feat: stabilize rag chat conversations and auto title sync

fastapi_modules/fastapi_leaudit/controllers/ragChatController.py

@@ -15,6 +15,7 @@ from fastapi_modules.fastapi_leaudit.domian.Dto.ragChatDto import (
     RagConversationRenameDTO,
     RagChatSendMessageDTO,
     RagMessageFeedbackDTO,
+    RagStopMessageDTO,
 )
 from fastapi_modules.fastapi_leaudit.domian.Dto.ragDatasetDto import (
     RagDatasetBatchDocumentDeleteDTO,
@@ -479,6 +480,17 @@ class RagChatController(BaseController):
                 headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no", "Connection": "keep-alive"},
             )
 
+        @self.router.post("/chat/messages/{MessageId}/stop", response_model=Result[RagOperationResultVO])
+        async def StopMessage(
+            MessageId: str,
+            Body: RagStopMessageDTO | None = None,
+            payload: dict[str, Any] = Depends(verify_access_token),
+        ):
+            if not await self._check_permission(int(payload["user_id"]), [self._PERMISSIONS["chat_use"]]):
+                return JSONResponse(status_code=403, content={"code": 403, "msg": "当前用户没有停止 RAG 对话权限", "data": None})
+            data = await self.RagChatService.StopMessage(int(payload["user_id"]), MessageId, Body)
+            return Result.success(data=data)
+
         @self.router.get("/chat/conversations", response_model=Result[RagConversationPageVO])
         async def GetConversations(
             appId: int | None = Query(None, description="聊天应用ID"),

fastapi_modules/fastapi_leaudit/domian/Dto/ragChatDto.py

@@ -13,3 +13,7 @@ class RagConversationRenameDTO(BaseModel):
 
 class RagMessageFeedbackDTO(BaseModel):
     rating: str | None = Field(None, description="反馈: like/dislike/None")
+
+
+class RagStopMessageDTO(BaseModel):
+    taskId: str | None = Field(None, description="流式任务ID")

fastapi_modules/fastapi_leaudit/domian/vo/ragChatVo.py

@@ -17,8 +17,10 @@ class RagConversationItemVO(BaseModel):
     id: str = Field(..., description="会话ID")
     name: str = Field(..., description="会话名称")
     introduction: str = Field("", description="会话简介")
+    titleSource: str = Field("default", description="标题来源: default/auto/manual")
     createdAt: int = Field(0, description="创建时间戳")
     updatedAt: int = Field(0, description="更新时间戳")
+    lastMessageAt: int = Field(0, description="最后一条消息完成时间戳")
 
 
 class RagConversationPageVO(BaseModel):
@@ -34,6 +36,8 @@ class RagMessageItemVO(BaseModel):
     answer: str = Field(...)
     feedback: dict | None = Field(None)
     retrieverResources: list[dict] | None = Field(None)
+    suggestedQuestions: list[str] = Field(default_factory=list)
+    status: str = Field("completed")
     createdAt: int = Field(0)
 
 

fastapi_modules/fastapi_leaudit/govdoc_engine/llm/client.py

@@ -38,6 +38,7 @@ from fastapi_admin.config import (
     LEAUDIT_LLM_RETRY_BACKOFF_BASE_SECONDS,
 )
 from fastapi_modules.fastapi_leaudit.govdoc_engine.llm.cache import LlmCache, make_key
+from fastapi_modules.fastapi_leaudit.rag_engine.config import normalize_openai_base_url
 
 _log = logging.getLogger(__name__)
 
@@ -153,8 +154,9 @@ class LlmClient:
                 "LLM_API_KEY is not configured. Set LLM_API_KEY in platform config."
             )
         else:
-            self._client = OpenAI(api_key=key, base_url=base_url or LLM_BASE_URL)
-            self._aclient = AsyncOpenAI(api_key=key, base_url=base_url or LLM_BASE_URL)
+            normalized_base_url = normalize_openai_base_url(base_url or LLM_BASE_URL)
+            self._client = OpenAI(api_key=key, base_url=normalized_base_url)
+            self._aclient = AsyncOpenAI(api_key=key, base_url=normalized_base_url)
         self.model = model or LLM_MODEL
         self.timeout = timeout_seconds if timeout_seconds is not None else LEAUDIT_LLM_REQUEST_TIMEOUT
         self.max_retries = max_retries if max_retries is not None else LEAUDIT_LLM_RETRY_MAX_ATTEMPTS

fastapi_modules/fastapi_leaudit/leaudit_bridge/client_factory.py

@@ -29,6 +29,7 @@ from fastapi_modules.fastapi_leaudit.leaudit_bridge.resilient_clients import (
     ResilientOpenAICompatibleClient,
     ResilientQwenVLMClient,
 )
+from fastapi_modules.fastapi_leaudit.rag_engine.config import normalize_openai_base_url
 
 if TYPE_CHECKING:
     from leaudit.llm.base import BaseLLMClient
@@ -68,7 +69,7 @@ def create_ocr_client() -> BaseOCRClient:
 
 def create_llm_client() -> BaseLLMClient:
     """Create a leaudit OpenAICompatibleClient from docauditai's LLM config."""
-    base_url = LLM_BASE_URL
+    base_url = normalize_openai_base_url(LLM_BASE_URL)
     model = LLM_MODEL
     api_key = LLM_API_KEY or "no-key"
 
@@ -93,7 +94,7 @@ def create_llm_client() -> BaseLLMClient:
 
 def create_vlm_client() -> BaseVLMClient | None:
     """Create a leaudit QwenVLMClient from docauditai's VLM config."""
-    base_url = VLM_BASE_URL
+    base_url = normalize_openai_base_url(VLM_BASE_URL)
     model = VLM_MODEL
     api_key = VLM_API_KEY or LLM_API_KEY or "no-key"
 

fastapi_modules/fastapi_leaudit/rag_engine/config.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from fastapi_admin.config._settings import llm
+from fastapi_admin.config._settings import embedding, llm
 
 
 def _get_str(name: str, default: str = "") -> str:
@@ -36,11 +36,23 @@ RAG_CONFIG = {
     "CHROMA_PORT": _get_int("RAG_CHROMA_PORT", 8010),
     "CHROMA_TOKEN": _get_str("RAG_CHROMA_TOKEN", ""),
     "CHROMA_AUTH_HEADER": _get_str("RAG_CHROMA_AUTH_HEADER", "X-Chroma-Token"),
-    "EMBED_URL": _get_str("RAG_EMBED_URL", _get_str("GRAPH_RAG_EMBED_URL", "")),
-    "EMBED_KEY": _get_str("RAG_EMBED_KEY", _get_str("GRAPH_RAG_EMBED_KEY", "")),
-    "EMBED_MODEL": _get_str("RAG_EMBED_MODEL", _get_str("GRAPH_RAG_EMBED_MODEL", "")),
-    "EMBED_DIM": _get_int("RAG_EMBED_DIM", 1024),
-    "EMBED_BATCH_SIZE": _get_int("RAG_EMBED_BATCH_SIZE", 10),
+    "EMBED_URL": _get_str(
+        "RAG_EMBED_URL",
+        _get_str("GRAPH_RAG_EMBED_URL", _get_str("EMBEDDING_BASE_URL", embedding.EMBEDDING_BASE_URL)),
+    ),
+    "EMBED_KEY": _get_str(
+        "RAG_EMBED_KEY",
+        _get_str("GRAPH_RAG_EMBED_KEY", _get_str("EMBEDDING_API_KEY", embedding.EMBEDDING_API_KEY)),
+    ),
+    "EMBED_MODEL": _get_str(
+        "RAG_EMBED_MODEL",
+        _get_str("GRAPH_RAG_EMBED_MODEL", _get_str("EMBEDDING_MODEL", embedding.EMBEDDING_MODEL)),
+    ),
+    "EMBED_DIM": _get_int("RAG_EMBED_DIM", _get_int("EMBEDDING_DIM", embedding.EMBEDDING_DIM)),
+    "EMBED_BATCH_SIZE": _get_int(
+        "RAG_EMBED_BATCH_SIZE",
+        _get_int("EMBEDDING_BATCH_SIZE", embedding.EMBEDDING_BATCH_SIZE),
+    ),
     "RERANKER_URL": _get_str("RAG_RERANKER_URL", _get_str("GRAPH_RAG_RERANKER_URL", "")),
     "RERANKER_KEY": _get_str("RAG_RERANKER_KEY", _get_str("GRAPH_RAG_RERANKER_KEY", "")),
     "RERANKER_MODEL": _get_str("RAG_RERANKER_MODEL", _get_str("GRAPH_RAG_RERANKER_MODEL", "")),
@@ -58,3 +70,34 @@ RAG_CONFIG = {
     "HYBRID_SEARCH": _get_bool("RAG_HYBRID_SEARCH", True),
     "RERANKING": _get_bool("RAG_RERANKING", True),
 }
+
+
+def build_openai_chat_completions_url(base_url: str) -> str:
+    normalized = (base_url or "").strip().rstrip("/")
+    if not normalized:
+        return "/chat/completions"
+    if normalized.endswith("/chat/completions"):
+        return normalized
+    return f"{normalized}/chat/completions"
+
+
+def build_openai_embeddings_url(base_url: str) -> str:
+    normalized = (base_url or "").strip().rstrip("/")
+    if not normalized:
+        return "/embeddings"
+    if normalized.endswith("/chat/completions"):
+        normalized = normalized[:-len("/chat/completions")]
+    if normalized.endswith("/embeddings"):
+        return normalized
+    return f"{normalized}/embeddings"
+
+
+def normalize_openai_base_url(base_url: str) -> str:
+    normalized = (base_url or "").strip().rstrip("/")
+    if not normalized:
+        return ""
+    if normalized.endswith("/chat/completions"):
+        return normalized[:-len("/chat/completions")]
+    if normalized.endswith("/embeddings"):
+        return normalized[:-len("/embeddings")]
+    return normalized

fastapi_modules/fastapi_leaudit/rag_engine/generator.py

@@ -7,7 +7,7 @@ from typing import AsyncGenerator
 
 import httpx
 
-from fastapi_modules.fastapi_leaudit.rag_engine.config import RAG_CONFIG
+from fastapi_modules.fastapi_leaudit.rag_engine.config import RAG_CONFIG, build_openai_chat_completions_url
 
 DEFAULT_SYSTEM_PROMPT = """你是烟草行业智慧法务小助手，专注于烟草专卖法规、合同管理、行政处罚等相关法律法规。\n\n回答要求：\n- 先用一句话直接回答，再展开详细说明\n- 多个要点用编号列表\n- 关键法条和数字用 **加粗**\n- 分类信息用表格\n- 层级结构用缩进子列表\n- 不要加标题，直接输出正文"""
 
@@ -17,13 +17,14 @@ async def generate_stream(
     context_chunks: list[dict],
     conversation_id: str,
     message_id: str,
+    task_id: str | None = None,
     system_prompt: str = "",
     model: str = "",
     temperature: float | None = None,
     max_tokens: int | None = None,
     dataset_name: str = "",
 ) -> AsyncGenerator[str, None]:
-    task_id = str(uuid.uuid4())
+    task_id = task_id or str(uuid.uuid4())
     created_at = int(time.time())
     _model = model or RAG_CONFIG["LLM_MODEL"]
     _temp = temperature if temperature is not None else RAG_CONFIG["LLM_TEMPERATURE"]
@@ -55,7 +56,7 @@ async def generate_stream(
         async with httpx.AsyncClient(timeout=RAG_CONFIG["LLM_TIMEOUT"]) as client:
             async with client.stream(
                 "POST",
-                f"{RAG_CONFIG['LLM_BASE_URL'].rstrip('/')}" + "/chat/completions",
+                build_openai_chat_completions_url(RAG_CONFIG["LLM_BASE_URL"]),
                 json={
                     "model": _model,
                     "messages": messages,

fastapi_modules/fastapi_leaudit/rag_engine/question_chains.py

@@ -4,7 +4,7 @@ import json
 
 import httpx
 
-from fastapi_modules.fastapi_leaudit.rag_engine.config import RAG_CONFIG
+from fastapi_modules.fastapi_leaudit.rag_engine.config import RAG_CONFIG, build_openai_chat_completions_url
 
 
 async def generate_followups(query: str, answer: str) -> list[str]:
@@ -15,7 +15,7 @@ async def generate_followups(query: str, answer: str) -> list[str]:
     )
     async with httpx.AsyncClient(timeout=30.0) as client:
         resp = await client.post(
-            f"{RAG_CONFIG['LLM_BASE_URL'].rstrip('/')}" + "/chat/completions",
+            build_openai_chat_completions_url(RAG_CONFIG["LLM_BASE_URL"]),
             json={
                 "model": RAG_CONFIG["LLM_MODEL"],
                 "messages": [{"role": "user", "content": prompt}],

fastapi_modules/fastapi_leaudit/services/impl/ragDatasetServiceImpl.py

@@ -36,7 +36,7 @@ from fastapi_modules.fastapi_leaudit.domian.vo.ragDatasetVo import (
 )
 from fastapi_modules.fastapi_leaudit.domian.vo.ragChatVo import RagOperationResultVO
 from fastapi_modules.fastapi_leaudit.rag_engine.chroma_client import get_chroma
-from fastapi_modules.fastapi_leaudit.rag_engine.config import RAG_CONFIG
+from fastapi_modules.fastapi_leaudit.rag_engine.config import RAG_CONFIG, build_openai_embeddings_url
 from fastapi_modules.fastapi_leaudit.services.ragDatasetService import IRagDatasetService
 
 
@@ -1503,7 +1503,7 @@ class RagDatasetServiceImpl(IRagDatasetService):
         return chunks
 
     async def _embed_texts(self, texts: list[str], model_name: str) -> list[list[float]]:
-        embed_url = (RAG_CONFIG.get("EMBED_URL") or "").strip() or f"{RAG_CONFIG['LLM_BASE_URL'].rstrip('/')}/embeddings"
+        embed_url = (RAG_CONFIG.get("EMBED_URL") or "").strip() or build_openai_embeddings_url(RAG_CONFIG["LLM_BASE_URL"])
         embed_key = (RAG_CONFIG.get("EMBED_KEY") or "").strip() or RAG_CONFIG["LLM_API_KEY"]
         embed_model = model_name or (RAG_CONFIG.get("EMBED_MODEL") or "").strip() or "text-embedding-v4"
         batch_size = max(1, int(RAG_CONFIG.get("EMBED_BATCH_SIZE") or 10))

fastapi_modules/fastapi_leaudit/services/ragChatService.py

@@ -6,6 +6,7 @@ from typing import AsyncGenerator
 from fastapi_modules.fastapi_leaudit.domian.Dto.ragChatDto import (
     RagConversationRenameDTO,
     RagMessageFeedbackDTO,
+    RagStopMessageDTO,
 )
 from fastapi_modules.fastapi_leaudit.domian.vo.ragChatVo import (
     RagAppParametersVO,
@@ -52,6 +53,9 @@ class IRagChatService(ABC):
     @abstractmethod
     async def UpdateFeedback(self, CurrentUserId: int, MessageId: str, Body: RagMessageFeedbackDTO) -> RagOperationResultVO: ...
 
+    @abstractmethod
+    async def StopMessage(self, CurrentUserId: int, MessageId: str, Body: RagStopMessageDTO | None = None) -> RagOperationResultVO: ...
+
     @abstractmethod
     async def GetAppParameters(
         self,