chore: import upstream snapshot with attribution

2026-07-13 12:36:28 +08:00
commit 9d3590ab86
509 changed files with 2512422 additions and 0 deletions
@@ -0,0 +1,87 @@
+from typing import Optional, List, Literal, Union
+
+from pydantic import Field
+
+from open_chatcaht._constants import MAX_TOKENS, LLM_MODEL, TEMPERATURE, SCORE_THRESHOLD, VECTOR_SEARCH_TOP_K
+from open_chatcaht.api_client import ApiClient
+from open_chatcaht.types.chat.chat_feedback_param import ChatFeedbackParam
+from open_chatcaht.types.chat.chat_message import ChatMessage
+from open_chatcaht.types.chat.file_chat_param import FileChatParam
+from open_chatcaht.types.chat.kb_chat_param import KbChatParam
+
+API_URI_CHAT_FEEDBACK = "/chat/feedback"
+API_URI_FILE_CHAT = "/chat/file_chat"
+API_URI_KB_CHAT = "/chat/kb_chat"
+
+
+class ChatClient(ApiClient):
+
+    def chat_feedback(self,
+                      message_id: str,
+                      score: int = 100,
+                      reason: str = ""):
+        data = ChatFeedbackParam(
+            message_id=message_id,
+            score=score,
+            reason=reason,
+        ).dict()
+        resp = self._post(API_URI_CHAT_FEEDBACK, json=data)
+        return self._get_response_value(resp, as_json=True)
+
+    def kb_chat(self,
+                query: str,
+                mode: Literal["local_kb", "temp_kb", "search_engine"] = "local_kb",
+                kb_name: str = "",
+                top_k: int = VECTOR_SEARCH_TOP_K,
+                score_threshold: float = SCORE_THRESHOLD,
+                history: List[Union[ChatMessage, dict]] = [],
+                stream: bool = True,
+                model: str = LLM_MODEL,
+                temperature: float = TEMPERATURE,
+                max_tokens: Optional[int] = MAX_TOKENS,
+                prompt_name: str = "default",
+                return_direct: bool = False,
+                ):
+        kb_chat_param = KbChatParam(
+            query=query,
+            mode=mode,
+            kb_name=kb_name,
+            top_k=top_k,
+            score_threshold=score_threshold,
+            history=history,
+            stream=stream,
+            model=model,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            prompt_name=prompt_name,
+            return_direct=return_direct,
+        ).dict()
+        response = self._post(API_URI_KB_CHAT, json=kb_chat_param, stream=True)
+        return self._httpx_stream2generator(response, as_json=True)
+
+    def file_chat(self,
+                  query: str,
+                  knowledge_id: str,
+                  top_k: int = VECTOR_SEARCH_TOP_K,
+                  score_threshold: float = SCORE_THRESHOLD,
+                  history: List[Union[dict, ChatMessage]] = [],
+                  stream: bool = True,
+                  model_name: str = LLM_MODEL,
+                  temperature: float = 0.01,
+                  max_tokens: Optional[int] = MAX_TOKENS,
+                  prompt_name: str = "default",
+                  ):
+        file_chat_param = FileChatParam(
+            query=query,
+            knowledge_id=knowledge_id,
+            top_k=top_k,
+            score_threshold=score_threshold,
+            history=history,
+            stream=stream,
+            model_name=model_name,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            prompt_name=prompt_name,
+        ).dict()
+        response = self._post(API_URI_FILE_CHAT, json=file_chat_param, stream=True)
+        return self._httpx_stream2generator(response, as_json=True)