cocoindex

Hi I've been working on adding multi-vector support natively in cocoindex for multi-modal RAG at scale. I wrote blog to help understand the concept of multi-vector and how it works underneath. The framework itself automatically infers types, so when defining a flow, we don’t need to explicitly specify any types. Felt these concept are fundamental to multimodal data processing so just wanted to share. This unlocks at scale: images, text, audio, video — all can be represented as structured multi-vectors that preserve the unique semantics of each modality. breakdown + Python examples: https://cocoindex.io/blogs/multi-vector/ Star GitHub if you like it! https://github.com/cocoindex-io/cocoindex

Hi HN, I’m George — I left Google after 10 years working on infrastructure and am building CocoIndex https://cocoindex.io with my friend Linghua. CocoIndex is an open source ETL framework that does incremental processing designed for AI workloads. We cut >90% of compute costs by processing only what’s changed — effortless fresh context for AI. It is easy to build scalable, production-grade pipelines like Lego in hours. Think it as n8n with python blocks but for large scale RAG pipelines. You can build vector index, knowledge graph and custom logic with any modality in the pipeline with AI. To get started, you can run `pip install -U cocoindex`. We’ve built 10+ examples to get you started. If you prefer to read blog: https://cocoindex.io/blogs/tags/examples If you prefer to read code: https://github.com/cocoindex-io/cocoindex?tab=readme-ov