refactor: integrate document consistency validation into pipeline processing
This ensures data consistency validation is part of the main processing pipeline and provides better monitoring of inconsistent document cleanup operations.
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yangdx
parent
b5ae84fac6
commit
17faeb2fb8
1 changed files with 66 additions and 35 deletions
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@ -1111,45 +1111,71 @@ class LightRAG:
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return track_id
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async def _validate_and_fix_document_consistency(
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self, to_process_docs: dict[str, DocProcessingStatus]
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self,
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to_process_docs: dict[str, DocProcessingStatus],
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pipeline_status: dict,
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pipeline_status_lock: asyncio.Lock,
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) -> dict[str, DocProcessingStatus]:
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"""Validate and fix document data consistency"""
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"""Validate and fix document data consistency by deleting inconsistent entries"""
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inconsistent_docs = []
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# Check each document's data consistency
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for doc_id, status_doc in to_process_docs.items():
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# Check if corresponding content exists in full_docs
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content_data = await self.full_docs.get_by_id(doc_id)
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if not content_data:
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inconsistent_docs.append(doc_id)
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logger.warning(
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f"Document {doc_id} has status record but missing content in full_docs"
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)
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async with pipeline_status_lock:
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log_message = f"Data inconsistency detected: Document {doc_id} ({status_doc.file_path}) missing content data"
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logger.warning(log_message)
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pipeline_status["latest_message"] = log_message
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pipeline_status["history_messages"].append(log_message)
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# Mark inconsistent documents as FAILED
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# Delete inconsistent document entries one by one
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if inconsistent_docs:
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failed_updates = {}
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async with pipeline_status_lock:
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summary_message = f"Starting cleanup of {len(inconsistent_docs)} inconsistent document entries"
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logger.info(summary_message)
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pipeline_status["latest_message"] = summary_message
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pipeline_status["history_messages"].append(summary_message)
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successful_deletions = 0
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for doc_id in inconsistent_docs:
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try:
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status_doc = to_process_docs[doc_id]
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failed_updates[doc_id] = {
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"status": DocStatus.FAILED,
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"error_msg": "Document content not found in full_docs storage - data inconsistency detected",
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"content_summary": status_doc.content_summary,
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"content_length": status_doc.content_length,
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"created_at": status_doc.created_at,
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"updated_at": datetime.now(timezone.utc).isoformat(),
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"file_path": status_doc.file_path,
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"track_id": status_doc.track_id,
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}
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file_path = getattr(status_doc, "file_path", "unknown_source")
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await self.doc_status.upsert(failed_updates)
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logger.info(
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f"Marked {len(inconsistent_docs)} inconsistent documents as FAILED"
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)
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# Delete doc_status entry
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await self.doc_status.delete([doc_id])
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successful_deletions += 1
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# Remove these documents from the processing list
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for doc_id in inconsistent_docs:
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# Log successful deletion
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async with pipeline_status_lock:
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log_message = f"Deleted inconsistent document entry: {doc_id} ({file_path})"
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logger.info(log_message)
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pipeline_status["latest_message"] = log_message
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pipeline_status["history_messages"].append(log_message)
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# Remove from processing list
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to_process_docs.pop(doc_id, None)
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except Exception as e:
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# Log deletion failure
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async with pipeline_status_lock:
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error_message = (
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f"Failed to delete document entry: {doc_id} - {str(e)}"
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)
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logger.error(error_message)
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pipeline_status["latest_message"] = error_message
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pipeline_status["history_messages"].append(error_message)
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# Final summary log
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async with pipeline_status_lock:
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final_message = f"Data consistency cleanup completed: successfully deleted {successful_deletions} entries"
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logger.info(final_message)
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pipeline_status["latest_message"] = final_message
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pipeline_status["history_messages"].append(final_message)
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return to_process_docs
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async def apipeline_process_enqueue_documents(
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@ -1192,15 +1218,6 @@ class LightRAG:
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logger.info("No documents to process")
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return
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# Validate document data consistency and fix any issues
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to_process_docs = await self._validate_and_fix_document_consistency(
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to_process_docs
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)
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if not to_process_docs:
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logger.info("No valid documents to process after consistency check")
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return
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pipeline_status.update(
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{
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"busy": True,
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@ -1233,6 +1250,20 @@ class LightRAG:
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pipeline_status["history_messages"].append(log_message)
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break
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# Validate document data consistency and fix any issues as part of the pipeline
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to_process_docs = await self._validate_and_fix_document_consistency(
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to_process_docs, pipeline_status, pipeline_status_lock
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)
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if not to_process_docs:
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log_message = (
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"No valid documents to process after consistency check"
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)
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logger.info(log_message)
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pipeline_status["latest_message"] = log_message
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pipeline_status["history_messages"].append(log_message)
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break
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log_message = f"Processing {len(to_process_docs)} document(s)"
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logger.info(log_message)
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