cocoindex

Automates real-time data transformation and indexing workflows via Python API or CLI.

4|Updated Dec 23, 2025

Search Tags:#cli #flow #etl #embedding #cocoindex #vectors

Ready to run

npx skills add https://github.com/89jobrien/steve --skill cocoindex

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cocoindex
Source: https://github.com/89jobrien/steve/tree/main/steve/skills/cocoindex
Command: npx skills add https://github.com/89jobrien/steve --skill cocoindex

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables building indexing flows for AI data processing, embedding, and exporting to various vector databases and knowledge graphs.

Core Features & Use Cases

Define indexing flows with Python
Write custom transformation functions
Run flows via CLI or Python API
Support incremental processing and live updates
Multiple data sources and targets

Quick Start

Create a basic CocoIndex flow with a simple source and export to a vector store.

Frequently Asked Questions about cocoindex

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build ETL pipelines that automatically transform and index data into vector stores?▼

ETL pipelines automate data extraction, transformation, and loading into vector databases. CocoIndex lets you define flows with Python decorators, apply custom transformations, and export to backends like Qdrant, Postgres, or Neo4j—all triggered via CLI or API with built-in text chunking and embedding support.

Can I set up incremental data synchronization across S3, Azure Blob, and Postgres?▼

Yes. CocoIndex supports incremental processing and live updates from multiple data sources including local files, S3, Azure Blob, and Postgres. Configure sources and targets via Python API or environment-driven settings, and CocoIndex manages synchronization automatically.

What's the best way to embed documents and index them for AI search?▼

Document embedding and indexing for AI search combines text chunking, vectorization, and storage in a vector database. CocoIndex automates this workflow with built-in text chunking, LLM extraction, and multi-target backend support so you can index documents once and query across Qdrant, LanceDB, or Neo4j.

Do I need to write custom code to orchestrate data flows, or can I use configuration?▼

You can do both. CocoIndex supports flow orchestration via Python decorators for custom logic, CLI commands for scripting, and environment-driven configuration for declarative setups—choose the approach that fits your workflow.

How do I construct knowledge graphs from unstructured data sources?▼

Knowledge graph construction extracts entities and relationships from unstructured data and stores them in a graph database. CocoIndex supports this via LLM extraction, Neo4j as a target backend, and flow definitions that transform raw data into graph-ready structure.

What's required before I can run indexing flows—do I need existing vector databases set up?▼

You need your data sources (local files, S3, Azure Blob, or Postgres) and target backends (Qdrant, Postgres, Neo4j, or LanceDB) configured. CocoIndex handles the transformation and indexing logic; you provide source and target connection details via Python or environment configuration.