接入新模型

config.py

@@ -87,7 +87,7 @@ AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
                     "api2d-gpt-3.5-turbo", 'api2d-gpt-3.5-turbo-16k', 
                     "gpt-4", "gpt-4-32k", "azure-gpt-4", "api2d-gpt-4", 
                     "chatglm", "moss", "newbing", "claude-2"]
-# P.S. 其他可用的模型还包括 ["qianfan", "llama2", "qwen", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613",  "gpt-3.5-random"
+# P.S. 其他可用的模型还包括 ["zhipuai", "qianfan", "llama2", "qwen", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613",  "gpt-3.5-random"
 # "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"]
 
 
@@ -172,6 +172,11 @@ XFYUN_API_SECRET = "bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
 XFYUN_API_KEY = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
 
 
+# 接入智谱大模型
+ZHIPUAI_API_KEY = ""
+ZHIPUAI_MODEL = "chatglm_turbo"
+
+
 # Claude API KEY
 ANTHROPIC_API_KEY = ""
 

request_llms/bridge_all.py

@@ -483,6 +483,22 @@ if "llama2" in AVAIL_LLM_MODELS:   # llama2
         })
     except:
         print(trimmed_format_exc())
+if "zhipuai" in AVAIL_LLM_MODELS:   # zhipuai
+    try:
+        from .bridge_zhipu import predict_no_ui_long_connection as zhipu_noui
+        from .bridge_zhipu import predict as zhipu_ui
+        model_info.update({
+            "zhipuai": {
+                "fn_with_ui": zhipu_ui,
+                "fn_without_ui": zhipu_noui,
+                "endpoint": None,
+                "max_token": 4096,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            }
+        })
+    except:
+        print(trimmed_format_exc())
 
 # <-- 用于定义和切换多个azure模型 -->
 AZURE_CFG_ARRAY, = get_conf("AZURE_CFG_ARRAY")

request_llms/bridge_chatglm.py

@@ -155,13 +155,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         history_feedin.append([history[2*i], history[2*i+1]] )
 
     # 开始接收chatglm的回复
-    response = "[Local Message]: 等待ChatGLM响应中 ..."
+    response = "[Local Message] 等待ChatGLM响应中 ..."
     for response in glm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)
 
     # 总结输出
-    if response == "[Local Message]: 等待ChatGLM响应中 ...":
+    if response == "[Local Message] 等待ChatGLM响应中 ...":
-        response = "[Local Message]: ChatGLM响应异常 ..."
+        response = "[Local Message] ChatGLM响应异常 ..."
     history.extend([inputs, response])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_chatglmft.py

@@ -195,13 +195,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         history_feedin.append([history[2*i], history[2*i+1]] )
 
     # 开始接收chatglmft的回复
-    response = "[Local Message]: 等待ChatGLMFT响应中 ..."
+    response = "[Local Message] 等待ChatGLMFT响应中 ..."
     for response in glmft_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)
 
     # 总结输出
-    if response == "[Local Message]: 等待ChatGLMFT响应中 ...":
+    if response == "[Local Message] 等待ChatGLMFT响应中 ...":
-        response = "[Local Message]: ChatGLMFT响应异常 ..."
+        response = "[Local Message] ChatGLMFT响应异常 ..."
     history.extend([inputs, response])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_jittorllms_llama.py

@@ -163,13 +163,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         history_feedin.append([history[2*i], history[2*i+1]] )
 
     # 开始接收jittorllms的回复
-    response = "[Local Message]: 等待jittorllms响应中 ..."
+    response = "[Local Message] 等待jittorllms响应中 ..."
     for response in llama_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)
 
     # 总结输出
-    if response == "[Local Message]: 等待jittorllms响应中 ...":
+    if response == "[Local Message] 等待jittorllms响应中 ...":
-        response = "[Local Message]: jittorllms响应异常 ..."
+        response = "[Local Message] jittorllms响应异常 ..."
     history.extend([inputs, response])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_jittorllms_pangualpha.py

@@ -163,13 +163,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         history_feedin.append([history[2*i], history[2*i+1]] )
 
     # 开始接收jittorllms的回复
-    response = "[Local Message]: 等待jittorllms响应中 ..."
+    response = "[Local Message] 等待jittorllms响应中 ..."
     for response in pangu_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)
 
     # 总结输出
-    if response == "[Local Message]: 等待jittorllms响应中 ...":
+    if response == "[Local Message] 等待jittorllms响应中 ...":
-        response = "[Local Message]: jittorllms响应异常 ..."
+        response = "[Local Message] jittorllms响应异常 ..."
     history.extend([inputs, response])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_jittorllms_rwkv.py

@@ -163,13 +163,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         history_feedin.append([history[2*i], history[2*i+1]] )
 
     # 开始接收jittorllms的回复
-    response = "[Local Message]: 等待jittorllms响应中 ..."
+    response = "[Local Message] 等待jittorllms响应中 ..."
     for response in rwkv_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)
 
     # 总结输出
-    if response == "[Local Message]: 等待jittorllms响应中 ...":
+    if response == "[Local Message] 等待jittorllms响应中 ...":
-        response = "[Local Message]: jittorllms响应异常 ..."
+        response = "[Local Message] jittorllms响应异常 ..."
     history.extend([inputs, response])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_moss.py

@@ -219,7 +219,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             moss_handle = None
             return
     else:
-        response = "[Local Message]: 等待MOSS响应中 ..."
+        response = "[Local Message] 等待MOSS响应中 ..."
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)
 
@@ -238,7 +238,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         yield from update_ui(chatbot=chatbot, history=history)
 
     # 总结输出
-    if response == "[Local Message]: 等待MOSS响应中 ...":
+    if response == "[Local Message] 等待MOSS响应中 ...":
-        response = "[Local Message]: MOSS响应异常 ..."
+        response = "[Local Message] MOSS响应异常 ..."
     history.extend([inputs, response.strip('<|MOSS|>: ')])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_newbingfree.py

@@ -199,7 +199,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 
     watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
     response = ""
-    if len(observe_window) >= 1: observe_window[0] = "[Local Message]: 等待NewBing响应中 ..."
+    if len(observe_window) >= 1: observe_window[0] = "[Local Message] 等待NewBing响应中 ..."
     for response in newbingfree_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         if len(observe_window) >= 1:  observe_window[0] = preprocess_newbing_out_simple(response)
         if len(observe_window) >= 2:  
@@ -212,7 +212,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         单线程方法
         函数的说明请见 request_llms/bridge_all.py
     """
-    chatbot.append((inputs, "[Local Message]: 等待NewBing响应中 ..."))
+    chatbot.append((inputs, "[Local Message] 等待NewBing响应中 ..."))
 
     global newbingfree_handle
     if (newbingfree_handle is None) or (not newbingfree_handle.success):
@@ -231,13 +231,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     for i in range(len(history)//2):
         history_feedin.append([history[2*i], history[2*i+1]] )
 
-    chatbot[-1] = (inputs, "[Local Message]: 等待NewBing响应中 ...")
+    chatbot[-1] = (inputs, "[Local Message] 等待NewBing响应中 ...")
-    response = "[Local Message]: 等待NewBing响应中 ..."
+    response = "[Local Message] 等待NewBing响应中 ..."
     yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
     for response in newbingfree_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         chatbot[-1] = (inputs, preprocess_newbing_out(response))
         yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
-    if response == "[Local Message]: 等待NewBing响应中 ...": response = "[Local Message]: NewBing响应异常，请刷新界面重试 ..."
+    if response == "[Local Message] 等待NewBing响应中 ...": response = "[Local Message] NewBing响应异常，请刷新界面重试 ..."
     history.extend([inputs, response])
     logging.info(f'[raw_input] {inputs}')
     logging.info(f'[response] {response}')

request_llms/bridge_qianfan.py

@@ -158,8 +158,8 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         return
     
     # 总结输出
-    response = f"[Local Message]: {model_name}响应异常 ..."
+    response = f"[Local Message] {model_name}响应异常 ..."
-    if response == f"[Local Message]: 等待{model_name}响应中 ...":
+    if response == f"[Local Message] 等待{model_name}响应中 ...":
-        response = f"[Local Message]: {model_name}响应异常 ..."
+        response = f"[Local Message] {model_name}响应异常 ..."
     history.extend([inputs, response])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_spark.py

@@ -42,7 +42,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     yield from update_ui(chatbot=chatbot, history=history)
 
     if validate_key() is False:
-        yield from update_ui_lastest_msg(lastmsg="[Local Message]: 请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET", chatbot=chatbot, history=history, delay=0)
+        yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET", chatbot=chatbot, history=history, delay=0)
         return
 
     if additional_fn is not None:
@@ -57,7 +57,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         yield from update_ui(chatbot=chatbot, history=history)
 
     # 总结输出
-    if response == f"[Local Message]: 等待{model_name}响应中 ...":
+    if response == f"[Local Message] 等待{model_name}响应中 ...":
-        response = f"[Local Message]: {model_name}响应异常 ..."
+        response = f"[Local Message] {model_name}响应异常 ..."
     history.extend([inputs, response])
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_stackclaude.py

@@ -222,7 +222,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 
     watch_dog_patience = 5  # 看门狗 (watchdog) 的耐心, 设置5秒即可
     response = ""
-    observe_window[0] = "[Local Message]: 等待Claude响应中 ..."
+    observe_window[0] = "[Local Message] 等待Claude响应中 ..."
     for response in claude_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
         observe_window[0] = preprocess_newbing_out_simple(response)
         if len(observe_window) >= 2:
@@ -236,7 +236,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         单线程方法
         函数的说明请见 request_llms/bridge_all.py
     """
-    chatbot.append((inputs, "[Local Message]: 等待Claude响应中 ..."))
+    chatbot.append((inputs, "[Local Message] 等待Claude响应中 ..."))
 
     global claude_handle
     if (claude_handle is None) or (not claude_handle.success):
@@ -255,14 +255,14 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     for i in range(len(history)//2):
         history_feedin.append([history[2*i], history[2*i+1]])
 
-    chatbot[-1] = (inputs, "[Local Message]: 等待Claude响应中 ...")
+    chatbot[-1] = (inputs, "[Local Message] 等待Claude响应中 ...")
-    response = "[Local Message]: 等待Claude响应中 ..."
+    response = "[Local Message] 等待Claude响应中 ..."
     yield from update_ui(chatbot=chatbot, history=history, msg="Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
     for response in claude_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt):
         chatbot[-1] = (inputs, preprocess_newbing_out(response))
         yield from update_ui(chatbot=chatbot, history=history, msg="Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
-    if response == "[Local Message]: 等待Claude响应中 ...":
+    if response == "[Local Message] 等待Claude响应中 ...":
-        response = "[Local Message]: Claude响应异常，请刷新界面重试 ..."
+        response = "[Local Message] Claude响应异常，请刷新界面重试 ..."
     history.extend([inputs, response])
     logging.info(f'[raw_input] {inputs}')
     logging.info(f'[response] {response}')

request_llms/bridge_zhipu.py

@@ -0,0 +1,59 @@
+
+import time
+from toolbox import update_ui, get_conf, update_ui_lastest_msg
+
+model_name = '智谱AI大模型'
+
+def validate_key():
+    ZHIPUAI_API_KEY, = get_conf("ZHIPUAI_API_KEY")
+    if ZHIPUAI_API_KEY == '': return False
+    return True
+
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+    """
+        ⭐多线程方法
+        函数的说明请见 request_llms/bridge_all.py
+    """
+    watch_dog_patience = 5
+    response = ""
+
+    if validate_key() is False:
+        raise RuntimeError('请配置ZHIPUAI_API_KEY')
+
+    from .com_zhipuapi import ZhipuRequestInstance
+    sri = ZhipuRequestInstance()
+    for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
+        if len(observe_window) >= 1:
+            observe_window[0] = response
+        if len(observe_window) >= 2:
+            if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
+    return response
+
+def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
+    """
+        ⭐单线程方法
+        函数的说明请见 request_llms/bridge_all.py
+    """
+    chatbot.append((inputs, ""))
+    yield from update_ui(chatbot=chatbot, history=history)
+
+    if validate_key() is False:
+        yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置ZHIPUAI_API_KEY", chatbot=chatbot, history=history, delay=0)
+        return
+
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+
+    # 开始接收回复    
+    from .com_zhipuapi import ZhipuRequestInstance
+    sri = ZhipuRequestInstance()
+    for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
+        chatbot[-1] = (inputs, response)
+        yield from update_ui(chatbot=chatbot, history=history)
+
+    # 总结输出
+    if response == f"[Local Message] 等待{model_name}响应中 ...":
+        response = f"[Local Message] {model_name}响应异常 ..."
+    history.extend([inputs, response])
+    yield from update_ui(chatbot=chatbot, history=history)

request_llms/com_zhipuapi.py

@@ -0,0 +1,67 @@
+from toolbox import get_conf
+import threading
+import logging
+
+timeout_bot_msg = '[Local Message] Request timeout. Network error.'
+
+class ZhipuRequestInstance():
+    def __init__(self):
+
+        self.time_to_yield_event = threading.Event()
+        self.time_to_exit_event = threading.Event()
+
+        self.result_buf = ""
+
+    def generate(self, inputs, llm_kwargs, history, system_prompt):
+        # import _thread as thread
+        import zhipuai
+        ZHIPUAI_API_KEY, ZHIPUAI_MODEL = get_conf("ZHIPUAI_API_KEY", "ZHIPUAI_MODEL")
+        zhipuai.api_key = ZHIPUAI_API_KEY
+        self.result_buf = ""
+        response = zhipuai.model_api.sse_invoke(
+            model=ZHIPUAI_MODEL,
+            prompt=generate_message_payload(inputs, llm_kwargs, history, system_prompt),
+            top_p=llm_kwargs['top_p'],
+            temperature=llm_kwargs['temperature'],
+        )
+        for event in response.events():
+            if event.event == "add":
+                self.result_buf += event.data
+                yield self.result_buf
+            elif event.event == "error" or event.event == "interrupted":
+                raise RuntimeError("Unknown error:" + event.data)
+            elif event.event == "finish":
+                yield self.result_buf
+                break
+            else:
+                raise RuntimeError("Unknown error:" + str(event))
+            
+        logging.info(f'[raw_input] {inputs}')
+        logging.info(f'[response] {self.result_buf}')
+        return self.result_buf
+
+def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
+    conversation_cnt = len(history) // 2
+    messages = [{"role": "user", "content": system_prompt}, {"role": "assistant", "content": "Certainly!"}]
+    if conversation_cnt:
+        for index in range(0, 2*conversation_cnt, 2):
+            what_i_have_asked = {}
+            what_i_have_asked["role"] = "user"
+            what_i_have_asked["content"] = history[index]
+            what_gpt_answer = {}
+            what_gpt_answer["role"] = "assistant"
+            what_gpt_answer["content"] = history[index+1]
+            if what_i_have_asked["content"] != "":
+                if what_gpt_answer["content"] == "":
+                    continue
+                if what_gpt_answer["content"] == timeout_bot_msg:
+                    continue
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+            else:
+                messages[-1]['content'] = what_gpt_answer['content']
+    what_i_ask_now = {}
+    what_i_ask_now["role"] = "user"
+    what_i_ask_now["content"] = inputs
+    messages.append(what_i_ask_now)
+    return messages

request_llms/local_llm_class.py

@@ -166,14 +166,14 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name):
             history_feedin.append([history[2*i], history[2*i+1]] )
 
         # 开始接收回复
-        response = f"[Local Message]: 等待{model_name}响应中 ..."
+        response = f"[Local Message] 等待{model_name}响应中 ..."
         for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
             chatbot[-1] = (inputs, response)
             yield from update_ui(chatbot=chatbot, history=history)
 
         # 总结输出
-        if response == f"[Local Message]: 等待{model_name}响应中 ...":
+        if response == f"[Local Message] 等待{model_name}响应中 ...":
-            response = f"[Local Message]: {model_name}响应异常 ..."
+            response = f"[Local Message] {model_name}响应异常 ..."
         history.extend([inputs, response])
         yield from update_ui(chatbot=chatbot, history=history)
 

tests/test_llms.py

@@ -17,7 +17,8 @@ if __name__ == "__main__":
     # from request_llms.bridge_claude import predict_no_ui_long_connection
     # from request_llms.bridge_internlm import predict_no_ui_long_connection
     # from request_llms.bridge_qwen import predict_no_ui_long_connection
-    from request_llms.bridge_spark import predict_no_ui_long_connection
+    # from request_llms.bridge_spark import predict_no_ui_long_connection
+    from request_llms.bridge_zhipu import predict_no_ui_long_connection
 
     llm_kwargs = {
         'max_length': 4096,

tests/test_markdown.py

@@ -0,0 +1,44 @@
+md = """
+作为您的写作和编程助手，我可以为您提供以下服务：
+
+1. 写作：
+    - 帮助您撰写文章、报告、散文、故事等。
+    - 提供写作建议和技巧。
+    - 协助您进行文案策划和内容创作。
+
+2. 编程：
+    - 帮助您解决编程问题，提供编程思路和建议。
+    - 协助您编写代码，包括但不限于 Python、Java、C++ 等。
+    - 为您解释复杂的技术概念，让您更容易理解。
+
+3. 项目支持：
+    - 协助您规划项目进度和任务分配。
+    - 提供项目管理和协作建议。
+    - 在项目实施过程中提供支持，确保项目顺利进行。
+
+4. 学习辅导：
+    - 帮助您巩固编程基础，提高编程能力。
+    - 提供计算机科学、数据科学、人工智能等相关领域的学习资源和建议。
+    - 解答您在学习过程中遇到的问题，让您更好地掌握知识。
+
+5. 行业动态和趋势分析：
+    - 为您提供业界最新的新闻和技术趋势。
+    - 分析行业动态，帮助您了解市场发展和竞争态势。
+    - 为您制定技术战略提供参考和建议。
+
+请随时告诉我您的需求，我会尽力提供帮助。如果您有任何问题或需要解答的议题，请随时提问。
+"""
+
+def validate_path():
+    import os, sys
+    dir_name = os.path.dirname(__file__)
+    root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
+    os.chdir(root_dir_assume)
+    sys.path.append(root_dir_assume)
+validate_path() # validate path so you can run from base directory
+from toolbox import markdown_convertion
+
+html = markdown_convertion(md)
+print(html)
+with open('test.html', 'w', encoding='utf-8') as f:
+    f.write(html)

toolbox.py

@@ -7,6 +7,7 @@ import os
 import gradio
 import shutil
 import glob
+import math
 from latex2mathml.converter import convert as tex2mathml
 from functools import wraps, lru_cache
 pj = os.path.join
@@ -372,6 +373,26 @@ def markdown_convertion(txt):
                 contain_any_eq = True
         return contain_any_eq
 
+    def fix_markdown_indent(txt):
+        # fix markdown indent
+        if (' - ' not in txt) or ('. ' not in txt): 
+            return txt # do not need to fix, fast escape
+        # walk through the lines and fix non-standard indentation
+        lines = txt.split("\n")
+        pattern = re.compile(r'^\s+-')
+        activated = False
+        for i, line in enumerate(lines):
+            if line.startswith('- ') or line.startswith('1. '):
+                activated = True
+            if activated and pattern.match(line):
+                stripped_string = line.lstrip()
+                num_spaces = len(line) - len(stripped_string)
+                if (num_spaces % 4) == 3:
+                    num_spaces_should_be = math.ceil(num_spaces/4) * 4
+                    lines[i] = ' ' * num_spaces_should_be + stripped_string
+        return '\n'.join(lines)
+
+    txt = fix_markdown_indent(txt)
     if is_equation(txt):  # 有$标识的公式符号，且没有代码段```的标识
         # convert everything to html format
         split = markdown.markdown(text='---')