接入讯飞星火Spark大模型

config.py

@@ -71,7 +71,7 @@ MAX_RETRY = 2
 # 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
 LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss", "newbing", "stack-claude"]
-# P.S. 其他可用的模型还包括 ["qwen", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+# P.S. 其他可用的模型还包括 ["qwen", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "spark", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
 
 
 # ChatGLM(2) Finetune Model Path （如果使用ChatGLM2微调模型，需要把"chatglmft"加入AVAIL_LLM_MODELS中）
@@ -137,6 +137,13 @@ ALIYUN_APPKEY=""    # 例如 RoPlZrM88DnAFkZK
 ALIYUN_ACCESSKEY="" # （无需填写）
 ALIYUN_SECRET=""    # （无需填写）
 
+
+# 接入讯飞星火大模型 https://console.xfyun.cn/services/iat
+XFYUN_APPID = "00000000"
+XFYUN_API_SECRET = "bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
+XFYUN_API_KEY = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
+
+
 # Claude API KEY
 ANTHROPIC_API_KEY = ""
 

request_llm/bridge_all.py

@@ -68,6 +68,10 @@ get_token_num_gpt35 = lambda txt: len(tokenizer_gpt35.encode(txt, disallowed_spe
 get_token_num_gpt4 = lambda txt: len(tokenizer_gpt4.encode(txt, disallowed_special=()))
 
 
+# 开始初始化模型
+AVAIL_LLM_MODELS, LLM_MODEL = get_conf("AVAIL_LLM_MODELS", "LLM_MODEL")
+AVAIL_LLM_MODELS = AVAIL_LLM_MODELS + [LLM_MODEL]
+# -=-=-=-=-=-=- 以下这部分是最早加入的最稳定的模型 -=-=-=-=-=-=-
 model_info = {
     # openai
     "gpt-3.5-turbo": {
@@ -164,9 +168,7 @@ model_info = {
 
 }
 
-
+# -=-=-=-=-=-=- 以下部分是新加入的模型，可能附带额外依赖 -=-=-=-=-=-=-
-AVAIL_LLM_MODELS, LLM_MODEL = get_conf("AVAIL_LLM_MODELS", "LLM_MODEL")
-AVAIL_LLM_MODELS = AVAIL_LLM_MODELS + [LLM_MODEL]
 if "claude-1-100k" in AVAIL_LLM_MODELS or "claude-2" in AVAIL_LLM_MODELS:
     from .bridge_claude import predict_no_ui_long_connection as claude_noui
     from .bridge_claude import predict as claude_ui
@@ -367,6 +369,24 @@ if "chatgpt_website" in AVAIL_LLM_MODELS:   # 接入一些逆向工程https://gi
         })
     except:
         print(trimmed_format_exc())
+if "spark" in AVAIL_LLM_MODELS:   # 接入一些逆向工程https://github.com/acheong08/ChatGPT-to-API/
+    try:
+        from .bridge_spark import predict_no_ui_long_connection as spark_noui
+        from .bridge_spark import predict as spark_ui
+        model_info.update({
+            "spark": {
+                "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())
+
+
 
 def LLM_CATCH_EXCEPTION(f):
     """

request_llm/bridge_spark.py

@@ -0,0 +1,49 @@
+
+import time
+import threading
+import importlib
+from toolbox import update_ui, get_conf
+from multiprocessing import Process, Pipe
+
+model_name = '星火认知大模型'
+
+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_llm/bridge_all.py
+    """
+    watch_dog_patience = 5
+    response = ""
+
+    from .com_sparkapi import SparkRequestInstance
+    sri = SparkRequestInstance()
+    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_llm/bridge_all.py
+    """
+    chatbot.append((inputs, ""))
+
+    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_sparkapi import SparkRequestInstance
+    sri = SparkRequestInstance()
+    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_llm/com_sparkapi.py

@@ -1,4 +1,4 @@
-import _thread as thread
+from toolbox import get_conf
 import base64
 import datetime
 import hashlib
@@ -10,8 +10,8 @@ from datetime import datetime
 from time import mktime
 from urllib.parse import urlencode
 from wsgiref.handlers import format_date_time
-
 import websocket
+import threading, time
 
 timeout_bot_msg = '[Local Message] Request timeout. Network error.'
 
@@ -37,13 +37,9 @@ class Ws_Param(object):
         signature_origin += "GET " + self.path + " HTTP/1.1"
 
         # 进行hmac-sha256进行加密
-        signature_sha = hmac.new(self.APISecret.encode('utf-8'), signature_origin.encode('utf-8'),
+        signature_sha = hmac.new(self.APISecret.encode('utf-8'), signature_origin.encode('utf-8'), digestmod=hashlib.sha256).digest()
-                                 digestmod=hashlib.sha256).digest()
-
         signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding='utf-8')
-
         authorization_origin = f'api_key="{self.APIKey}", algorithm="hmac-sha256", headers="host date request-line", signature="{signature_sha_base64}"'
-
         authorization = base64.b64encode(authorization_origin.encode('utf-8')).decode(encoding='utf-8')
 
         # 将请求的鉴权参数组合为字典
@@ -58,18 +54,84 @@ class Ws_Param(object):
         return url
 
 
-# 收到websocket错误的处理
+
-def on_error(ws, error):
+class SparkRequestInstance():
-    print("### error:", error)
+    def __init__(self):
+        XFYUN_APPID, XFYUN_API_SECRET, XFYUN_API_KEY = get_conf('XFYUN_APPID', 'XFYUN_API_SECRET', 'XFYUN_API_KEY')
+
+        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.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):
+        llm_kwargs = llm_kwargs
+        history = history
+        system_prompt = system_prompt
+        import _thread as thread
+        thread.start_new_thread(self.create_blocking_request, (inputs, llm_kwargs, history, system_prompt))
+        while True:
+            self.time_to_yield_event.wait(timeout=1)
+            if self.time_to_yield_event.is_set():
+                yield self.result_buf
+            if self.time_to_exit_event.is_set():
+                return self.result_buf
 
 
-# 收到websocket关闭的处理
+    def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt):
-def on_close(ws):
+        wsParam = Ws_Param(self.appid, self.api_key, self.api_secret, self.gpt_url)
-    print("### closed ###")
+        websocket.enableTrace(False)
+        wsUrl = wsParam.create_url()
 
+        # 收到websocket连接建立的处理
+        def on_open(ws):
+            import _thread as thread
+            thread.start_new_thread(run, (ws,))
 
+        def run(ws, *args):
+            data = json.dumps(gen_params(ws.appid, *ws.all_args))
+            ws.send(data)
 
-def generate_message_payload(inputs, llm_kwargs, history, system_prompt, stream):
+        # 收到websocket消息的处理
+        def on_message(ws, message):
+            data = json.loads(message)
+            code = data['header']['code']
+            if code != 0:
+                print(f'请求错误: {code}, {data}')
+                ws.close()
+                self.time_to_exit_event.set()
+            else:
+                choices = data["payload"]["choices"]
+                status = choices["status"]
+                content = choices["text"][0]["content"]
+                ws.content += content
+                self.result_buf += content
+                if status == 2:
+                    ws.close()
+                    self.time_to_exit_event.set()
+            self.time_to_yield_event.set()
+
+        # 收到websocket错误的处理
+        def on_error(ws, error):
+            print("error:", error)
+            self.time_to_exit_event.set()
+
+        # 收到websocket关闭的处理
+        def on_close(ws):
+            self.time_to_exit_event.set()
+
+        # websocket
+        ws = websocket.WebSocketApp(wsUrl, on_message=on_message, on_error=on_error, on_close=on_close, on_open=on_open)
+        ws.appid = self.appid
+        ws.content = ""
+        ws.all_args = (inputs, llm_kwargs, history, system_prompt)
+        ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE})
+
+def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
     conversation_cnt = len(history) // 2
     messages = [{"role": "system", "content": system_prompt}]
     if conversation_cnt:
@@ -94,7 +156,7 @@ def generate_message_payload(inputs, llm_kwargs, history, system_prompt, stream)
     return messages
 
 
-def gen_params(appid, inputs, llm_kwargs, history, system_prompt, stream):
+def gen_params(appid, inputs, llm_kwargs, history, system_prompt):
     """
     通过appid和用户的提问来生成请参数
     """
@@ -106,75 +168,17 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt, stream):
         "parameter": {
             "chat": {
                 "domain": "general",
+                "temperature": llm_kwargs["temperature"],
                 "random_threshold": 0.5,
-                "max_tokens": 2048,
+                "max_tokens": 4096,
                 "auditing": "default"
             }
         },
         "payload": {
             "message": {
-                "text": generate_message_payload(inputs, llm_kwargs, history, system_prompt, stream)
+                "text": generate_message_payload(inputs, llm_kwargs, history, system_prompt)
             }
         }
     }
     return data
 
-# 收到websocket消息的处理
-def on_message(ws, message):
-    print(message)
-    data = json.loads(message)
-    code = data['header']['code']
-    if code != 0:
-        print(f'请求错误: {code}, {data}')
-        ws.close()
-    else:
-        choices = data["payload"]["choices"]
-        status = choices["status"]
-        content = choices["text"][0]["content"]
-        ws.content += content
-        print(content, end='')
-        if status == 2:
-            ws.close()
-
-def commit_request(appid, api_key, api_secret, gpt_url, question):
-    inputs = question
-    llm_kwargs = {}
-    history = []
-    system_prompt = ""
-    stream = True
-
-    wsParam = Ws_Param(appid, api_key, api_secret, gpt_url)
-    websocket.enableTrace(False)
-    wsUrl = wsParam.create_url()
-
-    # 收到websocket连接建立的处理
-    def on_open(ws):
-        thread.start_new_thread(run, (ws,))
-
-    def run(ws, *args):
-        data = json.dumps(gen_params(ws.appid, *ws.all_args))
-        ws.send(data)
-
-    ws = websocket.WebSocketApp(wsUrl, on_message=on_message, on_error=on_error, on_close=on_close, on_open=on_open)
-    ws.appid = appid
-    ws.content = ""
-    
-    ws.all_args = (inputs, llm_kwargs, history, system_prompt, stream)
-    ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE})
-
-    return ws.content
-
-
-
-"""
-    1、配置好python、pip的环境变量
-    2、执行 pip install websocket 与 pip3 install websocket-client
-    3、去控制台https://console.xfyun.cn/services/cbm获取appid等信息填写即可
-"""
-if __name__ == "__main__":
-    # 测试时候在此处正确填写相关信息即可运行
-    commit_request(appid="929e7d53",
-         api_secret="NmFjNGE1ZDNhZWE2MzBkYzg5ZjNkZDcx",
-         api_key="896f9c5cf1b2b8669f523507f96e6c99",
-         gpt_url="ws://spark-api.xf-yun.com/v1.1/chat",
-         question="你是谁？你能做什么")

tests/test_llms.py

@@ -16,10 +16,11 @@ if __name__ == "__main__":
     # from request_llm.bridge_jittorllms_llama import predict_no_ui_long_connection
     # from request_llm.bridge_claude import predict_no_ui_long_connection
     # from request_llm.bridge_internlm import predict_no_ui_long_connection
-    from request_llm.bridge_qwen import predict_no_ui_long_connection
+    # from request_llm.bridge_qwen import predict_no_ui_long_connection
+    from request_llm.bridge_spark import predict_no_ui_long_connection
 
     llm_kwargs = {
-        'max_length': 512,
+        'max_length': 4096,
         'top_p': 1,
         'temperature': 1,
     }