feat: 知识库批量上传文档

This commit is contained in:
martsforever
2025-09-07 19:12:25 +08:00
parent 42d6604b28
commit 6940d24d53
2 changed files with 103 additions and 33 deletions
+62 -33
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@@ -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]}
+41
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@@ -0,0 +1,41 @@
import asyncio
from typing import List
from fastapi import UploadFile
from app.model.FileModel import FileSaveService
from app.model.KnowledgeDoc import KnowledgeDocModel, KnowledgeDocService
from app.utils.db_utils import AsyncSessionDep, async_session
class KnowledgeService:
def __init__(self):
pass
# 为每个文件创建独立的 session 进行文件保存
async def save_file_with_new_session(self, file: UploadFile):
# 这里需要根据您的数据库配置创建新的 session
# 假设您有一个 session factory
async with async_session() as session:
return await FileSaveService.saveFile(session=session, file=file, filename=file.filename, file_record={})
async def save_knowledge_doc_list(self, session: AsyncSessionDep, file_dict_list: List[dict], kb_code: str):
# 先插入文档对象
tobe_insert_kd_list = [
KnowledgeDocModel(id=file_dict["id"], name=file_dict["name"], path=file_dict["path"], parent_code=kb_code, status="process").model_dump()
for file_dict in file_dict_list
]
insert_cls_list: List[KnowledgeDocModel] = await KnowledgeDocService.batch_insert(session=session, row_dict_list=tobe_insert_kd_list)
return insert_cls_list
async def process_file_dict(self, file_dict: dict, kb_code: str):
print("process start", file_dict['name'])
await asyncio.sleep(3)
print("process end", file_dict['name'])
return {}
knowledge_service = KnowledgeService()