feat: 知识库批量上传文档

app/controller/add_knowledge_route.py

@@ -1,41 +1,70 @@
+import asyncio
+from http.client import HTTPException
 from typing import List
 
-from fastapi.params import Param
-from llama_index.core import Document
+from fastapi import UploadFile, File, Form
 
-from app.utils.milvus_utils import milvus_service
-from app.utils.next_id import next_id
+from app.utils.db_utils import AsyncSessionDep
+from app.utils.knowledge_utils import knowledge_service
 
 
 def add_knowledge_route(app):
-  @app.get("/knowledge/search")
-  async def knowledge_search(text: str = Param()):
-    print("text", text)
-    query_result = await  milvus_service.async_search(text or "hello")
-    return query_result
+  # @app.get("/knowledge/search")
+  # async def knowledge_search(text: str = Param()):
+  #   print("text", text)
+  #   query_result = await  milvus_service.async_search(text or "hello")
+  #   return query_result
+  #
+  # @app.post("/knowledge/embed_text")
+  # async def knowledge_embed_text(text_list: List[str]):
+  #   id_list = await next_id(len(text_list))
+  #   document_list = [
+  #     Document(
+  #       text=text,
+  #       id=id_list[index],
+  #       metadata={"kb_id": "cde"}
+  #     )
+  #     for index, text in enumerate(text_list)
+  #   ]
+  #   await milvus_service.async_create_index_from_documents(document_list)
+  #   search_result = await milvus_service.async_search("hello")
+  #   return {
+  #     "result": "嵌入成功:",
+  #     "origin_documents": [document.to_dict() for document in document_list],
+  #     "search_documents": search_result,
+  #   }
+  #
+  # @app.post("/knowledge/delete")
+  # async def knowledge_embed_text(body: dict):
+  #   await milvus_service.async_delete(body.get('id'))
+  #   return {
+  #     "result": "删除成功",
+  @app.post("/knowledge/upload_files")
+  async def knowledge_search(
+    session: AsyncSessionDep,
+    files: List[UploadFile] = File(...),
+    kb_code: str = Form(..., description="所属知识库的编码"),
+  ):
+    """
+    批量上传文档并嵌入到向量数据库（同步响应版本）
+    """
+    if not files:
+      raise HTTPException(status_code=400, detail="No files provided")
 
-  @app.post("/knowledge/embed_text")
-  async def knowledge_embed_text(text_list: List[str]):
-    id_list = await next_id(len(text_list))
-    document_list = [
-      Document(
-        text=text,
-        id=id_list[index],
-        metadata={"kb_id": "cde"}
-      )
-      for index, text in enumerate(text_list)
-    ]
-    await milvus_service.async_create_index_from_documents(document_list)
-    search_result = await milvus_service.async_search("hello")
-    return {
-      "result": "嵌入成功:",
-      "origin_documents": [document.to_dict() for document in document_list],
-      "search_documents": search_result,
-    }
+    # 限制文件数量
+    if len(files) > 20:
+      raise HTTPException(status_code=400, detail="Maximum 20 files allowed per upload")
 
-  @app.post("/knowledge/delete")
-  async def knowledge_embed_text(body: dict):
-    await milvus_service.async_delete(body.get('id'))
-    return {
-      "result": "删除成功",
-    }
+    # 先将所有文件直接保存到本地
+    task_list = [asyncio.create_task(knowledge_service.save_file_with_new_session(file=file)) for file in files]
+    task_result_list = await asyncio.gather(*task_list)
+    file_dict_list = [item["result"] for item in task_result_list]
+
+    # 插入对应的文档对象记录
+    doc_cls_list = await knowledge_service.save_knowledge_doc_list(session=session, file_dict_list=file_dict_list, kb_code=kb_code)
+
+    # 异步处理嵌入文档，不再等待
+    [asyncio.create_task(knowledge_service.process_file_dict(file_dict=file_dict, kb_code=kb_code)) for file_dict in file_dict_list]
+
+    # 直接返回文档对象
+    return {"message": f"正在处理 {len(doc_cls_list)} 个文档，请刷新列表查看文档状态。", "result": [item.model_dump() for item in doc_cls_list]}