From 1171b04e93f8d1e78d1357969df7d79ddd7843b0 Mon Sep 17 00:00:00 2001
From: zhayujie <yjzha1996@163.com>
Date: Fri, 25 Aug 2023 12:24:16 +0800
Subject: [PATCH] fix: wenxin token discard bug

---
 bot/baidu/baidu_wenxin.py         |  1 -
 bot/baidu/baidu_wenxin_session.py | 54 ++++++-------------------------
 2 files changed, 10 insertions(+), 45 deletions(-)

diff --git a/bot/baidu/baidu_wenxin.py b/bot/baidu/baidu_wenxin.py
index 315459e..54997f0 100644
--- a/bot/baidu/baidu_wenxin.py
+++ b/bot/baidu/baidu_wenxin.py
@@ -2,7 +2,6 @@
 
 import requests, json
 from bot.bot import Bot
-from bridge.reply import Reply, ReplyType
 from bot.session_manager import SessionManager
 from bridge.context import ContextType
 from bridge.reply import Reply, ReplyType
diff --git a/bot/baidu/baidu_wenxin_session.py b/bot/baidu/baidu_wenxin_session.py
index aad8f71..5ba2f17 100644
--- a/bot/baidu/baidu_wenxin_session.py
+++ b/bot/baidu/baidu_wenxin_session.py
@@ -9,6 +9,7 @@ from common.log import logger
     ]
 """
 
+
 class BaiduWenxinSession(Session):
     def __init__(self, session_id, system_prompt=None, model="gpt-3.5-turbo"):
         super().__init__(session_id, system_prompt)
@@ -17,7 +18,6 @@ class BaiduWenxinSession(Session):
         # self.reset()
 
     def discard_exceeding(self, max_tokens, cur_tokens=None):
-        # pdb.set_trace()
         precise = True
         try:
             cur_tokens = self.calc_tokens()
@@ -27,18 +27,9 @@ class BaiduWenxinSession(Session):
                 raise e
             logger.debug("Exception when counting tokens precisely for query: {}".format(e))
         while cur_tokens > max_tokens:
-            if len(self.messages) > 2:
-                self.messages.pop(1)
-            elif len(self.messages) == 2 and self.messages[1]["role"] == "assistant":
-                self.messages.pop(1)
-                if precise:
-                    cur_tokens = self.calc_tokens()
-                else:
-                    cur_tokens = cur_tokens - max_tokens
-                break
-            elif len(self.messages) == 2 and self.messages[1]["role"] == "user":
-                logger.warn("user message exceed max_tokens. total_tokens={}".format(cur_tokens))
-                break
+            if len(self.messages) >= 2:
+                self.messages.pop(0)
+                self.messages.pop(0)
             else:
                 logger.debug("max_tokens={}, total_tokens={}, len(messages)={}".format(max_tokens, cur_tokens, len(self.messages)))
                 break
@@ -52,36 +43,11 @@ class BaiduWenxinSession(Session):
         return num_tokens_from_messages(self.messages, self.model)
 
 
-# refer to https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
 def num_tokens_from_messages(messages, model):
     """Returns the number of tokens used by a list of messages."""
-    import tiktoken
-
-    if model in ["gpt-3.5-turbo-0301", "gpt-35-turbo"]:
-        return num_tokens_from_messages(messages, model="gpt-3.5-turbo")
-    elif model in ["gpt-4-0314", "gpt-4-0613", "gpt-4-32k", "gpt-4-32k-0613", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-16k-0613", "gpt-35-turbo-16k"]:
-        return num_tokens_from_messages(messages, model="gpt-4")
-
-    try:
-        encoding = tiktoken.encoding_for_model(model)
-    except KeyError:
-        logger.debug("Warning: model not found. Using cl100k_base encoding.")
-        encoding = tiktoken.get_encoding("cl100k_base")
-    if model == "gpt-3.5-turbo":
-        tokens_per_message = 4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
-        tokens_per_name = -1  # if there's a name, the role is omitted
-    elif model == "gpt-4":
-        tokens_per_message = 3
-        tokens_per_name = 1
-    else:
-        logger.warn(f"num_tokens_from_messages() is not implemented for model {model}. Returning num tokens assuming gpt-3.5-turbo.")
-        return num_tokens_from_messages(messages, model="gpt-3.5-turbo")
-    num_tokens = 0
-    for message in messages:
-        num_tokens += tokens_per_message
-        for key, value in message.items():
-            num_tokens += len(encoding.encode(value))
-            if key == "name":
-                num_tokens += tokens_per_name
-    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
-    return num_tokens
+    tokens = 0
+    for msg in messages:
+        # 官方token计算规则暂不明确： "大约为 token数为 "中文字 + 其他语种单词数 x 1.3"
+        # 这里先直接根据字数粗略估算吧，暂不影响正常使用，仅在判断是否丢弃历史会话的时候会有偏差
+        tokens += len(msg["content"])
+    return tokens