up

request_llm/bridge_all.py

@@ -398,6 +398,22 @@ if "spark" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
         })
     except:
         print(trimmed_format_exc())
+if "sparkv2" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
+    try:
+        from .bridge_spark import predict_no_ui_long_connection as spark_noui
+        from .bridge_spark import predict as spark_ui
+        model_info.update({
+            "sparkv2": {
+                "fn_with_ui": spark_ui,
+                "fn_without_ui": spark_noui,
+                "endpoint": None,
+                "max_token": 4096,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            }
+        })
+    except:
+        print(trimmed_format_exc())
 if "llama2" in AVAIL_LLM_MODELS:   # llama2
     try:
         from .bridge_llama2 import predict_no_ui_long_connection as llama2_noui

request_llm/bridge_chatglmft.py

@@ -63,9 +63,9 @@ class GetGLMFTHandle(Process):
                     # if not os.path.exists(conf): raise RuntimeError('找不到微调模型信息')
                     # with open(conf, 'r', encoding='utf8') as f:
                     #     model_args = json.loads(f.read())
-                    ChatGLM_PTUNING_CHECKPOINT, = get_conf('ChatGLM_PTUNING_CHECKPOINT')
-                    assert os.path.exists(ChatGLM_PTUNING_CHECKPOINT), "找不到微调模型检查点"
-                    conf = os.path.join(ChatGLM_PTUNING_CHECKPOINT, "config.json")
+                    CHATGLM_PTUNING_CHECKPOINT, = get_conf('CHATGLM_PTUNING_CHECKPOINT')
+                    assert os.path.exists(CHATGLM_PTUNING_CHECKPOINT), "找不到微调模型检查点"
+                    conf = os.path.join(CHATGLM_PTUNING_CHECKPOINT, "config.json")
                     with open(conf, 'r', encoding='utf8') as f:
                         model_args = json.loads(f.read())
                     if 'model_name_or_path' not in model_args:
@@ -78,9 +78,9 @@ class GetGLMFTHandle(Process):
                     config.pre_seq_len = model_args['pre_seq_len']
                     config.prefix_projection = model_args['prefix_projection']
 
-                    print(f"Loading prefix_encoder weight from {ChatGLM_PTUNING_CHECKPOINT}")
+                    print(f"Loading prefix_encoder weight from {CHATGLM_PTUNING_CHECKPOINT}")
                     model = AutoModel.from_pretrained(model_args['model_name_or_path'], config=config, trust_remote_code=True)
-                    prefix_state_dict = torch.load(os.path.join(ChatGLM_PTUNING_CHECKPOINT, "pytorch_model.bin"))
+                    prefix_state_dict = torch.load(os.path.join(CHATGLM_PTUNING_CHECKPOINT, "pytorch_model.bin"))
                     new_prefix_state_dict = {}
                     for k, v in prefix_state_dict.items():
                         if k.startswith("transformer.prefix_encoder."):

request_llm/bridge_chatgpt.py

@@ -137,6 +137,12 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     chatbot.append((inputs, ""))
     yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
 
+    # check mis-behavior
+    if raw_input.startswith('private_upload/') and len(raw_input) == 34:
+        chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需要点击“函数插件区”按钮进行处理，而不是点击“提交”按钮。")
+        yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
+        time.sleep(2)
+
     try:
         headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
     except RuntimeError as e:
@@ -178,7 +184,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                 return
             
             chunk_decoded = chunk.decode()
-            if is_head_of_the_stream and (r'"object":"error"' not in chunk_decoded) and (r"choices" not in chunk_decoded):
+            if is_head_of_the_stream and (r'"object":"error"' not in chunk_decoded) and (r"content" not in chunk_decoded):
                 # 数据流的第一帧不携带content
                 is_head_of_the_stream = False; continue
             

request_llm/bridge_qianfan.py

@@ -49,16 +49,17 @@ def get_access_token():
 
 def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
     conversation_cnt = len(history) // 2
+    if system_prompt == "": system_prompt = "Hello"
     messages = [{"role": "user", "content": system_prompt}]
     messages.append({"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_i_have_asked["content"] = history[index] if history[index]!="" else "Hello"
             what_gpt_answer = {}
             what_gpt_answer["role"] = "assistant"
-            what_gpt_answer["content"] = history[index+1]
+            what_gpt_answer["content"] = history[index+1] if history[index]!="" else "Hello"
             if what_i_have_asked["content"] != "":
                 if what_gpt_answer["content"] == "": continue
                 if what_gpt_answer["content"] == timeout_bot_msg: continue

request_llm/bridge_spark.py

@@ -2,11 +2,17 @@
 import time
 import threading
 import importlib
-from toolbox import update_ui, get_conf
+from toolbox import update_ui, get_conf, update_ui_lastest_msg
 from multiprocessing import Process, Pipe
 
 model_name = '星火认知大模型'
 
+def validate_key():
+    XFYUN_APPID,  = get_conf('XFYUN_APPID', )
+    if XFYUN_APPID == '00000000' or XFYUN_APPID == '': 
+        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
@@ -15,6 +21,9 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     watch_dog_patience = 5
     response = ""
 
+    if validate_key() is False:
+        raise RuntimeError('请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET')
+
     from .com_sparkapi import SparkRequestInstance
     sri = SparkRequestInstance()
     for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
@@ -30,6 +39,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         函数的说明请见 request_llm/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:
         from core_functional import handle_core_functionality

request_llm/com_sparkapi.py

@@ -58,11 +58,13 @@ class Ws_Param(object):
 class SparkRequestInstance():
     def __init__(self):
         XFYUN_APPID, XFYUN_API_SECRET, XFYUN_API_KEY = get_conf('XFYUN_APPID', 'XFYUN_API_SECRET', 'XFYUN_API_KEY')
-
+        if XFYUN_APPID == '00000000' or XFYUN_APPID == '': raise RuntimeError('请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET')
         self.appid = XFYUN_APPID
         self.api_secret = XFYUN_API_SECRET
         self.api_key = XFYUN_API_KEY
         self.gpt_url = "ws://spark-api.xf-yun.com/v1.1/chat"
+        self.gpt_url_v2 = "ws://spark-api.xf-yun.com/v2.1/chat"
+
         self.time_to_yield_event = threading.Event()
         self.time_to_exit_event = threading.Event()
 
@@ -83,7 +85,12 @@ class SparkRequestInstance():
 
 
     def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt):
-        wsParam = Ws_Param(self.appid, self.api_key, self.api_secret, self.gpt_url)
+        if llm_kwargs['llm_model'] == 'sparkv2':
+            gpt_url = self.gpt_url_v2
+        else:
+            gpt_url = self.gpt_url
+
+        wsParam = Ws_Param(self.appid, self.api_key, self.api_secret, gpt_url)
         websocket.enableTrace(False)
         wsUrl = wsParam.create_url()
 
@@ -167,7 +174,7 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
         },
         "parameter": {
             "chat": {
-                "domain": "general",
+                "domain": "generalv2" if llm_kwargs['llm_model'] == 'sparkv2' else "general",
                 "temperature": llm_kwargs["temperature"],
                 "random_threshold": 0.5,
                 "max_tokens": 4096,