Alternatives
Products that do what Papr Graph does
Upgrade to graph-native vector embeddings
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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
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2021 · github.com
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2020 · github.com
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Hi, I'm Ben, the co-creator of Embedbase. Embedbase lets you use OpenAI Embeddings and Pinecone seamlessly. For example, you can add Embedbase to your app and pair it with GPT3 to allow people to search using natural language (e.g. How many workouts did I complete last week?), or simply expanding your current search experience beyond full-text search (e.g. looking for "similar" documents in Notion to find other related information) Managing embeddings is uncharted territory, we needed to discover the best practices ourselves. Now we're happy to share our learnings with Embedbase. Shoot if…
2023 · embedbase.xyz
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Introducing embeds.ai: an embedding playground to compare how embedding models work on a real world use case (retrieval augmented generation for Wikipedia articles + Elad Gil's High growth handbook) A few weeks ago, Shreyan and I were looking for an embedding model to use for RAG. We eventually came across the MTEB leaderboard, but we struggled to understand the benchmark scores. We wanted a tool to test various embedding models with example queries on real-world datasets. After unsuccessfully looking for such a “playground”, we decided to just build one ourselves! We embedded HuggingFace’s…
2023 · embeds.ai
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I built https://ask.rivestack.io — a semantic search engine over Hacker News posts. Instead of keyword matching, it finds results by meaning, so you can search things like "best way to handle authentication in microservices" and get relevant threads even if they don't contain those exact words. How it works: Indexed HN posts and comments into PostgreSQL with pgvector (HNSW index) Embeddings generated with OpenAI's embedding model Queries run as nearest-neighbor vector searches — typical response under 50ms The whole thing runs on a single Postgres instance, no separate vector DB I…
Feb 2026 · ask.rivestack.io
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2020 · github.com
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Hi HN! We (Thomas and Stéphan, hello!) recently released Model2Vec, a Python library for distilling any sentence transformer into a small set of static embeddings. This makes inference with such a model up to 500x faster, and reduces model size by a factor of 15 (7.5M params or 15/30MB on disk, depending on whether you use float16 or float32). This allows you to embed 50-100k documents per second on a cpu on a macbook. This reduction of course comes at a cost: distilled models are worse than their parent models. Even so, they are actually a lot better than large sets of conventional…
2024 · github.com
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The implementation is at: https://github.com/nishnik/poincare_embeddings Corresponding blog post: https://medium.com/towards-data-science/facebook-research-ju... Corresponding paper: https://arxiv.org/abs/1705.08039 This paper explores Poincare disk model instead of Euclidean space for embedding hierarchical data
2017
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2023 · github.com
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2014 · opengraph.io
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Hey HN! My brothers and I have worked on this for the last 2 weeks. We use OpenAI's `text-embedding-ada-002` model to embed queries and a vector database to search for similar verses / blocks of verses. We'd like to see what you think and appreciate any feedback!
2023 · siliconscripture.org
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Hello HN, We are pleased to introduce you graphlearn-for-pytorch (https://github.com/alibaba/graphlearn-for-pytorch), an open-source distributed graph neural network library based on PyTorch and compatible with PyG. Our library is designed to make it easy for developers to build and train large-scale graph models in a distributed environment. With graphlearn-for-pytorch, you can leverage GPUs to accelerate graph sampling and utilize UVA to reduce the overheads of feature collection. Following a scalable design, graphlearn-for-pytorch supports training GNN models on…
2023 · github.com
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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
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Hi HN — I’m Abhi. We built Agint so PMs and engineers can design and edit software as a graph — architecture first — iterate with fast visual feedback, then generate deployable code from it when it’s ready. We presented underlying approach at NeurIPS (Deep Learning for Codegen) as an Agentic Graph Compiler: The graph (structure + types + semantic annotations) is the source of truth, and code is a compilation/export target. Paper: Agentic Graph Compilation for Software Engineering Agents: https://arxiv.org/abs/2511.19635 Live Demo: https://flow.agintai.com…
Jan 2026 · flow.agintai.com
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We are a small group of undergrads interested in building human in the loop coding agents. We dream of a world where building complex agent workflows feels as simple and creative as playing with legos. When we were building stuff we needed a tool that made it easy to try out different code embedding models so that we could see which ones worked best in different scenarios and understand their strengths and weaknesses. So to speed that process up we made PurpleSearch an 'instant' search engine for your local codebases. This tool lets you quickly deploy any open source embedding model on…
2025
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Hey, OpenAI recently released "assistants" - These have RAG built into the API, which means that you can provide up to 10.000 files to the assistant without the overhead of dealing with vector databases, splitting files into chunks and more. They work surprisingly well, so I've built a free simple tool to embed them on websites. It has no dependencies and simply uses fetch to communicate to my backend which proxies openai. In the future I plan to add more widget embedding options (currently only chat is available). Let me know if you have any specific questions about either the tool or…
2024 · rispose.com
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