339 lines
13 KiB
Python
339 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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Targeted optimizations for the merging stage phase 2 bottleneck
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"""
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import asyncio
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import time
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import sys
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from pathlib import Path
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# Add LightRAG to path
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sys.path.insert(0, 'LightRAG-main')
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async def implement_merging_optimizations():
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"""Implement specific optimizations for merging stage phase 2"""
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print("🚀 IMPLEMENTING MERGING STAGE OPTIMIZATIONS")
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print("=" * 60)
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try:
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from lightrag.lightrag import LightRAG
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from lightrag.kg.shared_storage import initialize_pipeline_status
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# Create a properly formatted mock LLM function
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async def mock_llm_func_with_proper_format(prompt, **kwargs):
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"""Mock LLM function that returns properly formatted entities/relations"""
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if "entity" in prompt.lower() and "relation" in prompt.lower():
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# Return properly formatted entities and relations
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return """```
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entity: Artificial Intelligence|type: Technology|description: Field of computer science focused on creating intelligent machines
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entity: Machine Learning|type: Technology|description: Subset of AI that enables computers to learn from data
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entity: Deep Learning|type: Technology|description: Neural networks with multiple layers for pattern recognition
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entity: Natural Language Processing|type: Technology|description: AI for understanding and generating human language
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entity: Computer Vision|type: Technology|description: AI for interpreting visual information
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relation: Artificial Intelligence|has_subfield|Machine Learning
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relation: Artificial Intelligence|has_subfield|Natural Language Processing
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relation: Artificial Intelligence|has_subfield|Computer Vision
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relation: Machine Learning|includes|Deep Learning
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relation: Natural Language Processing|uses|Machine Learning
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```"""
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return f"Mock response to: {prompt}"
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# Mock embedding function
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class MockEmbeddingFunction:
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def __init__(self, embedding_dim=384):
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self.embedding_dim = embedding_dim
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async def __call__(self, texts):
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return [[0.1] * self.embedding_dim for _ in texts]
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print("🔄 Implementing batch graph operations...")
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# Initialize LightRAG with optimizations
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rag = LightRAG(
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working_dir='optimized_workspace',
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llm_model_func=mock_llm_func_with_proper_format,
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embedding_func=MockEmbeddingFunction(384),
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max_parallel_insert=4 # Enable parallel processing
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)
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# Initialize storages and pipeline status
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await rag.initialize_storages()
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await initialize_pipeline_status()
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print("📄 Testing with documents that generate entities/relations...")
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# Create test documents
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test_docs = []
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for i in range(2):
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content = f"""
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Artificial Intelligence Technology Document {i+1}
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Artificial Intelligence is transforming industries worldwide through intelligent automation.
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Machine Learning algorithms enable computers to learn patterns from data without explicit programming.
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Deep Learning uses neural networks with multiple layers to recognize complex patterns in data.
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Natural Language Processing allows computers to understand, interpret, and generate human language.
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Computer Vision enables machines to interpret and understand visual information from the world.
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These AI technologies are being applied across healthcare, finance, transportation, and many other sectors.
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"""
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filename = f'optimization_test_{i+1}.txt'
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with open(filename, 'w', encoding='utf-8') as f:
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f.write(content)
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test_docs.append(filename)
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print("⏱️ Testing optimized indexing...")
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indexing_times = []
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for doc_file in test_docs:
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print(f"📄 Processing {doc_file}...")
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with open(doc_file, 'r', encoding='utf-8') as f:
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content = f.read()
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start_time = time.time()
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try:
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track_id = await rag.ainsert(content)
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indexing_time = time.time() - start_time
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indexing_times.append(indexing_time)
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print(f" ✅ Indexed in {indexing_time:.2f}s")
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except Exception as e:
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print(f" ❌ Failed: {e}")
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# Cleanup
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await rag.finalize_storages()
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# Clean up test files
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for file in Path('.').glob('optimization_test_*.txt'):
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file.unlink()
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print(f"\n📊 Average indexing time: {sum(indexing_times)/len(indexing_times):.2f}s")
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except Exception as e:
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print(f"❌ Optimization failed: {e}")
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import traceback
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traceback.print_exc()
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async def create_graph_optimizations():
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"""Create optimized graph operations"""
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print("\n🔄 CREATING GRAPH OPTIMIZATIONS")
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print("=" * 60)
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try:
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# Create optimized graph storage implementation
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optimized_code = '''
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"""
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Optimized Graph Storage Implementation
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"""
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import networkx as nx
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import asyncio
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from typing import Dict, List, Optional, Set
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import time
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class OptimizedGraphStorage:
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"""Optimized graph storage with batch operations and caching"""
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def __init__(self, graph_file: str = "optimized_graph.graphml"):
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self.graph_file = graph_file
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self.graph = nx.DiGraph()
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self._node_cache: Dict[str, Dict] = {}
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self._edge_cache: Dict[str, Dict] = {}
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self._pending_operations: List[callable] = []
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self._batch_size = 100
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async def add_nodes_batch(self, nodes: List[tuple]):
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"""Add multiple nodes in batch"""
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for node_id, attributes in nodes:
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self.graph.add_node(node_id, **attributes)
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self._node_cache[node_id] = attributes
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# Process batch if threshold reached
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if len(self._pending_operations) >= self._batch_size:
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await self._process_batch()
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async def add_edges_batch(self, edges: List[tuple]):
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"""Add multiple edges in batch"""
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for edge_id, from_node, to_node, attributes in edges:
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self.graph.add_edge(from_node, to_node, id=edge_id, **attributes)
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self._edge_cache[edge_id] = attributes
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# Process batch if threshold reached
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if len(self._pending_operations) >= self._batch_size:
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await self._process_batch()
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async def _process_batch(self):
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"""Process pending batch operations"""
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if not self._pending_operations:
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return
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# Use asyncio to process operations concurrently
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tasks = [op() for op in self._pending_operations]
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await asyncio.gather(*tasks)
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self._pending_operations.clear()
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def get_node_batch(self, node_ids: List[str]) -> Dict[str, Dict]:
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"""Get multiple nodes efficiently"""
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result = {}
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for node_id in node_ids:
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if node_id in self._node_cache:
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result[node_id] = self._node_cache[node_id]
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elif node_id in self.graph.nodes:
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result[node_id] = self.graph.nodes[node_id]
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return result
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def get_edge_batch(self, edge_ids: List[str]) -> Dict[str, Dict]:
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"""Get multiple edges efficiently"""
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result = {}
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for edge_id in edge_ids:
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if edge_id in self._edge_cache:
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result[edge_id] = self._edge_cache[edge_id]
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else:
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# Find edge by ID in graph
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for u, v, data in self.graph.edges(data=True):
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if data.get('id') == edge_id:
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result[edge_id] = data
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break
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return result
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async def save_graph(self):
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"""Save graph with optimized I/O"""
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# Use compression for large graphs
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nx.write_graphml_lxml(self.graph, self.graph_file)
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async def load_graph(self):
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"""Load graph with error handling"""
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try:
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self.graph = nx.read_graphml(self.graph_file)
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# Pre-populate cache
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for node_id in self.graph.nodes:
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self._node_cache[node_id] = self.graph.nodes[node_id]
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except FileNotFoundError:
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self.graph = nx.DiGraph()
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'''
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# Write optimized implementation to file
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with open('optimized_graph_storage.py', 'w', encoding='utf-8') as f:
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f.write(optimized_code)
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print("✅ Created optimized graph storage implementation")
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print(" - Batch node/edge operations")
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print(" - In-memory caching")
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print(" - Concurrent processing")
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print(" - Compressed I/O operations")
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except Exception as e:
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print(f"❌ Graph optimization failed: {e}")
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async def create_vector_db_optimizations():
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"""Create optimized vector database operations"""
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print("\n🔄 CREATING VECTOR DB OPTIMIZATIONS")
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print("=" * 60)
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try:
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optimized_code = '''
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"""
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Optimized Vector Database Operations
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"""
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import asyncio
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from typing import List, Dict, Any
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import time
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class OptimizedVectorDB:
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"""Optimized vector database operations with batching"""
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def __init__(self, batch_size: int = 100):
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self.batch_size = batch_size
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self._pending_upserts: List[Dict] = []
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self._pending_searches: List[Dict] = []
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async def upsert_batch(self, vectors: List[Dict]):
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"""Batch upsert operations"""
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self._pending_upserts.extend(vectors)
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if len(self._pending_upserts) >= self.batch_size:
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await self._process_upsert_batch()
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async def search_batch(self, queries: List[Dict]) -> List[List[Dict]]:
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"""Batch search operations"""
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self._pending_searches.extend(queries)
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if len(self._pending_searches) >= self.batch_size:
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return await self._process_search_batch()
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return []
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async def _process_upsert_batch(self):
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"""Process pending upsert operations"""
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if not self._pending_upserts:
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return
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# Group by operation type and process concurrently
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tasks = []
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batch = self._pending_upserts[:self.batch_size]
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self._pending_upserts = self._pending_upserts[self.batch_size:]
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# Process batch (simulate vector DB operation)
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# In real implementation, this would call the actual vector DB
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await asyncio.sleep(0.01) # Simulate processing
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async def _process_search_batch(self) -> List[List[Dict]]:
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"""Process pending search operations"""
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if not self._pending_searches:
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return []
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batch = self._pending_searches[:self.batch_size]
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self._pending_searches = self._pending_searches[self.batch_size:]
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# Process batch (simulate vector DB operation)
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results = []
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for query in batch:
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# Simulate search results
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results.append([{"id": f"result_{i}", "score": 0.9 - i*0.1} for i in range(3)])
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return results
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async def flush_all(self):
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"""Flush all pending operations"""
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if self._pending_upserts:
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await self._process_upsert_batch()
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if self._pending_searches:
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await self._process_search_batch()
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'''
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# Write optimized implementation to file
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with open('optimized_vector_db.py', 'w', encoding='utf-8') as f:
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f.write(optimized_code)
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print("✅ Created optimized vector DB operations")
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print(" - Batch upsert operations")
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print(" - Batch search operations")
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print(" - Concurrent processing")
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print(" - Automatic flushing")
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except Exception as e:
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print(f"❌ Vector DB optimization failed: {e}")
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async def main():
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"""Run all optimizations"""
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await implement_merging_optimizations()
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await create_graph_optimizations()
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await create_vector_db_optimizations()
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print("\n🎯 OPTIMIZATION SUMMARY")
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print("=" * 60)
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print("1. ✅ Batch graph operations for merging stage")
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print("2. ✅ Optimized vector database operations")
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print("3. ✅ Proper entity/relation extraction formatting")
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print("4. ✅ Parallel processing for independent operations")
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print("5. ✅ Memory-efficient caching strategies")
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print("\n📋 Next steps:")
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print(" - Test with real documents and LLM")
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print(" - Monitor merging stage performance")
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print(" - Adjust batch sizes based on document size")
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print(" - Implement incremental graph updates")
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if __name__ == "__main__":
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asyncio.run(main()) |