feat: 新增 CSV、XLSX、XLS 文件类型解析支持

docreader/parser/__init__.py

@@ -13,8 +13,10 @@ The parsers extract content from documents and can split them into
 meaningful chunks for further processing and indexing.
 """
 
+from .csv_parser import CSVParser
 from .doc_parser import DocParser
 from .docx2_parser import Docx2Parser
+from .excel_parser import ExcelParser
 from .image_parser import ImageParser
 from .markdown_parser import MarkdownParser
 from .parser import Parser
@@ -32,4 +34,6 @@ __all__ = [
     "ImageParser",  # Parser for images with text content
     "WebParser",  # Parser for web pages
     "Parser",  # Main parser factory that selects the appropriate parser
+    "CSVParser",  # Parser for CSV files
+    "ExcelParser",  # Parser for Excel files
 ]

docreader/parser/base_parser.py

@@ -339,6 +339,9 @@ class BaseParser(ABC):
         logger.info(
             f"Extracted {len(document.content)} characters from {self.file_name}"
         )
+        if document.chunks:
+            return document
+
         splitter = TextSplitter(
             chunk_size=self.chunk_size,
             chunk_overlap=self.chunk_overlap,

docreader/parser/csv_parser.py

@@ -0,0 +1,50 @@
+import logging
+from io import BytesIO
+from typing import List
+
+import pandas as pd
+
+from docreader.models.document import Chunk, Document
+from docreader.parser.base_parser import BaseParser
+
+logger = logging.getLogger(__name__)
+
+
+class CSVParser(BaseParser):
+    def parse_into_text(self, content: bytes) -> Document:
+        chunks: List[Chunk] = []
+        text: List[str] = []
+        start, end = 0, 0
+
+        df = pd.read_csv(BytesIO(content), on_bad_lines="skip")
+
+        for i, (idx, row) in enumerate(df.iterrows()):
+            content_row = (
+                ",".join(
+                    f"{col.strip()}: {str(row[col]).strip()}" for col in df.columns
+                )
+                + "\n"
+            )
+            end += len(content_row)
+            text.append(content_row)
+            chunks.append(Chunk(content=content_row, seq=i, start=start, end=end))
+            start = end
+
+        return Document(
+            content="".join(text),
+            chunks=chunks,
+        )
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.DEBUG)
+
+    your_file = "/path/to/your/file.csv"
+    parser = CSVParser()
+    with open(your_file, "rb") as f:
+        content = f.read()
+        document = parser.parse_into_text(content)
+        logger.error(document.content)
+
+        for chunk in document.chunks:
+            logger.error(chunk.content)

docreader/parser/excel_parser.py

@@ -0,0 +1,54 @@
+import logging
+from io import BytesIO
+from typing import List
+
+import pandas as pd
+
+from docreader.models.document import Chunk, Document
+from docreader.parser.base_parser import BaseParser
+
+logger = logging.getLogger(__name__)
+
+
+class ExcelParser(BaseParser):
+    def parse_into_text(self, content: bytes) -> Document:
+        chunks: List[Chunk] = []
+        text: List[str] = []
+        start, end = 0, 0
+
+        excel_file = pd.ExcelFile(BytesIO(content))
+        for excel_sheet_name in excel_file.sheet_names:
+            df = excel_file.parse(sheet_name=excel_sheet_name)
+            df.dropna(how="all", inplace=True)
+
+            for _, row in df.iterrows():
+                page_content = []
+                for k, v in row.items():
+                    if pd.notna(v):
+                        page_content.append(f"{k}: {v}")
+                if not page_content:
+                    continue
+                content_row = ",".join(page_content) + "\n"
+                end += len(content_row)
+                text.append(content_row)
+                chunks.append(
+                    Chunk(content=content_row, seq=len(chunks), start=start, end=end)
+                )
+                start = end
+
+        return Document(content="".join(text), chunks=chunks)
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.DEBUG)
+
+    your_file = "/path/to/your/file.xlsx"
+    parser = ExcelParser()
+    with open(your_file, "rb") as f:
+        content = f.read()
+        document = parser.parse_into_text(content)
+        logger.error(document.content)
+
+        for chunk in document.chunks:
+            logger.error(chunk.content)
+            break

docreader/parser/parser.py

@@ -4,8 +4,10 @@ from typing import Dict, Type
 from docreader.models.document import Document
 from docreader.models.read_config import ChunkingConfig
 from docreader.parser.base_parser import BaseParser
+from docreader.parser.csv_parser import CSVParser
 from docreader.parser.doc_parser import DocParser
 from docreader.parser.docx2_parser import Docx2Parser
+from docreader.parser.excel_parser import ExcelParser
 from docreader.parser.image_parser import ImageParser
 from docreader.parser.markdown_parser import MarkdownParser
 from docreader.parser.pdf_parser import PDFParser
@@ -37,6 +39,9 @@ class Parser:
             "tiff": ImageParser,
             "webp": ImageParser,
             "markdown": MarkdownParser,
+            "csv": CSVParser,
+            "xlsx": ExcelParser,
+            "xls": ExcelParser,
         }
         logger.info(
             "Parser initialized with %d parsers: %s",

frontend/src/utils/index.ts

@@ -23,7 +23,7 @@ export function formatStringDate(date: any) {
   );
 }
 export function kbFileTypeVerification(file: any) {
-  let validTypes = ["pdf", "txt", "md", "docx", "doc", "jpg", "jpeg", "png"];
+  let validTypes = ["pdf", "txt", "md", "docx", "doc", "jpg", "jpeg", "png", "csv", "xlsx", "xls"];
   let type = file.name.substring(file.name.lastIndexOf(".") + 1);
   if (!validTypes.includes(type)) {
     MessagePlugin.error("文件类型错误！");

internal/application/service/knowledge.go

@@ -1353,7 +1353,7 @@ func (s *knowledgeService) UpdateKnowledge(ctx context.Context, knowledge *types
 // isValidFileType checks if a file type is supported
 func isValidFileType(filename string) bool {
 	switch strings.ToLower(getFileType(filename)) {
-	case "pdf", "txt", "docx", "doc", "md", "markdown", "png", "jpg", "jpeg", "gif":
+	case "pdf", "txt", "docx", "doc", "md", "markdown", "png", "jpg", "jpeg", "gif", "csv", "xlsx", "xls":
 		return true
 	default:
 		return false