Dev tools · September 21, 2023
Client Vector Search – Embeddings and Semantic Search with 5 Lines
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…
Client Vector Search – Embeddings and Semantic Search with 5 Lines launched on September 21, 2023 with 11 votes, #1,483 of 4,228 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 3 years on.
In plain words
Client Vector Search is a client-side library that enables semantic search by embedding, storing, and caching vectors directly in browsers or Node.js environments. Designed for developers building search features, it requires only five lines of code to implement and eliminates the need for external embedding APIs. The library performs vector searches locally on user hardware, offering faster processing and zero server latency. It integrates with a backend API for applications that scale to millions of vectors.
written from the facts on this page · September 2026
From the sources
A client side vector search library that can embed, store, search, and cache vectors. Start with 5 lines of code and scale with our APIs. It outperforms OpenAI's text-embedding-ada-002 and is way faster than Pinecone and other VectorDBs.
You can test how fast you can compute embeddings by pasting a long article and you can load a wikipedia dataset and search through it and see how fast search is. You can find the dataset we use to search herefrom clientvectorsearch.com
In the maker’s words, at launch
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…
Does a similar job
all alternatives →- IMI made a website to semantically search ArXiv papers2024 · papermatch.mitanshu.tech · ▲324
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…
Building a web search engine from scratch with 3B neural embeddings2025 · blog.wilsonl.in · ▲699End-to-end deep dive of the project, spanning a large GPU cluster, distributed RocksDB, and terabytes of sharded HNSW.
I built a vector database API on Cloudflare2024 · github.com · ▲101🦉⚡️Serverless, distributed vector database as an API - TimeSurgeLabs/athenadb
Embeddinghub: A vector database built for Machine Learning embeddings2021 · github.com · ▲118The Virtual Feature Store. Turn your existing data infrastructure into a feature store. - featureform/featureform
- MDMy demo for vector embeddings for the Earth's surface2023 · louisquissetlabs.com · ▲106
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Source: Show HN post ↗ · launch text quoted in part
