From 0785ff2aedabbd1f4a4aee78b6b7c070ed30a069 Mon Sep 17 00:00:00 2001
From: binary-husky <505030475@qq.com>
Date: Sun, 23 Apr 2023 17:45:56 +0800
Subject: [PATCH] =?UTF-8?q?=E5=BE=AE=E8=B0=83=E5=AF=B9=E8=AF=9D=E8=A3=81?=
 =?UTF-8?q?=E5=89=AA?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 request_llm/bridge_chatgpt.py |  2 +-
 toolbox.py                    | 17 ++++++++++-------
 2 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/request_llm/bridge_chatgpt.py b/request_llm/bridge_chatgpt.py
index a1614b7..5e32f45 100644
--- a/request_llm/bridge_chatgpt.py
+++ b/request_llm/bridge_chatgpt.py
@@ -200,7 +200,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     if "reduce the length" in error_msg:
                         if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
                         history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'], 
-                                               max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])//2) # history至少释放二分之一
+                                               max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
                         chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
                         # history = []    # 清除历史
                     elif "does not exist" in error_msg:
diff --git a/toolbox.py b/toolbox.py
index 4340814..c9dc207 100644
--- a/toolbox.py
+++ b/toolbox.py
@@ -555,23 +555,26 @@ def run_gradio_in_subpath(demo, auth, port, custom_path):
 
 def clip_history(inputs, history, tokenizer, max_token_limit):
     """
-    reduce the length of input/history by clipping.
+    reduce the length of history by clipping.
     this function search for the longest entries to clip, little by little,
-    until the number of token of input/history is reduced under threshold.
-    通过剪辑来缩短输入/历史记录的长度。 
+    until the number of token of history is reduced under threshold.
+    通过裁剪来缩短历史记录的长度。 
     此函数逐渐地搜索最长的条目进行剪辑，
-    直到输入/历史记录的标记数量降低到阈值以下。
+    直到历史记录的标记数量降低到阈值以下。
     """
     import numpy as np
     from request_llm.bridge_all import model_info
     def get_token_num(txt): 
         return len(tokenizer.encode(txt, disallowed_special=()))
     input_token_num = get_token_num(inputs)
-    if input_token_num < max_token_limit * 3 / 4: 
-        # 当输入部分的token占比小于限制的3/4时，在裁剪时把input的余量留出来
+    if input_token_num < max_token_limit * 3 / 4:
+        # 当输入部分的token占比小于限制的3/4时，裁剪时
+        # 1. 把input的余量留出来
         max_token_limit = max_token_limit - input_token_num
+        # 2. 把输出用的余量留出来
+        max_token_limit = max_token_limit - 128
+        # 3. 如果余量太小了，直接清除历史
         if max_token_limit < 128:
-            # 余量太小了，直接清除历史
             history = []
             return history
     else: