Linghua Jin

There’s more need for open data infrastructure for AI, than ever.

Data for humans → to data for AI​

Traditionally, people build data frameworks heavily in this space to prepare data for humans. Over the years, we’ve seen massive progress in analytics-focused data infrastructure. Platforms like Spark and Flink fundamentally changed how the world processes and transforms data, at scale.

But with the rise of AI, entirely new needs — and new capabilities — have emerged. A new generation of data transformations is now required to support AI-native workloads.

PDFs are rich with both text and visual content — from descriptive paragraphs to illustrations and tables. This example builds an end-to-end flow that parses, embeds, and indexes both, with full traceability to the original page.

In this example, we split out both text and images, link them back to page metadata, and enable unified semantic search. We’ll use CocoIndex to define the flow, SentenceTransformers for text embeddings, and CLIP for image embeddings — all stored in Qdrant for retrieval.

We’re excited to announce Custom Sources — a new capability in CocoIndex that lets you read data from any system you want. Whether it’s APIs, databases, file systems, cloud storage, or other external services, CocoIndex can now ingest data incrementally, track changes efficiently, and integrate seamlessly into your flows.

After this change, users for CocoIndex are not bounded by any connectors, targets or some prebuilt libraries. You can use CocoIndex for anything, and enjoy the robust incremental computing to build fresh knowledge for AI.

Custom sources are the perfect complement to custom targets, giving you full control over both ends of your data pipelines.

🚀 Get started with custom sources by following the documentation now.

We are launching a major feature in both CocoIndex and CocoInsight to help users fast iterate with the indexing strategy, and trace back all the way to the data — to make the transformation experience more seamlessly integrated with the end goal.

We deeply care about making the overall experience seamless. With the new launch, you can define query handlers, so that you can easily run queries in tools like CocoInsight.

CocoIndex is one framework for building incremental data flows across structured and unstructured sources.

In CocoIndex, AI steps -- like generating embeddings -- are just transforms in the same flow as your other types of transformations, e.g. data mappings, calculations, etc.

Why One Framework for Structured + Unstructured?​

• One mental model: Treat files, APIs, and databases uniformly; AI steps are ordinary ops.
• Incremental by default: Use an ordinal column to sync only changes; no fragile glue jobs.
• Consistency: Embeddings are always derived from the exact transformed row state.
• Operational simplicity: One deployment, one lineage view, fewer moving parts.

This blog introduces the new PostgreSQL source and shows how to take data from PostgreSQL table as source, transform with both AI models and non-AI calculations, and write them into a new PostgreSQL table for semantic + structured search.

Do you have a messy collection of scanned documents, PDFs, academic papers, presentation slides, and standalone images — all mixed together with charts, tables, and figures — that you want to process into the same vector space for semantic search or to power an AI agent?

In this example, we’ll walk through how to build a visual document indexing pipeline using ColPali for embedding both PDFs and images — and then query the index using natural language.
We’ll skip OCR entirely — ColPali can directly understand document layouts, tables, and figures from images, making it perfect for semantic search across visual-heavy content.

We’ve shipped 20+ releases — packed with production-ready features, scalability upgrades, and runtime improvements. 🚀 Huge thanks to our amazing users for the feedback and for running CocoIndex at scale!

We’re excited to announce that CocoIndex now supports native integration with ColPali — enabling multi-vector, patch-level image indexing using cutting-edge multimodal models.

With just a few lines of code, you can now embed and index images with ColPali’s late-interaction architecture, fully integrated into CocoIndex’s composable flow system.

CocoIndex now provides robust and flexible support for typed vector data — from simple numeric arrays to deeply nested multi-dimensional vectors. This support is designed for seamless integration with high-performance vector databases such as Qdrant, and enables advanced indexing, embedding, and retrieval workflows across diverse data modalities.

We’re excited to announce that CocoIndex now officially supports custom targets — giving you the power to export data to any destination, whether it's a local file, cloud storage, a REST API, or your own bespoke system.

This new capability unlocks a whole new level of flexibility for integrating CocoIndex into your pipelines and allows you to bring your own "building blocks" into our flow model.

CocoIndex supports multi-modal processing natively - it could process both text and image with the same programming model and observe in the same user flow (in CocoInsight).

In this blog, we’ll walk through a comprehensive example of building a scalable face recognition pipeline using CocoIndex. We’ll show how to extract and embed faces from images, structure the data relationally, and export everything into a vector database for real-time querying.

CocoInsight can now visualize identified sections of an image based on the bounding boxes and makes it easier to understand and evaluate AI extractions - seamlessly attaching computed features in the context of unstructured visual data.

In this blog we will walk through a comprehensive example of indexing research papers with extracting different metadata — beyond full text chunking and embedding — and build semantic embeddings for indexing and querying.

From day zero, we envisioned CocoInsight as a fundamental companion to CocoIndex — not just a tool, but a philosophy: making data explainable, auditable, and actionable at every stage of the data pipeline with AI workloads. CocoInsight has been in private beta for a while, it is one of the most loved feature for our users building ETL with coco, with significant boost on developer velocity, and lowering the barrier to entry for data engineering.

We are officially launching CocoInsight today - it has zero pipeline data retention and connects to your on-premise CocoIndex server for pipeline insights. This makes data directly visible and easy to develop ETL pipelines.

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.