Dev tools · alternatives · 2026
24 alternatives to 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 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… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Client Vector Search – Embeddings and Semantic Search with 5 Lines launched in 2023; newer entries below may have overtaken it.
- 1IM
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 →
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End-to-end deep dive of the project, spanning a large GPU cluster, distributed RocksDB, and terabytes of sharded HNSW.
2025 · blog.wilsonl.in · its alternatives →
- 3
Supabase Vector▲398The open source backend for AI applications
2023 · supabase.com · its alternatives →
- 4
🦉⚡️Serverless, distributed vector database as an API - TimeSurgeLabs/athenadb
2024 · github.com · its alternatives →
- 5
Improved vector search with open-source AI embeddings editor
2023 · its alternatives →
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The Virtual Feature Store. Turn your existing data infrastructure into a feature store. - featureform/featureform
2021 · github.com · its alternatives →
- 8
Hey HN, we want to share HelixDB (https://github.com/HelixDB/helix-db/), a project a college friend and I are working on. It’s a new database that natively intertwines graph and vector types, without sacrificing performance. It’s written in Rust and our initial focus is on supporting RAG. Here’s a video runthrough: https://screen.studio/share/szgQu3yq. Why a hybrid? Vector databases are useful for similarity queries, while graph databases are useful for relationship queries. Each stores data in a way that’s best for its main type of query (e.g.…
2025 · github.com · its alternatives →
- 9
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 →
- 10MD
2023 · louisquissetlabs.com · its alternatives →
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Vector space model implemented in a few lines . Contribute to felipehummel/TinySearchEngine development by creating an account on GitHub.
2011 · github.com · its alternatives →
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HNSW implementation in Rust. Reference: https://arxiv.org/ftp/arxiv/papers/1603/1603.09320.pdf - swapneel/hnsw-rust
2024 · github.com · its alternatives →
- 13
Hi HN, I am Anubhav from Ramanlabs. We have been working on a native gui application to allow users to search any video data( mp4, mkv) or video streams (http/rtsp) using computer vision. Application is supposed to work like a video player which displays decoded frames and recognizes objects concurrently, making it an interactive experience. It works in super real-time and only expects a quad-core CPU with AVX2 instructions at minimum. Application is free to download (without any signup/account). We are only supporting WINDOWS for now [0]. Even though this is a binary application,…
2022 · ramanlabs.in · its alternatives →
- 14
Author here. I just wanted a quick and easy way to easily submit strings to a REST API and get back the embedding vectors in JSON using Llama2 and other similar LLMs, so I put this together over the past couple days. It's very quick and easy to set up and totally self-contained and self-hosted. You can easily add new models to it by simply adding the HuggingFace URL to the GGML format model weights. Two models are included by default, and these are automatically downloaded the first time it's run. It lets you not only submit text strings and get back the embeddings, but also to compare two…
2023 · github.com · its alternatives →
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Heirarchical Navigable Small Worlds. Contribute to dicroce/hnsw development by creating an account on GitHub.
2025 · github.com · its alternatives →
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Hi HN, I'm Daniel from Superlinked! We have built an open-source framework that improves vector search relevance and usefulness by combining structured metadata with unstructured data in your embeddings. We included self-hostable API server that sits between your data sources and vector database. Docs: https://docs.superlinked.com/ We're launching our cloud offering soon where you can use Superlinked to orchestrate high-performance retrieval for RAG, Search & Recommendation apps in your own cloud. Looking for feedback and happy to answer questions!
2024 · github.com · its alternatives →
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Vectorly▲92Get web data behind clicks, searches, user interactions
Dec 2025 · inkbox.ai · its alternatives →
- 18
Dear HN Community, I am a long time fan and first-time contributor. I just launched a developer focused semantic search platform and wanted to share it with the community. The idea is simple: upload structured or unstructured documents, select the fields you want to index and tag as metadata, and instantly get a clean search API you can use in your own app. Here is what it currently supports: - Manage your own tenants and projects - Upload .json and .txt files (support for .pdf, .docx, .xlsx, .yml, etc. coming soon) - Expose 3 APIs: search, upload document (embeddings), and delete document -…
2025 · aisearch.vpuna.com · its alternatives →
- 19AS
2013 · insightdatascience.com · its alternatives →
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🕸️🦀 A WASM vector similarity search written in Rust - tantaraio/voy
2023 · github.com · its alternatives →
- 21NN
Hi HN. Peter here. As a machine learning engineer, I mostly think in terms of feature vectors, embeddings, and matrices. One of the most useful byproducts of deep neural networks is embeddings because they allow us to represent high-dimensional data in terms of lower-dimensional latent vectors. These feature vectors can be used for downstream applications like similarly search, recommendation systems and near duplicate detection. As an ML engineer, I was frustrated by the lack of a datastore in which vectors are first-class citizens. As a result, most ML engineers, including myself, end up…
2021 · its alternatives →
- 22VD
Discover, evaluate, and access relevant embeddings in your go-to framework. Skip all the infra for scraping, cleaning, indexing, and updating high-quality embeddings.
2023 · embedding.store · its alternatives →
- 23
Search for your bookmarks by content! @ashwinlokkur and I built this Chrome extension that scrapes your bookmarks' content and does semantic search using transformer embeddings. Free and private since it's all in-browser. No LLM API calls ;)
2024 · github.com · its alternatives →
- 24AG
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 →
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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 →