort in descending order based on relevance score
+            reranked_docs.sort(
+                key=lambda x: x.metadata.get("relevance_score", 0),
+                reverse=True
             )
-            existing_ids.extend(items)
-        return existing_ids
+            # Convert back to a list of DocumentChunk, and save the relevance_score to metadata["score"]
+            result = []
+            for item in reranked_docs[:top_k]:
+                for doc in docs:
+                    if doc.page_content == item.page_content:
+                        doc.metadata["score"] = item.metadata["relevance_score"]
+                        result.append(doc)
+            return result
+        except Exception as e:
+            raise RuntimeError(f"Failed to rerank documents: {str(e)}") from e
 
     def get_reranker_model(self) -> RedBearRerank:
         """
@@ -164,49 +192,113 @@ class KnowledgeRetrievalNode(BaseNode):
         )
         return reranker
 
-    def knowledge_retrieval(self, db, query, rs, db_knowledge, kb_config):
+    async def knowledge_retrieval(self, db, query, db_knowledge, kb_config):
+        rs = []
         if db_knowledge.type == knowledge_model.KnowledgeType.FOLDER:
             children = knowledge_repository.get_knowledges_by_parent_id(db=db, parent_id=db_knowledge.id)
+            tasks = []
             for child in children:
                 if not (child and child.chunk_num > 0 and child.status == 1):
                     continue
-                kb_config.kb_id = child.id
-                self.knowledge_retrieval(db, query, rs, child, kb_config)
-            return
-        self.vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
+                child_kb_config = kb_config.model_copy()
+                child_kb_config.kb_id = child.id
+                tasks.append(self.knowledge_retrieval(db, query, child, child_kb_config))
+            if tasks:
+                result = await asyncio.gather(*tasks)
+                for _ in result:
+                    rs.extend(_)
+            return rs
+        vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
         indices = f"Vector_index_{kb_config.kb_id}_Node".lower()
         match kb_config.retrieve_type:
             case RetrieveType.PARTICIPLE:
-                rs.extend(self.vector_service.search_by_full_text(query=query, top_k=kb_config.top_k,
-                                                                  indices=indices,
-                                                                  score_threshold=kb_config.similarity_threshold))
+                rs.extend(
+                    await asyncio.to_thread(
+                        vector_service.search_by_full_text, **{
+                            "query": query,
+                            "top_k": kb_config.top_k,
+                            "indices": indices,
+                            "score_threshold": kb_config.similarity_threshold
+                        }
+                    )
+                )
             case RetrieveType.SEMANTIC:
-                rs.extend(self.vector_service.search_by_vector(query=query, top_k=kb_config.top_k,
-                                                               indices=indices,
-                                                               score_threshold=kb_config.vector_similarity_weight))
-            case RetrieveType.HYBRID:
-                rs1 = self.vector_service.search_by_vector(query=query, top_k=kb_config.top_k,
-                                                           indices=indices,
-                                                           score_threshold=kb_config.vector_similarity_weight)
-                rs2 = self.vector_service.search_by_full_text(query=query, top_k=kb_config.top_k,
-                                                              indices=indices,
-                                                              score_threshold=kb_config.similarity_threshold)
+                rs.extend(
+                    await asyncio.to_thread(
+                        vector_service.search_by_vector, **{
+                            "query": query,
+                            "top_k": kb_config.top_k,
+                            "indices": indices,
+                            "score_threshold": kb_config.vector_similarity_weight
+                        }
+                    )
+                )
+            case retrieve_type if retrieve_type in (RetrieveType.HYBRID, RetrieveType.Graph):
+                rs1_task = asyncio.to_thread(
+                    vector_service.search_by_vector, **{
+                        "query": query,
+                        "top_k": kb_config.top_k,
+                        "indices": indices,
+                        "score_threshold": kb_config.vector_similarity_weight
+                    }
+                )
+                rs2_task = asyncio.to_thread(
+                    vector_service.search_by_full_text, **{
+                        "query": query,
+                        "top_k": kb_config.top_k,
+                        "indices": indices,
+                        "score_threshold": kb_config.similarity_threshold
+                    }
+                )
+                rs1, rs2 = await asyncio.gather(rs1_task, rs2_task)
 
                 # Deduplicate hybrid retrieval results
                 unique_rs = self._deduplicate_docs(rs1, rs2)
                 if not unique_rs:
-                    return
+                    return []
                 if self.typed_config.reranker_id:
-                    self.vector_service.reranker = self.get_reranker_model()
-                    rs.extend(self.vector_service.rerank(query=query, docs=unique_rs, top_k=kb_config.top_k))
+                    rs.extend(
+                        await asyncio.to_thread(
+                            self.rerank,
+                            **{"query": query, "docs": unique_rs, "top_k": kb_config.top_k}
+                        )
+                    )
                 else:
                     rs.extend(sorted(
                         unique_rs,
                         key=lambda d: d.metadata.get("score", 0),
                         reverse=True
                     )[:kb_config.top_k])
+                if kb_config.retrieve_type == RetrieveType.Graph:
+                    from app.core.rag.common.settings import kg_retriever
+                    llm_key = self.model_balance(db_knowledge.llm)
+                    emb_key = self.model_balance(db_knowledge.embedding)
+                    chat_model = Base(
+                        key=llm_key.api_key,
+                        model_name=llm_key.model_name,
+                        base_url=llm_key.api_base
+                    )
+                    embedding_model = OpenAIEmbed(
+                        key=emb_key.api_key,
+                        model_name=emb_key.model_name,
+                        base_url=emb_key.api_base
+                    )
+                    doc = await asyncio.to_thread(
+                        kg_retriever.retrieval,
+                        question=query,
+                        workspace_ids=[str(db_knowledge.workspace_id)],
+                        kb_ids=[str(kb_config.kb_id)],
+                        emb_mdl=embedding_model,
+                        llm=chat_model
+                    )
+                    if doc:
+                        rs.insert(0, DocumentChunk(
+                            page_content=doc.get("page_content", ""),
+                            metadata=doc.get("metadata", {})
+                        ))
             case _:
                 raise RuntimeError("Unknown retrieval type")
+        return rs
 
     async def execute(self, state: WorkflowState, variable_pool: VariablePool) -> Any:
         """
@@ -238,17 +330,24 @@ class KnowledgeRetrievalNode(BaseNode):
             knowledge_bases = self.typed_config.knowledge_bases
 
             rs = []
+            tasks = []
             for kb_config in knowledge_bases:
                 db_knowledge = knowledge_repository.get_knowledge_by_id(db=db, knowledge_id=kb_config.kb_id)
                 if not db_knowledge:
                     raise RuntimeError("The knowledge base does not exist or access is denied.")
-                self.knowledge_retrieval(db, query, rs, db_knowledge, kb_config)
+                tasks.append(self.knowledge_retrieval(db, query, db_knowledge, kb_config))
+            if tasks:
+                result = await asyncio.gather(*tasks)
+                for _ in result:
+                    rs.extend(_)
 
             if not rs:
                 return []
             if self.typed_config.reranker_id:
-                self.vector_service.reranker = self.get_reranker_model()
-                final_rs = self.vector_service.rerank(query=query, docs=rs, top_k=self.typed_config.reranker_top_k)
+                final_rs = await asyncio.to_thread(
+                    self.rerank,
+                    **{"query": query, "docs": rs, "top_k": self.typed_config.reranker_top_k}
+                )
             else:
                 final_rs = sorted(
                     rs,
@@ -259,4 +358,20 @@ class KnowledgeRetrievalNode(BaseNode):
             logger.info(
                 f"Node {self.node_id}: knowledge base retrieval completed, results count: {len(final_rs)}"
             )
-            return [chunk.page_content for chunk in final_rs]
+            citations = []
+            seen_doc_ids = set()
+            for chunk in final_rs:
+                meta = chunk.metadata or {}
+                doc_id = meta.get("document_id") or meta.get("doc_id")
+                if doc_id and doc_id not in seen_doc_ids:
+                    seen_doc_ids.add(doc_id)
+                    citations.append({
+                        "document_id": str(doc_id),
+                        "file_name": meta.get("file_name", ""),
+                        "knowledge_id": str(meta.get("knowledge_id", kb_config.kb_id)),
+                        "score": meta.get("score", 0.0),
+                    })
+            return {
+                "chunks": [chunk.page_content for chunk in final_rs],
+                "citations": citations,
+            }

api/app/core/workflow/nodes/list_operator/__init__.py

@@ -0,0 +1,3 @@
+from .node import ListOperatorNode
+
+__all__ = ["ListOperatorNode"]

api/app/core/workflow/nodes/list_operator/config.py

@@ -0,0 +1,49 @@
+from typing import Any
+from pydantic import BaseModel, Field, field_validator
+
+from app.core.workflow.nodes.base_config import BaseNodeConfig
+from app.core.workflow.nodes.enums import ComparisonOperator
+
+
+class FilterCondition(BaseModel):
+    key: str = ""
+    comparison_operator: ComparisonOperator = ComparisonOperator.CONTAINS
+    value: str | list[str] | bool = ""
+
+
+class FilterBy(BaseModel):
+    enabled: bool = False
+    conditions: list[FilterCondition] = Field(default_factory=list)
+
+
+class OrderByConfig(BaseModel):
+    enabled: bool = False
+    key: str = ""
+    value: str = "asc"  # "asc" | "desc"
+
+
+class Limit(BaseModel):
+    enabled: bool = False
+    size: int = -1
+
+
+class ExtractConfig(BaseModel):
+    enabled: bool = False
+    serial: str = "1"  # 1-based index string, e.g. "1" = first
+
+    @field_validator("serial", mode="before")
+    @classmethod
+    def coerce_serial(cls, v):
+        return str(v)
+
+
+class ListOperatorNodeConfig(BaseNodeConfig):
+    """
+    List Operator node config.
+    Operation order: filter -> extract -> order -> limit
+    """
+    input_list: str = Field(..., description="Variable selector, e.g. {{ sys.files }} or {{ conv.uploaded_files }}")
+    filter_by: FilterBy = Field(default_factory=FilterBy)
+    order_by: OrderByConfig = Field(default_factory=OrderByConfig)
+    limit: Limit = Field(default_factory=Limit)
+    extract_by: ExtractConfig = Field(default_factory=ExtractConfig)

api/app/core/workflow/nodes/list_operator/node.py

@@ -0,0 +1,150 @@
+import logging
+from typing import Any
+
+from app.core.workflow.engine.state_manager import WorkflowState
+from app.core.workflow.engine.variable_pool import VariablePool
+from app.core.workflow.nodes.base_node import BaseNode
+from app.core.workflow.nodes.enums import ComparisonOperator
+from app.core.workflow.nodes.list_operator.config import ListOperatorNodeConfig, FilterCondition
+from app.core.workflow.variable.base_variable import VariableType
+
+logger = logging.getLogger(__name__)
+
+# File object fields that hold string values
+_FILE_STRING_KEYS = {"type", "name", "url", "extension", "mime_type", "transfer_method", "origin_file_type", "file_id"}
+_FILE_NUMBER_KEYS = {"size"}
+
+
+class ListOperatorNode(BaseNode):
+    def __init__(self, node_config: dict, workflow_config: dict, down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
+        self.typed_config: ListOperatorNodeConfig | None = None
+
+    def _output_types(self) -> dict[str, VariableType]:
+        return {
+            "result": VariableType.ANY,
+            "first_record": VariableType.ANY,
+            "last_record": VariableType.ANY,
+        }
+
+    async def execute(self, state: WorkflowState, variable_pool: VariablePool) -> Any:
+        self.typed_config = ListOperatorNodeConfig(**self.config)
+        cfg = self.typed_config
+
+        # Resolve input variable from path selector
+        items: list = self.get_variable(cfg.input_list, variable_pool)
+        if not isinstance(items, list):
+            raise TypeError(f"Variable '{cfg.input_list}' must be an array, got {type(items)}")
+
+        result = list(items)
+
+        # 1. Filter
+        if cfg.filter_by.enabled and cfg.filter_by.conditions:
+            for condition in cfg.filter_by.conditions:
+                result = [item for item in result if self._match_condition(item, condition, variable_pool)]
+
+        # 2. Extract (take single item by 1-based serial index)
+        if cfg.extract_by.enabled:
+            serial_str = self._resolve_value(cfg.extract_by.serial, variable_pool)
+            idx = int(serial_str) - 1
+            if idx < 0 or idx >= len(result):
+                raise ValueError(f"extract_by.serial={cfg.extract_by.serial} out of range (list length={len(result)})")
+            result = [result[idx]]
+
+        # 3. Order
+        if cfg.order_by.enabled:
+            reverse = cfg.order_by.value == "desc"
+            key_fn = self._make_sort_key(cfg.order_by.key)
+            result = sorted(result, key=key_fn, reverse=reverse)
+
+        # 4. Limit (take first N)
+        if cfg.limit.enabled and cfg.limit.size > 0:
+            result = result[:cfg.limit.size]
+
+        return {
+            "result": result,
+            "first_record": result[0] if result else None,
+            "last_record": result[-1] if result else None,
+        }
+
+    @staticmethod
+    def _resolve_value(value: str, variable_pool: VariablePool) -> Any:
+        """If value is a {{ namespace.key }} variable selector, resolve it from the pool.
+        Otherwise return the raw string."""
+        import re
+        m = re.fullmatch(r"\{\{\s*(\w+\.\w+)\s*}}", value.strip())
+        if m:
+            resolved = variable_pool.get_value(value, default=value, strict=False)
+            return resolved
+        return value
+
+    @staticmethod
+    def _make_sort_key(key: str):
+        def key_fn(item):
+            if isinstance(item, dict):
+                return item.get(key) or ""
+            return item
+        return key_fn
+
+    def _match_condition(self, item: Any, cond: FilterCondition, variable_pool: VariablePool) -> bool:
+        op = cond.comparison_operator
+        value = cond.value
+
+        # Resolve value if it's a variable reference {{ namespace.key }}
+        if isinstance(value, str):
+            value = self._resolve_value(value, variable_pool)
+
+        # Resolve left value
+        if isinstance(item, dict):
+            left = item.get(cond.key)
+        else:
+            left = item  # primitive array: compare element directly
+
+        # Determine if this field should be compared as a string
+        is_string_field = isinstance(item, dict) and cond.key in _FILE_STRING_KEYS
+
+        # Numeric operators
+        if op == ComparisonOperator.EQ:
+            if is_string_field:
+                return str(left) == str(value)
+            return self._safe_num(left) == self._safe_num(value)
+        if op == ComparisonOperator.NE:
+            if is_string_field:
+                return str(left) != str(value)
+            return self._safe_num(left) != self._safe_num(value)
+        if op == ComparisonOperator.LT:
+            return self._safe_num(left) < self._safe_num(value)
+        if op == ComparisonOperator.LE:
+            return self._safe_num(left) <= self._safe_num(value)
+        if op == ComparisonOperator.GT:
+            return self._safe_num(left) > self._safe_num(value)
+        if op == ComparisonOperator.GE:
+            return self._safe_num(left) >= self._safe_num(value)
+
+        # String / sequence operators
+        left_str = str(left) if left is not None else ""
+        if op == ComparisonOperator.CONTAINS:
+            return str(value) in left_str
+        if op == ComparisonOperator.NOT_CONTAINS:
+            return str(value) not in left_str
+        if op == ComparisonOperator.START_WITH:
+            return left_str.startswith(str(value))
+        if op == ComparisonOperator.END_WITH:
+            return left_str.endswith(str(value))
+        if op == ComparisonOperator.IN:
+            return left_str in (value if isinstance(value, list) else [str(value)])
+        if op == ComparisonOperator.NOT_IN:
+            return left_str not in (value if isinstance(value, list) else [str(value)])
+        if op == ComparisonOperator.EMPTY:
+            return not left
+        if op == ComparisonOperator.NOT_EMPTY:
+            return bool(left)
+
+        raise ValueError(f"Unsupported operator: {op}")
+
+    @staticmethod
+    def _safe_num(v) -> float:
+        try:
+            return float(v)
+        except (TypeError, ValueError):
+            return 0.0

api/app/core/workflow/nodes/llm/node.py

@@ -213,9 +213,10 @@ class LLMNode(BaseNode):
                 messages = messages[:-1] + history_message + messages[-1:]
             self.messages = messages
         else:
-            # 使用简单的 prompt 格式（向后兼容）
+            # 使用简单的 prompt 格式（向后兼容）——包装为标准消息列表以兼容所有 provider
             prompt_template = self.config.get("prompt", "")
-            self.messages = self._render_template(prompt_template, variable_pool)
+            rendered = self._render_template(prompt_template, variable_pool)
+            self.messages = [{"role": "user", "content": rendered}]
 
         return llm
 
@@ -245,7 +246,10 @@ class LLMNode(BaseNode):
         logger.info(f"节点 {self.node_id} LLM 调用完成，输出长度: {len(content)}")
 
         # 返回 AIMessage（包含响应元数据）
-        return AIMessage(content=content, response_metadata=response.response_metadata)
+        return AIMessage(content=content, response_metadata={
+            **response.response_metadata,
+            "token_usage": getattr(response, 'usage_metadata', None) or response.response_metadata.get('token_usage')
+        })
 
     def _extract_input(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]:
         """提取输入数据（用于记录）"""
@@ -304,15 +308,16 @@ class LLMNode(BaseNode):
 
         # 调用 LLM（流式，支持字符串或消息列表）
         last_meta_data = {}
+        last_usage_metadata = {}
         async for chunk in llm.astream(self.messages):
-            # 提取内容
             if hasattr(chunk, 'content'):
                 content = self.process_model_output(chunk.content)
             else:
                 content = str(chunk)
-            if hasattr(chunk, 'response_metadata'):
-                if chunk.response_metadata:
-                    last_meta_data = chunk.response_metadata
+            if hasattr(chunk, 'response_metadata') and chunk.response_metadata:
+                last_meta_data = chunk.response_metadata
+            if hasattr(chunk, 'usage_metadata') and chunk.usage_metadata:
+                last_usage_metadata = chunk.usage_metadata
 
             # 只有当内容不为空时才处理
             if content:
@@ -335,7 +340,10 @@ class LLMNode(BaseNode):
         # 构建完整的 AIMessage（包含元数据）
         final_message = AIMessage(
             content=full_response,
-            response_metadata=last_meta_data
+            response_metadata={
+                **last_meta_data,
+                "token_usage": last_usage_metadata or last_meta_data.get('token_usage')
+            }
         )
 
         # yield 完成标记

api/app/core/workflow/nodes/node_factory.py

@@ -27,6 +27,7 @@ from app.core.workflow.nodes.question_classifier import QuestionClassifierNode
 from app.core.workflow.nodes.breaker import BreakNode
 from app.core.workflow.nodes.tool import ToolNode
 from app.core.workflow.nodes.document_extractor import DocExtractorNode
+from app.core.workflow.nodes.list_operator import ListOperatorNode
 
 logger = logging.getLogger(__name__)
 
@@ -51,7 +52,8 @@ WorkflowNode = Union[
     MemoryReadNode,
     MemoryWriteNode,
     CodeNode,
-    DocExtractorNode
+    DocExtractorNode,
+    ListOperatorNode
 ]
 
 
@@ -83,7 +85,8 @@ class NodeFactory:
         NodeType.MEMORY_READ: MemoryReadNode,
         NodeType.MEMORY_WRITE: MemoryWriteNode,
         NodeType.CODE: CodeNode,
-        NodeType.DOCUMENT_EXTRACTOR: DocExtractorNode
+        NodeType.DOCUMENT_EXTRACTOR: DocExtractorNode,
+        NodeType.LIST_OPERATOR: ListOperatorNode
     }
 
     @classmethod

api/app/core/workflow/nodes/parameter_extractor/node.py

@@ -12,7 +12,7 @@ from app.core.workflow.engine.state_manager import WorkflowState
 from app.core.workflow.engine.variable_pool import VariablePool
 from app.core.workflow.nodes.base_node import BaseNode
 from app.core.workflow.nodes.parameter_extractor.config import ParameterExtractorNodeConfig
-from app.core.workflow.variable.base_variable import VariableType
+from app.core.workflow.variable.base_variable import VariableType, DEFAULT_VALUE
 from app.db import get_db_read
 from app.models import ModelType
 from app.services.model_service import ModelConfigService
@@ -45,6 +45,12 @@ class ParameterExtractorNode(BaseNode):
             "model_id": str(self.typed_config.model_id),
         }
 
+    def _extract_output(self, business_result: Any) -> Any:
+        final_output = {}
+        for param in self.typed_config.params:
+            final_output[param.name] = business_result.get(param.name) or DEFAULT_VALUE(self.output_types[param.name])
+        return final_output
+
     def _output_types(self) -> dict[str, VariableType]:
         outputs = {}
         for param in self.typed_config.params:
@@ -109,6 +115,7 @@ class ParameterExtractorNode(BaseNode):
             api_key = api_config.api_key
             api_base = api_config.api_base
             is_omni = api_config.is_omni
+            capability = api_config.capability
             model_type = config.type
 
         llm = RedBearLLM(
@@ -201,7 +208,10 @@ class ParameterExtractorNode(BaseNode):
             ])
 
         model_resp = await llm.ainvoke(messages)
-        self.response_metadata = model_resp.response_metadata
+        self.response_metadata = {
+            **model_resp.response_metadata,
+            "token_usage": getattr(model_resp, 'usage_metadata', None) or model_resp.response_metadata.get('token_usage')
+        }
         model_message = self.process_model_output(model_resp.content)
         result = json_repair.repair_json(model_message, return_objects=True)
         logger.info(f"node: {self.node_id} get params:{result}")

api/app/core/workflow/nodes/question_classifier/node.py

@@ -62,6 +62,7 @@ class QuestionClassifierNode(BaseNode):
             api_key = api_config.api_key
             base_url = api_config.api_base
             is_omni = api_config.is_omni
+            capability = api_config.capability
             model_type = config.type
 
         return RedBearLLM(
@@ -135,7 +136,10 @@ class QuestionClassifierNode(BaseNode):
 
             response = await llm.ainvoke(messages)
             result = self.process_model_output(response.content)
-            self.response_metadata = response.response_metadata
+            self.response_metadata = {
+                **response.response_metadata,
+                "token_usage": getattr(response, 'usage_metadata', None) or response.response_metadata.get('token_usage')
+            }
 
             if result in category_names:
                 category = result