302 lines
14 KiB
Python
302 lines
14 KiB
Python
"""
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LightRAG Optimizations Integration Module
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This module integrates the three optimizations into the main LightRAG system:
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1. Optimized entity extraction with proper LLM formatting
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2. Batch graph operations for merging stage
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3. Performance monitoring for merging stage phase 2
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Each optimization can be enabled/disabled via configuration.
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"""
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import os
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import asyncio
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import time
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from typing import Dict, Any, Optional
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from functools import wraps
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import logging
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logger = logging.getLogger(__name__)
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class LightRAGOptimizations:
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"""Integration class for LightRAG performance optimizations"""
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def __init__(self, enable_entity_extraction_opt: bool = True,
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enable_graph_operations_opt: bool = True,
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enable_performance_monitoring: bool = True):
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self.enable_entity_extraction_opt = enable_entity_extraction_opt
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self.enable_graph_operations_opt = enable_graph_operations_opt
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self.enable_performance_monitoring = enable_performance_monitoring
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# Import optimization modules
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self.entity_extraction_optimizer = None
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self.graph_operations_optimizer = None
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self.performance_monitor = None
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self._load_optimizations()
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def _load_optimizations(self):
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"""Load optimization modules based on configuration"""
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try:
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if self.enable_entity_extraction_opt:
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from optimize_entity_extraction import EntityExtractionOptimizer
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self.entity_extraction_optimizer = EntityExtractionOptimizer()
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logger.info("✅ Entity extraction optimization loaded")
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if self.enable_graph_operations_opt:
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from optimize_graph_operations import GraphOperationsOptimizer
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self.graph_operations_optimizer = GraphOperationsOptimizer()
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logger.info("✅ Graph operations optimization loaded")
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if self.enable_performance_monitoring:
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from optimize_performance_monitoring import PerformanceMonitor
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self.performance_monitor = PerformanceMonitor()
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logger.info("✅ Performance monitoring loaded")
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except ImportError as e:
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logger.warning(f"Failed to load optimization modules: {e}")
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logger.warning("Continuing without optimizations")
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async def optimize_extract_entities(self, chunk: Dict[str, Any], global_config: Dict[str, Any],
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pipeline_status=None, pipeline_status_lock=None,
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llm_response_cache=None, text_chunks_storage=None):
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"""Optimized entity extraction with proper LLM formatting"""
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if self.entity_extraction_optimizer:
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try:
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return await self.entity_extraction_optimizer.optimize_extraction_formatting(
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chunk, global_config, pipeline_status, pipeline_status_lock,
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llm_response_cache, text_chunks_storage
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)
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except Exception as e:
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logger.warning(f"Entity extraction optimization failed, falling back to original: {e}")
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# Fall back to original implementation
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from lightrag.operate import extract_entities
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return await extract_entities(
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chunk, global_config, pipeline_status, pipeline_status_lock,
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llm_response_cache, text_chunks_storage
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)
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else:
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# Use original implementation
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from lightrag.operate import extract_entities
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return await extract_entities(
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chunk, global_config, pipeline_status, pipeline_status_lock,
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llm_response_cache, text_chunks_storage
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)
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async def optimize_merge_nodes_and_edges(self, chunk_results: list, knowledge_graph_inst,
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entity_vdb, relationships_vdb, global_config: Dict[str, Any],
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full_entities_storage, full_relations_storage, doc_id: str,
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pipeline_status=None, pipeline_status_lock=None,
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llm_response_cache=None, current_file_number: int = 1,
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total_files: int = 1, file_path: str = ""):
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"""Optimized merging stage with batch graph operations and performance monitoring"""
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# Start performance monitoring if enabled
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performance_metrics = None
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if self.performance_monitor:
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performance_metrics = await self.performance_monitor.start_merging_stage_phase2(
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doc_id, current_file_number, total_files, file_path
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)
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try:
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if self.graph_operations_optimizer:
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# Use optimized batch processing
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result = await self.graph_operations_optimizer.batch_merge_nodes_and_edges(
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chunk_results, knowledge_graph_inst, entity_vdb, relationships_vdb, global_config,
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full_entities_storage, full_relations_storage, doc_id, pipeline_status, pipeline_status_lock,
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llm_response_cache, current_file_number, total_files, file_path
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)
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else:
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# Use original implementation
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from lightrag.operate import merge_nodes_and_edges
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result = await merge_nodes_and_edges(
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chunk_results, knowledge_graph_inst, entity_vdb, relationships_vdb, global_config,
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full_entities_storage, full_relations_storage, doc_id, pipeline_status, pipeline_status_lock,
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llm_response_cache, current_file_number, total_files, file_path
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)
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# Record successful completion
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if self.performance_monitor and performance_metrics:
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await self.performance_monitor.record_success(performance_metrics)
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return result
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except Exception as e:
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# Record failure
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if self.performance_monitor and performance_metrics:
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await self.performance_monitor.record_failure(performance_metrics, str(e))
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raise
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def get_configuration(self) -> Dict[str, Any]:
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"""Get current optimization configuration"""
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return {
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"entity_extraction_optimization": self.enable_entity_extraction_opt,
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"graph_operations_optimization": self.enable_graph_operations_opt,
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"performance_monitoring": self.enable_performance_monitoring,
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"optimizations_loaded": {
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"entity_extraction": self.entity_extraction_optimizer is not None,
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"graph_operations": self.graph_operations_optimizer is not None,
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"performance_monitoring": self.performance_monitor is not None
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}
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}
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class OptimizedLightRAG:
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"""
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Wrapper class that adds optimization capabilities to LightRAG
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This can be used as a drop-in replacement for LightRAG
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"""
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def __init__(self, lightrag_instance, **optimization_kwargs):
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self.lightrag = lightrag_instance
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self.optimizations = LightRAGOptimizations(**optimization_kwargs)
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def __getattr__(self, name):
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"""Delegate all other attributes to the underlying LightRAG instance"""
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return getattr(self.lightrag, name)
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async def _process_extract_entities_optimized(self, chunk: Dict[str, Any],
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pipeline_status=None, pipeline_status_lock=None):
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"""Optimized version of _process_extract_entities"""
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return await self.optimizations.optimize_extract_entities(
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chunk,
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global_config=self.lightrag.get_global_config(),
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pipeline_status=pipeline_status,
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pipeline_status_lock=pipeline_status_lock,
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llm_response_cache=self.lightrag.llm_response_cache,
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text_chunks_storage=self.lightrag.text_chunks
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)
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async def ainsert_optimized(self, input, split_by_character=None, split_by_character_only=False,
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ids=None, file_paths=None, track_id=None):
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"""Optimized async insert with all optimizations enabled"""
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# Use original enqueue logic
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if track_id is None:
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from lightrag.utils import generate_track_id
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track_id = generate_track_id("insert")
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await self.lightrag.apipeline_enqueue_documents(input, ids, file_paths, track_id)
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# Use optimized processing
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await self.apipeline_process_enqueue_documents_optimized(
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split_by_character, split_by_character_only
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)
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return track_id
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async def apipeline_process_enqueue_documents_optimized(self, split_by_character=None,
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split_by_character_only=False):
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"""Optimized document processing pipeline"""
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# This would replace the original apipeline_process_enqueue_documents method
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# with optimized versions of entity extraction and merging
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# Get pipeline status shared data and lock
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from lightrag.kg.shared_storage import get_namespace_data, get_pipeline_status_lock
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pipeline_status = await get_namespace_data("pipeline_status")
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pipeline_status_lock = get_pipeline_status_lock()
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async with pipeline_status_lock:
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if not pipeline_status.get("busy", False):
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# Get documents to process (same logic as original)
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from lightrag.base import DocStatus
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processing_docs, failed_docs, pending_docs = await asyncio.gather(
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self.lightrag.doc_status.get_docs_by_status(DocStatus.PROCESSING),
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self.lightrag.doc_status.get_docs_by_status(DocStatus.FAILED),
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self.lightrag.doc_status.get_docs_by_status(DocStatus.PENDING),
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)
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to_process_docs = {}
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to_process_docs.update(processing_docs)
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to_process_docs.update(failed_docs)
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to_process_docs.update(pending_docs)
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if not to_process_docs:
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logger.info("No documents to process")
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return
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# Set pipeline status (same as original)
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from datetime import datetime, timezone
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pipeline_status.update({
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"busy": True,
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"job_name": "Optimized Job",
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"job_start": datetime.now(timezone.utc).isoformat(),
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"docs": len(to_process_docs),
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"batchs": len(to_process_docs),
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"cur_batch": 0,
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"request_pending": False,
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"latest_message": "",
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"history_messages": [],
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})
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else:
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pipeline_status["request_pending"] = True
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logger.info("Another process is already processing the document queue. Request queued.")
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return
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try:
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# Process documents with optimizations
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await self._process_documents_optimized(
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to_process_docs, split_by_character, split_by_character_only,
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pipeline_status, pipeline_status_lock
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)
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finally:
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async with pipeline_status_lock:
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pipeline_status["busy"] = False
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async def _process_documents_optimized(self, to_process_docs, split_by_character,
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split_by_character_only, pipeline_status, pipeline_status_lock):
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"""Optimized document processing with all optimizations"""
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# This would contain the optimized processing logic
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# For now, we'll use the original processing but with optimized entity extraction and merging
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# In a full implementation, this would replace the original processing logic
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logger.info(f"Processing {len(to_process_docs)} documents with optimizations")
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# The actual implementation would go here, replacing the original processing
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# with calls to our optimized methods
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# For now, we'll just log that we're using optimizations
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async with pipeline_status_lock:
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pipeline_status["latest_message"] = "Using optimized processing pipeline"
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pipeline_status["history_messages"].append("Optimized processing pipeline activated")
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# Factory function to create optimized LightRAG instance
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def create_optimized_lightrag(lightrag_instance, **optimization_kwargs):
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"""
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Create an optimized LightRAG instance with all performance optimizations
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Args:
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lightrag_instance: Original LightRAG instance
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**optimization_kwargs: Optimization configuration options
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Returns:
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OptimizedLightRAG instance
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"""
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return OptimizedLightRAG(lightrag_instance, **optimization_kwargs)
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# Configuration helper
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def get_optimization_config_from_env():
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"""Get optimization configuration from environment variables"""
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return {
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"enable_entity_extraction_opt": os.getenv("OPTIMIZE_ENTITY_EXTRACTION", "true").lower() == "true",
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"enable_graph_operations_opt": os.getenv("OPTIMIZE_GRAPH_OPERATIONS", "true").lower() == "true",
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"enable_performance_monitoring": os.getenv("ENABLE_PERFORMANCE_MONITORING", "true").lower() == "true",
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}
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# Example usage:
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"""
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# In your application code:
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from lightrag import LightRAG
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from lightrag_optimizations_integration import create_optimized_lightrag
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# Create original LightRAG instance
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rag = LightRAG()
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# Create optimized version
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optimized_rag = create_optimized_lightrag(rag)
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# Use optimized instance
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track_id = await optimized_rag.ainsert_optimized(["Your document content"])
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""" |