19 posts tagged with "Examples"

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.

I recently worked with a clothing manufacturer who wanted to simplify their invoice process. Every day, they receive around 20–22 supplier invoices in PDF format. All these invoices are stored in Azure Blob Storage. The finance team used to open each PDF manually and copy the details into their system. This took a lot of time and effort. On top of that, they already had a backlog of 8,000 old invoices waiting to be processed.

At first, I built a flow using n8n. This solution read the invoices from Azure Blob Storage, used Mistral AI to pull out the fields from each PDF, and then loaded the results into Snowflake. The setup worked fine for a while. But as the number of invoices grew, the workflow started to break. Debugging errors inside a no-code tool like n8n became harder and harder. That’s when I decided to switch to a coding solution.

I came across CocoIndex, an open-source ETL framework designed to transform data for AI, with support for real-time incremental processing. It allowed me to build a pipeline that was both reliable and scalable for this use case.

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’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.

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.

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.

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.

In this blog, we will show you how to use OpenAI API to extract structured data from patient intake forms with different formats, like PDF, DOCX, etc.

In this blog, we will show you how to use CocoIndex to build text embeddings from Google Drive for RAG step by step including how to setup Google Cloud Service Account for Google Drive. CocoIndex is an open source framework to build fresh indexes from your data for AI. It is designed to be easy to use and extend.

In this blog, we will show you how to index a codebase for RAG with CocoIndex. CocoIndex provides built-in support for codebase chunking, with native Tree-sitter support and real-time update.

In this blog, we will show you how to use Ollama to extract structured data that you can run locally and deploy on your own cloud/server.