Alternatives
Products that do what Vectorize does
Build RAG pipelines that are optimized for your data.
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2014 · github.com
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Embeddings, Semantic Search & RAG Explained
23d ago · khayyamshah2007.blogspot.com
- 11SV
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
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docustore's aim is to provide up-to-date, off-the shelf and plug-and-play context for LLMs from a curated list of frameworks/sdks. It has a 4 step pipeline: scrape the documentation - clean it - vectorize it - package it. My vision is to host it somewhere and develop an API/MCP around it so it will be development-environment agnostic.
2025 · github.com
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Hello HN! We’ve been working hard on Vanna, our RAG framework for SQL generation and we’ve been updating our documentation. Please have a look — we have a ton of Jupyter notebooks for any combination of desired use cases. At it’s heart, we have abstractions that help you: - “train” a RAG “model” i.e. add metadata for the retrieval augmentation system to reference when constructing the LLM prompt (yes, we know that the terms “train” and “model” are somewhat confusing and we’re open to changing those terms if you can suggest better ones) - “ask” questions, which will generate SQL, run it,…
2023 · github.com
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Hi HN, I built OpenFable, an open-source retrieval engine that implements the FABLE algorithm (https://arxiv.org/abs/2601.18116) for RAG pipelines. I'm using it in another project and thought that others might benefit. Most RAG systems chunk documents into flat segments and retrieve by vector similarity. This works for simple lookups but breaks when answers span multiple sections, when relevant content is buried in a subsection, or when you need to control how many tokens you're sending to an LLM. OpenFable takes a different approach: when you ingest a document, it uses…
Apr 2026 · github.com
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Hey HN, We’re Basia, Fokke, and Geno from Liquidmetal AI, and we built something we wish we had a long time ago: SmartBuckets. We’ve spent a lot of time building RAG and AI systems, and honestly, the infrastructure side has always been a pain. Every project turned into a mess of vector databases, graph databases, and endless custom pipelines before you could even get to the AI part. SmartBuckets is our take on fixing that. It works like an object store, but under the hood it handles the messy stuff — vector search, graph relationships, metadata indexing — the kind of infrastructure you'd…
2025
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I’ve built vectorvector, an open-source CLI tool for working with elevation data. Key features: * Generates contour lines (vector), hillshade tiles (raster), and terrarium tiles (raster elevation) * Processes the entire planet in ~7 hours on consumer hardware * Algorithms: Marching Squares for contours, Horn for hillshade, and Ramer–Douglas–Peucker for line simplification Benchmarked on a MacBook Pro (M2, 64 GB RAM). Output is customizable and aimed at developers and GIS enthusiasts. Would love to hear your thoughts and feedback! Repo:…
2024 · github.com
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2014 · github.com
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I've built an airgapped Retrieval-Augmented Generation (RAG) system for question-answering on documents, running entirely offline with local inference. Using Llama 3, Mistral, and Gemini, this setup allows secure, private NLP on your own machine. Perfect for researchers, data scientists, and developers who need to process sensitive data without cloud dependencies. Built with Llama C++, LangChain, and Streamlit, it supports quantized models and provides a sleek UI for document processing. Check it out, contribute, or suggest new features!
2024 · github.com
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Hello everyone! I am Jan, CTO and one of the creators of Pathway, the real-time data processing framework. I’m excited to share Pathway’s ready-to-use AI Pipelines, configurable with just YAML! These frameworks offer out-of-the-box solutions for AI search, RAG, and more—optimized for real-time indexing and in-memory processing. What makes it simple? YAML templates! The pipeline templates are fully customizable using YAMLs to fit your needs, from changing the data sources to the choice of the LLM model, all without touching Pathway’s Python code. Thanks to the Pathway data processing engine,…
2024 · pathway.com
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Hi HN, I built EdgeVec, a vector database that runs entirely in the browser. It implements HNSW (Hierarchical Navigable Small World) graphs for approximate nearest neighbor search. Performance: - Sub-millisecond search at 100k vectors (768 dimensions, k=10) - 148 KB gzipped bundle - 3.6x memory reduction with scalar quantization Use cases: browser extensions with semantic search, local-first apps, privacy-preserving RAG. Technical: Written in Rust, compiled to WASM. Uses AVX2 SIMD on native, simd128 on WASM. IndexedDB for browser persistence. npm:…
Dec 2025 · github.com
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Hey HN, At Mintplex Labs are building developer tools for AI applications. One area we encountered frustration was the use of Vector Databases like Pinecone, Chroma, QDrant, or Weaviate to "unlock" long-term memory and contextual answers. It is nearly impossible to manage this data when in use for production. The craziest thing was how you cannot atomically CRUD any vectors in most of these vector databases. Let alone easily copy, clone, or migrate data or entire indexes without paying for re-embedding - among other things. With VectorAdmin you get a database level UI with the ability to…
2023 · vectoradmin.com
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2025 · github.com
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MyScale is designed for the storage and analysis of massive vector data with structured metadata. If you are eager to find a high-performance vector search using SQL queries, MyScale could be your preferred option. Thanks to the advantages of native structural database support, it provides you with a flexible filter with a WHERE clause, even JOIN when you want to jointly search vectors with filters on relevant metadata from other tables. MyScale is now open for registration and offers millions of vectors‘ free tier plan for you! (https://myscale.com/) Now you can also use…
2023 · myscale.com
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