Merge pull request #1965 from danielaskdd/rm-enqueued-file

Feat: Optimize error handling for document processing pipeline
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Daniel.y 2025-08-17 01:59:33 +08:00 committed by GitHub
commit 459b0e4c44
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61 changed files with 822 additions and 388 deletions

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@ -1 +1 @@
__api_version__ = "0201"
__api_version__ = "0202"

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@ -734,7 +734,7 @@ class DocumentManager:
new_files = []
for ext in self.supported_extensions:
logger.debug(f"Scanning for {ext} files in {self.input_dir}")
for file_path in self.input_dir.rglob(f"*{ext}"):
for file_path in self.input_dir.glob(f"*{ext}"):
if file_path not in self.indexed_files:
new_files.append(file_path)
return new_files
@ -746,6 +746,39 @@ class DocumentManager:
return any(filename.lower().endswith(ext) for ext in self.supported_extensions)
def get_unique_filename_in_enqueued(target_dir: Path, original_name: str) -> str:
"""Generate a unique filename in the target directory by adding numeric suffixes if needed
Args:
target_dir: Target directory path
original_name: Original filename
Returns:
str: Unique filename (may have numeric suffix added)
"""
from pathlib import Path
import time
original_path = Path(original_name)
base_name = original_path.stem
extension = original_path.suffix
# Try original name first
if not (target_dir / original_name).exists():
return original_name
# Try with numeric suffixes 001-999
for i in range(1, 1000):
suffix = f"{i:03d}"
new_name = f"{base_name}_{suffix}{extension}"
if not (target_dir / new_name).exists():
return new_name
# Fallback with timestamp if all 999 slots are taken
timestamp = int(time.time())
return f"{base_name}_{timestamp}{extension}"
async def pipeline_enqueue_file(
rag: LightRAG, file_path: Path, track_id: str = None
) -> tuple[bool, str]:
@ -759,201 +792,446 @@ async def pipeline_enqueue_file(
tuple: (success: bool, track_id: str)
"""
# Generate track_id if not provided
if track_id is None:
track_id = generate_track_id("unknown")
try:
content = ""
ext = file_path.suffix.lower()
file_size = 0
# Get file size for error reporting
try:
file_size = file_path.stat().st_size
except Exception:
file_size = 0
file = None
async with aiofiles.open(file_path, "rb") as f:
file = await f.read()
try:
async with aiofiles.open(file_path, "rb") as f:
file = await f.read()
except PermissionError as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "Permission denied - cannot read file",
"original_error": str(e),
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(f"Permission denied reading file: {file_path.name}")
return False, track_id
except FileNotFoundError as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "File not found",
"original_error": str(e),
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(f"File not found: {file_path.name}")
return False, track_id
except Exception as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "File reading error",
"original_error": str(e),
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(f"Error reading file {file_path.name}: {str(e)}")
return False, track_id
# Process based on file type
match ext:
case (
".txt"
| ".md"
| ".html"
| ".htm"
| ".tex"
| ".json"
| ".xml"
| ".yaml"
| ".yml"
| ".rtf"
| ".odt"
| ".epub"
| ".csv"
| ".log"
| ".conf"
| ".ini"
| ".properties"
| ".sql"
| ".bat"
| ".sh"
| ".c"
| ".cpp"
| ".py"
| ".java"
| ".js"
| ".ts"
| ".swift"
| ".go"
| ".rb"
| ".php"
| ".css"
| ".scss"
| ".less"
):
try:
# Try to decode as UTF-8
content = file.decode("utf-8")
try:
match ext:
case (
".txt"
| ".md"
| ".html"
| ".htm"
| ".tex"
| ".json"
| ".xml"
| ".yaml"
| ".yml"
| ".rtf"
| ".odt"
| ".epub"
| ".csv"
| ".log"
| ".conf"
| ".ini"
| ".properties"
| ".sql"
| ".bat"
| ".sh"
| ".c"
| ".cpp"
| ".py"
| ".java"
| ".js"
| ".ts"
| ".swift"
| ".go"
| ".rb"
| ".php"
| ".css"
| ".scss"
| ".less"
):
try:
# Try to decode as UTF-8
content = file.decode("utf-8")
# Validate content
if not content or len(content.strip()) == 0:
logger.error(f"Empty content in file: {file_path.name}")
return False, ""
# Check if content looks like binary data string representation
if content.startswith("b'") or content.startswith('b"'):
logger.error(
f"File {file_path.name} appears to contain binary data representation instead of text"
)
return False, ""
except UnicodeDecodeError:
logger.error(
f"File {file_path.name} is not valid UTF-8 encoded text. Please convert it to UTF-8 before processing."
)
return False, ""
case ".pdf":
if global_args.document_loading_engine == "DOCLING":
if not pm.is_installed("docling"): # type: ignore
pm.install("docling")
from docling.document_converter import DocumentConverter # type: ignore
converter = DocumentConverter()
result = converter.convert(file_path)
content = result.document.export_to_markdown()
else:
if not pm.is_installed("pypdf2"): # type: ignore
pm.install("pypdf2")
from PyPDF2 import PdfReader # type: ignore
from io import BytesIO
pdf_file = BytesIO(file)
reader = PdfReader(pdf_file)
for page in reader.pages:
content += page.extract_text() + "\n"
case ".docx":
if global_args.document_loading_engine == "DOCLING":
if not pm.is_installed("docling"): # type: ignore
pm.install("docling")
from docling.document_converter import DocumentConverter # type: ignore
converter = DocumentConverter()
result = converter.convert(file_path)
content = result.document.export_to_markdown()
else:
if not pm.is_installed("python-docx"): # type: ignore
try:
pm.install("python-docx")
except Exception:
pm.install("docx")
from docx import Document # type: ignore
from io import BytesIO
docx_file = BytesIO(file)
doc = Document(docx_file)
content = "\n".join(
[paragraph.text for paragraph in doc.paragraphs]
)
case ".pptx":
if global_args.document_loading_engine == "DOCLING":
if not pm.is_installed("docling"): # type: ignore
pm.install("docling")
from docling.document_converter import DocumentConverter # type: ignore
converter = DocumentConverter()
result = converter.convert(file_path)
content = result.document.export_to_markdown()
else:
if not pm.is_installed("python-pptx"): # type: ignore
pm.install("pptx")
from pptx import Presentation # type: ignore
from io import BytesIO
pptx_file = BytesIO(file)
prs = Presentation(pptx_file)
for slide in prs.slides:
for shape in slide.shapes:
if hasattr(shape, "text"):
content += shape.text + "\n"
case ".xlsx":
if global_args.document_loading_engine == "DOCLING":
if not pm.is_installed("docling"): # type: ignore
pm.install("docling")
from docling.document_converter import DocumentConverter # type: ignore
converter = DocumentConverter()
result = converter.convert(file_path)
content = result.document.export_to_markdown()
else:
if not pm.is_installed("openpyxl"): # type: ignore
pm.install("openpyxl")
from openpyxl import load_workbook # type: ignore
from io import BytesIO
xlsx_file = BytesIO(file)
wb = load_workbook(xlsx_file)
for sheet in wb:
content += f"Sheet: {sheet.title}\n"
for row in sheet.iter_rows(values_only=True):
content += (
"\t".join(
str(cell) if cell is not None else ""
for cell in row
)
+ "\n"
# Validate content
if not content or len(content.strip()) == 0:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "Empty file content",
"original_error": "File contains no content or only whitespace",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
content += "\n"
case _:
logger.error(
f"Unsupported file type: {file_path.name} (extension {ext})"
)
return False, ""
logger.error(f"Empty content in file: {file_path.name}")
return False, track_id
# Check if content looks like binary data string representation
if content.startswith("b'") or content.startswith('b"'):
error_files = [
{
"file_path": str(file_path.name),
"error_description": "Binary data in text file",
"original_error": "File appears to contain binary data representation instead of text",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"File {file_path.name} appears to contain binary data representation instead of text"
)
return False, track_id
except UnicodeDecodeError as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "UTF-8 encoding error",
"original_error": f"File is not valid UTF-8 encoded text: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"File {file_path.name} is not valid UTF-8 encoded text. Please convert it to UTF-8 before processing."
)
return False, track_id
case ".pdf":
try:
if global_args.document_loading_engine == "DOCLING":
if not pm.is_installed("docling"): # type: ignore
pm.install("docling")
from docling.document_converter import DocumentConverter # type: ignore
converter = DocumentConverter()
result = converter.convert(file_path)
content = result.document.export_to_markdown()
else:
if not pm.is_installed("pypdf2"): # type: ignore
pm.install("pypdf2")
from PyPDF2 import PdfReader # type: ignore
from io import BytesIO
pdf_file = BytesIO(file)
reader = PdfReader(pdf_file)
for page in reader.pages:
content += page.extract_text() + "\n"
except Exception as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "PDF processing error",
"original_error": f"Failed to extract text from PDF: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(f"Error processing PDF {file_path.name}: {str(e)}")
return False, track_id
case ".docx":
try:
if global_args.document_loading_engine == "DOCLING":
if not pm.is_installed("docling"): # type: ignore
pm.install("docling")
from docling.document_converter import DocumentConverter # type: ignore
converter = DocumentConverter()
result = converter.convert(file_path)
content = result.document.export_to_markdown()
else:
if not pm.is_installed("python-docx"): # type: ignore
try:
pm.install("python-docx")
except Exception:
pm.install("docx")
from docx import Document # type: ignore
from io import BytesIO
docx_file = BytesIO(file)
doc = Document(docx_file)
content = "\n".join(
[paragraph.text for paragraph in doc.paragraphs]
)
except Exception as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "DOCX processing error",
"original_error": f"Failed to extract text from DOCX: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"Error processing DOCX {file_path.name}: {str(e)}"
)
return False, track_id
case ".pptx":
try:
if global_args.document_loading_engine == "DOCLING":
if not pm.is_installed("docling"): # type: ignore
pm.install("docling")
from docling.document_converter import DocumentConverter # type: ignore
converter = DocumentConverter()
result = converter.convert(file_path)
content = result.document.export_to_markdown()
else:
if not pm.is_installed("python-pptx"): # type: ignore
pm.install("pptx")
from pptx import Presentation # type: ignore
from io import BytesIO
pptx_file = BytesIO(file)
prs = Presentation(pptx_file)
for slide in prs.slides:
for shape in slide.shapes:
if hasattr(shape, "text"):
content += shape.text + "\n"
except Exception as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "PPTX processing error",
"original_error": f"Failed to extract text from PPTX: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"Error processing PPTX {file_path.name}: {str(e)}"
)
return False, track_id
case ".xlsx":
try:
if global_args.document_loading_engine == "DOCLING":
if not pm.is_installed("docling"): # type: ignore
pm.install("docling")
from docling.document_converter import DocumentConverter # type: ignore
converter = DocumentConverter()
result = converter.convert(file_path)
content = result.document.export_to_markdown()
else:
if not pm.is_installed("openpyxl"): # type: ignore
pm.install("openpyxl")
from openpyxl import load_workbook # type: ignore
from io import BytesIO
xlsx_file = BytesIO(file)
wb = load_workbook(xlsx_file)
for sheet in wb:
content += f"Sheet: {sheet.title}\n"
for row in sheet.iter_rows(values_only=True):
content += (
"\t".join(
str(cell) if cell is not None else ""
for cell in row
)
+ "\n"
)
content += "\n"
except Exception as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "XLSX processing error",
"original_error": f"Failed to extract text from XLSX: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(
error_files, track_id
)
logger.error(
f"Error processing XLSX {file_path.name}: {str(e)}"
)
return False, track_id
case _:
error_files = [
{
"file_path": str(file_path.name),
"error_description": f"Unsupported file type: {ext}",
"original_error": f"File extension {ext} is not supported",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(
f"Unsupported file type: {file_path.name} (extension {ext})"
)
return False, track_id
except Exception as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "File format processing error",
"original_error": f"Unexpected error during file extracting: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(
f"Unexpected error during {file_path.name} extracting: {str(e)}"
)
return False, track_id
# Insert into the RAG queue
if content:
# Check if content contains only whitespace characters
if not content.strip():
error_files = [
{
"file_path": str(file_path.name),
"error_description": "File contains only whitespace",
"original_error": "File content contains only whitespace characters",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.warning(
f"File contains only whitespace characters. file_paths={file_path.name}"
f"File contains only whitespace characters: {file_path.name}"
)
return False, track_id
try:
await rag.apipeline_enqueue_documents(
content, file_paths=file_path.name, track_id=track_id
)
# Generate track_id if not provided
if track_id is None:
track_id = generate_track_id("unkown")
logger.info(f"Successfully fetched and enqueued file: {file_path.name}")
await rag.apipeline_enqueue_documents(
content, file_paths=file_path.name, track_id=track_id
)
# Move file to __enqueued__ directory after enqueuing
try:
enqueued_dir = file_path.parent / "__enqueued__"
enqueued_dir.mkdir(exist_ok=True)
logger.info(f"Successfully fetched and enqueued file: {file_path.name}")
return True, track_id
# Generate unique filename to avoid conflicts
unique_filename = get_unique_filename_in_enqueued(
enqueued_dir, file_path.name
)
target_path = enqueued_dir / unique_filename
# Move the file
file_path.rename(target_path)
logger.debug(
f"Moved file to enqueued directory: {file_path.name} -> {unique_filename}"
)
except Exception as move_error:
logger.error(
f"Failed to move file {file_path.name} to __enqueued__ directory: {move_error}"
)
# Don't affect the main function's success status
return True, track_id
except Exception as e:
error_files = [
{
"file_path": str(file_path.name),
"error_description": "Document enqueue error",
"original_error": f"Failed to enqueue document: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(f"Error enqueueing document {file_path.name}: {str(e)}")
return False, track_id
else:
logger.error(f"No content could be extracted from file: {file_path.name}")
return False, ""
error_files = [
{
"file_path": str(file_path.name),
"error_description": "No content extracted",
"original_error": "No content could be extracted from file",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(f"No content extracted from file: {file_path.name}")
return False, track_id
except Exception as e:
logger.error(f"Error processing or enqueueing file {file_path.name}: {str(e)}")
# Catch-all for any unexpected errors
try:
file_size = file_path.stat().st_size if file_path.exists() else 0
except Exception:
file_size = 0
error_files = [
{
"file_path": str(file_path.name),
"error_description": "Unexpected processing error",
"original_error": f"Unexpected error: {str(e)}",
"file_size": file_size,
}
]
await rag.apipeline_enqueue_error_documents(error_files, track_id)
logger.error(f"Enqueuing file {file_path.name} error: {str(e)}")
logger.error(traceback.format_exc())
return False, track_id
finally:
if file_path.name.startswith(temp_prefix):
try:
file_path.unlink()
except Exception as e:
logger.error(f"Error deleting file {file_path}: {str(e)}")
return False, ""
async def pipeline_index_file(rag: LightRAG, file_path: Path, track_id: str = None):

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@ -1 +1 @@
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import{s as a,c as s,a as e,C as t}from"./chunk-SZ463SBG-DomaNiTF.js";import{_ as i}from"./mermaid-vendor-J2ju872p.js";import"./chunk-E2GYISFI-DtDXRKC8.js";import"./chunk-BFAMUDN2-nPRHB0S3.js";import"./chunk-SKB7J2MH-Cgz1KI3w.js";import"./feature-graph-BgVuQYut.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var c={parser:e,get db(){return new t},renderer:s,styles:a,init:i(r=>{r.class||(r.class={}),r.class.arrowMarkerAbsolute=r.arrowMarkerAbsolute},"init")};export{c as diagram};

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import{s as a,c as s,a as e,C as t}from"./chunk-SZ463SBG-BpuXKCOy.js";import{_ as i}from"./mermaid-vendor-pCi8nQvB.js";import"./chunk-E2GYISFI-wNdHFpBQ.js";import"./chunk-BFAMUDN2-ioRYrumN.js";import"./chunk-SKB7J2MH-BsoIgbf0.js";import"./feature-graph-O43AXICd.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var c={parser:e,get db(){return new t},renderer:s,styles:a,init:i(r=>{r.class||(r.class={}),r.class.arrowMarkerAbsolute=r.arrowMarkerAbsolute},"init")};export{c as diagram};

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import{_ as e,l as o,K as i,e as n,L as p}from"./mermaid-vendor-J2ju872p.js";import{p as m}from"./treemap-75Q7IDZK-BiruUsnx.js";import"./feature-graph-BgVuQYut.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-Bd5eQEyI.js";import"./_basePickBy-C-Sjt21Q.js";import"./clone-CzjUKlzi.js";var g={parse:e(async r=>{const a=await m("info",r);o.debug(a)},"parse")},v={version:p.version+""},d=e(()=>v.version,"getVersion"),c={getVersion:d},l=e((r,a,s)=>{o.debug(`rendering info diagram
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View file

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import{p as N}from"./chunk-353BL4L5-leSB0xif.js";import{_ as i,g as B,s as U,a as q,b as H,t as K,q as V,l as C,c as Z,F as j,K as J,M as Q,N as z,O as X,e as Y,z as tt,P as et,H as at}from"./mermaid-vendor-J2ju872p.js";import{p as rt}from"./treemap-75Q7IDZK-BiruUsnx.js";import"./feature-graph-BgVuQYut.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-Bd5eQEyI.js";import"./_basePickBy-C-Sjt21Q.js";import"./clone-CzjUKlzi.js";var it=at.pie,D={sections:new Map,showData:!1},f=D.sections,w=D.showData,st=structuredClone(it),ot=i(()=>structuredClone(st),"getConfig"),nt=i(()=>{f=new Map,w=D.showData,tt()},"clear"),lt=i(({label:t,value:a})=>{f.has(t)||(f.set(t,a),C.debug(`added new section: ${t}, with value: ${a}`))},"addSection"),ct=i(()=>f,"getSections"),pt=i(t=>{w=t},"setShowData"),dt=i(()=>w,"getShowData"),F={getConfig:ot,clear:nt,setDiagramTitle:V,getDiagramTitle:K,setAccTitle:H,getAccTitle:q,setAccDescription:U,getAccDescription:B,addSection:lt,getSections:ct,setShowData:pt,getShowData:dt},gt=i((t,a)=>{N(t,a),a.setShowData(t.showData),t.sections.map(a.addSection)},"populateDb"),ut={parse:i(async t=>{const a=await rt("pie",t);C.debug(a),gt(a,F)},"parse")},mt=i(t=>`
import{p as N}from"./chunk-353BL4L5-DjMQec0j.js";import{_ as i,g as B,s as U,a as q,b as H,t as K,q as V,l as C,c as Z,F as j,K as J,M as Q,N as z,O as X,e as Y,z as tt,P as et,H as at}from"./mermaid-vendor-pCi8nQvB.js";import{p as rt}from"./treemap-75Q7IDZK-DhCk0PFk.js";import"./feature-graph-O43AXICd.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-D1ajN3w6.js";import"./_basePickBy-C9JQvwyt.js";import"./clone-DLVgXiWv.js";var it=at.pie,D={sections:new Map,showData:!1},f=D.sections,w=D.showData,st=structuredClone(it),ot=i(()=>structuredClone(st),"getConfig"),nt=i(()=>{f=new Map,w=D.showData,tt()},"clear"),lt=i(({label:t,value:a})=>{f.has(t)||(f.set(t,a),C.debug(`added new section: ${t}, with value: ${a}`))},"addSection"),ct=i(()=>f,"getSections"),pt=i(t=>{w=t},"setShowData"),dt=i(()=>w,"getShowData"),F={getConfig:ot,clear:nt,setDiagramTitle:V,getDiagramTitle:K,setAccTitle:H,getAccTitle:q,setAccDescription:U,getAccDescription:B,addSection:lt,getSections:ct,setShowData:pt,getShowData:dt},gt=i((t,a)=>{N(t,a),a.setShowData(t.showData),t.sections.map(a.addSection)},"populateDb"),ut={parse:i(async t=>{const a=await rt("pie",t);C.debug(a),gt(a,F)},"parse")},mt=i(t=>`
.pieCircle{
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stroke-width : ${t.pieStrokeWidth};

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@ -1 +1 @@
import{s as r,b as e,a,S as i}from"./chunk-OW32GOEJ-B9FViCRE.js";import{_ as s}from"./mermaid-vendor-J2ju872p.js";import"./chunk-BFAMUDN2-nPRHB0S3.js";import"./chunk-SKB7J2MH-Cgz1KI3w.js";import"./feature-graph-BgVuQYut.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var f={parser:a,get db(){return new i(2)},renderer:e,styles:r,init:s(t=>{t.state||(t.state={}),t.state.arrowMarkerAbsolute=t.arrowMarkerAbsolute},"init")};export{f as diagram};
import{s as r,b as e,a,S as i}from"./chunk-OW32GOEJ-WZhy3vze.js";import{_ as s}from"./mermaid-vendor-pCi8nQvB.js";import"./chunk-BFAMUDN2-ioRYrumN.js";import"./chunk-SKB7J2MH-BsoIgbf0.js";import"./feature-graph-O43AXICd.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var f={parser:a,get db(){return new i(2)},renderer:e,styles:r,init:s(t=>{t.state||(t.state={}),t.state.arrowMarkerAbsolute=t.arrowMarkerAbsolute},"init")};export{f as diagram};

View file

@ -1,4 +1,4 @@
import{_ as s,c as xt,l as E,d as q,a3 as kt,a4 as _t,a5 as bt,a6 as vt,N as nt,D as wt,a7 as St,z as Et}from"./mermaid-vendor-J2ju872p.js";import"./feature-graph-BgVuQYut.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var X=function(){var n=s(function(f,r,a,h){for(a=a||{},h=f.length;h--;a[f[h]]=r);return a},"o"),t=[6,8,10,11,12,14,16,17,20,21],e=[1,9],l=[1,10],i=[1,11],d=[1,12],c=[1,13],g=[1,16],m=[1,17],p={trace:s(function(){},"trace"),yy:{},symbols_:{error:2,start:3,timeline:4,document:5,EOF:6,line:7,SPACE:8,statement:9,NEWLINE:10,title:11,acc_title:12,acc_title_value:13,acc_descr:14,acc_descr_value:15,acc_descr_multiline_value:16,section:17,period_statement:18,event_statement:19,period:20,event:21,$accept:0,$end:1},terminals_:{2:"error",4:"timeline",6:"EOF",8:"SPACE",10:"NEWLINE",11:"title",12:"acc_title",13:"acc_title_value",14:"acc_descr",15:"acc_descr_value",16:"acc_descr_multiline_value",17:"section",20:"period",21:"event"},productions_:[0,[3,3],[5,0],[5,2],[7,2],[7,1],[7,1],[7,1],[9,1],[9,2],[9,2],[9,1],[9,1],[9,1],[9,1],[18,1],[19,1]],performAction:s(function(r,a,h,u,y,o,S){var k=o.length-1;switch(y){case 1:return o[k-1];case 2:this.$=[];break;case 3:o[k-1].push(o[k]),this.$=o[k-1];break;case 4:case 5:this.$=o[k];break;case 6:case 7:this.$=[];break;case 8:u.getCommonDb().setDiagramTitle(o[k].substr(6)),this.$=o[k].substr(6);break;case 9:this.$=o[k].trim(),u.getCommonDb().setAccTitle(this.$);break;case 10:case 11:this.$=o[k].trim(),u.getCommonDb().setAccDescription(this.$);break;case 12:u.addSection(o[k].substr(8)),this.$=o[k].substr(8);break;case 15:u.addTask(o[k],0,""),this.$=o[k];break;case 16:u.addEvent(o[k].substr(2)),this.$=o[k];break}},"anonymous"),table:[{3:1,4:[1,2]},{1:[3]},n(t,[2,2],{5:3}),{6:[1,4],7:5,8:[1,6],9:7,10:[1,8],11:e,12:l,14:i,16:d,17:c,18:14,19:15,20:g,21:m},n(t,[2,7],{1:[2,1]}),n(t,[2,3]),{9:18,11:e,12:l,14:i,16:d,17:c,18:14,19:15,20:g,21:m},n(t,[2,5]),n(t,[2,6]),n(t,[2,8]),{13:[1,19]},{15:[1,20]},n(t,[2,11]),n(t,[2,12]),n(t,[2,13]),n(t,[2,14]),n(t,[2,15]),n(t,[2,16]),n(t,[2,4]),n(t,[2,9]),n(t,[2,10])],defaultActions:{},parseError:s(function(r,a){if(a.recoverable)this.trace(r);else{var h=new Error(r);throw h.hash=a,h}},"parseError"),parse:s(function(r){var a=this,h=[0],u=[],y=[null],o=[],S=this.table,k="",M=0,C=0,B=2,J=1,O=o.slice.call(arguments,1),_=Object.create(this.lexer),N={yy:{}};for(var L in this.yy)Object.prototype.hasOwnProperty.call(this.yy,L)&&(N.yy[L]=this.yy[L]);_.setInput(r,N.yy),N.yy.lexer=_,N.yy.parser=this,typeof _.yylloc>"u"&&(_.yylloc={});var v=_.yylloc;o.push(v);var $=_.options&&_.options.ranges;typeof N.yy.parseError=="function"?this.parseError=N.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function R(T){h.length=h.length-2*T,y.length=y.length-T,o.length=o.length-T}s(R,"popStack");function A(){var T;return T=u.pop()||_.lex()||J,typeof T!="number"&&(T instanceof Array&&(u=T,T=u.pop()),T=a.symbols_[T]||T),T}s(A,"lex");for(var w,H,I,K,F={},j,P,et,G;;){if(H=h[h.length-1],this.defaultActions[H]?I=this.defaultActions[H]:((w===null||typeof w>"u")&&(w=A()),I=S[H]&&S[H][w]),typeof I>"u"||!I.length||!I[0]){var Q="";G=[];for(j in S[H])this.terminals_[j]&&j>B&&G.push("'"+this.terminals_[j]+"'");_.showPosition?Q="Parse error on line "+(M+1)+`:
import{_ as s,c as xt,l as E,d as q,a3 as kt,a4 as _t,a5 as bt,a6 as vt,N as nt,D as wt,a7 as St,z as Et}from"./mermaid-vendor-pCi8nQvB.js";import"./feature-graph-O43AXICd.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var X=function(){var n=s(function(f,r,a,h){for(a=a||{},h=f.length;h--;a[f[h]]=r);return a},"o"),t=[6,8,10,11,12,14,16,17,20,21],e=[1,9],l=[1,10],i=[1,11],d=[1,12],c=[1,13],g=[1,16],m=[1,17],p={trace:s(function(){},"trace"),yy:{},symbols_:{error:2,start:3,timeline:4,document:5,EOF:6,line:7,SPACE:8,statement:9,NEWLINE:10,title:11,acc_title:12,acc_title_value:13,acc_descr:14,acc_descr_value:15,acc_descr_multiline_value:16,section:17,period_statement:18,event_statement:19,period:20,event:21,$accept:0,$end:1},terminals_:{2:"error",4:"timeline",6:"EOF",8:"SPACE",10:"NEWLINE",11:"title",12:"acc_title",13:"acc_title_value",14:"acc_descr",15:"acc_descr_value",16:"acc_descr_multiline_value",17:"section",20:"period",21:"event"},productions_:[0,[3,3],[5,0],[5,2],[7,2],[7,1],[7,1],[7,1],[9,1],[9,2],[9,2],[9,1],[9,1],[9,1],[9,1],[18,1],[19,1]],performAction:s(function(r,a,h,u,y,o,S){var k=o.length-1;switch(y){case 1:return o[k-1];case 2:this.$=[];break;case 3:o[k-1].push(o[k]),this.$=o[k-1];break;case 4:case 5:this.$=o[k];break;case 6:case 7:this.$=[];break;case 8:u.getCommonDb().setDiagramTitle(o[k].substr(6)),this.$=o[k].substr(6);break;case 9:this.$=o[k].trim(),u.getCommonDb().setAccTitle(this.$);break;case 10:case 11:this.$=o[k].trim(),u.getCommonDb().setAccDescription(this.$);break;case 12:u.addSection(o[k].substr(8)),this.$=o[k].substr(8);break;case 15:u.addTask(o[k],0,""),this.$=o[k];break;case 16:u.addEvent(o[k].substr(2)),this.$=o[k];break}},"anonymous"),table:[{3:1,4:[1,2]},{1:[3]},n(t,[2,2],{5:3}),{6:[1,4],7:5,8:[1,6],9:7,10:[1,8],11:e,12:l,14:i,16:d,17:c,18:14,19:15,20:g,21:m},n(t,[2,7],{1:[2,1]}),n(t,[2,3]),{9:18,11:e,12:l,14:i,16:d,17:c,18:14,19:15,20:g,21:m},n(t,[2,5]),n(t,[2,6]),n(t,[2,8]),{13:[1,19]},{15:[1,20]},n(t,[2,11]),n(t,[2,12]),n(t,[2,13]),n(t,[2,14]),n(t,[2,15]),n(t,[2,16]),n(t,[2,4]),n(t,[2,9]),n(t,[2,10])],defaultActions:{},parseError:s(function(r,a){if(a.recoverable)this.trace(r);else{var h=new Error(r);throw h.hash=a,h}},"parseError"),parse:s(function(r){var a=this,h=[0],u=[],y=[null],o=[],S=this.table,k="",M=0,C=0,B=2,J=1,O=o.slice.call(arguments,1),_=Object.create(this.lexer),N={yy:{}};for(var L in this.yy)Object.prototype.hasOwnProperty.call(this.yy,L)&&(N.yy[L]=this.yy[L]);_.setInput(r,N.yy),N.yy.lexer=_,N.yy.parser=this,typeof _.yylloc>"u"&&(_.yylloc={});var v=_.yylloc;o.push(v);var $=_.options&&_.options.ranges;typeof N.yy.parseError=="function"?this.parseError=N.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function R(T){h.length=h.length-2*T,y.length=y.length-T,o.length=o.length-T}s(R,"popStack");function A(){var T;return T=u.pop()||_.lex()||J,typeof T!="number"&&(T instanceof Array&&(u=T,T=u.pop()),T=a.symbols_[T]||T),T}s(A,"lex");for(var w,H,I,K,F={},j,P,et,G;;){if(H=h[h.length-1],this.defaultActions[H]?I=this.defaultActions[H]:((w===null||typeof w>"u")&&(w=A()),I=S[H]&&S[H][w]),typeof I>"u"||!I.length||!I[0]){var Q="";G=[];for(j in S[H])this.terminals_[j]&&j>B&&G.push("'"+this.terminals_[j]+"'");_.showPosition?Q="Parse error on line "+(M+1)+`:
`+_.showPosition()+`
Expecting `+G.join(", ")+", got '"+(this.terminals_[w]||w)+"'":Q="Parse error on line "+(M+1)+": Unexpected "+(w==J?"end of input":"'"+(this.terminals_[w]||w)+"'"),this.parseError(Q,{text:_.match,token:this.terminals_[w]||w,line:_.yylineno,loc:v,expected:G})}if(I[0]instanceof Array&&I.length>1)throw new Error("Parse Error: multiple actions possible at state: "+H+", token: "+w);switch(I[0]){case 1:h.push(w),y.push(_.yytext),o.push(_.yylloc),h.push(I[1]),w=null,C=_.yyleng,k=_.yytext,M=_.yylineno,v=_.yylloc;break;case 2:if(P=this.productions_[I[1]][1],F.$=y[y.length-P],F._$={first_line:o[o.length-(P||1)].first_line,last_line:o[o.length-1].last_line,first_column:o[o.length-(P||1)].first_column,last_column:o[o.length-1].last_column},$&&(F._$.range=[o[o.length-(P||1)].range[0],o[o.length-1].range[1]]),K=this.performAction.apply(F,[k,C,M,N.yy,I[1],y,o].concat(O)),typeof K<"u")return K;P&&(h=h.slice(0,-1*P*2),y=y.slice(0,-1*P),o=o.slice(0,-1*P)),h.push(this.productions_[I[1]][0]),y.push(F.$),o.push(F._$),et=S[h[h.length-2]][h[h.length-1]],h.push(et);break;case 3:return!0}}return!0},"parse")},x=function(){var f={EOF:1,parseError:s(function(a,h){if(this.yy.parser)this.yy.parser.parseError(a,h);else throw new Error(a)},"parseError"),setInput:s(function(r,a){return this.yy=a||this.yy||{},this._input=r,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:s(function(){var r=this._input[0];this.yytext+=r,this.yyleng++,this.offset++,this.match+=r,this.matched+=r;var a=r.match(/(?:\r\n?|\n).*/g);return a?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),r},"input"),unput:s(function(r){var a=r.length,h=r.split(/(?:\r\n?|\n)/g);this._input=r+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-a),this.offset-=a;var u=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),h.length-1&&(this.yylineno-=h.length-1);var y=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:h?(h.length===u.length?this.yylloc.first_column:0)+u[u.length-h.length].length-h[0].length:this.yylloc.first_column-a},this.options.ranges&&(this.yylloc.range=[y[0],y[0]+this.yyleng-a]),this.yyleng=this.yytext.length,this},"unput"),more:s(function(){return this._more=!0,this},"more"),reject:s(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
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@ -8,16 +8,16 @@
<link rel="icon" type="image/png" href="favicon.png" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Lightrag</title>
<script type="module" crossorigin src="/webui/assets/index-MT1PAKNY.js"></script>
<script type="module" crossorigin src="/webui/assets/index-CfXratTv.js"></script>
<link rel="modulepreload" crossorigin href="/webui/assets/react-vendor-DEwriMA6.js">
<link rel="modulepreload" crossorigin href="/webui/assets/ui-vendor-CeCm8EER.js">
<link rel="modulepreload" crossorigin href="/webui/assets/graph-vendor-B-X5JegA.js">
<link rel="modulepreload" crossorigin href="/webui/assets/utils-vendor-BysuhMZA.js">
<link rel="modulepreload" crossorigin href="/webui/assets/feature-graph-BgVuQYut.js">
<link rel="modulepreload" crossorigin href="/webui/assets/feature-documents-DfpqEFJm.js">
<link rel="modulepreload" crossorigin href="/webui/assets/mermaid-vendor-J2ju872p.js">
<link rel="modulepreload" crossorigin href="/webui/assets/feature-graph-O43AXICd.js">
<link rel="modulepreload" crossorigin href="/webui/assets/feature-documents-XY-qP1x8.js">
<link rel="modulepreload" crossorigin href="/webui/assets/mermaid-vendor-pCi8nQvB.js">
<link rel="modulepreload" crossorigin href="/webui/assets/markdown-vendor-DmIvJdn7.js">
<link rel="modulepreload" crossorigin href="/webui/assets/feature-retrieval-Q3pnpDUM.js">
<link rel="modulepreload" crossorigin href="/webui/assets/feature-retrieval-lTh8tRF9.js">
<link rel="stylesheet" crossorigin href="/webui/assets/feature-graph-BipNuM18.css">
<link rel="stylesheet" crossorigin href="/webui/assets/index-CafJWW1u.css">
</head>

View file

@ -971,11 +971,10 @@ class LightRAG:
"""
Pipeline for Processing Documents
1. Validate ids if provided or generate MD5 hash IDs
2. Remove duplicate contents
3. Generate document initial status
4. Filter out already processed documents
5. Enqueue document in status
1. Validate ids if provided or generate MD5 hash IDs and remove duplicate contents
2. Generate document initial status
3. Filter out already processed documents
4. Enqueue document in status
Args:
input: Single document string or list of document strings
@ -1008,7 +1007,7 @@ class LightRAG:
# If no file paths provided, use placeholder
file_paths = ["unknown_source"] * len(input)
# 1. Validate ids if provided or generate MD5 hash IDs
# 1. Validate ids if provided or generate MD5 hash IDs and remove duplicate contents
if ids is not None:
# Check if the number of IDs matches the number of documents
if len(ids) != len(input):
@ -1018,22 +1017,25 @@ class LightRAG:
if len(ids) != len(set(ids)):
raise ValueError("IDs must be unique")
# Generate contents dict of IDs provided by user and documents
# Generate contents dict and remove duplicates in one pass
unique_contents = {}
for id_, doc, path in zip(ids, input, file_paths):
cleaned_content = clean_text(doc)
if cleaned_content not in unique_contents:
unique_contents[cleaned_content] = (id_, path)
# Reconstruct contents with unique content
contents = {
id_: {"content": doc, "file_path": path}
for id_, doc, path in zip(ids, input, file_paths)
id_: {"content": content, "file_path": file_path}
for content, (id_, file_path) in unique_contents.items()
}
else:
# Clean input text and remove duplicates
cleaned_input = [
(clean_text(doc), path) for doc, path in zip(input, file_paths)
]
# Clean input text and remove duplicates in one pass
unique_content_with_paths = {}
# Keep track of unique content and their paths
for content, path in cleaned_input:
if content not in unique_content_with_paths:
unique_content_with_paths[content] = path
for doc, path in zip(input, file_paths):
cleaned_content = clean_text(doc)
if cleaned_content not in unique_content_with_paths:
unique_content_with_paths[cleaned_content] = path
# Generate contents dict of MD5 hash IDs and documents with paths
contents = {
@ -1044,21 +1046,7 @@ class LightRAG:
for content, path in unique_content_with_paths.items()
}
# 2. Remove duplicate contents
unique_contents = {}
for id_, content_data in contents.items():
content = content_data["content"]
file_path = content_data["file_path"]
if content not in unique_contents:
unique_contents[content] = (id_, file_path)
# Reconstruct contents with unique content
contents = {
id_: {"content": content, "file_path": file_path}
for content, (id_, file_path) in unique_contents.items()
}
# 3. Generate document initial status (without content)
# 2. Generate document initial status (without content)
new_docs: dict[str, Any] = {
id_: {
"status": DocStatus.PENDING,
@ -1074,22 +1062,24 @@ class LightRAG:
for id_, content_data in contents.items()
}
# 4. Filter out already processed documents
# 3. Filter out already processed documents
# Get docs ids
all_new_doc_ids = set(new_docs.keys())
# Exclude IDs of documents that are already in progress
# Exclude IDs of documents that are already enqueued
unique_new_doc_ids = await self.doc_status.filter_keys(all_new_doc_ids)
# Log ignored document IDs
ignored_ids = [
doc_id for doc_id in unique_new_doc_ids if doc_id not in new_docs
]
# Log ignored document IDs (documents that were filtered out because they already exist)
ignored_ids = list(all_new_doc_ids - unique_new_doc_ids)
if ignored_ids:
logger.warning(
f"Ignoring {len(ignored_ids)} document IDs not found in new_docs"
)
for doc_id in ignored_ids:
logger.warning(f"Ignored document ID: {doc_id}")
file_path = new_docs.get(doc_id, {}).get("file_path", "unknown_source")
logger.warning(
f"Ignoring document ID (already exists): {doc_id} ({file_path})"
)
if len(ignored_ids) > 3:
logger.warning(
f"Total Ignoring {len(ignored_ids)} document IDs that already exist in storage"
)
# Filter new_docs to only include documents with unique IDs
new_docs = {
@ -1099,11 +1089,11 @@ class LightRAG:
}
if not new_docs:
logger.info("No new unique documents were found.")
logger.warning("No new unique documents were found.")
return
# 5. Store document content in full_docs and status in doc_status
# Store full document content separately
# 4. Store document content in full_docs and status in doc_status
# Store full document content separately
full_docs_data = {
doc_id: {"content": contents[doc_id]["content"]}
for doc_id in new_docs.keys()
@ -1118,23 +1108,114 @@ class LightRAG:
return track_id
async def apipeline_enqueue_error_documents(
self,
error_files: list[dict[str, Any]],
track_id: str | None = None,
) -> None:
"""
Record file extraction errors in doc_status storage.
This function creates error document entries in the doc_status storage for files
that failed during the extraction process. Each error entry contains information
about the failure to help with debugging and monitoring.
Args:
error_files: List of dictionaries containing error information for each failed file.
Each dictionary should contain:
- file_path: Original file name/path
- error_description: Brief error description (for content_summary)
- original_error: Full error message (for error_msg)
- file_size: File size in bytes (for content_length, 0 if unknown)
track_id: Optional tracking ID for grouping related operations
Returns:
None
"""
if not error_files:
logger.debug("No error files to record")
return
# Generate track_id if not provided
if track_id is None or track_id.strip() == "":
track_id = generate_track_id("error")
error_docs: dict[str, Any] = {}
current_time = datetime.now(timezone.utc).isoformat()
for error_file in error_files:
file_path = error_file.get("file_path", "unknown_file")
error_description = error_file.get(
"error_description", "File extraction failed"
)
original_error = error_file.get("original_error", "Unknown error")
file_size = error_file.get("file_size", 0)
# Generate unique doc_id with "error-" prefix
doc_id_content = f"{file_path}-{error_description}"
doc_id = compute_mdhash_id(doc_id_content, prefix="error-")
error_docs[doc_id] = {
"status": DocStatus.FAILED,
"content_summary": error_description,
"content_length": file_size,
"error_msg": original_error,
"chunks_count": 0, # No chunks for failed files
"created_at": current_time,
"updated_at": current_time,
"file_path": file_path,
"track_id": track_id,
"metadata": {
"error_type": "file_extraction_error",
},
}
# Store error documents in doc_status
if error_docs:
await self.doc_status.upsert(error_docs)
# Log each error for debugging
for doc_id, error_doc in error_docs.items():
logger.error(
f"File processing error: - ID: {doc_id} {error_doc['file_path']}"
)
async def _validate_and_fix_document_consistency(
self,
to_process_docs: dict[str, DocProcessingStatus],
pipeline_status: dict,
pipeline_status_lock: asyncio.Lock,
) -> dict[str, DocProcessingStatus]:
"""Validate and fix document data consistency by deleting inconsistent entries"""
"""Validate and fix document data consistency by deleting inconsistent entries, but preserve failed documents"""
inconsistent_docs = []
failed_docs_to_preserve = []
# Check each document's data consistency
for doc_id, status_doc in to_process_docs.items():
# Check if corresponding content exists in full_docs
content_data = await self.full_docs.get_by_id(doc_id)
if not content_data:
inconsistent_docs.append(doc_id)
# Check if this is a failed document that should be preserved
if (
hasattr(status_doc, "status")
and status_doc.status == DocStatus.FAILED
):
failed_docs_to_preserve.append(doc_id)
else:
inconsistent_docs.append(doc_id)
# Delete inconsistent document entries one by one
# Log information about failed documents that will be preserved
if failed_docs_to_preserve:
async with pipeline_status_lock:
preserve_message = f"Preserving {len(failed_docs_to_preserve)} failed document entries for manual review"
logger.info(preserve_message)
pipeline_status["latest_message"] = preserve_message
pipeline_status["history_messages"].append(preserve_message)
# Remove failed documents from processing list but keep them in doc_status
for doc_id in failed_docs_to_preserve:
to_process_docs.pop(doc_id, None)
# Delete inconsistent document entries(excluding failed documents)
if inconsistent_docs:
async with pipeline_status_lock:
summary_message = (
@ -1156,7 +1237,9 @@ class LightRAG:
# Log successful deletion
async with pipeline_status_lock:
log_message = f"Deleted entry: {doc_id} ({file_path})"
log_message = (
f"Deleted inconsistent entry: {doc_id} ({file_path})"
)
logger.info(log_message)
pipeline_status["latest_message"] = log_message
pipeline_status["history_messages"].append(log_message)
@ -1174,7 +1257,7 @@ class LightRAG:
# Final summary log
async with pipeline_status_lock:
final_message = f"Data consistency cleanup completed: successfully deleted {successful_deletions} entries"
final_message = f"Data consistency cleanup completed: successfully deleted {successful_deletions} inconsistent entries, preserved {len(failed_docs_to_preserve)} failed documents"
logger.info(final_message)
pipeline_status["latest_message"] = final_message
pipeline_status["history_messages"].append(final_message)

View file

@ -1,4 +1,4 @@
import { useState, useCallback, useEffect } from 'react'
import { useState, useCallback, useEffect, useRef } from 'react'
import Button from '@/components/ui/Button'
import {
Dialog,
@ -15,10 +15,10 @@ import { toast } from 'sonner'
import { errorMessage } from '@/lib/utils'
import { clearDocuments, clearCache } from '@/api/lightrag'
import { EraserIcon, AlertTriangleIcon } from 'lucide-react'
import { EraserIcon, AlertTriangleIcon, Loader2Icon } from 'lucide-react'
import { useTranslation } from 'react-i18next'
// 简单的Label组件
// Simple Label component
const Label = ({
htmlFor,
className,
@ -43,18 +43,51 @@ export default function ClearDocumentsDialog({ onDocumentsCleared }: ClearDocume
const [open, setOpen] = useState(false)
const [confirmText, setConfirmText] = useState('')
const [clearCacheOption, setClearCacheOption] = useState(false)
const [isClearing, setIsClearing] = useState(false)
const timeoutRef = useRef<ReturnType<typeof setTimeout> | null>(null)
const isConfirmEnabled = confirmText.toLowerCase() === 'yes'
// 重置状态当对话框关闭时
// Timeout constant (30 seconds)
const CLEAR_TIMEOUT = 30000
// Reset state when dialog closes
useEffect(() => {
if (!open) {
setConfirmText('')
setClearCacheOption(false)
setIsClearing(false)
// Clear timeout timer
if (timeoutRef.current) {
clearTimeout(timeoutRef.current)
timeoutRef.current = null
}
}
}, [open])
// Cleanup when component unmounts
useEffect(() => {
return () => {
// Clear timeout timer when component unmounts
if (timeoutRef.current) {
clearTimeout(timeoutRef.current)
}
}
}, [])
const handleClear = useCallback(async () => {
if (!isConfirmEnabled) return
if (!isConfirmEnabled || isClearing) return
setIsClearing(true)
// Set timeout protection
timeoutRef.current = setTimeout(() => {
if (isClearing) {
toast.error(t('documentPanel.clearDocuments.timeout'))
setIsClearing(false)
setConfirmText('') // Reset confirmation text after timeout
}
}, CLEAR_TIMEOUT)
try {
const result = await clearDocuments()
@ -81,13 +114,20 @@ export default function ClearDocumentsDialog({ onDocumentsCleared }: ClearDocume
onDocumentsCleared().catch(console.error)
}
// 所有操作成功后关闭对话框
// Close dialog after all operations succeed
setOpen(false)
} catch (err) {
toast.error(t('documentPanel.clearDocuments.error', { error: errorMessage(err) }))
setConfirmText('')
} finally {
// Clear timeout timer
if (timeoutRef.current) {
clearTimeout(timeoutRef.current)
timeoutRef.current = null
}
setIsClearing(false)
}
}, [isConfirmEnabled, clearCacheOption, setOpen, t, onDocumentsCleared])
}, [isConfirmEnabled, isClearing, clearCacheOption, setOpen, t, onDocumentsCleared, CLEAR_TIMEOUT])
return (
<Dialog open={open} onOpenChange={setOpen}>
@ -125,6 +165,7 @@ export default function ClearDocumentsDialog({ onDocumentsCleared }: ClearDocume
onChange={(e: React.ChangeEvent<HTMLInputElement>) => setConfirmText(e.target.value)}
placeholder={t('documentPanel.clearDocuments.confirmPlaceholder')}
className="w-full"
disabled={isClearing}
/>
</div>
@ -133,6 +174,7 @@ export default function ClearDocumentsDialog({ onDocumentsCleared }: ClearDocume
id="clear-cache"
checked={clearCacheOption}
onCheckedChange={(checked: boolean | 'indeterminate') => setClearCacheOption(checked === true)}
disabled={isClearing}
/>
<Label htmlFor="clear-cache" className="text-sm font-medium cursor-pointer">
{t('documentPanel.clearDocuments.clearCache')}
@ -141,15 +183,26 @@ export default function ClearDocumentsDialog({ onDocumentsCleared }: ClearDocume
</div>
<DialogFooter>
<Button variant="outline" onClick={() => setOpen(false)}>
<Button
variant="outline"
onClick={() => setOpen(false)}
disabled={isClearing}
>
{t('common.cancel')}
</Button>
<Button
variant="destructive"
onClick={handleClear}
disabled={!isConfirmEnabled}
disabled={!isConfirmEnabled || isClearing}
>
{t('documentPanel.clearDocuments.confirmButton')}
{isClearing ? (
<>
<Loader2Icon className="mr-2 h-4 w-4 animate-spin" />
{t('documentPanel.clearDocuments.clearing')}
</>
) : (
t('documentPanel.clearDocuments.confirmButton')
)}
</Button>
</DialogFooter>
</DialogContent>

View file

@ -532,15 +532,25 @@ export default function DocumentManager() {
// Reset health check timer with 1 second delay to avoid race condition
useBackendState.getState().resetHealthCheckTimerDelayed(1000);
// Schedule a health check 2 seconds after successful scan
// Start fast refresh with 2-second interval immediately after scan
startPollingInterval(2000);
// Set recovery timer to restore normal polling interval after 15 seconds
setTimeout(() => {
if (isMountedRef.current && currentTab === 'documents' && health) {
// Restore intelligent polling interval based on document status
const hasActiveDocuments = (statusCounts.processing || 0) > 0 || (statusCounts.pending || 0) > 0;
const normalInterval = hasActiveDocuments ? 5000 : 30000;
startPollingInterval(normalInterval);
}
}, 15000); // Restore after 15 seconds
} catch (err) {
// Only show error if component is still mounted
if (isMountedRef.current) {
toast.error(t('documentPanel.documentManager.errors.scanFailed', { error: errorMessage(err) }));
}
}
}, [t, startPollingInterval])
}, [t, startPollingInterval, currentTab, health, statusCounts])
// Handle page size change - update state and save to store
const handlePageSizeChange = useCallback((newPageSize: number) => {

View file

@ -50,6 +50,8 @@
"confirmPlaceholder": "اكتب yes للتأكيد",
"clearCache": "مسح كاش نموذج اللغة",
"confirmButton": "نعم",
"clearing": "جارٍ المسح...",
"timeout": "انتهت مهلة عملية المسح، يرجى المحاولة مرة أخرى",
"success": "تم مسح المستندات بنجاح",
"cacheCleared": "تم مسح ذاكرة التخزين المؤقت بنجاح",
"cacheClearFailed": "فشل مسح ذاكرة التخزين المؤقت:\n{{error}}",

View file

@ -50,6 +50,8 @@
"confirmPlaceholder": "Type yes to confirm",
"clearCache": "Clear LLM cache",
"confirmButton": "YES",
"clearing": "Clearing...",
"timeout": "Clear operation timed out, please try again",
"success": "Documents cleared successfully",
"cacheCleared": "Cache cleared successfully",
"cacheClearFailed": "Failed to clear cache:\n{{error}}",

View file

@ -50,6 +50,8 @@
"confirmPlaceholder": "Tapez yes pour confirmer",
"clearCache": "Effacer le cache LLM",
"confirmButton": "OUI",
"clearing": "Effacement en cours...",
"timeout": "L'opération d'effacement a expiré, veuillez réessayer",
"success": "Documents effacés avec succès",
"cacheCleared": "Cache effacé avec succès",
"cacheClearFailed": "Échec de l'effacement du cache :\n{{error}}",

View file

@ -50,6 +50,8 @@
"confirmPlaceholder": "输入 yes 确认",
"clearCache": "清空LLM缓存",
"confirmButton": "确定",
"clearing": "正在清除...",
"timeout": "清除操作超时,请重试",
"success": "文档清空成功",
"cacheCleared": "缓存清空成功",
"cacheClearFailed": "清空缓存失败:\n{{error}}",

View file

@ -50,6 +50,8 @@
"confirmPlaceholder": "輸入 yes 以確認",
"clearCache": "清空 LLM 快取",
"confirmButton": "確定",
"clearing": "正在清除...",
"timeout": "清除操作逾時,請重試",
"success": "文件清空成功",
"cacheCleared": "快取清空成功",
"cacheClearFailed": "清空快取失敗:\n{{error}}",