Dev tools · alternatives · 2026
24 alternatives to Client-Side WASM Vector Search
🕸️🦀 A WASM vector similarity search written in Rust - tantaraio/voy
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Client-Side WASM Vector Search launched in 2023; newer entries below may have overtaken it.
- 1
HNSW implementation in Rust. Reference: https://arxiv.org/ftp/arxiv/papers/1603/1603.09320.pdf - swapneel/hnsw-rust
2024 · github.com · its alternatives →
- 2
I recently found myself computing the similarity between lots of very high dimensional vectors (i.e., sentence embedding vectors from LLMs), and I wanted to try some more powerful measures of similarity/dependency than just Cosine similarity, which seems to be the default for everything nowadays because of its computational efficiency. There are many other more involved measures that can detect more subtle relationships, but the problem is that some of them are quite slow to compute, especially if you're trying to do it in Python. For my favorite measure of statistical dependency,…
2023 · github.com · its alternatives →
- 3IM
As a grad student (and an ADHDer), I had trouble doing literature review systematically. To combat this, I made a website that finds similar papers using the meaning of the thing I am looking for. I used MixedBread's [^1] embedding model to generate vectors from the abstracts. I store and search similar vectors using Milvus [^2] and finally use Gradio [^3] to serve the frontend. I update the vector database weekly by pulling the metadata dataset from Kaggle [^4]. To speed up the search process on my free oracle instance, I binarise the embeddings and use Hamming distance as a metric. I would…
2024 · papermatch.mitanshu.tech · its alternatives →
- 4
TurboQuant WASM SIMD vector compression — 3 bits/dim with fast dot product. Requires relaxed SIMD (Chrome 114+, Firefox 128+, Safari 18+, Node 20+) - teamchong/turboquant-wasm
Apr 2026 · github.com · its alternatives →
- 5
A high-performance observability data pipeline. Contribute to vectordotdev/vector development by creating an account on GitHub.
2019 · github.com · its alternatives →
- 6
Hey HN, we're excited to show you client-vector-search, a client-side library that helps you embed, store, search, and cache vectors in your browser or node env. We needed it at https://searchbase.app and that's why we've built it. with it you get: 1. easy setup: you only need to add 5 lines of code to build a semantic search 2. no embedding api needed: you don't need an api and have to pay for it unless ure scaling up millions 3. faster search: modern hardware is better than cheap cloud computers (0.5vCPUs) 4. zero latency: no back-and-forth with server-side 5. easy integration…
2023 · clientvectorsearch.com · its alternatives →
- 7
Vector space model implemented in a few lines . Contribute to felipehummel/TinySearchEngine development by creating an account on GitHub.
2011 · github.com · its alternatives →
- 8
"Game" is a stretch, but I put together this repo containing a pure Rust app which uses SDL2 and compiles to WASM (and can be run in a browser). Older resources were a bit out of date and a bit too verbose for my purposes, so here's a minimal working example.
2024 · github.com · its alternatives →
- 9
Tantivy▲68A full-text, horse-speed search engine library in Rust
2022 · github.com · its alternatives →
- 10
Hi HN, I built EdgeVec, a vector database that runs entirely in the browser. It implements HNSW (Hierarchical Navigable Small World) graphs for approximate nearest neighbor search. Performance: - Sub-millisecond search at 100k vectors (768 dimensions, k=10) - 148 KB gzipped bundle - 3.6x memory reduction with scalar quantization Use cases: browser extensions with semantic search, local-first apps, privacy-preserving RAG. Technical: Written in Rust, compiled to WASM. Uses AVX2 SIMD on native, simd128 on WASM. IndexedDB for browser persistence. npm:…
Dec 2025 · github.com · its alternatives →
- 11
Hi HN, I am Jiayuan, and I'm here to introduce a tool we've been building over the past few months: Devv (https://devv.ai). In simple terms, it is an AI-powered search engine specifically designed for developers. Now, you might ask, with so many AI search engines already available—Perplexity, You.com, Phind, and several open-source projects—why do we need another one? We all know that Generative Search Engines are built on RAG (Retrieval-Augmented Generation)[1] combined with Large Language Models (LLMs). Most of the products mentioned above use indexes from general search engines…
2024 · devv.ai · its alternatives →
- 12
Using wasm-bindgen, and the tween-rs crate
2024 · rustween.mtassoumt.uk · its alternatives →
- 13
A vector index built on TurboQuant, written in Rust with Python bindings - RyanCodrai/turbovec
Mar 2026 · github.com · its alternatives →
- 14AG
This is a vector index I built that supports insertion and k-nearest neighbors (k-NN) querying, optimized for GPUs. It operates entirely in CUDA and can process queries on half a billion vectors in under 200 milliseconds. The codebase is structured as a standalone library with an HTTP API for remote access. It’s intended for high-performance search tasks—think similarity search, AI model retrieval, or reinforcement learning replay buffers. The codebase is located at https://github.com/rodlaf/BinaryGPUIndex.
2025 · rlafuente.com · its alternatives →
- 15
LinkingMem — Graph-native RAG Engine
Jun 2026 · its alternatives →
- 16
- 17MA
2020 · milvus.io · its alternatives →
- 18
LinkingMem — Graph-native RAG Engine
Jul 2026 · linking-mem.vercel.app · its alternatives →
- 19RW
Hi HN, I'm Holger, a developer who worked in the lighting industry. I built this to scratch my own itch and put it on crates.io and PyPI where nothing like it existed. The old file formats (EULUMDAT from 1990, IES from 1991) still work fine for basic photometry. But the industry is moving toward spectral data – full wavelength distributions instead of just lumen values. The new standards (TM-33, ATLA-S001) are barely supported by existing tools. So this handles both: legacy formats for compatibility, spectral data for anyone who wants to work with the new standards. Stack: Rust core, then…
Dec 2025 · eulumdat.icu · its alternatives →
- 20
Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs
2023 · github.com · its alternatives →
- 21
Happy to release FastPlaid, which aim to ease and accelerate ColBERT and ColPali retrieval
2025 · github.com · its alternatives →
- 22
Hey HN, My friend Sam and I were bored one weekend, and wanted to make something self-contained but still useful. We ended up with Victor [0], a vector database designed to run in the browser. It’s written in Rust, which gives us access to things like super-fast and compact serialization and deserialization, but it’s compiled to WASM and uses web-sys to interact with the browser’s JS APIs to write the database contents to disk. We worked hard on designing it in a way that makes sense for the browser. It uses the private origin file system [1] and stores the vectors in a compact format (one…
2023 · github.com · its alternatives →
- 23
A modern, embedded product search for modern platforms
Jul 2026 · vectoriasearch.com · its alternatives →
- 24TA
In this post, we document the results of some experiments comparing vanilla Graph RAG (just a single pass of text2cypher) vs. a router agent Graph RAG approach that can call vector search tools alongside text2cypher. The routing agent uses an LLM to decide which vector search tool to call, depending on the terms identified in the question, and it works quite well. The results show that recent frontier LLMs like `gpt-4.1` and the trusty workhorse `gemini-2.0-flash` produce great quality Cypher reliably and reproducibly, with some prompt engineering to ensure that the graph schema is formatted…
2025 · blog.kuzudb.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →