new branch

src/blackbox/fastchat.py

@@ -19,40 +19,68 @@ class Fastchat(Blackbox):
         return isinstance(data, list)
 
     # model_name有 Qwen1.5-14B-Chat , internlm2-chat-20b
-    def processing(self, model_name, prompt, template, context: list)  -> str:
+    def processing(self, model_name, prompt, template, context: list, temperature, top_p, top_k, n, max_tokens)  -> str:
         if context == None:
             context = []
         url = 'http://120.196.116.194:48892/v1/chat/completions'
 
-        # history可以为空列表，也可以是用户的对话历史
-        # history = [
+        # context可以为空列表，也可以是用户的对话历史
+        # context = [
         #     {
         #         "role": "user",
-        #         "content": "你吃饭了吗"
+        #         "content": "智能体核心思想"
         #     },
         #     {
         #         "role": "assistant", 
-        #         "content": "作为一个AI模型，我没有吃饭的需要，因为我并不具备实体形态。我专注于提供信息和帮助回答你的问题。你有什么需要帮助的吗？"
+        #         "content": "智能体的核心思想是将人工智能应用于问题求解者角色，它通过算法模拟人类决策过程，通过感知环境、学习、规划和执行行动，以实现特定任务或目标。其目标是通过自我适应和优化，实现高效问题解决。"
         #     },
         # ]
 
+        prompt_template = [
+           {"role": "system", "content": template},
+        ]
+
         fastchat_inputs={
             "model": model_name,
-            "messages": context + [
+            "messages": prompt_template + context + [
                 {
                     "role": "user",
-                    "content": template + prompt
+                    "content": prompt
                 }
-            ]
+            ],
+            "temperature": temperature,
+            "top_p": top_p,
+            "top_k": top_k,
+            "n": n,
+            "max_tokens": max_tokens,
+            "stream": False,
         }
 
+
+        # {
+        #     "model": "string",
+        #     "messages": "string",
+        #     "temperature": 0.7, #  between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
+        #     "top_p": 1, # 控制生成下一个单词的概率分布，即从所有可能的单词中，只选择概率最高的一部分作为候选单词
+        #     "top_k": -1, #  top-k 参数设置为 3意味着选择前三个tokens。
+        #     "n": 1, # How many chat completion choices to generate for each input message.
+        #     "max_tokens": 1024, # The maximum number of tokens to generate in the chat completion.
+        #     "stop": [
+        #         "string"
+        #     ],
+        #     "stream": False,
+        #     "presence_penalty": 0, # Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
+        #     "frequency_penalty": 0, # Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim.
+        #     "user": "string"
+        # }
+
         fastchat_response = requests.post(url, json=fastchat_inputs)
 
-        user_message = fastchat_inputs["messages"]
-        context.append(user_message)
+        # user_message = fastchat_inputs["messages"]
+        # context.append(user_message)
 
         assistant_message = fastchat_response.json()["choices"][0]["message"]
-        context.append(assistant_message)
+        # context.append(assistant_message)
 
         fastchat_content = assistant_message["content"]
 
@@ -68,17 +96,37 @@ class Fastchat(Blackbox):
         user_context = data.get("context")
         user_prompt = data.get("prompt")
         user_template = data.get("template")
+        user_temperature = data.get("temperature")
+        user_top_p = data.get("top_p")
+        user_top_k = data.get("top_k")
+        user_n = data.get("n")
+        user_max_tokens = data.get("max_tokens")
 
         if user_prompt is None:
             return JSONResponse(content={"error": "question is required"}, status_code=status.HTTP_400_BAD_REQUEST)
 
-        if user_model_name is None or user_model_name.isspace():
+        if user_model_name is None or user_model_name.isspace() or user_model_name == "":
             user_model_name = "Qwen1.5-14B-Chat"
 
         if user_template is None or user_template.isspace():
-            # user_template 是定义LLM的语气，例如template = "使用小丑的语气说话。"，user_template可以为空字串，或者是用户自定义的语气，或者是使用我们提供的语气
+            # user_template 是定义LLM的语气，例如template = "使用小丑的语气说话。"，user_template可以为空字串，或者是用户自定义的语气
             user_template = ""
-        else:
-            user_template = f"使用{user_template}的语气说话。"
+        
+        if user_temperature is None or user_temperature == "":
+            user_temperature = 0.7
 
-        return JSONResponse(content={"response": self.processing(user_model_name, user_prompt, user_template, user_context)}, status_code=status.HTTP_200_OK)
+        if user_top_p is None or user_top_p == "":
+            user_top_p = 1
+        
+        if user_top_k is None or user_top_k == "":
+            user_top_k = -1
+
+        if user_n is None or user_n == "":
+            user_n = 3
+        
+        if user_max_tokens is None or user_max_tokens == "":
+            user_max_tokens = 1024
+
+
+        return JSONResponse(content={"response": self.processing(user_model_name, user_prompt, user_template, user_context, 
+        user_temperature, user_top_p, user_top_k, user_n, user_max_tokens)}, status_code=status.HTTP_200_OK)