Revert "Refa: Chats /chat API to RESTFul (#13871)" (#13877)

### What problem does this PR solve?

This reverts commit 1a608ac411.

### Type of change

- [x] Other (please describe):

sdk/python/ragflow_sdk/modules/base.py

@@ -54,9 +54,5 @@ class Base:
         res = self.rag.put(path, json)
         return res
 
-    def patch(self, path, json):
-        res = self.rag.patch(path, json)
-        return res
-
     def __str__(self):
         return str(self.to_json())

sdk/python/ragflow_sdk/modules/chat.py

@@ -23,22 +23,50 @@ class Chat(Base):
     def __init__(self, rag, res_dict):
         self.id = ""
         self.name = "assistant"
-        self.icon = ""
-        self.dataset_ids = []
-        self.llm_id = None
-        self.llm_setting = {}
-        self.prompt_config = {}
-        self.similarity_threshold = 0.2
-        self.vector_similarity_weight = 0.3
-        self.top_n = 6
-        self.top_k = 1024
-        self.rerank_id = ""
+        self.avatar = "path/to/avatar"
+        self.llm = Chat.LLM(rag, {})
+        self.prompt = Chat.Prompt(rag, {})
         super().__init__(rag, res_dict)
 
+    class LLM(Base):
+        def __init__(self, rag, res_dict):
+            self.model_name = None
+            self.temperature = 0.1
+            self.top_p = 0.3
+            self.presence_penalty = 0.4
+            self.frequency_penalty = 0.7
+            self.max_tokens = 512
+            super().__init__(rag, res_dict)
+
+    class Prompt(Base):
+        def __init__(self, rag, res_dict):
+            self.similarity_threshold = 0.2
+            self.keywords_similarity_weight = 0.7
+            self.top_n = 8
+            self.top_k = 1024
+            self.variables = [{"key": "knowledge", "optional": True}]
+            self.rerank_model = ""
+            self.empty_response = None
+            self.opener = "Hi! I'm your assistant. What can I do for you?"
+            self.show_quote = True
+            self.prompt = (
+                "You are an intelligent assistant. Your primary function is to answer questions based strictly on the provided knowledge base."
+                "**Essential Rules:**"
+                "- Your answer must be derived **solely** from this knowledge base: `{knowledge}`."
+                "- **When information is available**: Summarize the content to give a detailed answer."
+                "- **When information is unavailable**: Your response must contain this exact sentence: 'The answer you are looking for is not found in the knowledge base!' "
+                "- **Always consider** the entire conversation history."
+            )
+            super().__init__(rag, res_dict)
+
     def update(self, update_message: dict):
         if not isinstance(update_message, dict):
             raise Exception("ValueError('`update_message` must be a dict')")
-        res = self.patch(f"/chats/{self.id}", update_message)
+        if update_message.get("llm") == {}:
+            raise Exception("ValueError('`llm` cannot be empty')")
+        if update_message.get("prompt") == {}:
+            raise Exception("ValueError('`prompt` cannot be empty')")
+        res = self.put(f"/chats/{self.id}", update_message)
         res = res.json()
         if res.get("code") != 0:
             raise Exception(res["message"])

sdk/python/ragflow_sdk/ragflow.py

@@ -49,10 +49,6 @@ class RAGFlow:
         res = requests.put(url=self.api_url + path, json=json, headers=self.authorization_header)
         return res
 
-    def patch(self, path, json):
-        res = requests.patch(url=self.api_url + path, json=json, headers=self.authorization_header)
-        return res
-
     def create_dataset(
         self,
         name: str,
@@ -115,25 +111,55 @@ class RAGFlow:
             return result_list
         raise Exception(res["message"])
 
-    def create_chat(
-        self,
-        name: str,
-        icon: str = "",
-        dataset_ids: list[str] | None = None,
-        llm_id: str | None = None,
-        llm_setting: dict | None = None,
-        prompt_config: dict | None = None,
-        **kwargs,
-    ) -> Chat:
-        payload = {"name": name, "icon": icon, "dataset_ids": dataset_ids or []}
-        if llm_id is not None:
-            payload["llm_id"] = llm_id
-        if llm_setting is not None:
-            payload["llm_setting"] = llm_setting
-        if prompt_config is not None:
-            payload["prompt_config"] = prompt_config
-        payload.update(kwargs)
-        res = self.post("/chats", payload)
+    def create_chat(self, name: str, avatar: str = "", dataset_ids=None, llm: Chat.LLM | None = None, prompt: Chat.Prompt | None = None) -> Chat:
+        if dataset_ids is None:
+            dataset_ids = []
+        dataset_list = []
+        for id in dataset_ids:
+            dataset_list.append(id)
+
+        if llm is None:
+            llm = Chat.LLM(
+                self,
+                {
+                    "model_name": None,
+                    "temperature": 0.1,
+                    "top_p": 0.3,
+                    "presence_penalty": 0.4,
+                    "frequency_penalty": 0.7,
+                    "max_tokens": 512,
+                },
+            )
+        if prompt is None:
+            prompt = Chat.Prompt(
+                self,
+                {
+                    "similarity_threshold": 0.2,
+                    "keywords_similarity_weight": 0.7,
+                    "top_n": 8,
+                    "top_k": 1024,
+                    "variables": [{"key": "knowledge", "optional": True}],
+                    "rerank_model": "",
+                    "empty_response": None,
+                    "opener": None,
+                    "show_quote": True,
+                    "prompt": None,
+                },
+            )
+            if prompt.opener is None:
+                prompt.opener = "Hi! I'm your assistant. What can I do for you?"
+            if prompt.prompt is None:
+                prompt.prompt = (
+                    "You are an intelligent assistant. Your primary function is to answer questions based strictly on the provided knowledge base."
+                    "**Essential Rules:**"
+                    "- Your answer must be derived **solely** from this knowledge base: `{knowledge}`."
+                    "- **When information is available**: Summarize the content to give a detailed answer."
+                    "- **When information is unavailable**: Your response must contain this exact sentence: 'The answer you are looking for is not found in the knowledge base!' "
+                    "- **Always consider** the entire conversation history."
+                )
+
+        temp_dict = {"name": name, "avatar": avatar, "dataset_ids": dataset_list if dataset_list else [], "llm": llm.to_json(), "prompt": prompt.to_json()}
+        res = self.post("/chats", temp_dict)
         res = res.json()
         if res.get("code") == 0:
             return Chat(self, res["data"])
@@ -145,24 +171,7 @@ class RAGFlow:
         if res.get("code") != 0:
             raise Exception(res["message"])
 
-    def get_chat(self, chat_id: str) -> Chat:
-        res = self.get(f"/chats/{chat_id}")
-        res = res.json()
-        if res.get("code") == 0:
-            return Chat(self, res["data"])
-        raise Exception(res["message"])
-
-    def list_chats(
-        self,
-        page: int = 1,
-        page_size: int = 30,
-        orderby: str = "create_time",
-        desc: bool = True,
-        id: str | None = None,
-        name: str | None = None,
-        keywords: str | None = None,
-        owner_ids: str | list[str] | None = None,
-    ) -> list[Chat]:
+    def list_chats(self, page: int = 1, page_size: int = 30, orderby: str = "create_time", desc: bool = True, id: str | None = None, name: str | None = None) -> list[Chat]:
         res = self.get(
             "/chats",
             {
@@ -172,14 +181,12 @@ class RAGFlow:
                 "desc": desc,
                 "id": id,
                 "name": name,
-                "keywords": keywords,
-                "owner_ids": owner_ids,
             },
         )
         res = res.json()
         result_list = []
         if res.get("code") == 0:
-            for data in res["data"]["chats"]:
+            for data in res["data"]:
                 result_list.append(Chat(self, data))
             return result_list
         raise Exception(res["message"])