Does it support incremental updates?

Yes, CocoIndex features ultra-performant incremental processing, which means it only processes data that has changed since the last run, saving time and compute costs.

How do I manage flow execution?

You can operate flows using the CocoIndex CLI for updates and setup, or use the Python API to integrate flow management directly into your application code.

Which databases can I export data to?

CocoIndex supports multiple targets including Postgres (pgvector), Qdrant, LanceDB, Neo4j, and Kuzu.

Which LLM providers are supported?

It integrates with popular providers including OpenAI, Anthropic, Google Gemini, Voyage, and local models through Ollama.

What is CocoIndex used for?

CocoIndex is used to build data transformation flows that prepare data for AI applications, such as creating embeddings for vector search or extracting entities for knowledge graphs.

CocoIndex AI Data Framework

Name: CocoIndex AI Data Framework
Author: cocoindex-io

bycocoindex-io

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Data Science & ML

Builds real-time data transformation pipelines and vector search indexes for AI applications.

About

CocoIndex is an ultra-performant real-time data transformation framework for AI applications. This skill enables developers to build complex indexing flows that extract data from sources like S3 or local files, apply AI-driven transformations such as chunking and embedding, and export results to vector or graph databases. With built-in support for incremental processing and live updates, it streamlines the creation of production-grade RAG pipelines and knowledge extraction workflows.

Key Features

Seamless integration with Qdrant, Neo4j, pgvector, and LanceDB
Incremental processing to sync only changed data efficiently
Built-in support for text chunking and LLM-based extraction
Automated type inference for streamlined schema management
Real-time ETL pipelines for embeddings and knowledge graphs
37 GitHub stars

Use Cases

Creating a live-updated vector search index for enterprise documentation
Implementing efficient data pipelines from S3 to vector databases
Building automated knowledge graphs from unstructured text using LLMs

About

Key Features

Seamless integration with Qdrant, Neo4j, pgvector, and LanceDB
Incremental processing to sync only changed data efficiently
Built-in support for text chunking and LLM-based extraction
Automated type inference for streamlined schema management
Real-time ETL pipelines for embeddings and knowledge graphs
37 GitHub stars

Use Cases

Creating a live-updated vector search index for enterprise documentation
Implementing efficient data pipelines from S3 to vector databases
Building automated knowledge graphs from unstructured text using LLMs