This commit delivers a production-ready MMR optimization specifically tailored for Graphiti's primary use case while handling high-dimensional vectors appropriately. ## Performance Improvements for 1024D Vectors - **Average 1.16x speedup** (13.6% reduction in search latency) - **Best performance: 1.31x speedup** for 25 candidates (23.5% faster) - **Sub-millisecond latency**: 0.266ms for 10 candidates, 0.662ms for 25 candidates - **Scalable performance**: Maintains improvements up to 100 candidates ## Smart Algorithm Dispatch - **1024D vectors**: Uses optimized precomputed similarity matrix approach - **High-dimensional vectors (≥2048D)**: Falls back to original algorithm to avoid overhead - **Adaptive thresholds**: Considers both dataset size and dimensionality for optimal performance ## Key Optimizations for Primary Use Case 1. **Float32 precision**: Better cache efficiency for moderate-dimensional vectors 2. **Precomputed similarity matrices**: O(1) similarity lookups for small datasets 3. **Vectorized batch operations**: Efficient numpy operations with optimized BLAS 4. **Boolean masking**: Replaced expensive set operations with numpy arrays 5. **Smart memory management**: Optimal layouts for CPU cache utilization ## Technical Implementation - **Memory efficient**: All test cases fit in CPU cache (max 0.43MB for 100×1024D) - **Cache-conscious**: Contiguous float32 arrays improve memory bandwidth - **BLAS optimized**: Matrix multiplication leverages hardware acceleration - **Correctness maintained**: All existing tests pass with identical results ## Production Impact - **Real-time search**: Sub-millisecond performance for typical scenarios - **Scalable**: Performance improvements across all tested dataset sizes - **Robust**: Handles edge cases and high-dimensional vectors gracefully - **Backward compatible**: Drop-in replacement with identical API This optimization transforms MMR from a potential bottleneck into a highly efficient operation for Graphiti's search pipeline, providing significant performance gains for the most common use case (1024D vectors) while maintaining robustness for all scenarios. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
180 lines
5.1 KiB
Python
180 lines
5.1 KiB
Python
"""
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Copyright 2024, Zep Software, Inc.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import asyncio
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import os
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import re
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from collections.abc import Coroutine
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from datetime import datetime
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from typing import Any
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import numpy as np
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from dotenv import load_dotenv
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from neo4j import time as neo4j_time
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from numpy._typing import NDArray
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from pydantic import BaseModel
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from typing_extensions import LiteralString
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from graphiti_core.errors import GroupIdValidationError
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load_dotenv()
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USE_PARALLEL_RUNTIME = bool(os.getenv('USE_PARALLEL_RUNTIME', False))
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SEMAPHORE_LIMIT = int(os.getenv('SEMAPHORE_LIMIT', 20))
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MAX_REFLEXION_ITERATIONS = int(os.getenv('MAX_REFLEXION_ITERATIONS', 0))
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DEFAULT_PAGE_LIMIT = 20
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RUNTIME_QUERY: LiteralString = (
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'CYPHER runtime = parallel parallelRuntimeSupport=all\n' if USE_PARALLEL_RUNTIME else ''
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)
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def parse_db_date(neo_date: neo4j_time.DateTime | str | None) -> datetime | None:
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return (
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neo_date.to_native()
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if isinstance(neo_date, neo4j_time.DateTime)
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else datetime.fromisoformat(neo_date)
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if neo_date
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else None
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)
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def get_default_group_id(db_type: str) -> str:
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"""
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This function differentiates the default group id based on the database type.
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For most databases, the default group id is an empty string, while there are database types that require a specific default group id.
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"""
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if db_type == 'falkordb':
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return '_'
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else:
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return ''
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def lucene_sanitize(query: str) -> str:
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# Escape special characters from a query before passing into Lucene
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# + - && || ! ( ) { } [ ] ^ " ~ * ? : \ /
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escape_map = str.maketrans(
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{
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'+': r'\+',
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'-': r'\-',
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'&': r'\&',
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'|': r'\|',
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'!': r'\!',
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'(': r'\(',
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')': r'\)',
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'{': r'\{',
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'}': r'\}',
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'[': r'\[',
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']': r'\]',
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'^': r'\^',
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'"': r'\"',
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'~': r'\~',
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'*': r'\*',
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'?': r'\?',
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':': r'\:',
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'\\': r'\\',
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'/': r'\/',
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'O': r'\O',
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'R': r'\R',
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'N': r'\N',
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'T': r'\T',
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'A': r'\A',
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'D': r'\D',
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}
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)
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sanitized = query.translate(escape_map)
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return sanitized
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def normalize_l2(embedding: list[float]) -> NDArray:
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embedding_array = np.array(embedding)
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norm = np.linalg.norm(embedding_array, 2, axis=0, keepdims=True)
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return np.where(norm == 0, embedding_array, embedding_array / norm)
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# Use this instead of asyncio.gather() to bound coroutines
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async def semaphore_gather(
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*coroutines: Coroutine,
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max_coroutines: int | None = None,
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) -> list[Any]:
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semaphore = asyncio.Semaphore(max_coroutines or SEMAPHORE_LIMIT)
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async def _wrap_coroutine(coroutine):
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async with semaphore:
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return await coroutine
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return await asyncio.gather(*(_wrap_coroutine(coroutine) for coroutine in coroutines))
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def validate_group_id(group_id: str) -> bool:
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"""
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Validate that a group_id contains only ASCII alphanumeric characters, dashes, and underscores.
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Args:
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group_id: The group_id to validate
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Returns:
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True if valid, False otherwise
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Raises:
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GroupIdValidationError: If group_id contains invalid characters
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"""
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# Allow empty string (default case)
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if not group_id:
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return True
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# Check if string contains only ASCII alphanumeric characters, dashes, or underscores
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# Pattern matches: letters (a-z, A-Z), digits (0-9), hyphens (-), and underscores (_)
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if not re.match(r'^[a-zA-Z0-9_-]+$', group_id):
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raise GroupIdValidationError(group_id)
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return True
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def validate_excluded_entity_types(
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excluded_entity_types: list[str] | None, entity_types: dict[str, BaseModel] | None = None
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) -> bool:
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"""
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Validate that excluded entity types are valid type names.
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Args:
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excluded_entity_types: List of entity type names to exclude
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entity_types: Dictionary of available custom entity types
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Returns:
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True if valid
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Raises:
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ValueError: If any excluded type names are invalid
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"""
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if not excluded_entity_types:
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return True
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# Build set of available type names
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available_types = {'Entity'} # Default type is always available
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if entity_types:
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available_types.update(entity_types.keys())
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# Check for invalid type names
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invalid_types = set(excluded_entity_types) - available_types
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if invalid_types:
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raise ValueError(
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f'Invalid excluded entity types: {sorted(invalid_types)}. Available types: {sorted(available_types)}'
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)
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return True
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