CocoIndex supports Qdrant natively - the integration features a high performance Rust stack with incremental processing end to end for scale and data freshness. 🎉 We just rolled out our latest change that handles automatic target schema setup with Qdrant from CocoIndex indexing flow.
CocoIndex now provides native support for Kuzu as a target graph data store. This integration features a high performance knowledge graph stack with real-time updates.
CocoIndex now provides native support for Amazon S3 as a data source. Additionally, CocoIndex integrates with AWS Simple Queue Service (SQS), enabling true real-time incremental processing of your S3 data.
In this project, we will build image search and query it with natural language. You can search for “a cute animal” or “a red car”, and the system returns visually relevant results — no manual tagging needed.
In this blog, we will build index with text embeddings and query it with natural language. We try to keep it minimalistic and focus on the gist of the indexing flow.
We have been working on CocoIndex - a real-time data framework for AI for a while, with lots of excitement from the community. We officially crossed 1k stars earlier this week. Huge thanks to everyone who starred, forked, contributed, or shared the love ❤️!
In this blog, we will build a real-time product recommendation engine with LLM and graph database. In particular, we will use LLM to understand the category (taxonomy) of a product. In addition, we will use LLM to enumerate the complementary products - users are likely to buy together with the current product (pencil and notebook). We will use Graph to explore the relationships between products that can be further used for product recommendations or labeling.
CocoIndex makes it easy to build and maintain knowledge graphs with continuous source updates. In this blog, we will process a list of documents (using CocoIndex documentation as an example). We will use LLM to extract relationships between the concepts in each document.