diff --git a/lab/test.py b/lab/test.py
index 422bd81..498f72d 100644
--- a/lab/test.py
+++ b/lab/test.py
@@ -8,6 +8,7 @@
 import time
 from typing import List
 
+from llm_kira.client.llms import ChatGptParam
 from llm_kira.radio.anchor import SearchCraw
 from loguru import logger
 from llm_kira import radio
@@ -68,19 +69,6 @@ async def completion():
     conversation_id=12094,  # random.randint(1, 10000000),
 )
 
-llm = llm_kira.client.llms.OpenAi(
-    profile=conversation,
-    api_key=openaiApiKey,
-    token_limit=4000,
-    auto_penalty=False,
-    call_func=None,
-)
-
-mem = receiver.MemoryManager(profile=conversation)
-chat_client = receiver.ChatBot(profile=conversation,
-                               llm_model=llm
-                               )
-
 
 async def mood_hook():
     _think = ThinkEngine(profile=conversation)
@@ -90,8 +78,84 @@ async def mood_hook():
     print(_think.build_status(rank=5))
 
 
+async def chatGpt():
+    llm = llm_kira.client.llms.ChatGpt(
+        profile=conversation,
+        api_key=openaiApiKey,
+        token_limit=4000,
+        auto_penalty=False,
+        call_func=None,
+    )
+
+    mem = llm_kira.client.MemoryManager(profile=conversation)
+    chat_client = llm_kira.client.ChatBot(
+        profile=conversation,
+        llm_model=llm
+    )
+    promptManager = llm_kira.creator.engine.PromptEngine(
+        reverse_prompt_buffer=False,
+        profile=conversation,
+        connect_words="\n",
+        memory_manger=mem,
+        llm_model=llm,
+        description="晚上了，这里是河边",
+        reference_ratio=0.5,
+        forget_words=["忘掉对话"],
+        optimizer=Optimizer.SinglePoint,
+    )
+    # 大型数据对抗测试
+    # promptManager.insert_prompt(prompt=PromptItem(start="Neko", text=random_string(8000)))
+    # promptManager.insert_prompt(prompt=PromptItem(start="Neko", text=random_string(500)))
+
+    # 多 prompt 对抗测试
+    testPrompt = input("TestPrompt:")
+    promptManager.insert_prompt(prompt=PromptItem(start="Neko", text="喵喵喵"))
+    promptManager.insert_interaction(Interaction(single=True, ask=PromptItem(start="alice", text="MewMewMewMew")))
+    _result = await promptManager.build_skeleton(query=testPrompt,
+                                                 llm_task="Summary Text" if len(
+                                                     testPrompt) > 20 else None,
+                                                 skeleton=random.choice([SearchCraw(
+                                                     deacon=["https://www.bing.com/search?q={}&form=QBLH"])])
+                                                 )
+    _index = 1
+    for item in _result:
+        logger.trace(item.content)
+        item.ask.start = f"[{_index}]"
+        promptManager.insert_knowledge(knowledge=item)
+        _index += 1
+    promptManager.insert_knowledge(Interaction(single=True, ask=PromptItem(start="alice", text="MewMewMewMew")))
+    # 测试
+    promptManager.insert_prompt(prompt=PromptItem(start=conversation.start_name, text=testPrompt))
+    response = await chat_client.predict(
+        prompt=promptManager,
+        llm_param=ChatGptParam(model_name="gpt-3.5-turbo", temperature=0.8, presence_penalty=0.1, n=1, best_of=1),
+        predict_tokens=1000,
+    )
+    print(f"id {response.conversation_id}")
+    print(f"ask {response.ask}")
+    print(f"reply {response.reply}")
+    print(f"usage:{response.llm.usage}")
+    print(f"raw:{response.llm.raw}")
+    print(f"---{response.llm.time}---")
+    promptManager.clean(clean_prompt=True, clean_knowledge=False, clean_memory=False)
+    return "End"
+
+
 async def chat():
-    promptManager = llm_kira.creator.PromptEngine(
+    llm = llm_kira.client.llms.OpenAi(
+        profile=conversation,
+        api_key=openaiApiKey,
+        token_limit=4000,
+        auto_penalty=False,
+        call_func=None,
+    )
+
+    mem = llm_kira.client.MemoryManager(profile=conversation)
+    chat_client = llm_kira.client.ChatBot(
+        profile=conversation,
+        llm_model=llm
+    )
+    promptManager = llm_kira.creator.engine.PromptEngine(
         reverse_prompt_buffer=False,
         profile=conversation,
         connect_words="\n",
@@ -181,7 +245,13 @@ async def Sentiment():
 async def Sim():
     # response = llm_kira.utils.chat.Utils.edit_similarity(pre="4552", aft="1224")
     # print(response)
-    response = llm_kira.utils.chat.Sim.cosion_similarity(pre="", aft="你是不是啊")
+    test1 = """
+    早苗（さなえ）
+耕种水稻时刚刚种植的幼苗
+原型是守矢早苗（もりやさなえ，生于1945年），守矢家第七十八代当主，是实际存在的人物。
+守矢家是洩矢神的子孙，现任诹访神社下社神长官。洩矢神的祭祀司守矢家代代口传的祭神秘法。那个秘传是一脉相承的，在半夜没有火光的祈祷殿之中秘密传授。但是随着时代的变迁，世袭神官制度在明治五年被取消了。到明治六年，家传之宝（包括：印（印文「卖神祝印」）与镜、太刀等）从诹访大社上社被移走家里只残留下用佐奈伎铃（在大御立座祭神中所使用的）祭祀御左口神的方法。在明治时代，守矢实久（第七十六代当主）被取消了神长官一职，可惜当时口传秘法已失，实久只告诉了守矢真幸（第七十七代当主，实久之弟，诹访大社的祢宜宫司）剩下的部分。到守矢早苗（第七十八代当主，真幸之孙，平成18年(注)3月末从校长(注)[6]一职退下之后，一直致力于环境保护的演讲）这一代，已经不再继承代代相传的已消失的秘法了。到现在，再也没有人知道守矢祭神秘法了。
+    """
+    response = llm_kira.utils.chat.Sim.cosion_similarity(pre=test1, aft="守矢家第七十八代当主")
     print(response)
 
 
@@ -230,7 +300,8 @@ async def Web():
 # asyncio.run(completion())
 # asyncio.run(mood_hook())
 # asyncio.run(Web())
-asyncio.run(chat())
+# asyncio.run(chat())
+asyncio.run(chatGpt())
 # asyncio.run(Moderation())
 # asyncio.run(Sentiment())
 # asyncio.run(KeyParse())
diff --git a/pyproject.toml b/pyproject.toml
index 137a7bc..9b2549d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "llm_kira"
-version = "0.6.50"
+version = "0.7.0"
 description = "chatbot client for llm"
 authors = ["sudoskys <coldlando@hotmail.com>"]
 maintainers = [
diff --git a/src/llm_kira/client/Optimizer.py b/src/llm_kira/client/Optimizer.py
index 38ba94b..db01f3a 100644
--- a/src/llm_kira/client/Optimizer.py
+++ b/src/llm_kira/client/Optimizer.py
@@ -179,7 +179,10 @@ def run(self) -> List[Interaction]:
         knowledge = Scorer.build_weight(self.knowledge)
         _knowledge_token_limit = int(self.token_limit * self.reference_ratio)
         _interaction_token_limit = self.token_limit - _knowledge_token_limit
-        _returner = [Interaction(single=True, ask=PromptItem(start="*", text=self.desc))]
+
+        # Desc
+        _returner = [Interaction(single=True, ask=PromptItem(start="system", text=self.desc))]
+
         _old_prompt = interaction[:1]
         # Desc
         if self.tokenizer(self.desc) > self.token_limit:
diff --git a/src/llm_kira/client/__init__.py b/src/llm_kira/client/__init__.py
index b019ae4..464c144 100644
--- a/src/llm_kira/client/__init__.py
+++ b/src/llm_kira/client/__init__.py
@@ -6,5 +6,5 @@
 
 from .llms import openai
 from .enhance import Support
-from .agent import Conversation,MemoryManager
+from .agent import Conversation, MemoryManager
 from .anchor import ChatBot
diff --git a/src/llm_kira/client/agent.py b/src/llm_kira/client/agent.py
index 8829385..7b8716a 100644
--- a/src/llm_kira/client/agent.py
+++ b/src/llm_kira/client/agent.py
@@ -76,3 +76,4 @@ def read_context(self) -> List[Interaction]:
     def save_context(self, message: List[Interaction], override: bool = True):
         self._DataManager.save(interaction_flow=message, override=override)
         return message
+
diff --git a/src/llm_kira/client/anchor.py b/src/llm_kira/client/anchor.py
index 67020e0..be0da90 100644
--- a/src/llm_kira/client/anchor.py
+++ b/src/llm_kira/client/anchor.py
@@ -10,12 +10,10 @@
 # from loguru import logger
 from .llms.base import LlmBaseParam
 from .llms.openai import LlmBase
-from .types import LlmReturn, Interaction, PromptItem
+from .types import LlmReturn, PromptItem
 from ..creator.engine import PromptEngine
-from ..error import LLMException
 
-# Utils
-from ..utils.chat import Sim
+from ..error import LLMException
 
 # Completion
 from .types import ChatBotReturn
@@ -37,40 +35,17 @@ def __init__(self,
         if llm_model is None:
             raise LLMException("Whats your llm model?")
 
-    def __person(self, prompt, prompt_list):
-        _person_list = [f"{self.profile.start_name}:",
-                        f"{self.profile.restart_name}:",
-                        f"{self.profile.start_name}：",
-                        f"{self.profile.restart_name}：",
-                        ]
-        for item in prompt_list:
-            if item.ask.connect_words.strip() in [":", "："]:
-                _person_list.append(f"{item.ask.start}{item.ask.connect_words}")
-        _person_list = self.__rank_name(prompt=prompt.prompt, users=_person_list)
-        return _person_list
-
-    @staticmethod
-    def __rank_name(prompt: str, users: List[str]):
-        __temp = {}
-        for item in users:
-            __temp[item] = 0
-        users = list(__temp.keys())
-        _ranked = list(sorted(users, key=lambda i: Sim.cosion_similarity(pre=str(prompt), aft=str(i)), reverse=True))
-        return _ranked
-
     async def predict(self,
                       prompt: PromptEngine,
                       predict_tokens: Union[int] = 100,
                       llm_param: LlmBaseParam = None,
                       parse_reply: Callable[[list], str] = None,
-                      rank_name: bool = True,
                       ) -> ChatBotReturn:
         """
         :param prompt: PromptEngine
         :param predict_tokens: 预测 Token 位
         :param llm_param: 大语言模型参数
         :param parse_reply: Callable[[list], str] 覆写解析方法
-        :param rank_name: 自动排序停止词减少第三人称的冲突出现
         """
         self.prompt = prompt
         # ReWrite
@@ -79,28 +54,17 @@ async def predict(self,
         if predict_tokens > self.llm.get_token_limit():
             # Or Auto Cut?
             raise LLMException("Why your predict token > set token limit?")
-        _llm_result_limit = self.llm.get_token_limit() - predict_tokens
-        _llm_result_limit = _llm_result_limit if _llm_result_limit > 0 else 1
-        # Get
-        _prompt_index, _prompt = self.prompt.build_prompt(predict_tokens=predict_tokens)
-        _prompt_list = []
-        _person_list = None if not rank_name else self.__person(prompt=_prompt_index, prompt_list=_prompt)
-
-        # Prompt 构建
-        for item in _prompt:
-            _prompt_list.extend(item.content)
 
-        prompt_build = "\n".join(_prompt_list) + f"\n{self.profile.restart_name}:"
-        prompt_build = self.llm.resize_sentence(prompt_build, token=_llm_result_limit)
-        # ODO
-        # logger.trace(prompt_build)
+        # Get Question Index
+        _prompt_index = self.prompt.prompt
         # Get
         llm_result: LlmReturn = await self.llm.run(
-            prompt=prompt_build,
+            prompt=prompt,
             predict_tokens=predict_tokens,
-            llm_param=llm_param,
-            stop_words=_person_list
+            llm_param=llm_param
         )
+        prompt.clean(clean_prompt=True)
+
         self.prompt.build_interaction(
             ask=_prompt_index,
             response=PromptItem(
diff --git a/src/llm_kira/client/llms/base.py b/src/llm_kira/client/llms/base.py
index 0a86883..dc237ce 100644
--- a/src/llm_kira/client/llms/base.py
+++ b/src/llm_kira/client/llms/base.py
@@ -8,10 +8,8 @@
 import os
 from abc import abstractmethod, ABC
 from typing import Union, Optional, Callable, Any, Dict, Tuple, Mapping, List
-
 from loguru import logger
 from pydantic import BaseModel
-
 from ..types import LlmReturn
 
 
@@ -87,7 +85,7 @@ def parse_usage(response) -> Optional[int]:
 
     @abstractmethod
     async def run(self,
-                  prompt: str,
+                  prompt: Any,
                   validate: Union[List[str], None] = None,
                   predict_tokens: int = 500,
                   llm_param: LlmBaseParam = None,
diff --git a/src/llm_kira/client/llms/chatgpt.py b/src/llm_kira/client/llms/chatgpt.py
index 3e991ef..8ab2669 100644
--- a/src/llm_kira/client/llms/chatgpt.py
+++ b/src/llm_kira/client/llms/chatgpt.py
@@ -5,42 +5,69 @@
 # @Github    ：sudoskys
 import math
 import time
+import random
 import tiktoken
-from typing import Union, Optional, Callable, Any, Dict, Tuple, Mapping, List
+from typing import Union, Optional, Callable, Any, Dict, Tuple, Mapping, List, Literal
 
+from loguru import logger
+
+from ...creator.engine import PromptEngine
 # from loguru import logger
 
 from ...error import RateLimitError, ServiceUnavailableError
+from ...tool import openai as openai_api
 from pydantic import BaseModel, Field
 from tenacity import retry_if_exception_type, retry, stop_after_attempt, wait_exponential
 from ..agent import Conversation
 from ..llms.base import LlmBase, LlmBaseParam
-from ..types import LlmReturn
+from ..types import LlmReturn, Interaction, LlmException
+from ...tool.openai import ChatPrompt
+from ...utils.chat import Sim
 from ...utils.data import DataUtils
 from ...utils.setting import llmRetryAttempt, llmRetryTime, llmRetryTimeMax, llmRetryTimeMin
-from ...utils import network
 
 
 class ChatGptParam(LlmBaseParam, BaseModel):
-    api: str
-    """Mew Mew API"""
-    model_name: str = "text-davinci-003"
+    model_name: str = "gpt-3.5-turbo"
     """Model name to use."""
+    temperature: float = 0.8
+    """What sampling temperature to use."""
     max_tokens: int = 256
     """The maximum number of tokens to generate in the completion.
     -1 returns as many tokens as possible given the prompt and
     the models maximal context size."""
-    request_timeout: Optional[Union[float, Tuple[float, float]]] = None
-    """Timeout for requests to OpenAI completion API. Default is 600 seconds."""
+    top_p: float = 1
+    """Total probability mass of tokens to consider at each step."""
+    frequency_penalty: float = 0
+    """Penalizes repeated tokens according to frequency."""
+    presence_penalty: float = 0
+    """Penalizes repeated tokens."""
+    n: int = 1
+    """How many completions to generate for each prompt."""
+    best_of: int = 1
+    """Generates best_of completions server-side and returns the "best"."""
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for create call not explicitly specified."""
+    batch_size: int = 20
+    """Batch size to use when passing multiple documents to generate."""
+    request_timeout: Optional[Union[float, Tuple[float, float]]] = None
+    """Timeout for requests to OpenAI completion API. Default is 600 seconds."""
+    logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
+    """Adjust the probability of specific tokens being generated."""
 
     @property
     def _default_params(self) -> Dict[str, Any]:
         """Get the default parameters for calling OpenAI API."""
         normal_params = {
+            "temperature": self.temperature,
             "max_tokens": self.max_tokens,
+            "top_p": self.top_p,
+            "frequency_penalty": self.frequency_penalty,
+            "presence_penalty": self.presence_penalty,
+            "n": self.n,
+            "best_of": self.best_of,
             "request_timeout": self.request_timeout,
+            "logit_bias": self.logit_bias,
         }
         return {**normal_params, **self.model_kwargs}
 
@@ -56,10 +83,9 @@ def identifying_params(self) -> Mapping[str, Any]:
 
 
 class ChatGpt(LlmBase):
-    """CatGpt"""
 
-    def __init__(self,
-                 profile: Conversation,
+    def __init__(self, profile: Conversation,
+                 api_key: Union[str, list] = None,
                  token_limit: int = 3700,
                  auto_penalty: bool = False,
                  call_func: Callable[[dict, str], Any] = None,
@@ -74,6 +100,17 @@ def __init__(self,
         """
         self.auto_penalty = auto_penalty
         self.profile = profile
+        # if api_key is None:
+        #     api_key = setting.openaiApiKey
+        if isinstance(api_key, list):
+            api_key: list
+            if not api_key:
+                raise RuntimeError("NO KEY")
+            api_key = random.choice(api_key)
+            api_key: str
+        self.__api_key = api_key
+        if not api_key:
+            raise RuntimeError("NO KEY")
         self.__start_sequence = self.profile.start_name
         self.__restart_sequence = self.profile.restart_name
         self.__call_func = call_func
@@ -92,16 +129,22 @@ def tokenizer(self, text, raw: bool = False) -> Union[int, list]:
     @staticmethod
     def parse_response(response) -> list:
         REPLY = []
-        Choice = response.get("response")
+        Choice = response.get("choices")
         if Choice:
-            REPLY.append(Choice)
+            for item in Choice:
+                _text = item["message"]["content"]
+                REPLY.append(_text)
         if not REPLY:
             REPLY = [""]
         return REPLY
 
     @staticmethod
     def parse_usage(response) -> Optional[int]:
-        return 0
+        usage = None
+        usage_dict = response.get("usage")
+        if usage_dict:
+            usage = usage_dict["total_tokens"]
+        return usage
 
     @property
     def _llm_type(self) -> str:
@@ -141,7 +184,7 @@ async def task_context(self, task: str, prompt: str, predict_tokens: int = 500)
         _prompt = f"Text:{prompt}\n{task}: "
         llm_result = await self.run(prompt=_prompt,
                                     predict_tokens=predict_tokens,
-                                    llm_param=ChatGptParam(model_name="text-davinci-003"),
+                                    llm_param=ChatGptParam(model_name="gpt-3.5-turbo"),
                                     stop_words=["Text:", "\n\n"]
                                     )
         return llm_result
@@ -190,14 +233,16 @@ def model_context_size(model_name: str) -> int:
                                           ServiceUnavailableError)),
            stop=stop_after_attempt(llmRetryAttempt),
            wait=wait_exponential(multiplier=llmRetryTime, min=llmRetryTimeMin, max=llmRetryTimeMax),
-           reraise=True)
+           reraise=True,
+           )
     async def run(self,
-                  prompt: str,
+                  prompt: Union[PromptEngine, str],
                   validate: Union[List[str], None] = None,
                   predict_tokens: int = 500,
                   llm_param: ChatGptParam = None,
                   stop_words: list = None,
-                  anonymous_user: bool = True,
+                  anonymous_user: bool = False,
+                  rank_name: bool = False,
                   **kwargs
                   ) -> LlmReturn:
         """
@@ -208,6 +253,7 @@ async def run(self,
         :param llm_param: 参数表
         :param anonymous_user:
         :param stop_words:
+        :param rank_name:
         :return:
         """
         _request_arg = {
@@ -215,44 +261,92 @@ async def run(self,
             "n": 1
         }
         _request_arg: dict
-        if stop_words is None:
-            stop_words = [f"{self.profile.start_name}:",
-                          f"{self.profile.restart_name}:",
-                          f"{self.profile.start_name}：",
-                          f"{self.profile.restart_name}："]
-        # Kwargs
+        _llm_result_limit = self.get_token_limit() - predict_tokens
+        _llm_result_limit = _llm_result_limit if _llm_result_limit > 0 else 1
+        _prompt_input, _prompt = prompt.build_prompt(predict_tokens=_llm_result_limit)
+        _prompt: List[Interaction]
+
+        # Get
+        if not _prompt_input:
+            raise LlmException("Input Is Empty")
+        _prompt_input = _prompt_input.prompt
+
+        # Temp
+        _message = []
+        for item in _prompt:
+            item: Interaction
+            _message.extend(item.message)
+        # Prompt
+        _message_list = [ChatPrompt(role="system", content=prompt.description)]
+        for item in _message:
+            item: List[str]
+            # 对齐 _role
+            _role = self.__role_edit(item[0])
+            _content = item[1]
+            if _content != prompt.description:
+                _message_list.append(ChatPrompt(role=_role, content=_content))
+        print(_message_list)
+        # 补全参数
         if llm_param:
             _request_arg.update(llm_param.invocation_params)
         if validate is None:
             validate = []
+
+        # 构造覆盖信息
+        _request_arg.update(model=str(llm_param.model_name),
+                            max_tokens=int(predict_tokens),
+                            user=str(self.profile.get_conversation_hash()),
+                            )
         # Anonymous
         if anonymous_user:
             _request_arg.pop("user", None)
-        headers = {"User-Agent": "Mozilla/5.0", "Content-Type": "application/json"}
-        _message_arg = {
-            "message": prompt
-        }
+        if _request_arg.get("frequency_penalty") == 0:
+            _request_arg.pop("frequency_penalty", None)
+        if _request_arg.get("presence_penalty") == 0:
+            _request_arg.pop("presence_penalty", None)
+        # 校准字节参数
+        if not _request_arg.get("logit_bias"):
+            _request_arg["logit_bias"] = {}
+            _request_arg.pop("logit_bias", None)
+        # 校准温度和惩罚参数
+        if _request_arg.get("frequency_penalty"):
+            _frequency_penalty = _request_arg["frequency_penalty"]
+            _frequency_penalty = _frequency_penalty if -2.0 < _frequency_penalty else -1.9
+            _frequency_penalty = _frequency_penalty if _frequency_penalty < 2.0 else 1.9
+            _request_arg["frequency_penalty"] = _frequency_penalty
+        if _request_arg.get("presence_penalty"):
+            _presence_penalty = _request_arg["presence_penalty"]
+            _presence_penalty = _presence_penalty if -2.0 < _presence_penalty else -1.9
+            _presence_penalty = _presence_penalty if _presence_penalty < 2.0 else 1.9
+            _request_arg["presence_penalty"] = _presence_penalty
+        if _request_arg.get("temperature"):
+            _temperature = _request_arg["temperature"]
+            _request_arg["temperature"] = _temperature if 0 < _temperature < 1 else 0.9
+
         # 自维护 Api 库
-        try:
-            response = await network.request(
-                method="POST",
-                url=llm_param.api + "/message",
-                data=_message_arg,
-                headers=headers,
-                json_body=True,
-            )
-            _ = response.json()
-        except Exception as e:
-            raise ServiceUnavailableError(f"Server:{e}")
-        if response.status_code != 200:
-            raise ServiceUnavailableError(f"Server:{response.json().get('error')}")
+        response = await openai_api.ChatCompletion(api_key=self.__api_key, call_func=self.__call_func).create(
+            prompt=_message_list,
+            **_request_arg
+        )
+
         # Reply
         reply = self.parse_response(response)
         self.profile.update_usage(usage=self.parse_usage(response))
         return LlmReturn(model_flag=llm_param.model_name,
-                         raw=response.json(),
-                         prompt=prompt,
+                         raw=response,
+                         prompt=_prompt_input,
                          usage=self.profile.get_round_usage(),
                          time=int(time.time()),
-                         reply=reply,
+                         reply=reply
                          )
+
+    def __role_edit(self, _role) -> Literal["system", "user", "assistant"]:
+        if _role in ["system", "user", "assistant"]:
+            return _role
+        if Sim.cosion_similarity(pre=str(_role), aft=self.profile.start_name) > 0.85:
+            return "user"
+        if Sim.cosion_similarity(pre=str(_role), aft=self.profile.restart_name) > 0.85:
+            return "assistant"
+        if _role not in ["system", "user", "assistant"]:
+            return "user"
+        return "user"
diff --git a/src/llm_kira/client/llms/openai.py b/src/llm_kira/client/llms/openai.py
index ac33440..ecac374 100644
--- a/src/llm_kira/client/llms/openai.py
+++ b/src/llm_kira/client/llms/openai.py
@@ -8,6 +8,8 @@
 import random
 import tiktoken
 from typing import Union, Optional, Callable, Any, Dict, Tuple, Mapping, List
+
+from loguru import logger
 # from loguru import logger
 
 from ...error import RateLimitError, ServiceUnavailableError
@@ -15,8 +17,10 @@
 from pydantic import BaseModel, Field
 from tenacity import retry_if_exception_type, retry, stop_after_attempt, wait_exponential
 from ..agent import Conversation
+from ...creator.engine import PromptEngine
 from ..llms.base import LlmBase, LlmBaseParam
-from ..types import LlmReturn
+from ..types import LlmReturn, LlmException
+from ...utils.chat import Sim
 from ...utils.data import DataUtils
 from ...utils.setting import llmRetryAttempt, llmRetryTime, llmRetryTimeMax, llmRetryTimeMin
 
@@ -229,12 +233,13 @@ def model_context_size(model_name: str) -> int:
            wait=wait_exponential(multiplier=llmRetryTime, min=llmRetryTimeMin, max=llmRetryTimeMax),
            reraise=True)
     async def run(self,
-                  prompt: str,
+                  prompt: Union[PromptEngine, str],
                   validate: Union[List[str], None] = None,
                   predict_tokens: int = 500,
                   llm_param: OpenAiParam = None,
                   stop_words: list = None,
-                  anonymous_user: bool = True,
+                  anonymous_user: bool = False,
+                  rank_name: bool = False,
                   **kwargs
                   ) -> LlmReturn:
         """
@@ -245,6 +250,7 @@ async def run(self,
         :param llm_param: 参数表
         :param anonymous_user:
         :param stop_words:
+        :param rank_name:
         :return:
         """
         _request_arg = {
@@ -252,25 +258,48 @@ async def run(self,
             "n": 1
         }
         _request_arg: dict
+        if not isinstance(prompt, str):
+            # Prompt
+            _prompt_list = []
+            # Get
+            _llm_result_limit = self.get_token_limit() - predict_tokens
+            _llm_result_limit = _llm_result_limit if _llm_result_limit > 0 else 1
+            _prompt_input, _prompt = prompt.build_prompt(predict_tokens=_llm_result_limit)
+            if not _prompt_input:
+                raise LlmException("Input Is Empty")
+            for item in _prompt:
+                _prompt_list.extend(item.content)
+            prompt_build = "\n".join(_prompt_list) + f"\n{self.profile.restart_name}:"
+            prompt_build = self.resize_sentence(prompt_build, token=self.get_token_limit())
+            stop_words = None if not rank_name else self.__person(prompt=_prompt_input, prompt_list=_prompt)
+        else:
+            prompt_build = self.resize_sentence(prompt, token=predict_tokens)
+
+        # 停止符号
         if stop_words is None:
             stop_words = [f"{self.profile.start_name}:",
                           f"{self.profile.restart_name}:",
                           f"{self.profile.start_name}：",
                           f"{self.profile.restart_name}："]
-        # Kwargs
+
+        # 补全参数
         if llm_param:
             _request_arg.update(llm_param.invocation_params)
         if validate is None:
             validate = []
+
+        # 构造覆盖信息
         _request_arg.update(model=str(llm_param.model_name),
-                            prompt=str(prompt),
+                            prompt=prompt_build,  # IMPORTANT
                             max_tokens=int(predict_tokens),
                             user=str(self.profile.get_conversation_hash()),
                             stop=stop_words[:4],
                             )
+
         # Anonymous
         if anonymous_user:
             _request_arg.pop("user", None)
+
         # Adjust Penalty
         """
         if self.auto_penalty and validate:
@@ -313,18 +342,6 @@ async def run(self,
             _temperature = _request_arg["temperature"]
             _request_arg["temperature"] = _temperature if 0 < _temperature < 1 else 0.9
 
-        # 继承重写
-        # openai_client.api_key = self.__api_key
-        # try:
-        #     response = await openai_client.Completion.acreate(**_request_arg)
-        # except openai_client.error.OpenAIError as e:
-        #     if self.__call_func:
-        #         self.__call_func(e.json_body, self.__api_key)
-        #     openai_error_handler(e.code, e.error)
-        #     raise
-        # except Exception as e:
-        #     raise LLMException(e)
-
         # 自维护 Api 库
         response = await openai_api.Completion(api_key=self.__api_key, call_func=self.__call_func).create(
             **_request_arg
@@ -335,8 +352,29 @@ async def run(self,
         self.profile.update_usage(usage=self.parse_usage(response))
         return LlmReturn(model_flag=llm_param.model_name,
                          raw=response,
-                         prompt=prompt,
+                         prompt=prompt_build,
                          usage=self.profile.get_round_usage(),
                          time=int(time.time()),
-                         reply=reply,
+                         reply=reply
                          )
+
+    @staticmethod
+    def __rank_name(prompt: str, users: List[str]):
+        __temp = {}
+        for item in users:
+            __temp[item] = 0
+        users = list(__temp.keys())
+        _ranked = list(sorted(users, key=lambda i: Sim.cosion_similarity(pre=str(prompt), aft=str(i)), reverse=True))
+        return _ranked
+
+    def __person(self, prompt, prompt_list: list):
+        _person_list = [f"{self.profile.start_name}:",
+                        f"{self.profile.restart_name}:",
+                        f"{self.profile.start_name}：",
+                        f"{self.profile.restart_name}：",
+                        ]
+        for item in prompt_list:
+            if item.ask.connect_words.strip() in [":", "："]:
+                _person_list.append(f"{item.ask.start}{item.ask.connect_words}")
+        _person_list = self.__rank_name(prompt=prompt, users=_person_list)
+        return _person_list
diff --git a/src/llm_kira/client/types.py b/src/llm_kira/client/types.py
index 2c1d33d..df84880 100644
--- a/src/llm_kira/client/types.py
+++ b/src/llm_kira/client/types.py
@@ -55,6 +55,13 @@ def content(self):
     def raw(self):
         return "\n".join(self.content)
 
+    @property
+    def message(self):
+        if self.single:
+            return [[self.ask.start, self.ask.text]]
+        else:
+            return [[self.ask.start, self.ask.text], [self.reply.start, self.reply.text]]
+
 
 class InteractionWeight(BaseModel):
     interaction: Interaction
@@ -83,3 +90,7 @@ class ChatBotReturn(BaseModel):
     llm: LlmReturn
     ask: str
     reply: str
+
+
+class LlmException(Exception):
+    pass
diff --git a/src/llm_kira/creator/__init__.py b/src/llm_kira/creator/__init__.py
index 309d3dd..4532cea 100644
--- a/src/llm_kira/creator/__init__.py
+++ b/src/llm_kira/creator/__init__.py
@@ -3,5 +3,5 @@
 # @FileName: __init__.py
 # @Software: PyCharm
 # @Github    ：sudoskys
-from .engine import PromptEngine, Preset
-from .think import Hook, ThinkEngine, HookPool
+
+__all__ = ["base", "engine", "think"]
diff --git a/src/llm_kira/creator/base.py b/src/llm_kira/creator/base.py
new file mode 100644
index 0000000..ddb92cc
--- /dev/null
+++ b/src/llm_kira/creator/base.py
@@ -0,0 +1,14 @@
+# -*- coding: utf-8 -*-
+# @Time    : 3/2/23 4:13 PM
+# @FileName: base.py
+# @Software: PyCharm
+# @Github    ：sudoskys
+from abc import ABC, abstractmethod
+
+class BaseEngine(ABC):
+
+    @abstractmethod
+    def build_prompt(self, predict_tokens: int = 500):
+        pass
+
+
diff --git a/src/llm_kira/creator/engine.py b/src/llm_kira/creator/engine.py
index 8763c94..d83c3c3 100644
--- a/src/llm_kira/creator/engine.py
+++ b/src/llm_kira/creator/engine.py
@@ -4,12 +4,12 @@
 # @Software: PyCharm
 # @Github    ：sudoskys
 import time
-from typing import List, Union, Tuple
+from typing import List, Union, Tuple, Optional
 
 from loguru import logger
 
-from llm_kira.client import Optimizer
-
+from .base import BaseEngine
+from ..client import Optimizer
 from ..client.agent import Conversation, MemoryManager
 from ..client.llms.base import LlmBase
 from ..client.types import Interaction, PromptItem
@@ -17,7 +17,7 @@
 from ..utils.data import MsgFlow
 
 
-class PromptEngine(object):
+class PromptEngine(BaseEngine):
     """
     设计用于维护提示系统和接入外骨骼
     """
@@ -95,10 +95,11 @@ def _build_prompt_buffer(self):
         _buffer = self.prompt_buffer.copy()
         if self.reverse_prompt_buffer:
             _buffer = list(reversed(_buffer))
+        if not _buffer:
+            return None
         _index = _buffer.pop(-1)
         for item in _buffer:
             self.build_interaction(ask=item, single=True)
-        self.clean(clean_prompt=True)
         return _index
 
     def read_interaction(self):
@@ -193,11 +194,14 @@ def build_context(self, prompt: PromptItem, predict_tokens) -> List[Interaction]
         _optimized_prompt.append(Interaction(single=True, ask=prompt))
         return _optimized_prompt
 
-    def build_prompt(self, predict_tokens: int = 500) -> Tuple[PromptItem, List[Interaction]]:
+    def build_prompt(self, predict_tokens: int = 500) -> Tuple[Optional[PromptItem], List[Interaction]]:
         """
         Optimising context and re-cutting
         """
         user_input = self._build_prompt_buffer()
+        if not user_input:
+            logger.warning("No Buffer")
+            return None, []
         prompt = self.build_context(user_input, predict_tokens=predict_tokens)
         return user_input, prompt
 
@@ -285,3 +289,9 @@ def head(self,
             head = f"{start_name}{restart_name}の会話,"
             head = self.add_tail(prompt_iscode, sentence=head, tail="プログラミング指導を提供する,")
         return f"{head}"
+
+
+class MiddlePrompt(object):
+    def __init__(self, prompt: PromptEngine = None, limit_token: int = 2000):
+        self.prompt = prompt
+        self.limit_token: int = limit_token
diff --git a/src/llm_kira/radio/__init__.py b/src/llm_kira/radio/__init__.py
index 725f158..6b4b176 100644
--- a/src/llm_kira/radio/__init__.py
+++ b/src/llm_kira/radio/__init__.py
@@ -4,4 +4,4 @@
 # @Software: PyCharm
 # @Github    ：sudoskys
 
-__all__ = ["anchor", "crawer", "decomposer", "setting"]
+# __all__ = ["anchor", "crawer", "decomposer", "setting"]
diff --git a/src/llm_kira/tool/openai/__init__.py b/src/llm_kira/tool/openai/__init__.py
index 3311bbc..13db58c 100644
--- a/src/llm_kira/tool/openai/__init__.py
+++ b/src/llm_kira/tool/openai/__init__.py
@@ -5,3 +5,4 @@
 # @Github    ：sudoskys
 from .resouce import Completion
 from .resouce import Moderations
+from .resouce import ChatCompletion, ChatPrompt
diff --git a/src/llm_kira/tool/openai/api/api_url.json b/src/llm_kira/tool/openai/api/api_url.json
index 169344d..77a6aee 100644
--- a/src/llm_kira/tool/openai/api/api_url.json
+++ b/src/llm_kira/tool/openai/api/api_url.json
@@ -12,6 +12,86 @@
         }
       }
     },
+    "chat": {
+      "completions": {
+        "desc": "write",
+        "url": "https://api.openai.com/v1/chat/completions",
+        "params": {
+          "model": {
+            "type": "string",
+            "intro": "ID of the model to use.",
+            "must": true,
+            "Defaults": ""
+          },
+          "messages": {
+            "type": "string,array",
+            "intro": "The prompt(s) to generate completions for, encoded as a string, array of strings, array of tokens, or array of token arrays.",
+            "must": true,
+            "Defaults": ""
+          },
+          "temperature": {
+            "type": "number",
+            "intro": "What sampling temperature to use. ",
+            "must": false,
+            "Defaults": 1
+          },
+          "top_p": {
+            "type": "number",
+            "intro": "An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.",
+            "must": false,
+            "Defaults": 1
+          },
+          "n": {
+            "type": "integer",
+            "intro": "How many completions to generate for each prompt.",
+            "must": false,
+            "Defaults": 1
+          },
+          "stream": {
+            "type": "string",
+            "intro": "Whether to stream back partial progress.",
+            "must": false,
+            "Defaults": false
+          },
+          "max_tokens": {
+            "type": "integer",
+            "intro": "The maximum number of tokens to generate in the completion.",
+            "must": false,
+            "Defaults": 16
+          },
+          "logprobs": {
+            "type": "boolean",
+            "intro": "Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. ",
+            "must": false,
+            "Defaults": false
+          },
+          "presence_penalty": {
+            "type": "number",
+            "intro": "Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.",
+            "must": false,
+            "Defaults": 0
+          },
+          "frequency_penalty": {
+            "type": "number",
+            "intro": "Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.",
+            "must": false,
+            "Defaults": 0
+          },
+          "logit_bias": {
+            "type": "map",
+            "intro": "Modify the likelihood of specified tokens appearing in the completion.",
+            "must": false,
+            "Defaults": {}
+          },
+          "user": {
+            "type": "string",
+            "intro": "A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.",
+            "must": false,
+            "Defaults": ""
+          }
+        }
+      }
+    },
     "completions": {
       "desc": "write",
       "url": "https://api.openai.com/v1/completions",
diff --git a/src/llm_kira/tool/openai/resouce/__init__.py b/src/llm_kira/tool/openai/resouce/__init__.py
index 5d0a70a..32426a8 100644
--- a/src/llm_kira/tool/openai/resouce/__init__.py
+++ b/src/llm_kira/tool/openai/resouce/__init__.py
@@ -3,7 +3,7 @@
 # @FileName: __init__.py
 # @Software: PyCharm
 # @Github    ：sudoskys
-
+from .chat import ChatCompletion, ChatPrompt
 from .completion import Completion
 from .moderations import Moderations
 
diff --git a/src/llm_kira/tool/openai/resouce/chat.py b/src/llm_kira/tool/openai/resouce/chat.py
new file mode 100644
index 0000000..dec864a
--- /dev/null
+++ b/src/llm_kira/tool/openai/resouce/chat.py
@@ -0,0 +1,99 @@
+# -*- coding: utf-8 -*-
+# @Time    : 3/2/23 4:44 PM
+# @FileName: chat.py
+# @Software: PyCharm
+# @Github    ：sudoskys
+import random
+from typing import Union, Optional, Literal, List
+
+from pydantic import BaseModel
+
+from ...openai.api.api_utils import load_api
+from ...openai.api.network import request
+from ....utils import setting
+
+API = load_api()
+
+
+class ChatPrompt(BaseModel):
+    role: Literal["system", "user", "assistant"] = "user"
+    content: str = ""
+
+
+class ChatCompletion(object):
+    def __init__(self, api_key: Union[str, list] = None, proxy_url: str = "", call_func=None):
+        # if api_key is None:
+        #     api_key = setting.openaiApiKey
+        if isinstance(api_key, list):
+            api_key: list
+            if not api_key:
+                raise RuntimeError("Use Out")
+            random.shuffle(api_key)
+            api_key = random.choice(api_key)
+            api_key: str
+        if not api_key:
+            raise RuntimeError("NO KEY")
+        self.__api_key = api_key
+        if not proxy_url:
+            proxy_url = setting.proxyUrl
+        self.__proxy = proxy_url
+        self.__call_func = call_func
+
+    def get_api_key(self):
+        return self.__api_key
+
+    async def create(self,
+                     model: str = "gpt-3.5-turbo",
+                     prompt: Union[List[ChatPrompt], dict] = None,
+                     temperature: float = 0,
+                     max_tokens: int = 7,
+                     **kwargs
+                     ):
+        """
+        得到一个对话，预设了一些参数，其实还有很多参数，如果你有api文档
+        :param model: 模型
+        :param prompt: 提示
+        :param temperature: unknown
+        :param max_tokens: 返回数量
+        :return:
+        """
+        """
+        curl https://api.openai.com/v1/chat/completions \
+          -H 'Content-Type: application/json' \
+          -H 'Authorization: Bearer YOUR_API_KEY' \
+          -d '{
+          "model": "gpt-3.5-turbo",
+          "messages": [{"role": "user", "content": "Hello!"}]
+        }'
+        """
+        api = API["v1"]["chat"]["completions"]
+        if not isinstance(prompt, dict):
+            prompt = [item.dict() for item in prompt]
+        # 参数决定
+        params = {
+            "model": model,
+            "messages": prompt,
+            "temperature": temperature,
+            "max_tokens": max_tokens
+        }
+        _api_config = {
+            param: api["params"][param]["Defaults"]
+            for param in api["params"].keys()
+            if (param in kwargs) or (param in params)
+        }
+        _api_config.update(params)
+        _api_config.update(kwargs)
+        _api_config = {key: item
+                       for key, item in _api_config.items()
+                       if key in api["params"].keys()
+                       }
+        # 返回请求
+        return await request(
+            method="POST",
+            url=api["url"],
+            data=_api_config,
+            auth=self.__api_key,
+            proxy=self.__proxy,
+            json_body=True,
+            call_func=self.__call_func
+        )
diff --git a/src/llm_kira/tool/openai/resouce/completion.py b/src/llm_kira/tool/openai/resouce/completion.py
index 80dfaf8..7b6795e 100644
--- a/src/llm_kira/tool/openai/resouce/completion.py
+++ b/src/llm_kira/tool/openai/resouce/completion.py
@@ -7,7 +7,7 @@
 from typing import Union
 from ...openai.api.api_utils import load_api
 from ...openai.api.network import request
-from llm_kira.utils import setting
+from ....utils import setting
 
 API = load_api()
 
diff --git a/src/llm_kira/tool/openai/resouce/moderations.py b/src/llm_kira/tool/openai/resouce/moderations.py
index 1025dd3..d7cafb0 100644
--- a/src/llm_kira/tool/openai/resouce/moderations.py
+++ b/src/llm_kira/tool/openai/resouce/moderations.py
@@ -8,7 +8,7 @@
 
 from ..api.api_utils import load_api
 from ..api.network import request
-from llm_kira.utils import setting
+from ....utils import setting
 
 API = load_api()