bug修复和提示词修改 (#2230)

* 更新Langchain依赖到0.0.343以上版本

* 更改了一些提示词和依赖，修改了openai异常的问题

* 注释data的打印日志

configs/prompt_config.py.example

@@ -1,7 +1,6 @@
 # prompt模板使用Jinja2语法，简单点就是用双大括号代替f-string的单大括号
 # 本配置文件支持热加载，修改prompt模板后无需重启服务。
 
-
 # LLM对话支持的变量：
 #   - input: 用户输入内容
 
@@ -76,9 +75,6 @@ Answer the following questions as best you can. If it is in order, you can use s
 
 {tools}
 
-Please note that the "知识库查询工具" is information about the "西交利物浦大学" ,and if a question is asked about it, you must answer with the knowledge base，
-Please note that the "天气查询工具" can only be used once since Question begin.
-
 Use the following format:
 Question: the input question you must answer1
 Thought: you should always think about what to do and what tools to use.

requirements.txt

@@ -1,6 +1,6 @@
 # API requirements
 
-langchain>=0.0.342
+langchain>=0.0.343
 langchain-experimental>=0.0.42
 fschat[model_worker]>=0.2.33
 xformers>=0.0.22.post7

requirements_api.txt

@@ -1,6 +1,6 @@
 # API requirements
 
-langchain>=0.0.342
+langchain>=0.0.343
 langchain-experimental>=0.0.42
 fschat[model_worker]>=0.2.33
 xformers>=0.0.22.post7

requirements_lite.txt

@@ -1,4 +1,4 @@
-langchain>=0.0.342
+langchain>=0.0.343
 fschat>=0.2.33
 openai>=1.3.5
 # sentence_transformers

server/utils.py

@@ -40,11 +40,9 @@ def get_ChatOpenAI(
         verbose: bool = True,
         **kwargs: Any,
 ) -> ChatOpenAI:
-    ## 以下模型是Langchain原生支持的模型，这些模型不会走Fschat封装
-    config_models = list_config_llm_models()
-
-    ## 非Langchain原生支持的模型，走Fschat封装
     config = get_model_worker_config(model_name)
+    if config.get("openai-api"):
+        model_name = config.get("model_name")
     model = ChatOpenAI(
         streaming=streaming,
         verbose=verbose,
@@ -57,10 +55,7 @@ def get_ChatOpenAI(
         openai_proxy=config.get("openai_proxy"),
         **kwargs
     )
-
     return model
-
-
 def get_OpenAI(
         model_name: str,
         temperature: float,
@@ -71,67 +66,22 @@ def get_OpenAI(
         verbose: bool = True,
         **kwargs: Any,
 ) -> OpenAI:
-    ## 以下模型是Langchain原生支持的模型，这些模型不会走Fschat封装
+    config = get_model_worker_config(model_name)
-    config_models = list_config_llm_models()
+    if config.get("openai-api"):
-    if model_name in config_models.get("langchain", {}):
+        model_name = config.get("model_name")
-        config = config_models["langchain"][model_name]
+    model = OpenAI(
-        if model_name == "Azure-OpenAI":
+        streaming=streaming,
-            model = AzureOpenAI(
+        verbose=verbose,
-                streaming=streaming,
+        callbacks=callbacks,
-                verbose=verbose,
+        openai_api_key=config.get("api_key", "EMPTY"),
-                callbacks=callbacks,
+        openai_api_base=config.get("api_base_url", fschat_openai_api_address()),
-                deployment_name=config.get("deployment_name"),
+        model_name=model_name,
-                model_version=config.get("model_version"),
+        temperature=temperature,
-                openai_api_type=config.get("openai_api_type"),
+        max_tokens=max_tokens,
-                openai_api_base=config.get("api_base_url"),
+        openai_proxy=config.get("openai_proxy"),
-                openai_api_version=config.get("api_version"),
+        echo=echo,
-                openai_api_key=config.get("api_key"),
+        **kwargs
-                openai_proxy=config.get("openai_proxy"),
+    )
-                temperature=temperature,
-                max_tokens=max_tokens,
-                echo=echo,
-            )
-
-        elif model_name == "OpenAI":
-            model = OpenAI(
-                streaming=streaming,
-                verbose=verbose,
-                callbacks=callbacks,
-                model_name=config.get("model_name"),
-                openai_api_base=config.get("api_base_url"),
-                openai_api_key=config.get("api_key"),
-                openai_proxy=config.get("openai_proxy"),
-                temperature=temperature,
-                max_tokens=max_tokens,
-                echo=echo,
-            )
-        elif model_name == "Anthropic":
-            model = Anthropic(
-                streaming=streaming,
-                verbose=verbose,
-                callbacks=callbacks,
-                model_name=config.get("model_name"),
-                anthropic_api_key=config.get("api_key"),
-                echo=echo,
-            )
-    ## TODO 支持其他的Langchain原生支持的模型
-    else:
-        ## 非Langchain原生支持的模型，走Fschat封装
-        config = get_model_worker_config(model_name)
-        model = OpenAI(
-            streaming=streaming,
-            verbose=verbose,
-            callbacks=callbacks,
-            openai_api_key=config.get("api_key", "EMPTY"),
-            openai_api_base=config.get("api_base_url", fschat_openai_api_address()),
-            model_name=model_name,
-            temperature=temperature,
-            max_tokens=max_tokens,
-            openai_proxy=config.get("openai_proxy"),
-            echo=echo,
-            **kwargs
-        )
-
     return model
 
 
@@ -630,7 +580,7 @@ def get_httpx_client(
     for host in os.environ.get("no_proxy", "").split(","):
         if host := host.strip():
             # default_proxies.update({host: None}) # Origin code
-            default_proxies.update({'all://' + host: None}) # PR 1838 fix, if not add 'all://', httpx will raise error
+            default_proxies.update({'all://' + host: None})  # PR 1838 fix, if not add 'all://', httpx will raise error
 
     # merge default proxies with user provided proxies
     if isinstance(proxies, str):
@@ -714,7 +664,7 @@ def get_temp_dir(id: str = None) -> Tuple[str, str]:
     from configs.basic_config import BASE_TEMP_DIR
     import tempfile
 
-    if id is not None: # 如果指定的临时目录已存在，直接返回
+    if id is not None:  # 如果指定的临时目录已存在，直接返回
         path = os.path.join(BASE_TEMP_DIR, id)
         if os.path.isdir(path):
             return path, id

webui_pages/utils.py

@@ -276,8 +276,8 @@ class ApiRequest:
             "max_tokens": max_tokens,
         }
 
-        print(f"received input message:")
+        # print(f"received input message:")
-        pprint(data)
+        # pprint(data)
 
         response = self.post(
             "/chat/fastchat",
@@ -288,16 +288,16 @@ class ApiRequest:
         return self._httpx_stream2generator(response)
 
     def chat_chat(
-        self,
+            self,
-        query: str,
+            query: str,
-        conversation_id: str = None,
+            conversation_id: str = None,
-        history: List[Dict] = [],
+            history: List[Dict] = [],
-        stream: bool = True,
+            stream: bool = True,
-        model: str = LLM_MODELS[0],
+            model: str = LLM_MODELS[0],
-        temperature: float = TEMPERATURE,
+            temperature: float = TEMPERATURE,
-        max_tokens: int = None,
+            max_tokens: int = None,
-        prompt_name: str = "default",
+            prompt_name: str = "default",
-        **kwargs,
+            **kwargs,
     ):
         '''
         对应api.py/chat/chat接口 #TODO: 考虑是否返回json