CocoIndex – Open-Source Data framework for AI, built for data freshness
Hi HN, I’ve been working on CocoIndex, an open-source Data ETL framework to transform data for AI, optimized for data freshness. You can start a CocoIndex project with `pip install cocoindex` and declare a data flow that can build ETL like LEGO - build a RAG pipeline for vector embeddings, knowledge graphs, or extract, transform data with LLMs. It is a data processing framework beyond text. When you run the data flow either with live mode or batch mode, it will process the data incrementally with minimal recomputation and make it super fast to update the target stores on source changes. Get…
In plain words
CocoIndex is an open-source data ETL framework designed to transform and prepare data for AI applications with a focus on keeping data fresh. Built with a modular approach similar to LEGO blocks, it enables users to construct data pipelines for RAG systems, vector embeddings, knowledge graphs, and data extraction or transformation using LLMs. The framework processes data incrementally in both live and batch modes, minimizing redundant computation and enabling fast updates to target stores when source data changes. It handles multiple data types beyond text.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, I’ve been working on CocoIndex, an open-source Data ETL framework to transform data for AI, optimized for data freshness. You can start a CocoIndex project with `pip install cocoindex` and declare a data flow that can build ETL like LEGO - build a RAG pipeline for vector embeddings, knowledge graphs, or extract, transform data with LLMs. It is a data processing framework beyond text. When you run the data flow either with live mode or batch mode, it will process the data incrementally with minimal recomputation and make it super fast to update the target stores on source changes. Get started video: https://www.youtube.com/watch?v=gv5R8nOXsWU Demo video: https://www.youtube.com/watch?v=ZnmyoHslBSc Previously, I’ve worked at Google on projects like search indexing and ETL infra for 8 years. After I left Google last year, I built various projects and went through pivoting hell. In all the projects I’ve built, data still sits in the center of the problem and I find myself focusing on building data infra other than the business logic I need for data transformation. The current prepackaged RAG-as-service doesn't serve my needs, because I need to choose a different strategy for the context, and I also need deduplication, clustering (items are related), and other custom features that are commonly needed. That’s where CocoIndex starts. A simple philosophy behind it - data transformation is similar to formulas in spreadsheets. The ground of truth is at the source data, and all the steps to transform, and final target store are derived data, and should be reactive based on the source change. If you use CocoIndex, you only need to worry about defining transformations like formulas. *Data flow paradigm* came in as an immediate choice - because there’s no side effect, lineage and observability just come out of the box. *Incremental processing* - If you are a data expert, an analogy would be a materialized view beyond SQL. The framework tracks pipeline states in database, and only reprocessing necessary portions. When data has changed, framework handles the change data capture comprehensively and combines the mechanism for push and pull. Then clear stale derived data/versions and re-index data based on tracking data/logic changes or data TTL settings. There’s lots of edge cases to do it right, for example, when a row is referenced in other places, and the row changes. These should be handled at the level of the framework. *At the compute engine level* - the framework should consider the multiple processes and concurrent updates. It should consider how to resume existing states from terminated execution. In the end, we want to build a framework that is easy to build with exceptional velocity, but scalable and robust in production. *Standardized the interface throughout the data flow* - really easy to plugin custom logic like LEGO; with a variety of native built-in components. One example is that it takes a few lines to switch among Qdrant, Postgres, Neo4j. CocoIndex is licensed under Apache 2.0 https://github.com/cocoindex-io/cocoindex Getting started: https://cocoindex.io/docs/getting_started/quickstart Excited to learn your thoughts, and thank you so much! Linghua
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, April 2025
the whole month →- IB
Hi everyone, I built PyXL — a hardware processor that executes a custom assembly generated from Python programs, without using a traditional interpreter or virtual machine. It compiles Python -> CPython Bytecode -> Instruction set designed for direct hardware execution. I’m sharing an early benchmark: a GPIO test where PyXL achieves a 480ns round-trip toggle — compared to 14-25 micro seconds on a MicroPython Pyboard - even though PyXL runs at a lower clock (100MHz vs. 168MHz). The design is stack-based, fully pipelined, and preserves Python's dynamic typing without static type restrictions.…
Dev tools · 2025 · runpyxl.com
- UC
Life & fun · 2025 · filiph.github.io
- IB
https://the-pocket.github.io/Tutorial-Codebase-Knowledge/
AI · 2025 · github.com
