Thread by meng shao (@shao__meng)

[Open Source Recommendation] CocoIndex: A high-performance open-source data ETL framework designed specifically for AI applications such as RAG and semantic search. Core positioning: The "data processing pipeline" of the AI ​​era When building AI applications, the most challenging problem is often not the model itself, but how to process the data. CocoIndex was created to solve this problem. It is an intelligent data processing engine responsible for extracting, transforming, and processing messy data into a format that AI can understand. Key Highlights ⚡ Incremental Updates (Core Killer Feature) This is CocoIndex's biggest feature. Traditional data processing often involves a "full reload"—even if you only change one sentence in a file, the entire database may need to be re-indexed, which is both slow and expensive. CocoIndex supports fine-grained incremental updates. It can precisely identify which data has changed and only process the changed parts. This is similar to an Excel formula; changing a cell only updates the relevant calculation result, while the rest remains unchanged. This means your AI data can always be kept "fresh" with extremely low computational cost. 🧩 As flexible as building blocks (modular design) It adopts a "LEGO brick" design concept. While it offers many out-of-the-box features, you can fully insert custom logic. Whether it's segmentation, embedding, deduplication, or cleansing, you can freely combine different modules according to your business needs. 🚀 Rust Kernel + Python Ease of Use: To ensure processing speed, its underlying core engine is written in the high-performance language Rust; however, to facilitate developer use, it provides a user-friendly Python interface. You can enjoy Python development efficiency while achieving top-tier runtime performance. Main application scenarios: RAG system: When building a knowledge base, newly uploaded documents are automatically converted into vectors and stored in the database for large models to query. • Semantic search: Build a search system that can understand natural language, such as "search for all meeting minutes related to last year's financial report". • Knowledge graph construction: Extracting entities and relationships from unstructured text to build complex knowledge networks. Project address: