0.2.4 动态惩罚机器，但是暂时不可用

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "llm_kira"
-version = "0.2.3"
+version = "0.2.4"
 description = "chatbot client for llm"
 authors = ["sudoskys <[email protected]>"]
 maintainers = [

src/llm_kira/client/Optimizer.py

@@ -132,7 +132,7 @@ def forgetting_curve(x):
         # 相似度检索
         for i in range(0, len(memory)):
             ask, reply = MsgFlow.get_content(memory[i], sign=False)
-            _ask_diff = Utils.cosion_sismilarity(pre=prompt, aft=ask)
+            _ask_diff = Utils.cosion_similarity(pre=prompt, aft=ask)
             _ask_diff = _ask_diff * 100
             score = _ask_diff if _ask_diff < 90 else 0
             if score != 0:
@@ -256,7 +256,7 @@ def forgetting_curve(x):
         # 相似度检索
         for i in range(0, len(memory)):
             ask, reply = MsgFlow.get_content(memory[i], sign=False)
-            _ask_diff = Utils.cosion_sismilarity(pre=prompt, aft=ask)
+            _ask_diff = Utils.cosion_similarity(pre=prompt, aft=ask)
             _ask_diff = _ask_diff * 100
             score = _ask_diff if _ask_diff < 90 else 0
             if score != 0:

src/llm_kira/client/anchor.py

@@ -328,19 +328,16 @@ async def predict(self,
                                           foot=_prompt_foot,
                                           token=_llm_result_limit)
 
-        # Clean
-        _prompt_body = [item for item in _prompt_body if item]
-
         # Stick Them
         _prompt = _prompt + f"\n{self.profile.restart_name}:"
         if not prompt_iscode:
             _prompt = _prompt.replace("\n\n", "\n")
 
-        # ODO
-        # logger.warning(_prompt)
-
         # Get
-        llm_result = await self.llm.run(prompt=_prompt, predict_tokens=predict_tokens, llm_param=llm_param)
+        llm_result = await self.llm.run(prompt=_prompt,
+                                        validate=_prompt_body,
+                                        predict_tokens=predict_tokens,
+                                        llm_param=llm_param)
         llm_result: LlmReturn
 
         # Parse Result
@@ -350,4 +347,4 @@ async def predict(self,
         return ChatBotReturn(conversation_id=f"{self.profile.conversation_id}",
                              llm=llm_result,
                              ask=prompt_text,
-                             reply=self.llm.parse_reply(llm_result.reply))
+                             reply=self.llm.parse_reply(llm_result.reply).rstrip("<im_end>"))

src/llm_kira/client/llms/base.py

@@ -46,7 +46,9 @@ def get_token_limit(self) -> int:
         return 2000
 
     @abstractmethod
-    def tokenizer(self, text):
+    def tokenizer(self, text, raw=False) -> Union[int, list]:
+        if raw:
+            return []
         return len(text)
 
     @abstractmethod
@@ -80,6 +82,7 @@ def parse_usage(response) -> Optional[int]:
     @abstractmethod
     async def run(self,
                   prompt: str,
+                  validate: Union[List[str], None] = None,
                   predict_tokens: int = 500,
                   llm_param: LlmBaseParam = None,
                   ) -> Optional[LlmReturn]:

src/llm_kira/client/llms/openai.py

@@ -111,10 +111,12 @@ def __init__(self, profile: Conversation,
     def get_token_limit(self) -> int:
         return self.token_limit
 
-    def tokenizer(self, text) -> int:
+    def tokenizer(self, text, raw: bool = False) -> Union[int, list]:
         gpt_tokenizer = tiktoken.get_encoding("gpt2")
-        _token = len(gpt_tokenizer.encode(text))
-        return _token
+        _token = gpt_tokenizer.encode(text)
+        if raw:
+            return _token
+        return len(_token)
 
     @staticmethod
     def parse_response(response) -> list:
@@ -152,6 +154,7 @@ def resize_sentence(self, text: str, token: int) -> str:
         return text
 
     def resize_context(self, head: list, body: list, foot: list, token: int) -> str:
+        body = [item for item in body if item]
         token = token if token > 5 else 5
         _head = '\n'.join(head) + "\n"
         _body = "\n".join(body) + "\n"
@@ -188,25 +191,29 @@ def model_context_size(model_name: str) -> int:
 
     async def run(self,
                   prompt: str,
+                  validate: Union[List[str], None] = None,
                   predict_tokens: int = 500,
                   llm_param: OpenAiParam = None
                   ) -> LlmReturn:
         """
         异步的，得到对话上下文
         :param predict_tokens: 限制返回字符数量
+        :param validate: 惩罚验证列表
         :param prompt: 提示词
         :param llm_param: 参数表
         :return:
         """
-
         _request_arg = {
             "top_p": 1,
             "n": 1
         }
+        _request_arg: dict
+
         # Kwargs
         if llm_param:
             _request_arg.update(llm_param.invocation_params)
-
+        if validate is None:
+            validate = []
         _request_arg.update(model=str(llm_param.model_name),
                             prompt=str(prompt),
                             max_tokens=int(predict_tokens),
@@ -217,26 +224,46 @@ async def run(self,
                                   f"{self.profile.restart_name}："],
                             )
 
-        # Penalty
-        if self.auto_penalty:
-            # THINK ABOUT HOT CAKE
-            _frequency_penalty, _presence_penalty, _temperature = Detect().gpt_tendency_arg(prompt=prompt)
-            # SOME HOT CAKE
+        # Adjust Penalty
+        if self.auto_penalty and validate:
+            # Cook
+            _frequency_penalty, _presence_penalty, _temperature = Detect().gpt_tendency_arg(prompt=prompt,
+                                                                                            memory=validate,
+                                                                                            tokenizer=self.tokenizer
+                                                                                            )
+            # Some Update
             _request_arg.update({
                 "frequency_penalty": float(_frequency_penalty),
                 "presence_penalty": float(_presence_penalty),
                 "temperature": float(_temperature),
             })
-        # logit_bias
+
+        # 校准字节参数
         if not _request_arg.get("logit_bias"):
             _request_arg["logit_bias"] = {}
             _request_arg.pop("logit_bias")
-        # Req
+
+        # 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
+
+        # 请求
         response = await Completion(api_key=self.__api_key, call_func=self.__call_func).create(
             **_request_arg
         )
-
-        # Reply
         reply = self.parse_response(response)
         usage = self.parse_usage(response)
         return LlmReturn(model_flag=llm_param.model_name,

src/llm_kira/client/module/plugin/_plugin_tool.py

@@ -115,7 +115,7 @@ def nlp_filter_list(prompt, material: list):
                     continue
                 _pre = material[i]
                 _afe = material[i + 1]
-                sim = Utils.cosion_sismilarity(pre=_pre, aft=_afe)
+                sim = Utils.cosion_similarity(pre=_pre, aft=_afe)
                 if sim > 0.7:
                     _remo = _afe if len(_afe) > len(_pre) else _pre
                     # 移除过于相似的
@@ -129,7 +129,7 @@ def nlp_filter_list(prompt, material: list):
         material = list(material_.keys())
         _top_table = {}
         for item in material:
-            _top_table[item] = Utils.cosion_sismilarity(pre=prompt, aft=item)
+            _top_table[item] = Utils.cosion_similarity(pre=prompt, aft=item)
         material = {k: v for k, v in _top_table.items() if v > 0.15}
         # 搜索引擎比相似度算法靠谱所以注释掉了
         # material = OrderedDict(sorted(material.items(), key=lambda t: t[1]))

src/llm_kira/client/module/plugin/time.py

@@ -17,7 +17,13 @@ class Week(object):
     def __init__(self):
         self._server = None
         self._text = None
-        self._time = ["time", "多少天", "几天", "时间", "几点", "今天", "昨天", "明天", "几月", "几月", "几号",
+        self._time = ["time", "今年", "2022",
+                      "2023", "2024", "year", "day",
+                      "多少天", "几几年",
+                      "几天", "时间",
+                      "几点", "今天",
+                      "昨天", "明天",
+                      "几月", "几号",
                       "几个月",
                       "天前"]
 

src/llm_kira/client/module/plugin/week.py

@@ -18,7 +18,7 @@ def __init__(self):
         self._server = None
         self._text = None
         self._week_list = ["星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"]
-        self._week_key = ["星期", "星期几", "时间", "周几", "周一", "周二", "周三", "周四", "周五", "周六"]
+        self._week_key = ["星期", "星期几", "week", "时间", "周几", "周一", "周二", "周三", "周四", "周五", "周六"]
 
     def requirements(self):
         return []

src/llm_kira/client/text_analysis_tools/__init__.py

@@ -14,3 +14,5 @@
 from .api.summarization.textrank_summarization import TextRankSummarization
 from .api.text_similarity.simhash import SimHashSimilarity
 from .api.sentiment.sentiment import SentimentAnalysis
+from .api.text_similarity.edit import EditSimilarity
+from .api.text_similarity.cosion import CosionSimilarity

src/llm_kira/utils/chat.py

@@ -5,6 +5,7 @@
 # @Github    ：sudoskys
 import re
 import random
+from typing import Union, Callable
 
 from .langdetect.langdetect import LangDetector
 from ..client.text_analysis_tools.api.keywords.tfidf import TfidfKeywords
@@ -13,12 +14,20 @@
 from ..client.text_analysis_tools.api.summarization.tfidf_summarization import TfidfSummarization
 from ..client.text_analysis_tools.api.text_similarity.simhash import SimHashSimilarity
 from ..client.text_analysis_tools.api.text_similarity.cosion import CosionSimilarity
+from ..client.text_analysis_tools.api.text_similarity.edit import EditSimilarity
 from ..client.text_analysis_tools.api.keyphrase.keyphrase import KeyPhraseExtraction
 import tiktoken
 
 gpt_tokenizer = tiktoken.get_encoding("gpt2")
 
 
+def default_gpt_tokenizer(text, raw: bool = False) -> Union[int, list]:
+    _token = gpt_tokenizer.encode(text)
+    if raw:
+        return _token
+    return len(_token)
+
+
 class Detect(object):
     @staticmethod
     def isNeedHelp(sentence) -> bool:
@@ -87,59 +96,52 @@ def get_text_language(sentence: str):
             lang_type = detect(text=sentence.replace("\n", "").replace("\r", ""))[0][0].upper()
         return lang_type
 
-    def gpt_tendency_arg(self, prompt: str, memory: list = None, lang: str = "CN") -> tuple:
+    def gpt_tendency_arg(self, prompt: str,
+                         memory: list = None,
+                         tokenizer: Callable[[str, bool], Union[int, list]] = default_gpt_tokenizer,
+                         lang: str = "CN") -> tuple:
+
         if memory is None:
             memory = []
-        # 代码
+        temperature = 0.9
+        frequency_penalty = 0
+        presence_penalty = 0
+
         if self.isCode(sentence=prompt):
-            temperature = 0.9
-            frequency_penalty = 0
-            presence_penalty = 0
-            return frequency_penalty, presence_penalty, temperature
-        if self.isNeedHelp(sentence=prompt):
-            temperature = 0.9
-            frequency_penalty = 0
-            presence_penalty = 0
             return frequency_penalty, presence_penalty, temperature
 
-        # 普通情况
-        temperature = 0.9
+        if self.isNeedHelp(sentence=prompt):
+            temperature -= 0.2
+            frequency_penalty -= 0.1
+            presence_penalty -= 0.1
 
         # 控制随机数的精度round(数值，精度)
-        presence_penalty = 0 + round(random.uniform(-1, 1) / 10, 2)
-        frequency_penalty = 0 + round(random.uniform(-1, 1) / 10, 2)
+        # presence_penalty += round(random.uniform(-1, 1) / 10, 2)
+        # frequency_penalty += round(random.uniform(-1, 1) / 10, 2)
         _sentiment_score = Utils.sentiment(sentence=prompt).get("score")
         while _sentiment_score > 1.5 or _sentiment_score < -1.5:
             _sentiment_score = _sentiment_score / 10
         _sentiment_score = 0.1 if 0.05 < _sentiment_score < 0.1 else _sentiment_score
         _sentiment_score = -0.1 if -0.1 < _sentiment_score < -0.05 else _sentiment_score
+        # 不谈论新话题
+        presence_penalty -= _sentiment_score * 0.4
+        # 拒绝重复
+        frequency_penalty += _sentiment_score * 0.4
 
-        # NEW 高兴正数，就不扭转
-        presence_penalty -= _sentiment_score * 1.2
-
-        # REPEAT 高兴正数，则采用默认加法惩罚
-        frequency_penalty += _sentiment_score * 0.8
-        _memory_len = len(memory)
-        # 对话结束就拒绝复读，扭转为正数！
-        if _memory_len > 20:
-            while _memory_len > 20:
-                _memory_len = _memory_len - 20
-            if _memory_len / 20 > 0.7:
-                frequency_penalty = abs(frequency_penalty)
+        # 验证记忆体
+        if len(memory) > 3:
+            # 计算回复指数指标
+            _token = tokenizer("".join(memory[-4:]), True)
+            _repeat_score = 2 * (0.8 - len(set(_token)) / len(_token))
+            frequency_penalty = frequency_penalty + _repeat_score
+            print(_repeat_score)
 
-        # FIX
+        # Fix
         temperature = round(temperature, 1)
         presence_penalty = round(presence_penalty, 1)
         frequency_penalty = round(frequency_penalty, 1)
 
-        # CHECK
-        temperature = temperature if 0 < temperature < 1 else 0.9
-
-        presence_penalty = presence_penalty if -1.8 < presence_penalty else -0.1
-        presence_penalty = presence_penalty if presence_penalty < 1.8 else 0.1
-
-        frequency_penalty = frequency_penalty if -1.8 < frequency_penalty else -0.1
-        frequency_penalty = frequency_penalty if frequency_penalty < 1.8 else 0.1
+        # Check
         return frequency_penalty, presence_penalty, temperature
 
 
@@ -178,7 +180,7 @@ def tfidf_summarization(sentence: str, ratio=0.5):
         return _sum
 
     @staticmethod
-    def cosion_sismilarity(pre, aft):
+    def cosion_similarity(pre, aft):
         """
         基于余弦计算文本相似性 0 - 1 (1为最相似)
         :return: 余弦值
@@ -187,6 +189,16 @@ def cosion_sismilarity(pre, aft):
         _sim = _cos.similarity(pre, aft)
         return _sim
 
+    @staticmethod
+    def edit_similarity(pre, aft):
+        """
+        基于余弦计算文本相似性 0 - 1 (1为最相似)
+        :return: 余弦值
+        """
+        _cos = EditSimilarity()
+        _sim = _cos.edit_dist(pre, aft)
+        return _sim
+
     @staticmethod
     def simhash_similarity(pre, aft):
         """