feat: 添加agentChat功能及相关API支持

- 在API路由中新增agentChat路由，支持与AI大模型的交互
- 引入AgentChatRequest和AgentChatResponse Pydantic模型，定义请求和响应结构
- 更新util_schemas.py以包含新的agentChat请求和响应字段，增强数据验证逻辑
- 更新数据库文件和二进制数据，确保数据一致性

th_agenter/api/endpoints/agent_chat.py

@@ -0,0 +1,145 @@
+"""agentChat 接口：根据 AI 大模型、提示词、关联知识库输出结果。"""
+
+from fastapi import APIRouter, Depends, HTTPException, status
+from sqlalchemy import select
+from sqlalchemy.orm import Session
+from loguru import logger
+
+from ...db.database import get_session
+from ...models.user import User
+from ...models.llm_config import LLMConfig
+from ...models.knowledge_base import KnowledgeBase
+from ...services.llm_service import LLMService
+from ...services.document_processor import get_document_processor
+from ...core.simple_permissions import require_authenticated_user
+from utils.util_exceptions import HxfResponse
+from utils.util_schemas import AgentChatRequest, AgentChatResponse
+
+router = APIRouter()
+
+
+@router.post("/", response_model=AgentChatResponse, summary="agentChat：按大模型、提示词、关联知识库输出结果")
+@router.post("", response_model=AgentChatResponse, include_in_schema=False)
+async def agent_chat(
+    body: AgentChatRequest,
+    current_user: User = Depends(require_authenticated_user),
+    session: Session = Depends(get_session),
+):
+    """
+    根据选择的大模型、关联的知识库和提示词，返回模型输出。
+    - model_id: AI 大模型配置 ID（需为对话型，非嵌入型）
+    - prompt 或 message: 提示词（二选一）
+    - knowledge_base_id 或 knowledge_base_ids: 关联知识库（单个或列表，如 [1, 2] 或 ['3']）
+    """
+    prompt_text = (body.prompt or body.message or "").strip()
+    # 解析知识库 ID 列表：优先 knowledge_base_ids，否则 [knowledge_base_id]
+    kb_ids: list[int] = []
+    if body.knowledge_base_ids:
+        try:
+            kb_ids = [int(x) for x in body.knowledge_base_ids if x is not None and str(x).strip() != ""]
+            kb_ids = [i for i in kb_ids if i >= 1]
+        except (ValueError, TypeError):
+            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="knowledge_base_ids 须为数字或数字字符串")
+    elif body.knowledge_base_id is not None and body.knowledge_base_id >= 1:
+        kb_ids = [body.knowledge_base_id]
+
+    session.title = "agentChat"
+    session.desc = f"START: agentChat model_id={body.model_id}, prompt_len={len(prompt_text)}, knowledge_base_ids={kb_ids}"
+
+    # 1. 校验并获取大模型配置
+    stmt = select(LLMConfig).where(LLMConfig.id == body.model_id)
+    llm_config = (await session.execute(stmt)).scalar_one_or_none()
+    if not llm_config:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="大模型配置不存在")
+    if not llm_config.is_active:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="该大模型配置未启用")
+    if llm_config.is_embedding:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="请选择对话型大模型，不能使用嵌入模型")
+
+    # 2. 若指定知识库，校验并检索（支持多知识库，结果合并后按相似度取 top_k）
+    knowledge_base_used = False
+    references = None
+    final_prompt = prompt_text
+    first_kb_id_used: int | None = None
+
+    if kb_ids:
+        doc_processor = await get_document_processor(session)
+        all_results: list = []
+
+        for kb_id in kb_ids:
+            kb_stmt = select(KnowledgeBase).where(KnowledgeBase.id == kb_id)
+            kb = (await session.execute(kb_stmt)).scalar_one_or_none()
+            if not kb:
+                raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f"知识库不存在: id={kb_id}")
+            if not kb.is_active:
+                raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=f"该知识库未启用: id={kb_id}")
+
+            part = doc_processor.search_similar_documents(
+                knowledge_base_id=kb_id,
+                query=prompt_text,
+                k=body.top_k,
+            )
+            all_results.extend(part)
+
+        def _score(r):
+            return float(r.get("normalized_score") or r.get("similarity_score") or 0)
+
+        all_results.sort(key=_score, reverse=True)
+        results = all_results[: body.top_k]
+        max_score = _score(results[0]) if results else 0.0
+
+        if results and max_score >= 0.5:
+            knowledge_base_used = True
+            first_kb_id_used = kb_ids[0]
+            refs = []
+            for i, r in enumerate(results[:5], 1):
+                content = (r.get("content") or "").strip()
+                if content:
+                    if len(content) > 1000:
+                        content = content[:1000] + "..."
+                    refs.append({"index": i, "content": content, "score": r.get("normalized_score")})
+            references = refs
+
+            context = "\n\n".join([f"【参考文档{ref['index']}】\n{ref['content']}" for ref in refs])
+            final_prompt = f"""你是一个专业的助手。请仔细阅读以下参考文档，然后回答用户的问题。
+
+{context}
+
+【用户问题】
+{prompt_text}
+
+【重要提示】
+- 参考文档中包含了与用户问题相关的信息
+- 请仔细阅读参考文档，提取相关信息来回答用户的问题
+- 即使文档没有直接定义，也要基于文档中的相关内容进行解释和说明
+- 如果文档中提到了相关概念、政策、法规等，请基于这些内容进行回答
+- 回答要准确、详细、有条理，尽量引用文档中的具体内容"""
+            logger.info(f"agentChat 使用 RAG，知识库 {kb_ids}，检索 {len(results)} 条，最高相似度 {max_score:.3f}")
+        else:
+            logger.info(f"agentChat：知识库 {kb_ids} 检索结果相似度较低（最高 {max_score:.3f}），仅用提示词")
+
+    # 3. 调用大模型
+    llm_service = LLMService()
+    try:
+        response_text = await llm_service.chat_completion(
+            model_config=llm_config,
+            messages=[{"role": "user", "content": final_prompt}],
+            temperature=body.temperature if body.temperature is not None else llm_config.temperature,
+            max_tokens=body.max_tokens if body.max_tokens is not None else llm_config.max_tokens,
+        )
+    except Exception as e:
+        logger.error(f"agentChat LLM 调用失败: {e}")
+        raise HTTPException(status_code=status.HTTP_502_BAD_GATEWAY, detail=f"大模型调用失败: {str(e)}")
+
+    session.desc = f"SUCCESS: agentChat 完毕 model_id={body.model_id}, model_name={llm_config.model_name}"
+
+    return HxfResponse(
+        AgentChatResponse(
+            response=response_text,
+            model_id=llm_config.id,
+            model_name=llm_config.model_name,
+            knowledge_base_id=first_kb_id_used if knowledge_base_used else None,
+            knowledge_base_used=knowledge_base_used,
+            references=references,
+        )
+    )

th_agenter/api/routes.py

@@ -3,6 +3,7 @@
 from fastapi import APIRouter
 
 from .endpoints import chat
+from .endpoints import agent_chat
 from .endpoints import auth
 from .endpoints import knowledge_base
 from .endpoints import smart_query
@@ -63,6 +64,11 @@ router.include_router(
     prefix="/chat",
     tags=["chat"]
 )
+router.include_router(
+    agent_chat.router,
+    prefix="/agentChat",
+    tags=["agentChat"]
+)
 
 router.include_router(
     smart_chat.router,

utils/util_schemas.py

@@ -1,8 +1,8 @@
 """Pydantic schemas for API requests and responses."""
 
-from typing import Optional, List, Any, Dict, TYPE_CHECKING
+from typing import Optional, List, Any, Dict, TYPE_CHECKING, Union
 from datetime import datetime
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, model_validator
 from enum import Enum
 
 if TYPE_CHECKING:
@@ -363,6 +363,34 @@ class ChatResponse(BaseModel):
     model_used: str
 
 
+class AgentChatRequest(BaseModel):
+    """agentChat 请求：AI大模型、提示词、关联知识库"""
+    model_id: int = Field(..., ge=1, description="AI大模型配置ID")
+    prompt: Optional[str] = Field(default=None, max_length=20000, description="提示词，与 message 二选一")
+    message: Optional[str] = Field(default=None, max_length=20000, description="提示词（与 prompt 等价，二选一）")
+    knowledge_base_id: Optional[int] = Field(default=None, ge=1, description="关联知识库ID（单个），与 knowledge_base_ids 二选一")
+    knowledge_base_ids: Optional[List[Union[int, str]]] = Field(default=None, description="关联知识库ID列表，如 [1, 2] 或 ['3']")
+    top_k: int = Field(default=5, ge=1, le=20, description="知识库检索返回条数")
+    temperature: Optional[float] = Field(default=None, ge=0.0, le=2.0)
+    max_tokens: Optional[int] = Field(default=None, ge=1, le=32768)
+
+    @model_validator(mode="after")
+    def require_prompt_or_message(self):
+        if not ((self.prompt or "").strip() or (self.message or "").strip()):
+            raise ValueError("prompt 或 message 至少提供一个")
+        return self
+
+
+class AgentChatResponse(BaseModel):
+    """agentChat 响应"""
+    response: str = Field(..., description="模型输出结果")
+    model_id: int = Field(..., description="使用的大模型配置ID")
+    model_name: str = Field(..., description="使用的大模型名称")
+    knowledge_base_id: Optional[int] = Field(default=None, description="关联的知识库ID（若使用）")
+    knowledge_base_used: bool = Field(default=False, description="是否使用了知识库RAG")
+    references: Optional[List[Dict[str, Any]]] = Field(default=None, description="引用的知识库片段（若使用RAG）")
+
+
 class StreamChunk(BaseModel):
     """流式响应块模型"""
     content: str