feat: enhance dataset file fetching with improved pagination and document loading support (#156)

This commit is contained in:
Dallas98
2025-12-10 22:39:24 +08:00
committed by GitHub
parent e9fd6a3ae1
commit 2f3ae21f8a
7 changed files with 158 additions and 112 deletions

View File

@@ -46,7 +46,7 @@ public class DatasetFileController {
@PathVariable("datasetId") String datasetId,
@RequestParam(value = "page", required = false, defaultValue = "0") Integer page,
@RequestParam(value = "size", required = false, defaultValue = "20") Integer size,
@RequestParam(value = "prefix", required = false) String prefix) {
@RequestParam(value = "prefix", required = false, defaultValue = "") String prefix) {
PagingQuery pagingQuery = new PagingQuery(page, size);
PagedResponse<DatasetFile> filesPage = datasetFileApplicationService.getDatasetFilesWithDirectory(
datasetId, prefix, pagingQuery);

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@@ -78,6 +78,7 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
const fetchDatasets = async () => {
const { data } = await queryDatasetsUsingGet({
// Ant Design Table pagination.current is 1-based; ensure backend also receives 1-based value
page: datasetPagination.current,
size: datasetPagination.pageSize,
keyword: datasetSearch,
@@ -98,29 +99,49 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
300
);
const fetchFiles = useCallback(async () => {
if (!selectedDataset) return;
const { data } = await queryDatasetFilesUsingGet(selectedDataset.id, {
page: filesPagination.current - 1,
size: filesPagination.pageSize,
keyword: filesSearch,
});
setFiles(
(data.content || []).map((item: DatasetFile) => ({
...item,
key: item.id,
datasetName: selectedDataset.name,
}))
);
setFilesPagination((prev) => ({
...prev,
total: data.totalElements,
}));
}, [filesPagination.current, filesPagination.pageSize, filesSearch, selectedDataset]);
const fetchFiles = useCallback(
async (
options?: Partial<{ page: number; pageSize: number; keyword: string }>
) => {
if (!selectedDataset) return;
const page = options?.page ?? filesPagination.current;
const pageSize = options?.pageSize ?? filesPagination.pageSize;
const keyword = options?.keyword ?? filesSearch;
const { data } = await queryDatasetFilesUsingGet(selectedDataset.id, {
page,
size: pageSize,
keyword,
});
setFiles(
(data.content || []).map((item: DatasetFile) => ({
...item,
key: item.id,
datasetName: selectedDataset.name,
}))
);
setFilesPagination((prev) => ({
...prev,
current: page,
pageSize,
total: data.totalElements,
}));
},
[selectedDataset, filesPagination.current, filesPagination.pageSize, filesSearch]
);
useEffect(() => {
fetchFiles().catch(() => {});
}, [fetchFiles]);
// 当数据集变化时,重置文件分页并拉取第一页文件,避免额外的循环请求
if (selectedDataset) {
setFilesPagination({ current: 1, pageSize: 10, total: 0 });
fetchFiles({ page: 1, pageSize: 10 }).catch(() => {});
} else {
setFiles([]);
setFilesPagination({ current: 1, pageSize: 10, total: 0 });
}
// 只在 selectedDataset 变化时触发
// eslint-disable-next-line react-hooks/exhaustive-deps
}, [selectedDataset]);
useEffect(() => {
onDatasetSelect?.(selectedDataset);
@@ -238,7 +259,18 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
size="small"
dataSource={files}
columns={fileCols.slice(1, fileCols.length)}
pagination={filesPagination}
pagination={{
...filesPagination,
onChange: (page, pageSize) => {
const nextPageSize = pageSize || filesPagination.pageSize;
setFilesPagination((prev) => ({
...prev,
current: page,
pageSize: nextPageSize,
}));
fetchFiles({ page, pageSize: nextPageSize }).catch(() => {});
},
}}
onRow={(record: DatasetFile) => ({
onClick: () => toggleSelectFile(record),
})}
@@ -247,7 +279,7 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
selectedRowKeys: Object.keys(selectedFilesMap),
// 单选
onSelect: (record: DatasetFile, selected: boolean) => {
onSelect: (record: DatasetFile) => {
toggleSelectFile(record);
},
@@ -255,7 +287,7 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
onSelectAll: (selected, selectedRows: DatasetFile[]) => {
if (selected) {
// ✔ 全选 -> 将 files 列表全部加入 selectedFilesMap
const newMap: Record<string, DatasetFile> = {};
const newMap: Record<string, DatasetFile> = { ...selectedFilesMap };
selectedRows.forEach((f) => {
newMap[f.id] = f;
});
@@ -264,7 +296,7 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
// ✘ 取消全选 -> 清空 map
const newMap = { ...selectedFilesMap };
Object.keys(newMap).forEach((id) => {
if (files.find((f) => f.id === id)) {
if (files.some((f) => String(f.id) === id)) {
// 仅移除当前页对应文件
delete newMap[id];
}
@@ -277,15 +309,6 @@ const DatasetFileTransfer: React.FC<DatasetFileTransferProps> = ({
name: record.fileName,
}),
}}
// rowSelection={{
// type: "checkbox",
// selectedRowKeys: Object.keys(selectedFilesMap),
// onSelect: toggleSelectFile,
// getCheckboxProps: (record: DatasetFile) => ({
// name: record.fileName,
// }),
// }}
/>
</div>
</div>

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@@ -0,0 +1,3 @@
"""
公共模块
"""

View File

@@ -0,0 +1,93 @@
from typing import List, Union, Optional
from pathlib import Path
from langchain_core.documents import Document
from langchain_community.document_loaders import (
TextLoader,
JSONLoader,
CSVLoader,
UnstructuredMarkdownLoader,
PyPDFLoader,
Docx2txtLoader
)
from app.core.logging import get_logger
log = get_logger(__name__)
class UniversalDocLoader:
"""
通用泛文本文档加载类
支持格式:TXT/JSON/CSV/Markdown/Word(.docx)/PPT(.pptx)/PDF
"""
# 格式-加载器映射(轻量优先)
SUPPORTED_FORMATS = {
# 纯文本类
".txt": TextLoader,
".json": JSONLoader,
".csv": CSVLoader,
".md": UnstructuredMarkdownLoader,
# 办公文档类
".docx": Docx2txtLoader,
".doc": Docx2txtLoader,
# PDF 类
".pdf": PyPDFLoader
}
def __init__(self, file_path: Union[str, Path]):
self.file_path = Path(file_path).resolve()
self.file_suffix = self.file_path.suffix.lower()
log.info(f"初始化文档加载器: {self.file_path} (格式: {self.file_suffix})")
self._validate_file()
def _validate_file(self) -> None:
"""验证文件存在性和格式支持性"""
if not self.file_path.exists():
raise FileNotFoundError(f"文件不存在: {self.file_path}")
if self.file_suffix not in self.SUPPORTED_FORMATS:
raise ValueError(
f"不支持的格式: {self.file_suffix} | 支持格式: {list(self.SUPPORTED_FORMATS.keys())}"
)
def load(
self,
file_format: Optional[str] = None,
**loader_kwargs
) -> List[Document]:
"""
加载文档并返回 LangChain Document 列表
:param file_format: 手动指定格式(如 ".pdf"),默认自动识别
:param loader_kwargs: 传递给具体加载器的参数(如 JSONLoader 的 jq_schema)
:return: List[Document]
"""
# 确定目标格式
target_format = file_format.lower() if file_format else self.file_suffix
loader_cls = self.SUPPORTED_FORMATS[target_format]
# 加载器默认参数优化
loader_kwargs = self._set_default_kwargs(loader_cls, loader_kwargs)
# 初始化并加载
loader = loader_cls(str(self.file_path), **loader_kwargs)
return loader.load()
@staticmethod
def _set_default_kwargs(loader_cls, kwargs: dict) -> dict:
"""为不同加载器设置默认参数,简化调用"""
if loader_cls == JSONLoader and "jq_schema" not in kwargs:
kwargs.setdefault("jq_schema", ".")
kwargs.setdefault("text_content", False)
if loader_cls == CSVLoader and "csv_args" not in kwargs:
kwargs["csv_args"] = {"delimiter": ","}
return kwargs
# 文档加载器便捷函数
def load_documents(
file_path: Union[str, Path],
file_format: Optional[str] = None,
**loader_kwargs
) -> List[Document]:
"""快速加载文档的便捷函数"""
loader = UniversalDocLoader(file_path)
return loader.load(file_format=file_format, **loader_kwargs)

View File

@@ -1,20 +1,8 @@
import asyncio
import uuid
import json
import uuid
from pathlib import Path
from langchain_community.document_loaders import (
TextLoader,
CSVLoader,
JSONLoader,
UnstructuredMarkdownLoader,
UnstructuredHTMLLoader,
UnstructuredFileLoader,
PyPDFLoader,
UnstructuredWordDocumentLoader,
UnstructuredPowerPointLoader,
UnstructuredExcelLoader,
)
from langchain_text_splitters import RecursiveCharacterTextSplitter
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
@@ -30,6 +18,7 @@ from app.db.models.model_config import get_model_by_id
from app.db.session import logger
from app.module.shared.util.model_chat import _extract_json_substring
from app.module.system.service.common_service import get_chat_client, chat
from app.common.document_loaders import load_documents
class GenerationService:
@@ -250,8 +239,7 @@ class GenerationService:
保留每个 Document 的 metadata,方便后续追加例如文件ID、chunk序号等信息。
"""
loader = self._build_loader(file_path)
docs = loader.load()
docs = load_documents(file_path)
splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
@@ -262,67 +250,6 @@ class GenerationService:
split_docs = splitter.split_documents(docs)
return split_docs
@staticmethod
def _build_loader(file_path: str):
"""根据文件扩展名选择合适的 LangChain 文本加载器,尽量覆盖常见泛文本格式。
优先按格式选择专门的 Loader,找不到匹配时退回到 TextLoader。
"""
path = Path(file_path)
suffix = path.suffix.lower()
path_str = str(path)
# 1. 纯文本类
if suffix in {".txt", "", ".log"}: # "" 兼容无扩展名
return TextLoader(path_str, encoding="utf-8")
# 2. Markdown
if suffix in {".md", ".markdown"}:
# UnstructuredMarkdownLoader 会保留更多结构信息
return UnstructuredMarkdownLoader(path_str)
# 3. HTML / HTM
if suffix in {".html", ".htm"}:
return UnstructuredHTMLLoader(path_str)
# 4. JSON
if suffix == ".json":
# 使用 JSONLoader 将 JSON 中的内容展开成文档
# 这里使用默认 jq_schema,后续需要更精细地提取可以在此调整
return JSONLoader(file_path=path_str, jq_schema=".")
# 5. CSV / TSV
if suffix in {".csv", ".tsv"}:
# CSVLoader 默认将每一行作为一条 Document
return CSVLoader(file_path=path_str)
# 6. YAML
if suffix in {".yaml", ".yml"}:
# 暂时按纯文本加载
return TextLoader(path_str, encoding="utf-8")
# 7. PDF
if suffix == ".pdf":
return PyPDFLoader(path_str)
# 8. Word 文档
if suffix in {".docx", ".doc"}:
# UnstructuredWordDocumentLoader 支持 .docx/.doc 文本抽取
return UnstructuredWordDocumentLoader(path_str)
# 9. PowerPoint
if suffix in {".ppt", ".pptx"}:
return UnstructuredPowerPointLoader(path_str)
# 10. Excel
if suffix in {".xls", ".xlsx"}:
return UnstructuredExcelLoader(path_str)
# 11. 兜底:使用 UnstructuredFileLoader 或 TextLoader 作为纯文本
try:
return UnstructuredFileLoader(path_str)
except Exception:
return TextLoader(path_str, encoding="utf-8")
@staticmethod
def _build_qa_prompt(chunk: str, synthesis_cfg: dict) -> str:

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@@ -32,7 +32,7 @@ RUN --mount=type=cache,target=$POETRY_CACHE_DIR \
poetry install --no-root --only main
# Download NLTK data
RUN python -c "import nltk; nltk.download('punkt_tab', download_dir='/usr/local/nltk_data')"
RUN python -c "import nltk; nltk.download(['punkt_tab','averaged_perceptron_tagger_eng'], download_dir='/usr/local/nltk_data')"
ENV NLTK_DATA=/usr/local/nltk_data
# Copy the rest of the application