Merge pull request #2 from sudoskys/fix_

修复严重错误

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "llm_kira"
-version = "0.2.0"
+version = "0.2.1"
 description = "chatbot client for llm"
 authors = ["sudoskys <[email protected]>"]
 maintainers = [
@@ -23,9 +23,8 @@ pydantic = "^1.10.4"
 loguru = "^0.6.0"
 elara = "^0.5.4"
 openai-async = "^0.0.2"
-numpy = "^1.24.1"
+numpy = "^1.22.1"
 tiktoken = "^0.1.2"
-scikit-learn = "^1.2.0"
 # pycurl = "^7.43.0.6"
 
 

src/llm_kira/client/anchor.py

@@ -250,6 +250,7 @@ async def predict(self,
         prompt_text: str = self.prompt.run(raw_list=False)
         # prompt 前向注入
         prompt_raw: list = self.prompt.run(raw_list=True)
+        prompt_raw.pop(-1)
         __extra_memory = []
         for item in prompt_raw:
             index = round(len(item) / 3) if round(len(item) / 3) > 3 else 10

src/llm_kira/utils/data.py

@@ -6,7 +6,7 @@
 import ast
 import time
 import json
-from typing import Union, Optional, List
+from typing import Union, Optional, List, Tuple
 from ..client.types import Memory_Flow, MemoryItem
 from ..utils import setting
 from pydantic import BaseModel
@@ -199,7 +199,7 @@ def _set_uid(self, uid, message_streams):
         return self.MsgFlowData.setKey(uid, message_streams)
 
     @staticmethod
-    def get_content(memory_flow: Memory_Flow, sign: bool = False) -> tuple:
+    def get_content(memory_flow: Memory_Flow, sign: bool = False) -> Tuple[str, str]:
         """
         得到单条消息的内容
         :param sign: 是否署名
@@ -214,7 +214,7 @@ def get_content(memory_flow: Memory_Flow, sign: bool = False) -> tuple:
         if isinstance(memory_flow, dict):
             _ask_ = memory_flow["content"]["ask"]
             _reply_ = memory_flow["content"]["reply"]
-        if not sign and ":" in _ask_ and '：' in _reply_:
+        if not sign and ":" in _ask_ and ':' in _reply_:
             _ask_ = _ask_.split(":", 1)[1]
             _reply_ = _reply_.split(":", 1)[1]
         if _ask_ == _reply_:
@@ -227,7 +227,8 @@ def saveMsg(self, msg: MemoryItem) -> None:
         content = Memory_Flow(content=msg, time=time_s).dict()
         _message_streams = self._get_uid(self.uid)
         if "msg" in _message_streams:
-            _message_streams["msg"] = sorted(_message_streams["msg"], key=lambda x: x['time'], reverse=False)
+            # 倒序
+            _message_streams["msg"] = sorted(_message_streams["msg"], key=lambda x: x['time'], reverse=True)
             # 记忆容量重整
             if len(_message_streams["msg"]) > self.memory:
                 for i in range(len(_message_streams["msg"]) - self.memory + 1):

test/test.py

@@ -50,7 +50,7 @@ async def completion():
 conversation = receiver.Conversation(
     start_name="Human:",
     restart_name="AI:",
-    conversation_id=10093,  # random.randint(1, 10000000),
+    conversation_id=10096,  # random.randint(1, 10000000),
 )
 
 llm = llm_kira.client.llms.OpenAi(
@@ -70,36 +70,39 @@ async def completion():
 
 async def chat():
     promptManager = receiver.PromptManager(profile=conversation,
-                                         connect_words="\n",
-                                         )
+                                           connect_words="\n",
+                                           )
     # 大型数据对抗测试
     # promptManager.insert(item=PromptItem(start="Neko", text=random_string(4000)))
     # promptManager.insert(item=PromptItem(start="Neko", text=random_string(500)))
 
     # 多 prompt 对抗测试
     # promptManager.insert(item=PromptItem(start="Neko", text="喵喵喵"))
 
-    promptManager.insert(item=PromptItem(start=conversation.start_name, text="我的账号是 2216444"))
+    promptManager.insert(item=PromptItem(start=conversation.start_name, text="换谁都被吓走吧"))
     response = await chat_client.predict(
-        llm_param=OpenAiParam(model_name="text-davinci-003", temperature=0.8, presence_penalty=0.1, n=2, best_of=2),
+        llm_param=OpenAiParam(model_name="text-davinci-003", temperature=0.8, presence_penalty=0.1, n=1, best_of=1),
         prompt=promptManager,
         predict_tokens=500,
         increase="外部增强:每句话后面都要带 “喵”",
     )
+
     print(f"id {response.conversation_id}")
     print(f"ask {response.ask}")
     print(f"reply {response.reply}")
     print(f"usage:{response.llm.usage}")
     print(f"usage:{response.llm.raw}")
     print(f"---{response.llm.time}---")
     promptManager.clean()
+    return "END"
     promptManager.insert(item=PromptItem(start=conversation.start_name, text="说出我的账号？"))
-    response = await chat_client.predict(llm_param=OpenAiParam(model_name="text-davinci-003", logit_bias=None),
-                                         prompt=promptManager,
-                                         predict_tokens=500,
-                                         increase="外部增强:每句话后面都要带 “喵”",
-                                         # parse_reply=None
-                                         )
+    response = await chat_client.predict(
+        llm_param=OpenAiParam(model_name="text-davinci-003", temperature=0.8, presence_penalty=0.1, n=2, best_of=2),
+        prompt=promptManager,
+        predict_tokens=500,
+        increase="外部增强:每句话后面都要带 “喵”",
+        # parse_reply=None
+        )
     _info = "parse_reply 回调会处理 llm 的回复字段，比如 list 等，传入list，传出 str 的回复。必须是 str。",
     print(f"id {response.conversation_id}")
     print(f"ask {response.ask}")
@@ -121,9 +124,10 @@ async def Sentiment():
         "什么是？",
         "玉玉了，紫砂了",
         "我知道了",
-        "主播抑郁了，自杀了",
+        "抑郁了，自杀了",
         "公主也能看啊",
-        "换谁都被吓走吧"
+        "换谁都被吓走吧",
+        "错了"
     ]
     for item in _sentence_list:
         print(item)