Alternatives
Products that do what Vicinity – Fast, Lightweight Nearest Neighbors with Flexible Back Ends does
We’ve just open-sourced Vicinity, a lightweight approximate nearest neighbors (ANN) search package that allows for fast experimentation and comparison of a larger number of well known algorithms. Main features: - Lightweight: the base package only uses Numpy - Unified interface: use any of the supported algorithms and backends with a single interface: HNSW, Annoy, FAISS, and many more algorithms and libraries are supported - Easy evaluation: evaluate the performance of your backend with a simple function to measure queries per second vs recall - Serialization: save and load your index for…
- 1AF
2024 · github.com
- 2FV
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
- 3TT
In an effort to understand it, I put together a simple, pure python implementation of HNSW, an approximate nearest neighbor library. Learned a lot, and I think for anyone interested in vector search it's an exercise that's absolutely worth doing. The code is optimized (imo) for readability, and working (albeit, quite slowly) on putting together a tutorial that walks through the motivation and implementation of HNSW. There's also working code examples for using the library for text and image search with sentence transformers and CLIP!
2025 · github.com
- 4IV
The video demo runs a 7b Model on a normal gaming GPU. I think it already works quite well (accounting for the limited hardware power). :)
2024 · github.com
- 5HI
2025 · github.com
- 6PG
2017 · github.com
- 7LA
I built LocalGPT over 4 nights as a Rust reimagining of the OpenClaw assistant pattern (markdown-based persistent memory, autonomous heartbeat tasks, skills system). It compiles to a single ~27MB binary — no Node.js, Docker, or Python required. Key features: - Persistent memory via markdown files (MEMORY, HEARTBEAT, SOUL markdown files) — compatible with OpenClaw's format - Full-text search (SQLite FTS5) + semantic search (local embeddings, no API key needed) - Autonomous heartbeat runner that checks tasks on a configurable interval - CLI + web interface + desktop GUI - Multi-provider:…
Feb 2026 · github.com
- 8UJ
Hello HN! I became frustrated with the unpredictible/poor match quality and opaqueness of "relevance scores" in existing fuzzy and fulltext search libs, so I tried something different and this is the result. The main selling point is the result quality / ordering, with best-in-class memory overhead and excellent performance being bonuses. The API is pretty stable at this point, but looking for feedback before committing to 1.0. TL;DR The test corpus is a 4MB json file with 162k words/phrases, so give it a second for initial download. You can also drag/drop your own…
2022 · github.com
- 9IM
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
- 10JA
2012 · madlag.github.com
- 11DC
Hey folks! As someone doing hybrid search daily and wishing I could have a pgvector-like experience but with actual prefiltered approximate nearest neighbours, I decided to just take a punt on implementing ACORN on a fork of the DuckDB VSS extension. I had to make some changes to (vendored) usearch that I'm thinking of submitting upstream. But this does the business. Approximate nearest neighbours with WHERE prefiltering. Edit: Just to clarify, this has been accepted into the community extensions repo. So you can use it like: ``` INSTALL hnsw_acorn FROM community; LOAD hnsw_acorn; ```
Mar 2026 · github.com
- 12PF
Hey HN! I’ve recently open-sourced Pyversity, a lightweight library for diversifying retrieval results. Most retrieval systems optimize only for relevance, which can lead to top-k results that look almost identical. Pyversity efficiently re-ranks results to balance relevance and diversity, surfacing items that remain relevant but are less redundant. This helps with improving retrieval, recommendation, and RAG pipelines without adding latency or complexity. Main features: - Unified API: one function (diversify) supporting several well-known strategies: MMR, MSD, DPP, and COVER (with more to…
Oct 2025 · github.com
- 13PA
2019 · github.com
- 14EH
Frustrated by the inability to play with semantic search / ANN in Go w/o a heavy-weight external DB, I created this HNSW library. I hope other gophers find it useful.
2024 · github.com
- 15IM
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
- 16AP
2018 · github.com
- 17RW
Been messing with cosine similarity and decided to try calculating nearest neighbors over the entire link graph for the marginalia search engine. Turns out that you can just bruteforce that in a day or two. And the results are pretty good. One drawback is that depending on if you're looking at an older website, a lot of the links are dead. The deduplication isn't great either.
2022 · explore2.marginalia.nu
- 18SA
Hi everyone, I've made an open-source library for fast spatial search in Rust. It's called Spart, and it currently provides the following features: - Five tree implementations: Quadtree, Octree, Kd-tree, R-tree, and R*-tree - Python bindings (`pyspart` on PyPI) - Fast k-nearest neighbor (kNN) and radius search - Bulk data loading for efficient tree construction Project's GitHub repo: https://github.com/habedi/spart
2025
- 19WA
2021 · github.com
- 20AG
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
- 21WA
Here's a small demonstration of the fundamental aspects of the word-to-vec algorithm. It's implemented in a single python script and depends only on a single text file for training. It's not meant to be blazingly fast or anything, just a toy example to aid my understanding of how word vectors might be learnt from a corpus.
2023 · github.com
- 22
- 23HO
For the last year I’ve been developing Hyperparam — a collection of small, fast, dependency-free open-source libraries designed for data scientists and ML engineers to actually look at their data. - Hyparquet: Read any Parquet file in browser/node.js - Icebird: Explore Iceberg tables without needing Spark/Presto - HighTable: Virtual scrolling of millions of rows - Hyparquet-Writer: Export Parquet easily from JS - Hyllama: Read llama.cpp .gguf LLM metadata efficiently CLI for viewing local files: npx hyperparam dataset.parquet Example dataset on Hugging Face Space:…
2025 · hyperparam.app
- 24OF
OctaneDB is an open-source vector database for Python that focuses on ultra-fast similarity search for high-dimensional data—perfect for AI/ML, semantic search, and large-scale document or embedding retrieval. What does it do? Store, index, and search millions of embeddings (text, images, etc.) with sub-millisecond query time. Supports in-memory and efficient HDF5 persistent storage. Integrates seamlessly with sentence-transformers for automatic text embedding. Key Features: 10x faster than Pinecone or ChromaDB for vector search and batch insertions. Advanced indexing: HNSW (approximate…
2025 · github.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →