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Products that do what RustyRAG does

The Fastest RAG API

  1. 1FB

    Hey there HN! We’re Antonio, Luca, and Yuhang, and we’re excited to introduce Fast GraphRAG, an open-source RAG approach that leverages knowledge graphs and the 25 years old PageRank for better information retrieval and reasoning. Building a good RAG pipeline these days takes a lot of manual optimizations. Most engineers intuitively start from naive RAG: throw everything in a vector database and hope that semantic search is powerful enough. This can work for use cases where accuracy isn’t too important and hallucinations are tolerable, but it doesn’t work for more difficult queries that…

    2024 · github.com

  2. 2

    Multimodal RAG platform, from POC to production in minutes

    Apr 2026 · ignitionrag.com

  3. 3SO
  4. 4
    Super RAG114

    Super performant RAG pipelines for AI apps

    2024

  5. 5FF

    A few months ago, I benchmarked FastAPI on an i9 MacBook Pro. I couldn't believe my eyes. A primary REST endpoint to `sum` two integers took 6 milliseconds to evaluate. It is okay if you are targeting a server in another city, but it should be less when your client and server apps are running on the same machine. FastAPI would have bottleneck-ed the inference of our lightweight UForm neural networks recently trending on HN under the title "Beating OpenAI CLIP with 100x less data and compute". (Thank you all for the kind words!) So I wrote another library. It has been a while since I have…

    2023 · github.com

  6. 6SO

    Hey HN, I’m Zach from Superpowered AI (YC S22). We’ve been working in the RAG space for a little over a year now, and we’ve recently decided to open-source all of our core retrieval tech. spRAG is a retrieval system that’s designed to handle complex real-world queries over dense text, like legal documents and financial reports. As far as we know, it produces the most accurate and reliable results of any RAG system for these kinds of tasks. For example, on FinanceBench, which is an especially challenging open-book financial question answering benchmark, spRAG gets 83% of questions correct,…

    2024 · github.com

  7. 7DR

    I'd like to invite everyone to try out DontBeEvil.rip, an experimental search engine for developers. tl;dr $ alias rip="curl -G -H 'Accept: text/plain' --url https://dontbeevil.rip/search --data-urlencode " $ rip 'q=Heartbleed bug' DontBeEvil.rip is a year long experiment to see if a small team can build a developer-focused search engine that is self-sustaining on $10 monthly subscriptions. It works by only indexing high-quality resources that are relevant to developers. You won't get useless listicles because we'll never crawl them. Relevant urls are harvested from HN,…

    2022

  8. 8RA
  9. 9WA

    Hey HN, this is Will and David from Fortress (https://news.ycombinator.com/item?id=41426998). We use a lot of async Rust internally, and created this library out of a need for an async-aware concurrent hashmap since there weren’t many available in the Rust ecosystem. Whirlwind is a sharded HashMap with a fully asynchronous API. Just as dashmap is a replacement for std::sync::RwLock, whirlwind aims to be a replacement for tokio::sync::RwLock. It has a similar design and performance characteristics to dashmap, but seems to perform better in read-heavy workloads with tokio's…

    2024 · github.com

  10. 10DA

    I've built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. Key Takeaway:…

    2023 · github.com

  11. 11VW
  12. 12RO

    Ragas is an open-source library for evaluating and testing RAG and other LLM applications. Github: https://docs.ragas.io/en/stable/, docs: https://docs.ragas.io/. Ragas provides you with different sets of metrics and methods like synthetic test data generation to help you evaluate your RAG applications. Ragas started off by scratching our own itch for evaluating our RAG chatbots last year. Problems Ragas can solve - How do you choose the best components for your RAG, such as the retriever, reranker, and LLM? - How do you formulate a test dataset…

    2024 · github.com

  13. 13

    Enterprise grade solution for building RAG

    2024

  14. 14RI

    Got tired of wiring up vector stores, embedding models, and chunking logic every time I needed RAG. So I built piragi. from piragi import Ragi kb = Ragi(\["./docs", "./code/\*\*/\*.py", "https://api.example.com/docs"\]) answer = kb.ask("How do I deploy this?") That's the entire setup. No API keys required - runs on Ollama + sentence-transformers locally. What it does: - All formats - PDF, Word, Excel, Markdown, code, URLs, images, audio - Auto-updates - watches sources, refreshes in background, zero query latency - Citations - every answer includes sources…

    Dec 2025 · pypi.org

  15. 15

    Implement RAG in Minutes - Fast, Simple, and Scalable

    2025

  16. 16AA

    - Discovering the most effective RAG pipeline for your specific data and use case can be daunting. It requires experimenting with various RAG modules and configurations, which are both time-consuming and complex. - AutoRAG addresses this challenge by automatically evaluating different combinations of RAG modules and their parameters. You don't need to write implementation code yourself; everything is set up through a single YAML file. - Our aim is to save you the hassle of continuously adapting to new RAG modules and configurations. Instead, you can focus on developing robust data for your…

    2024 · github.com

  17. 17AF

    Serialization framework with some interesting numbers: 10-20x faster on nested objects than json/protobuf. Technical approach: compile-time codegen (no reflection), compact binary protocol with meta-packing, little-endian layout optimized for modern CPUs. Unique features that other fast serializers don't have: - Cross-language without IDL files (Rust ↔ Python/Java/Go) - Trait object serialization (Box) - Automatic circular reference handling - Schema evolution without coordination Happy to discuss design trade-offs. Benchmarks:…

    Oct 2025 · fory.apache.org

  18. 18
    Tantivy68

    A full-text, horse-speed search engine library in Rust

    2022

  19. 19
    ShinRAG10

    Visual RAG and Agent Pipelines, Instantly

    Dec 2025 · shinrag.com

  20. 20

    APIs for building AI chat and search

    Feb 2026 · agentset.ai

  21. 21

    Go from dataset to custom RAG prototype in 5 minutes

    2024

  22. 22
    RAGBOT12

    Real-time Chat Interface with React and RAG from scratch

    2025

  23. 23

    An ultra-fast, single-binary MCP server written in Rust as a lightweight alternative to Node.js/Python. - StamManif/mcp-stama

    25d ago · github.com

  24. 24

    Rust-native doc chunker for RAG-40x faster than LangChain

    Mar 2026 · kriralabs.com

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