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feat(im): mirror WeChat bot chats into AI chat as WeXin Bot conversation; add conversations.source; migrate history; remove redundant history page

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qyyfw committed 2026-07-10 10:09:49 +08:00
1 parent 7c71d193fc
commit 7b7871a14d
5 files changed
+156 -67
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backend/python/app/api/chat.py
+96 -64
View File
@@ -691,6 +691,7 @@ def list_conversations(
"status": conv.status,
"pinned": conv.pinned,
"knowledge_graph_count": conv.knowledge_graph_count or 0,
"source": getattr(conv, "source", None),
"created_at": str(conv.created_at) if conv.created_at else None,
"updated_at": str(conv.updated_at) if conv.updated_at else None,
})
@@ -1140,69 +1141,8 @@ def clear_conversation(
return {"code": 0, "message": "对话上下文已清空"}
@router.post("/conversations/{conv_id}/to-knowledge-graph")
def convert_to_knowledge_graph(
conv_id: str,
db: Session = Depends(get_db),
current_user: User = Depends(get_current_user),
):
"""将对话内容转换为知识图谱"""
conv = db.query(Conversation).filter(
and_(Conversation.id == conv_id, Conversation.user_id == current_user.id)
).first()
if not conv:
raise HTTPException(status_code=404, detail="对话不存在")
messages = (
db.query(Message)
.filter(Message.conversation_id == conv_id)
.order_by(asc(Message.created_at))
.all()
)
if not messages:
raise HTTPException(status_code=400, detail="对话中没有消息, 无法生成知识图谱")
kb_name = f"对话图谱-{conv.title}"
kb = KnowledgeBase(
id=gen_uuid(),
user_id=current_user.id,
name=kb_name,
description=f"从对话「{conv.title}」自动生成的知识图谱",
directory_path="/",
document_count=0,
created_at=cst_now(),
updated_at=cst_now(),
)
db.add(kb)
db.flush()
doc_count = 0
for idx, msg in enumerate(messages):
role_label = "用户" if msg.role == "user" else "AI"
preview = re.sub(r'[\\n\\r\\t]+', ' ', msg.content[:40]).strip()
filename = f"msg_{idx+1:03d}_{role_label}_{preview}.txt"
doc = KnowledgeDocument(
id=gen_uuid(),
knowledge_base_id=kb.id,
name=filename,
filename=filename,
file_type="txt",
file_size=len(msg.content.encode('utf-8')),
content=msg.content,
status="completed",
chunk_count=max(1, len(msg.content) // 500 + 1),
created_at=cst_now(),
updated_at=cst_now(),
)
db.add(doc)
doc_count += 1
kb.document_count = doc_count
db.flush()
# 构建知识图谱节点和边
def _build_knowledge_graph(db: Session, kb: KnowledgeBase) -> dict:
"""从知识库文档构建知识图谱节点和边，返回 graph_data。"""
documents = db.query(KnowledgeDocument).filter(
and_(
KnowledgeDocument.knowledge_base_id == kb.id,
@@ -1268,7 +1208,96 @@ def convert_to_knowledge_graph(
kb.knowledge_graph_count = len(graph_nodes)
kb.updated_at = cst_now()
conv.knowledge_graph_count = (conv.knowledge_graph_count or 0) + 1
return graph_data
@router.post("/conversations/{conv_id}/to-knowledge-graph")
def convert_to_knowledge_graph(
conv_id: str,
db: Session = Depends(get_db),
current_user: User = Depends(get_current_user),
):
"""将对话内容转换为知识图谱（幂等：同一对话仅生成一次知识库）"""
conv = db.query(Conversation).filter(
and_(Conversation.id == conv_id, Conversation.user_id == current_user.id)
).first()
if not conv:
raise HTTPException(status_code=404, detail="对话不存在")
messages = (
db.query(Message)
.filter(Message.conversation_id == conv_id)
.order_by(asc(Message.created_at))
.all()
)
if not messages:
raise HTTPException(status_code=400, detail="对话中没有消息, 无法生成知识图谱")
# 幂等：若该对话已生成过知识图谱且知识库仍存在，则直接刷新返回
if conv.knowledge_graph_base_id:
existing_kb = db.query(KnowledgeBase).filter(
KnowledgeBase.id == conv.knowledge_graph_base_id
).first()
if existing_kb:
graph_data = _build_knowledge_graph(db, existing_kb)
conv.knowledge_graph_count = 1
conv.updated_at = cst_now()
db.commit()
return {
"code": 0,
"data": {
"knowledge_base_id": existing_kb.id,
"knowledge_base_name": existing_kb.name,
"graph": graph_data,
"knowledge_graph_count": 1,
"already_exists": True,
},
"message": "知识图谱已存在, 已刷新展示",
}
kb_name = f"对话图谱-{conv.title}"
kb = KnowledgeBase(
id=gen_uuid(),
user_id=current_user.id,
name=kb_name,
description=f"从对话「{conv.title}」自动生成的知识图谱",
directory_path="/",
document_count=0,
created_at=cst_now(),
updated_at=cst_now(),
)
db.add(kb)
db.flush()
doc_count = 0
for idx, msg in enumerate(messages):
role_label = "用户" if msg.role == "user" else "AI"
preview = re.sub(r'[\\n\\r\\t]+', ' ', msg.content[:40]).strip()
filename = f"msg_{idx+1:03d}_{role_label}_{preview}.txt"
doc = KnowledgeDocument(
id=gen_uuid(),
knowledge_base_id=kb.id,
filename=filename,
file_type="txt",
file_size=len(msg.content.encode('utf-8')),
content=msg.content,
status="completed",
chunk_count=max(1, len(msg.content) // 500 + 1),
created_at=cst_now(),
updated_at=cst_now(),
)
db.add(doc)
doc_count += 1
kb.document_count = doc_count
db.flush()
# 构建知识图谱节点和边
graph_data = _build_knowledge_graph(db, kb)
conv.knowledge_graph_base_id = kb.id
conv.knowledge_graph_count = 1
conv.updated_at = cst_now()
db.commit()
@@ -1285,6 +1314,8 @@ def convert_to_knowledge_graph(
"knowledge_base_id": kb.id,
"knowledge_base_name": kb.name,
"graph": graph_data,
"knowledge_graph_count": 1,
"already_exists": False,
},
"message": f"知识图谱构建完成",
}
@@ -1299,6 +1330,7 @@ def get_models(db: Session = Depends(get_db)):
result.append({
"id": m.id,
"name": m.name,
"display_name": getattr(m, "display_name", None) or m.name,
"provider_id": m.provider_id,
"provider_name": provider.name if provider else "",
"status": m.status,
backend/python/app/api/im_bot_user.py
+59
View File
@@ -264,6 +264,63 @@ def _log_chat(user_id, platform, from_user, context_token, role, content):
db.close()
# ============================================================
# 镜像到 AI 聊天（标准 conversations/messages）
# 个人微信对话作为一个独立会话窗（默认名 "WeXin Bot"）出现在 AI 聊天中，
# 从而复用 AI 聊天模块的全部功能（滚动翻阅、重命名、置顶、删除等）。
# ============================================================
WECHAT_CONV_TITLE = "WeXin Bot"
def _get_or_create_wechat_conv(db: Session, user_id: str) -> str:
"""按 user_id 找/建 source='wechat_bot' 的会话，返回会话 id。"""
rows = _execute_raw(db, text(
"SELECT id FROM conversations WHERE user_id=:uid AND source='wechat_bot' "
"AND status<>'archived' ORDER BY updated_at DESC LIMIT 1"
), {"uid": user_id})
if rows:
return _row_to_dict(rows[0])["id"]
import uuid as _uuid
cid = str(_uuid.uuid4())
now = datetime.now()
_execute_raw(db, text(
"INSERT INTO conversations "
"(id, user_id, title, status, pinned, knowledge_graph_count, source, created_at, updated_at) "
"VALUES (:id, :uid, :title, 'active', 0, 0, 'wechat_bot', :c, :u)"
), {"id": cid, "uid": user_id, "title": WECHAT_CONV_TITLE, "c": now, "u": now})
return cid
def _append_ai_message(db: Session, conv_id: str, role: str, content: str, created=None):
import uuid as _uuid
_execute_raw(db, text(
"INSERT INTO messages (id, conversation_id, role, content, token_used, created_at) "
"VALUES (:id, :cid, :role, :content, 0, :c)"
), {
"id": str(_uuid.uuid4()), "cid": conv_id, "role": role,
"content": content, "c": created or datetime.now(),
})
def _mirror_to_ai_chat(user_id: str, user_content: str, assistant_content: str):
"""把一轮微信问答写入用户的 WeXin Bot AI 聊天会话。"""
if not user_id:
return
db = None
try:
db = SessionLocal()
cid = _get_or_create_wechat_conv(db, user_id)
_append_ai_message(db, cid, "user", user_content)
_append_ai_message(db, cid, "assistant", assistant_content)
_execute_raw(db, text("UPDATE conversations SET updated_at=:u WHERE id=:id"),
{"u": datetime.now(), "id": cid})
except Exception:
pass
finally:
if db:
db.close()
# ============================================================
# 后台接收线程（每用户独立）
# ============================================================
@@ -326,6 +383,8 @@ def _wechat_receiver_loop(account_id: int):
if owner_uid:
_log_chat(owner_uid, "personal_wechat", sender, ctx, "user", content)
_log_chat(owner_uid, "personal_wechat", sender, ctx, "assistant", reply)
# 同步镜像到 AI 聊天的 WeXin Bot 会话窗
_mirror_to_ai_chat(owner_uid, content, reply)
else:
try:
with open("/tmp/wechat_send.log", "a", encoding="utf-8") as _f:
backend/python/app/models.py
+1
View File
@@ -85,6 +85,7 @@ class Conversation(Base):
pinned = Column(Boolean, default=False)
knowledge_graph_count = Column(Integer, default=0)
knowledge_graph_base_id = Column(String(64), default=None, comment="已生成的知识图谱知识库ID")
source = Column(String(32), default=None, comment="对话来源，如 wechat_bot")
created_at = Column(DateTime, default=datetime.now)
updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
frontend/src/App.tsx
-2
View File
@@ -23,7 +23,6 @@ import AssetsPage from '@/pages/front/AssetsPage';
import NovelPreviewPage from '@/pages/front/NovelPreviewPage';
import EvolutionDashboard from '@/pages/front/EvolutionDashboard';
import WechatBot from '@/pages/front/WechatBot';
import WechatHistory from '@/pages/front/WechatHistory';
// Admin Pages
import AdminDashboard from '@/pages/admin/Dashboard';
@@ -120,7 +119,6 @@ const App: React.FC = () => {
<Route path="assets/novel/:id" element={<NovelPreviewPage />} />
<Route path="evolution" element={<EvolutionDashboard />} />
<Route path="wechat-bot" element={<WechatBot />} />
<Route path="wechat-history" element={<WechatHistory />} />
</Route>
{/* Admin Routes */}
frontend/src/components/layout/FrontLayout.tsx
-1
View File
@@ -18,7 +18,6 @@ const navGroups = [
items: [
{ path: '/channels', icon: 'channel', label: '频道管理' },
{ path: '/wechat-bot', icon: 'wechat', label: '微信机器人' },
{ path: '/wechat-history', icon: 'chat', label: '对话记录' },
],
},
{