diff --git a/main/api.py b/main/api.py
new file mode 100644
index 0000000..6b3907e
--- /dev/null
+++ b/main/api.py
@@ -0,0 +1,671 @@
+from typing import Union
+
+from fastapi import FastAPI,Query,UploadFile,File,HTTPException,Request,Path,Form
+from fastapi.responses import StreamingResponse,FileResponse,RedirectResponse
+from fastapi.staticfiles import StaticFiles
+
+from pydantic import BaseModel,Field
+from typing import Optional,List,Dict,Any
+import json
+import time
+import datetime
+import json5
+
+import shutil
+
+############################  模块代码 ########################
+#全文检索
+from full_index_search import full_search,doc_search,c_or_u_baike_index,c_or_u_fs_index,one_doc_search,delete_index_by_docid,adjust_ctx_size,full_search_by_baike_catalog,full_search_by_doc_list
+
+#初始化信息
+from init import *
+
+from utils import regular_filename
+
+#文件仓库
+from ff import sftp_list_directory,smb_list_directory,linux_list_directory,ftp_list_directory,create_doc_cache
+
+
+#数据库
+from k_database import QA,BaiKe,BaiKe_Catalog,Doc,KAgent
+
+#
+from peewee import fn
+
+from agent_chat import Agent_chat_build
+
+#得到客户端ip
+def get_client_ip(request):
+    x_forwarded_for = request.headers.get("X-Forwarded-For")
+    if x_forwarded_for:
+        client_ip = x_forwarded_for.split(",")[0]
+    else:
+        # 如果没有 X-Forwarded-For，尝试从 X-Real-IP 获取
+        client_ip = request.headers.get("X-Real-IP")
+
+    # 如果以上都没有，使用 request.client.host
+    if not client_ip:
+        client_ip = request.client.host
+    return client_ip
+
+
+#KAgent的sn查询,对外使用
+#@app.get("/api/kagent_cfg/{kasn}")
+def get_kagent_by_sn(kasn: str,request:Request):
+
+    if kasn=='superman':
+        title,demo,atype,guide,files = "超级个人助理","AI革命数字永生",3,"你好！我是你的超级个人助手！我可以调用我的发布和我的订阅智能体来帮你完成任务规划和执行，有事不决请问我！","S"
+    else:
+        #根据sn获得知识体的详细信息
+        try:
+            query = KAgent.select(KAgent.title,KAgent.demo,KAgent.atype,KAgent.guide,KAgent.files).where(KAgent.kasn==kasn)
+            title,demo,atype,guide,files = query.scalar(as_tuple=True)
+        except:
+            return {"title":"未知应用","guide":"未能正确识别SN",'atype':"","files":"","demo":"SN不正确,无法访问应用"}
+    
+    username = request.cookies.get("username", "匿名用户")
+    username = decode_username(username)
+
+    cfg = {"title":title,"guide":guide,'atype':atype,"files":files,"demo":demo,"username":username}
+    #钉钉参数
+    if gcfg["dingtalk"]["app_key"]!="":
+        dingtalk={"client_id":gcfg["dingtalk"]["app_key"],
+                "close_login":gcfg["dingtalk"]["exclusiveLogin"],
+                "corpid":gcfg["dingtalk"]["exclusiveCorpId"],
+                }
+        cfg["dingtalk"]=dingtalk
+    #微信参数
+    if gcfg["wechat"]["app_key"]!="":
+        cfg["wechat"] = gcfg["wechat"]["app_key"]
+
+    #logo
+    cfg["logo"] = gcfg["fs"]["logo"]
+    #官网
+    cfg["url"]=gcfg["fs"]["url"]
+    #服务码
+    cfg["aicode"]=gcfg["fs"]["aicode"]
+
+    #分类:
+    cfg["agent_catalogs"] = get_gcfg_mem()["agent_catalogs"]
+    
+    return cfg
+
+#KAgent对话
+#写入对话上下文
+def write_chat_context(chat_id,context):
+    with open(f"/tmp/k3gpt/{chat_id}","w+") as f:
+        json.dump(context,f,ensure_ascii=False)
+
+#删除对话上下文
+def delete_chat_by_id(chat_id):
+    try:
+        os.remove(f"/tmp/k3gpt/{chat_id}")
+    except:
+        pass
+
+#获取上下文
+def get_chat_context(chat_id):
+    try:
+        with open(f"/tmp/k3gpt/{chat_id}","r") as f:
+            context = json.load(f)
+            return context
+    except Exception as e:
+        raise Exception(f"上下问读取失败:{e}")
+
+from typing import Optional
+
+class Chat(BaseModel):
+    question: str = Field(..., example="你是谁？")
+    ai_mode: Optional[str] = Field("", example="对话模式")
+    base:Optional[str] = Field(None, example="知识库")
+    file: Optional[str] = Field(None, example="文件名")
+    path: Optional[str] = Field(None, example="路径")
+    history: Optional[list] = Field(None, example="历史对话")
+    kagent_sn: Optional[str] = Field(None, example="Agent的sn,识别码")
+    mem: Optional[list] = Field([], example="可选的短期记忆")
+
+#KAgent对话
+def chat_kagent(chat:Chat):
+    chat_id=f"chat_{time.time()}"
+    count=0
+    ctx=[] #上下文信息
+
+    #根据sn获得知识体的详细信息
+    if chat.kagent_sn=='superman':
+        title,atype,prompt,files = "超级个人助理",3,"# 你是一个超级个人助理,优先使用100来回答问题,回答不好的再使用工具和其它智能体,你可以根据成功案例和失败案例进行参考","S"
+    else:
+        query = KAgent.select(KAgent.title,KAgent.atype,KAgent.prompt,KAgent.files).where(KAgent.kasn==chat.kagent_sn)
+        title,atype,prompt,files = query.scalar(as_tuple=True)
+
+    #上传文件
+    upload_files=[]
+    if chat.base and chat.path:
+        upload_files.append([chat.base,chat.path])
+
+    if not files:
+        files=""
+    
+    #根据类型进行相应的处理
+    if atype in [0,1]:
+        doc_list=[]
+        #全文检索相关知识
+        if atype==0:
+            doc_list = files.split(",")
+        elif atype==1: #关键字检索文档列表
+            #支持多个目录
+            from functools import reduce
+            from operator import or_
+            conditions =[]
+            for file in files.split(","):
+                conditions.append(Doc.abs_path.contains(file))
+
+            # 使用 reduce 将多个条件用 OR 合并
+            combined_condition = reduce(or_, conditions)
+            query= (Doc
+               .select(Doc.base,Doc.abs_path)
+               .where( combined_condition )
+               .order_by(Doc.f_up_at.desc())
+               .limit(10000)
+            )
+            for r in query:
+                doc_list.append(f"{r.base}_{r.abs_path}")
+        #end if
+        print("相关文档数量",len(doc_list))
+
+        #搜索相关的百科
+        context0 = []
+        
+        #确认是否有满足问题的百科数据
+        context0 = full_search_by_baike_catalog(chat.question,f"智能体数据/{title}")
+
+        #搜索文件
+        context = full_search_by_doc_list(chat.question,doc_list)
+
+        if len(context0)==0: #没有百科相关信息
+            context = adjust_ctx_size(context)
+            count = len(context)
+            if chat.ai_mode.find("Detailed")==0:
+                for i in range(count):
+                    ctx.append({"name":i,"path":i,"base":i,"ctx":context[i],"size":len(context[i])})
+            write_chat_context(chat_id,{"title":title,"req":chat.question,"atype":atype,"ctx":context,"mem":chat.mem,
+                                        "prompt":prompt,"history":chat.history,"upload_files":upload_files})
+        elif len(context0) < 10 or len(context) < 10:  
+            #百科信息比较少的情况，合并知识百科的内容，进行回答
+            context0.extend(context)
+            context = adjust_ctx_size(context0)
+            count = len(context)
+            if chat.ai_mode.find("Detailed")==0:
+                for i in range(count):
+                    ctx.append({"name":i,"path":i,"base":i,"ctx":context[i],"size":len(context[i])})
+            write_chat_context(chat_id,{"title":title,"req":chat.question,"atype":atype,"ctx":context,"mem":chat.mem,
+                                        "prompt":prompt,"history":chat.history,"upload_files":upload_files})
+        else:
+            #百科的相关内容很多
+            #依赖知识百科，二次回答，时间比较长
+
+            #第一步根据检索信息，得到大模型回答的结果
+            context = adjust_ctx_size(context)
+            count = len(context)
+            answer = do_chat_agent_nostream({"title":title,"req":chat.question,"atype":atype,"ctx":context,"mem":chat.mem,
+                                             "prompt":prompt,"history":chat.history,"upload_files":upload_files})
+
+            #第二步，结合结合知识百科的信息，进行综合回答
+            context = adjust_ctx_size(context0)
+            context.insert(0,f"搜索答案: {answer}")
+            count = len(context)
+
+            prompt="""
+# 智能助手
+你是一个问答机器人的助手,请使用提供的上下问来回答问题.
+
+# 回答逻辑
+1. 上下文中标有知识百科的数据可信度很高，可以优先采纳
+2. 搜索答案是通过大模型RAG得到的答案
+3. 如果知识百科的数据和问题关联性很高时，可以讲百科的数据和搜索答案进行融合来回答
+4. 如果知识百科的数据和问题关联性不高时，可以直接使用搜索答案来回答
+
+# 上下文
+{context}
+
+
+# 问题
+{question}
+
+"""
+
+            if chat.ai_mode.find("Detailed")==0:
+                for i in range(count):
+                    ctx.append({"name":i,"path":i,"base":i,"ctx":context[i],"size":len(context[i])})
+            write_chat_context(chat_id,{"title":title,"req":chat.question,"atype":atype,"ctx":context,
+                                        "prompt":prompt,"history":chat.history,"upload_files":upload_files,"mem":chat.mem})
+
+    else:
+        #非检索的、融合检索的、上传文件的
+        if chat.mem:
+            ctx.append(chat.mem)
+        write_chat_context(chat_id,{"title":title,"req":chat.question,"atype":atype,"ctx":ctx,
+                                    "prompt":prompt,"ai_mode":chat.ai_mode,
+                                    "history":chat.history,"fun_def":files,"upload_files":upload_files})
+
+    return {"chat_id":chat_id,"count":count,"ctx":ctx}
+
+
+def decode_username(username):
+    if username !="匿名用户":
+        # 原始字符串
+        #raw_string = "b'\\xe9\\x83\\x9c'"
+
+        # 提取字节部分
+        byte_sequence = username[2:-1]  # 去掉前后的 `b'` 和 `'`
+
+        # 将转义的字节字符串转换为实际的字节对象
+        byte_string = bytes(byte_sequence, 'utf-8').decode('unicode_escape').encode('latin1')
+
+        # 解码为正常字符串
+        username = byte_string.decode('utf-8')
+    return username
+
+
+#KAgent对话的流输出，采用事件流机制，实时输出
+#@app.get("/api/chat_kagent/{chat_id}")
+def chat_event(chat_id: str,request:Request):
+    userid = request.cookies.get("userid", "id-00001")
+    if userid=="id-00001":
+        return StreamingResponse(sse_do_response(do_chat_error(chat_id),chat_id,
+            "k3gpt"),media_type="text/event-stream")
+    username = request.cookies.get("username", "匿名用户")
+    username = decode_username(username)
+
+    client_ip = get_client_ip(request)
+    
+    return StreamingResponse(
+        sse_do_response(
+            do_chat_agent(chat_id,userid,username,client_ip),
+            chat_id,
+            "k3gpt"), 
+        media_type="text/event-stream")
+
+
+def do_chat_error(chat_id):
+    delete_chat_by_id(chat_id)
+    yield "用户认证信息丢失，请重新认证后再访问！"
+
+
+#SSE+不同的返回format()
+def sse_do_response(gen,chat_id,format="k3gpt"):
+    if format=="k3gpt":
+        for content in gen:
+            if content.endswith("<new>") and len(content)>5:
+                yield gen_k3gpt_result(content[0:-5])
+                yield gen_k3gpt_result("<new>")
+            elif content.startswith("<tool>"):#工具+数据
+                data_start = content.find("<data>")
+                yield gen_k3gpt_result2(content[6:data_start],content[data_start+6:])
+            else:
+                yield gen_k3gpt_result(content)
+    else:
+        for content in gen:
+            if content.endswith("<new>") and len(content)>5:
+                yield gen_openai_result(chat_id,content[0:-5])
+            else:
+                yield gen_openai_result(chat_id,content)
+
+
+#生成类OpenAI的流式diego结果
+def gen_openai_result(chat_id,data):
+    messages=[]
+    messages.append({"delta":{"role":"assistant","content":data}})
+    response={"id":chat_id,"object":"chat","choices":messages}
+    json_str = "data: " + json.dumps(response) + "\n\n"
+    return json_str.encode("utf-8")
+
+#返回结果信息和UI保持一致，rsp为真正内容
+def gen_k3gpt_result(data):
+    json_data={}
+    json_data["rsp"] = data
+    json_str = "data: " + json.dumps(json_data) + "\n\n"
+    return json_str.encode("utf-8")
+
+#返回结果信息和UI保持一致，rsp为真正内容,tool_name为工具名称,用来处理chart和表格的json数据
+def gen_k3gpt_result2(tool_name,data):
+    json_data={}
+    json_data["rsp"] = data
+    json_data["type"] = tool_name
+    json_str = "data: " + json.dumps(json_data) + "\n\n"
+    return json_str.encode("utf-8")
+
+
+def found_llm_think_data(data):
+    think=""
+    if data.find("<think>")>=0 and data.find("</think>")==-1:
+        #还未思考结束
+        think = data[8:]
+        data = ""
+    elif data.find("<think>")>=0 and data.find("</think>")>0:
+        #思考结束
+        end = data.find("</think>")
+        think = data[8:end]
+        data = data[end+8:]
+
+    if data.find("```json") >=0:
+        #找到json数据，只返回json数据
+        begin = data.find("{")
+        end = data.rfind("}")
+        data = data[begin:end+1]
+    return think,data
+
+
+#KAgent chat的主函数
+def do_chat_agent(chat_id,userid="10000",username="Admin",client_ip="127.0.0.1",delta_stream=False):
+    chat = get_chat_context(chat_id)
+    chat["chat_id"]=chat_id
+    title = chat["title"]
+    question = chat["req"]
+    atype = chat["atype"] #智能体类型
+    prompt = chat["prompt"]
+    history = chat["history"]
+    context = chat["ctx"]
+    
+    answer = ""
+    if atype==3:
+        #融合智能检索模式
+        agent_chat = Agent_chat_build(chat,delta_stream,username,userid)
+        answer = yield from agent_chat.do_chat_loop()
+    
+    else:
+        if "mem" in chat:
+            mem = chat["mem"]
+        else:
+            mem = []
+        # 获取当前日期和星期
+        today = datetime.datetime.now()
+        # 获取今天是星期几，使用weekday()方法（周一=0, 周日=6）
+        weekday_number = today.weekday()
+
+        now=f"{today.strftime('%Y-%m-%d %H:%M:%S')} 星期{weekday_number+1}"
+
+        #用户不用再添加部分内容
+        if prompt.find("{context}")==-1 or prompt.find("{question}")==-1:
+            prompt +="""
+### 当前时间
+{now}
+### 问题
+{question}
+### 上下文
+{context}
+        """
+        
+        upload_files = chat["upload_files"]
+        file_content=""
+
+        if upload_files:
+            print("上传文件",upload_files)
+            #只取第一个文件
+            f1 = [f'{upload_files[0][0]}_{upload_files[0][1]}']
+            filename = upload_files[0][1]
+            file_contents = full_search_by_doc_list("",f1)
+            file_prompt = prompt +"""
+            ### 文件
+            {filename}
+            ### 文件内容
+            {file_content}
+            """
+
+            if len(file_contents)>1:
+                
+                answers = []
+                for i,file_content in enumerate(file_contents):
+                    print(f"分片[{i}], 大小: {len(file_content)}")
+                    system_prompt = file_prompt.format(**{"query":question,"question":question,"context":context,"now":now,"file_content":file_content,"filename":filename})
+                    answer = yield from llm_ask(system_prompt,delta_stream)
+                    answers.append(answer)
+
+                #汇总答案
+                sum_prompt = prompt +"""
+# 上下文是文件的多个分片分别计算的到的结果，现在合并上下问，结合用户的问题，做最后的回答输出
+"""
+                system_prompt = sum_prompt.format(**{"query":question,"question":question,"context":answers,"now":now,"history":history})
+                sum_answers = yield from llm_ask(system_prompt,delta_stream)
+                answer = answers
+
+            elif len(file_contents)==1:
+                file_content = file_contents[0]
+                system_prompt = file_prompt.format(**{"query":question,"question":question,"context":context,"now":now,"history":history,"file_content":file_content,"filename":filename})
+                answer = yield from llm_ask(system_prompt,delta_stream)
+            else:
+                yield f"没有找到文件的数据分片:{filename}"
+        else:
+            prompt +="""
+### 上下文2 
+{mem}
+"""
+            system_prompt = prompt.format(**{"query":question,"question":question,"context":context,"now":now,"history":history,"mem":mem})
+            answer = yield from llm_ask(system_prompt,delta_stream)
+        
+    #删除文件
+    QA.create(userid=userid,username=username, question=question,answer=answer,ref=f"知识体:{title}",client_ip=client_ip)
+    delete_chat_by_id(chat_id)
+    return answer
+
+#大模型回答,
+def llm_ask(system_prompt,delta_stream):
+    messages = [{'role': 'user', 'content': system_prompt}]
+    responses = llm.chat(
+        messages=messages,
+        stream=True,
+        delta_stream=delta_stream
+    ) # get a new response from the model where it can see the function response
+
+    answer=""
+    try:
+        
+        for response in responses:
+            #print(response[0]["content"])
+            yield response[0]["content"]
+            if delta_stream==True:
+                answer +=response[0]["content"]
+        if not delta_stream:
+            think,data = found_llm_think_data(response[0]["content"])
+            answer = data
+    except Exception as e:
+        yield "大模型运行出现错误:{e}, 请检查配置是否正确或者网络是否通畅!\n如果是本地大模型还请检查大模型是否正常启动!"
+    return answer
+
+
+#KAgent chat内部使用函数，非流输出
+def do_chat_agent_nostream(chat):
+    question = chat["req"]
+    mode = chat["mode"]
+    prompt = chat["prompt"]
+    history = chat["history"]
+    context = chat["ctx"]
+    doc_list = chat["doc_list"]
+
+    # 获取当前日期和星期
+    today = datetime.datetime.now()
+    # 获取今天是星期几，使用weekday()方法（周一=0, 周日=6）
+    weekday_number = today.weekday()
+
+    now=f"{today.strftime('%Y-%m-%d %H:%M:%S')} 星期{weekday_number+1}"
+
+    system_prompt = prompt.format(**{"query":question,"question":question,"context":context,"now":now,"history":history,"doc_list":doc_list})
+    
+    messages = [{'role': 'user', 'content': system_prompt}]
+    responses = llm.chat(
+        messages=messages,
+        stream=False,
+    ) # get a new response from the model where it can see the function response
+
+    return responses[0]["content"]
+
+
+#文件中心上传文件
+#@app.post("/api/upload")
+async def upload_file(request:Request,curpath:str=Form(''), file: UploadFile = File(...)):
+    try:
+        username = request.cookies.get("username", "匿名用户")
+        username = decode_username(username)
+        #上传文件空间
+        if not os.path.exists(f'{gcfg["fs"]["path"]}/个人文件夹/{username}'):
+            os.makedirs(f'{gcfg["fs"]["path"]}/个人文件夹/{username}')
+
+        #去除空格
+        filename = regular_filename(file.filename)
+
+        with open(f"{gcfg['fs']['path']}/个人文件夹/{username}/{filename}", "wb") as buf:
+            shutil.copyfileobj(file.file, buf)
+            fsize = buf.tell()
+    except Exception as e:
+        return {"errno":1,"message":f"上传出错: {e}"}
+    
+    #建立索引和文件记录
+    c_or_u_fs_index(filename,f"{gcfg['fs']['path']}/个人文件夹/{username}/{filename}",fsize,username)
+
+    
+    return {"errno":0,"file":{"name":filename,"path":f"{gcfg['fs']['path']}/个人文件夹/{username}/{filename}","base":"文件中心"}}
+
+#对外提供的大模型对外服务
+def chat_completions(chat_req:dict,req:Request):
+    #先认证
+    auth_header = req.headers.get("Authorization")
+    if not auth_header:
+        raise HTTPException(status_code=401, detail="Authorization header missing")
+    
+    # 示例：Bearer token
+    if not auth_header.startswith("Bearer "):
+        raise HTTPException(status_code=401, detail="Invalid authorization scheme")
+    
+    token = auth_header.split(" ")[1]  # 提取 token
+    try:
+        #print(token)
+        import base64
+        sn,username_base64 = token.split(":")
+        username = base64.b64decode(username_base64.encode("utf-8")).decode("utf-8")
+    except:
+        raise HTTPException(status_code=401, detail="api_key is error")
+
+    #再内容
+    #chat_req = await req.json()
+    print(chat_req)
+    if 'messages' not in  chat_req or not chat_req['messages']:
+        raise HTTPException(status_code=501, detail="message format error")
+    
+    question = ""
+    for msg in chat_req['messages']:
+        if msg["role"]=="user":
+            if isinstance(msg["content"],list): #列表类型
+                for content in msg["content"]:
+                    if content["type"]=="text":
+                        question += content["text"]
+            elif isinstance(msg["content"],str):
+                question += msg["content"]
+        elif msg["role"]=="system":
+            if isinstance(msg["content"],list):#列表类型
+                for content in msg["content"]:
+                    if content["type"]=="text":
+                        question = content["text"]
+            elif isinstance(msg["content"],str):
+                question = msg["content"]
+
+    chat = Chat(kagent_sn=sn,question=question)
+    chat_info = chat_kagent(chat)
+
+    userid = "50000"
+    client_ip = get_client_ip(req)
+    chat_id = chat_info["chat_id"]
+    if "stream" in chat_req and chat_req["stream"]: #流式输出
+        return StreamingResponse(sse_do_response(
+            do_chat_agent(chat_id,userid,username,client_ip,True),
+            chat_id,
+            "openai"), media_type="text/event-stream")
+
+    #非流式输出，取得最后的结果
+    try:
+        anwser = ""
+        gen = do_chat_agent(chat_id,userid,username,client_ip,True)
+        while True:
+            next(gen)
+    except StopIteration as e:
+        anwser = e.value
+    messages=[]
+    messages.append({"message":{"role":"assistant","content":anwser}})
+    response={"id":chat_id,"object":"chat","choices":messages}
+    print(response)
+    return response
+
+
+#当前用户的名字和IP
+def get_user_info(req):
+    # 从请求中获取 Cookie
+    userid = req.cookies.get("userid", "id-00001")
+    username = req.cookies.get("username", "匿名用户")
+    username = decode_username(username)
+    client_ip = get_client_ip(req)
+    return username,client_ip
+
+
+#百科的保存
+class New_BaiKe(BaseModel):
+    id: int =  Field(None, example="你好，世界！")
+    title: str = Field(..., example="你好，世界！")
+    catalog: str = Field(..., example="你好，世界！")
+    html: str = Field(..., example="你好，世界！")
+    style: Optional[Dict[str, Any]] = None  #生成样式
+    trans: Optional[bool] = True
+
+
+
+#百科的保存
+#@app.post("/api/baike")
+def new_baike(baike:New_BaiKe,req:Request):
+
+    username,client = get_user_info(req)
+
+    #增加个人空间的知识百科
+    if baike.catalog=="个人空间":
+        baike.catalog = f"个人空间/{username}"
+
+    if baike.trans:
+        from parse_html import trans_html_to_format
+        #print(baike.html)
+        #print("============================================")
+        baike.html = trans_html_to_format(baike.html)
+
+    if baike.id and baike.id !=0:
+
+        #更新
+        # query = BaiKe.select(BaiKe.full_id).where(BaiKe.id == baike.id)
+        # old_full_id = query.scalar()
+        
+        
+        # baike_u = BaiKe(id=baike.id)
+
+        # try:
+        #     full_id = c_or_u_baike_index(baike.html,baike.title,baike.id,baike_u.full_id,baike.catalog)
+        # except:
+        #     full_id = baike_u.full_id
+
+        # baike_u.html = baike.html
+        # baike_u.title = baike.title
+        # baike_u.catalog = baike.catalog
+        # baike_u.m_time = datetime.datetime.now()
+        # baike_u.full_id = full_id
+        # baike_u.save(only=[BaiKe.html,BaiKe.title,BaiKe.catalog,BaiKe.m_time,BaiKe.full_id])
+        
+        #这个可以和上面的效果一样
+        c_or_u_baike_index(baike.html,baike.title,baike.id,baike.catalog)
+        BaiKe.update(title=baike.title,catalog=baike.catalog,html=baike.html,m_time=datetime.datetime.now(),modifier=username).where(BaiKe.id == baike.id).execute()
+
+    else:
+        #新建
+        baike=BaiKe.create(title=baike.title,catalog=baike.catalog,html=baike.html,creator=username,modifier=username)
+        
+        c_or_u_baike_index(baike.html,baike.title,baike.id,baike.catalog)
+
+        #Operate_Log(req,"新建",f'[百科] id:{baike.id},标题:{baike.title}')
+    
+        # baike.full_id = full_id
+        # baike.save(only=[BaiKe.full_id])
+
+        
+    return {"msg":"OK","id":baike.id}
\ No newline at end of file