diff --git a/PROJECT.md b/PROJECT.md
index f27d7e7..0bc58a7 100644
--- a/PROJECT.md
+++ b/PROJECT.md
@@ -17,6 +17,7 @@
 * [ChatGLM-web](https://github.com/NCZkevin/chatglm-web)：基于FastAPI和Vue3搭建的ChatGLM演示网站(支持chatglm流式输出、前端调整模型参数、上下文选择、保存图片、知识库问答等功能)
 * [ChatGLM-6B-Engineering](https://github.com/LemonQu-GIT/ChatGLM-6B-Engineering)：基于 ChatGLM-6B 后期调教，网络爬虫及 [Stable Diffusion](https://github.com/AUTOMATIC1111/stable-diffusion-webui) 实现的网络搜索及图片生成
 * [ChatGLM-OpenAI-API](https://github.com/ninehills/chatglm-openai-api): 将 ChatGLM-6B 封装为 OpenAI API 风格，并通过 ngrok/cloudflare 对外提供服务，从而将 ChatGLM 快速集成到 OpenAI 的各种生态中。
+* [ChatSQL](https://github.com/yysirs/ChatSQL): 基于ChatGLM+SBERT实现NL2SQL本地化，并直接连接数据库查询数据返回结果，使得生成的SQL语句更具有实用性。
 
 对 ChatGLM-6B 进行微调的开源项目：
 * [InstructGLM](https://github.com/yanqiangmiffy/InstructGLM)：基于ChatGLM-6B进行指令学习，汇总开源中英文指令数据，基于Lora进行指令数据微调，开放了Alpaca、Belle微调后的Lora权重，修复web_demo重复问题
diff --git a/cli_demo.py b/cli_demo.py
index da80fff..3559840 100644
--- a/cli_demo.py
+++ b/cli_demo.py
@@ -2,6 +2,7 @@ import os
 import platform
 import signal
 from transformers import AutoTokenizer, AutoModel
+import readline
 
 tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
 model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
diff --git a/ptuning/main.py b/ptuning/main.py
index 43ecdf8..49b08b0 100644
--- a/ptuning/main.py
+++ b/ptuning/main.py
@@ -382,9 +382,10 @@ def main():
 
     # Evaluation
     results = {}
+    max_seq_length = data_args.max_source_length + data_args.max_target_length + 1
     if training_args.do_eval:
         logger.info("*** Evaluate ***")
-        metrics = trainer.evaluate(metric_key_prefix="eval", do_sample=True, top_p=0.7, max_length=512, temperature=0.95)
+        metrics = trainer.evaluate(metric_key_prefix="eval", do_sample=True, top_p=0.7, max_length=max_seq_length, temperature=0.95)
         max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
         metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
 
@@ -393,8 +394,7 @@ def main():
 
     if training_args.do_predict:
         logger.info("*** Predict ***")
-
-        predict_results = trainer.predict(predict_dataset, metric_key_prefix="predict", max_length=512, do_sample=True, top_p=0.7, temperature=0.95)
+        predict_results = trainer.predict(predict_dataset, metric_key_prefix="predict", max_length=max_seq_length, do_sample=True, top_p=0.7, temperature=0.95)
         metrics = predict_results.metrics
         max_predict_samples = (
             data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
diff --git a/web_demo2.py b/web_demo2.py
index 226682e..ce976b3 100644
--- a/web_demo2.py
+++ b/web_demo2.py
@@ -28,6 +28,8 @@ def predict(input, max_length, top_p, temperature, history=None):
 
     with container:
         if len(history) > 0:
+            if len(history)>MAX_BOXES:
+                history = history[-MAX_TURNS:]
             for i, (query, response) in enumerate(history):
                 message(query, avatar_style="big-smile", key=str(i) + "_user")
                 message(response, avatar_style="bottts", key=str(i))
@@ -66,4 +68,4 @@ if 'state' not in st.session_state:
 if st.button("发送", key="predict"):
     with st.spinner("AI正在思考，请稍等........"):
         # text generation
-        st.session_state["state"] = predict(prompt_text, max_length, top_p, temperature, st.session_state["state"])
\ No newline at end of file
+        st.session_state["state"] = predict(prompt_text, max_length, top_p, temperature, st.session_state["state"])