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packages/leann-backend-diskann/third_party/DiskANN/python/src/defaults.py

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+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT license.
+
+"""
+# Parameter Defaults
+These parameter defaults are re-exported from the C++ extension module, and used to keep the pythonic wrapper in sync with the C++.
+"""
+from ._diskannpy import defaults as _defaults
+
+ALPHA = _defaults.ALPHA
+""" 
+Note that, as ALPHA is a `float32` (single precision float) in C++, when converted into Python it becomes a 
+`float64` (double precision float). The actual value is 1.2f. The alpha parameter (>=1) is used to control the nature 
+and number of points that are added to the graph. A higher alpha value (e.g., 1.4) will result in fewer hops (and IOs) 
+to convergence, but probably more distance comparisons compared to a lower alpha value.
+"""
+NUM_THREADS = _defaults.NUM_THREADS
+""" Number of threads to use. `0` will use all available detected logical processors """
+MAX_OCCLUSION_SIZE = _defaults.MAX_OCCLUSION_SIZE
+""" 
+The maximum number of points that can be occluded by a single point. This is used to  prevent a single point from 
+dominating the graph structure. If a point has more than `max_occlusion_size` neighbors closer to it than the current 
+point, it will not be added to the graph. This is a tradeoff between index build time and search quality. 
+"""
+FILTER_COMPLEXITY = _defaults.FILTER_COMPLEXITY
+""" 
+Complexity (a.k.a. `L`) references the size of the list we store candidate approximate neighbors in while doing a 
+filtered search. This value must be larger than `k_neighbors`, and larger values tend toward higher recall in the 
+resultant ANN search at the cost of more time. 
+"""
+NUM_FROZEN_POINTS_STATIC = _defaults.NUM_FROZEN_POINTS_STATIC
+""" Number of points frozen by default in a StaticMemoryIndex """
+NUM_FROZEN_POINTS_DYNAMIC = _defaults.NUM_FROZEN_POINTS_DYNAMIC
+""" Number of points frozen by default in a DynamicMemoryIndex """
+SATURATE_GRAPH = _defaults.SATURATE_GRAPH
+""" Whether to saturate the graph or not. Default is `True` """
+GRAPH_DEGREE = _defaults.GRAPH_DEGREE
+""" 
+Graph degree (a.k.a. `R`) is the maximum degree allowed for a node in the index's graph structure. This degree will be 
+pruned throughout the course of the index build, but it will never grow beyond this value. Higher R values require 
+longer index build times, but may result in an index showing excellent recall and latency characteristics. 
+"""
+COMPLEXITY = _defaults.COMPLEXITY
+""" 
+Complexity (a.k.a `L`) references the size of the list we store candidate approximate neighbors in while doing build
+or search tasks. It's used during index build as part of the index optimization processes. It's used in index search 
+classes both to help mitigate poor latencies during cold start, as well as on subsequent queries to conduct the search. 
+Large values will likely increase latency but also may improve recall, and tuning these values for your particular 
+index is certainly a reasonable choice.
+"""
+PQ_DISK_BYTES = _defaults.PQ_DISK_BYTES
+""" 
+Use `0` to store uncompressed data on SSD. This allows the index to asymptote to 100% recall. If your vectors are 
+too large to store in SSD, this parameter provides the option to compress the vectors using PQ for storing on SSD. 
+This will trade off recall. You would also want this to be greater than the number of bytes used for the PQ 
+compressed data stored in-memory. Default is `0`. 
+"""
+USE_PQ_BUILD = _defaults.USE_PQ_BUILD
+"""
+ Whether to use product quantization in the index building process. Product quantization is an approximation 
+technique that can vastly speed up vector computations and comparisons in a spatial neighborhood, but it is still an 
+approximation technique. It should be preferred when index creation times take longer than you can afford for your 
+use case.
+"""
+NUM_PQ_BYTES = _defaults.NUM_PQ_BYTES
+""" 
+The number of product quantization bytes to use. More bytes requires more resources in both memory and time, but is 
+like to result in better approximations. 
+"""
+USE_OPQ = _defaults.USE_OPQ
+""" Whether to use Optimized Product Quantization or not. """