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Products that do what Embeddable HNSW Library for Go does
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.
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2024 · github.com
- 2HI
2025 · github.com
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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
- 4HB
Hey all, I just wanted to share a project I've been working on for the past month. After years of heavy frameworks, I really like the idea of using htmx, but it’s a little too low level for me and needs a thin layer above it to facilitate things like components, better syntax with complex JS inside of an attribute, etc To try and solve this problem with a very minimal stack (golang + htmx) that I've been really enjoying, I'm building this project to cater to my needs and was thinking it would be useful for other developers.
2024 · htmgo.dev
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2018 · github.com
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2021 · github.com
- 7IM
Hey HN! I love finding new books to read on here. I wanted to gather the most mentioned books and recreate the serendipity of physical browsing. I scraped 20k comments from HN threads related to reading, extracted the references and opinions using GPT-4o mini, and visualised their embeddings as a map. - OpenAI's embeddings were processed using UMAP and HDBSCAN. A direct 2D projection from the text embeddings didn't yield visually interesting results. Instead, HDBSCAN is first applied on a high-dimensional projection. Those clusters tend to correspond to different genres. The genre…
2024 · hnbooks.pieterma.es
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2020 · 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
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2014 · github.com
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No gc, No goroutines, Produces small binaries while using the unmodified official go toolchain, and comes with complete Web SDK (generated from w3c/webref). We are building `pcz` to provide a reimagination of Go the language, in an effort to make it suitable for all kinds of programming tasks, and currently you can use it to build efficient web applications in Go using the generated Web SDK (as shown with the live web demo[1]). The journey is just starting, any suggestions? or any critics? [1]: https://primecitizens.github.io/livedemos/10-plat-web/
2023 · github.com
- 12BK
Hey HN! I got nerd-sniped by Bloom Filters this weekend, specifically for searching datasets with high "cardinality" (number of unique items). They're an _amazing_ data structure that, at a fixed size, tracks potential set membership. That means unlike normal b-tree indexes, they don't grow with the number of unique items in the dataset. This makes them great for "needle in a haystack" search (logs, document) as implementations like VictoriaMetrics and Bing's BitFunnel show. I've used them in the past, but they've never been center-stage in my projects. I wanted high cardinality keyword…
2025 · github.com
- 13GB
Hey HN, I just published v0.1.0 of go-bt and would love some feedback from the Go veterans here. Thanks in advance!
Apr 2026 · github.com
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2019 · gocode.io
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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…
2024 · github.com
- 16CH
Hi HN! We're thrilled to share CozoDB v0.6, a monumental update to our FOSS database, which already unifies relational and graph features. With the addition of vector search, CozoDB becomes an even better companion for LLMs like ChatGPT. This release introduces vector search using HNSW indices within Datalog, enabling seamless integration with powerful features such as ad-hoc joins, recursive Datalog, and classical whole-graph algorithms. This update significantly broadens CozoDB's capabilities. Check out the linked release note for an in-depth look at the new features, comparisons to other…
2023 · docs.cozodb.org
- 17GG
2016 · github.com
- 18EP
2021 · github.com
- 19DC
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
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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
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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
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2015 · github.com
- 23ST
2023 · gothrough.dev
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2017 · github.com
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