Alternatives
Products that do what Krira Chunker does
Rust-native doc chunker for RAG-40x faster than LangChain
- 1CA
I built Chonkie because I was tired of rewriting chunking code for RAG applications. Existing libraries were either too bloated (80MB+) or too basic, with no middle ground. Core features: - 21MB default install vs 80-171MB alternatives - 33x faster token chunking than popular alternatives - Supports multiple chunking strategies: token, word, sentence, and semantic - Works with all major tokenizers (transformers, tokenizers, tiktoken) - Zero external dependencies for basic functionality Technical optimizations: - Uses tiktoken with multi-threading for faster tokenization - Implements…
2024 · github.com
- 2FB
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
- 3GA
Hi all, I've talked about glidesort a few times on HN already, but it's finally ready for release. If you have any questions, feel free to ask. An academic paper on glidesort that goes into a lot more detail than the readme is upcoming, but is not ready yet. I will be giving a talk on glidesort tomorrow at FOSDEM 2023 in the Rust Devroom at 16:10, you can seek me out there as well. In other news, I am leaving academia soon, so if you have interesting (Rust) jobs the coming months feel free to approach me.
2023 · github.com
- 4FF
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
- 5WA
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
- 6AF
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
- 7
- 8RO
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
- 9CA
ChunkHound’s goal is simple: local-first codebase intelligence that helps you pull deep, core-dev-level insights on demand, generate always-up-to-date docs, and scale from small repos to enterprise monorepos — while staying free + open source and provider-agnostic (VoyageAI / OpenAI / Qwen3, Anthropic / OpenAI / Gemini / Grok, and more). I’d love your feedback — and if you have, thank you for being part of the journey!
Jan 2026 · github.com
- 10PA
I’m sure many of you are familiar, but there’s a treacherous gap between finding (or building) a model that works in PyTorch, and getting that deployed into your application, especially in consumer-facing applications. I’ve been very interested in solving this problem with a great developer experience. Over time, I gradually realized that the highest-impact thing to have was a way to go from existing Python code to a self-contained native binary—in other words, a Python compiler. I was already pretty familiar with a successful attempt: when Apple introduced armv8 on the iPhone 5s, they…
2025 · blog.fxn.ai
- 11OL
I've been working on Fast LiteLLM - a Rust acceleration layer for the popular LiteLLM library - and I had some interesting learnings that might resonate with other developers trying to squeeze performance out of existing systems. My assumption was that LiteLLM, being a Python library, would have plenty of low-hanging fruit for optimization. I set out to create a Rust layer using PyO3 to accelerate the performance-critical parts: token counting, routing, rate limiting, and connection pooling. The Approach - Built Rust implementations for token counting using tiktoken-rs - Added lock-free data…
Nov 2025 · github.com
- 12BA
Hi HN, We’ve all accepted the "Python tax"—you trade execution speed for developer happiness. But what if that trade-off was a thing of the past? I just came across BustAPI, and it’s basically a "cheat code" for Python web services. It’s not just another wrapper; it’s a hybrid engine that embeds a Rust (Actix-Web) core directly into the Python runtime. Is this the end of the "slow Python" era? The benchmarks are pretty shocking. I’d love to see someone stress-test this against a production-grade Go or Node.js setup. Repo: https://github.com/GrandpaEJ/BustAPI Benchmarks:…
Dec 2025 · github.com
- 13
- 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
- 15RA
RAGLite is a Python package for building Retrieval-Augmented Generation (RAG) applications. RAG applications can be magical when they work well, but anyone who has built one knows how much the output quality depends on the quality of retrieval and augmentation. With RAGLite, we set out to unhobble RAG by mapping out all of its subproblems and implementing the best solutions to those subproblems. For example, RAGLite solves the chunking problem by partitioning documents in provably optimal level 4 semantic chunks. Another unique contribution is its optimal closed-form linear query adapter…
2024 · github.com
- 16
This is an upgrade of a tool I created 15 years ago in Python to learn OOP and solve some inadequacies in the HDR stacking tools I could find at the time. The problem was, none of them were really "batch friendly". None of them properly preserved the metadata I wanted them to stuff into the output file. There were probably some other reasons also, I just can't remember them now. It got the job done, but was very slow. Python was what I knew at the time and even with NumPy, I was limited in the speed I could squeeze out of it. (I also made some very specific, conscious, architectural choices…
Jun 2026 · github.com
- 17RH
A RAG has several moving parts: data ingestion, retrieval, re-ranking, generation etc.. Each part comes with numerous options. If we consider a toy example, where you could choose from: 5 different chunking methods, 5 different chunk sizes, 5 different embedding models, 5 different retrievers, 5 different re-rankers/ compressors 5 different prompts 5 different LLMs That’s 78,125 distinct RAG configurations! If you could try evaluating each one in just 5 mins, that’d still take 271 days of non-stop trial-and-error effort! In short, it’s kinda impossible to find your optimal RAG setup…
2024 · github.com
- 18
Hi everyone, I was recently much frustrated with speed of k9s that I use regularly and decided to create a rust alternative that is much faster. Architecture comes from me, and (shocking) Fable coded most of the things. My whole company switched to it and experience has been great - much faster, integrated tightly with Flux/helm/externalsecrets and develops much faster. I know that there is an overall sentiment on HN with using LLMs, so YMMV. In terms of speed, you can check the benchmarks:…
20d ago · github.com
- 19RC
Nov 2025 · github.com
- 20PS
tl;dr we’re releasing python bindings for writing stream processing jobs using the Denormalized Rust library. Hey HN, A few months ago we released Denormalized, our embeddable stream processing engine written in rust [0]. Today we’re excited to showcase python bindings for the library. Python is still very much the language of data and we wanted to make it easy for more engineers to write fast streaming jobs without having to learn Rust. Currently, you can use the library to write streaming aggregations, stream joins, and user defined aggregate functions (thought the API for this is quite…
2024 · github.com
- 21FA
Hello HN! I want to introduce fastgron, my new project. fastgron is a JSON to GRON converter, built to be incredibly fast – it's 40 times faster than Gron. GRON is a tool for making JSON greppable, but it can slow down with larger files. With fastgron, even a 200MB JSON file can be converted in just 1 second. Key features include streaming conversion for memory efficiency and an optimized path reconstruction for faster operations. It leverages C++ and the simdjson and fast_io libraries for speed. I welcome all feedback, suggestions, or questions. Thank you!
2023 · github.com
- 22WA
The Problem I’ve been using BeautifulSoup for sometime. It’s the standard for ease-of-use in Python scraping, but it almost always becomes the performance bottleneck when processing large-scale datasets. Parsing complex or massive HTML trees in Python typically suffers from high memory allocation costs and the overhead of the Python object model during tree traversal. In my production scraping workloads, the parser was consuming more CPU cycles than the network I/O. Lxml is fast but again uses up a lot of memory when processing large documents and has can cause trouble with malformed…
Apr 2026
- 23RM
2025 · github.com
- 24AA
- 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
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