Original): Edgar Bermudez
Originally published in the direction of artificial intelligence.
Introduction: Why should Vector search local?
As AI evolutions, many applications that we use every day, such as recommendation engines, searching for images and chatting assistants, what it is Vector search. This technique allows machines to quickly find “similar” things, regardless of whether they are related documents, nearby images or reactions contextual. But here is the hook: most of it happens in the cloud, because storage and inquiry about the high dimensional vector data is expensive and heavy.
This is two main problems.
First, privacy: AI searching for cloud often requires sending personal data to remote servers. Secondly, availability: people with limited communication or working on EDGE devices cannot fully use these powerful tools. Wouldn't that be useful if your phone or laptop could do it locally without sending data elsewhere?
This is a challenge resolved in a new article entitled “Leann: low foot vector index” Author: Wang et al. (2025). The authors present a method that allows quick, accurate and economical search for vectors on small, limited resources of the device without relying on cloud infrastructure.
Basics: What is vector search and what is HNSW?
To understand the Leann cartridge, we must unpack two basic ideas: Vector search AND HNSW indexing.
In vector search, data elements (such as text, images or sound) are transformed into Vectors, Basically, long lists of numbers that capture the meaning or features of each element. Finding similar elements becomes a matter of measuring the distance between these vectors. But when you are dealing with millions, a comparison of brutal strength is too slow. The approximate algorithms of the nearest neighbor (Ann) appear here.
One of the most popular Ann algorithms is HNSWOr Hierarchical, sailing little world. You can think about how a friendship network: every data point (or “knot”) has links to some others. To find a close fit, you start with a random point and “go” through the neighbors, jumping through the network until you reach something similar to the inquiry. HNSW is fast and accurate, but there is also intensive storage. This requires storing both the index chart (all these connections) and the original data vectors that add up quickly.
This makes HNSW impractical for mobile or built -in devices, in which memory is limited.
The basic idea of Leanna: Pune, do not store
Leann refers to this challenge with two key innovations:
- Pruning the chart: Instead of storing the full HNSW chart, Leann cuts it to a much smaller version, which still retains the possibility of effective navigation. He does it with Trimming algorithms This reduces unnecessary connections, maintaining a sufficient structure to maintain search accuracy.
- Reconstruction of vectors in flight: Leann does not store all the original data vectors. Instead, it stores small seed set And reconstructs the needed vectors during the inquiry using a lightweight model. This dramatically reduces memory consumption, because the full seating matrix no longer has to live in memory.
Together, these strategies reduce storage to 45 times compared to the standard HNSW implementation without significant loss of accuracy or speed. This is a breakthrough of games for local artificial intelligence.
The authors demonstrate Leann on several data sets in the real world and show that it works comparable to full HNSW both in terms of delays and withdrawal, using only a fraction of memory.
Why does it matter: making AI more private, available and personal
For me, this article is interesting because it offers a practical way to introduce powerful AI possibilities to smaller, offline. Think about a few specific examples:
- Searching for documents on the device: Imagine that you can ask your phone to “find this PDF file that I read last week about neural networks” and obtaining a significant result, even if you are on a plane or in a distant location.
- Private photo download: Instead of sending photos to the cloud for searching according to visual similarity, the device can handle it locally.
- Tools supporting health care or education: In regions with limited internet access, light search for vectors can supply diagnostic tools or personalized learning without the need for external servers.
This type of local computing model is consistent with the wider AI change away from centralized systems towards more distributed savings architecture.
Try it yourself: Demo of low warehouse vector toys
Although I did not find the implementation of Leann Open Source, which could be used to demonstrate this post, here is a simple example using hnswlib To build a vector index, simulate reduced storage using a smaller set of seeds and estimate memory savings.
# Install hnswlib if not available
!pip install hnswlib -qimport hnswlib
import numpy as np
import random
import sys
import gc
# Helper to estimate size in MB
def get_size(obj):
return sys.getsizeof(obj) / (1024 * 1024)
# 1. Generate synthetic data
dim = 128 # Vector dimension
num_elements = 10000 # Number of vectors
data = np.random.randn(num_elements, dim).astype(np.float32)
# 2. Build full HNSW index
p = hnswlib.Index(space='l2', dim=dim)
p.init_index(max_elements=num_elements, ef_construction=200, M=16)
p.add_items(data)
p.set_ef(50)
print(f"Full index size (approx): {get_size(p)} MB")
print(f"Full vector storage size: {get_size(data)} MB")
# 3. Simulate storing only a small seed set (e.g. 5% of vectors)
seed_ratio = 0.05
seed_indices = random.sample(range(num_elements), int(seed_ratio * num_elements))
seed_vectors = data(seed_indices)
# Simulated vector reconstruction (dummy here: just return nearest seed vector)
def reconstruct_vector(query_vec, seed_vectors):
dists = np.linalg.norm(seed_vectors - query_vec, axis=1)
nearest = seed_vectors(np.argmin(dists))
return nearest
# 4. Search using reconstructed vectors
query = np.random.randn(1, dim).astype(np.float32)
reconstructed_query = reconstruct_vector(query, seed_vectors)
labels, distances = p.knn_query(reconstructed_query, k=5)
print(f"Search result using reconstructed vector: {labels}")
# 5. Print simulated memory usage
print(f"Simulated reduced vector storage size: {get_size(seed_vectors)} MB")
What shows it
- Full matrix for 10,000 vectors requires ~ 5-10 MB (depending on DTYPE and dimension).
- By storing only 5% of vectors and recreating others, we can Significantly reduce memory consumption.
- The HNSW indicator itself is also compact, but cutting it further (not shown here) can bring more savings.
In the real Leann system, the reconstruction of vectors is carried out using the learned model, and the pruning stage is optimized to maintain accuracy. This example of a toy simply helps visualize Basic compromises.
Final thoughts: what does the future of AI search look like?
Leann shows that you do not have to choose between performance and performance when searching for vectors. Thanks to intelligent algorithmic design, it is possible to build AI systems that are both capable and available operating directly on the devices we use each day.
This leads to an open question:
How will a light, local search for vectors change the design of future AI applications? Will more systems transfer to offline models or will the cloud infrastructure remain dominant?
Reference
paper: https://arxiv.org/pdf/2506.08276
GitHub-NmSlib/Hnswlib: C ++/Python library for quick approximate neighbors
Library C ++/Python Library for quick approximate closest neighbors-NMSLib/Hnswlib
github.com
Hnswlib | 🦜️🔗 Langchain
Hnswlib is a vector store in memory that can be saved in a file. This
Js.langchain.com
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