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cocoindex

Design and run CocoIndex data transformation flows with incremental exports.

75|9|Updated Oct 20, 2025
Search Tags:#knowledge graph#embeddings#ETL#LLM#data transformation#cocoindex
One-click install
npx skills add https://github.com/cocoindex-io/cocoindex-claude --skill cocoindex-cocoindex-io
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cocoindex
Source: https://github.com/cocoindex-io/cocoindex-claude/tree/main/cocoindex
Command: npx skills add https://github.com/cocoindex-io/cocoindex-claude --skill cocoindex-cocoindex-io

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables teams to design, implement, and maintain CocoIndex-powered data transformation flows, including embeddings, LLM-based extraction, and knowledge-graph exports, with incremental updates.

Core Features & Use Cases

  • Build indexing flows to extract, transform, and load data to vector stores, graph databases, or relational stores
  • Create reusable transform flows and custom functions to accelerate development
  • Use with CLI or Python API to orchestrate data ingestion, transformations, and exports at scale

Quick Start

Install CocoIndex in your project, define a basic flow using the cocoindex.flow_def decorator, add a LocalFile source, compute an embedding, and export to a Postgres target. Use environment variables for database connections and API keys to enable LLM and embedding features.

Frequently Asked Questions about cocoindex

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

FAQPage Schema
How do I build an ETL pipeline that exports embeddings to a vector store?▼

To build an ETL pipeline for embeddings, use CocoIndex to define a data transformation flow that ingests sources, computes embeddings, and incrementally exports the results to vector stores like LanceDB or Postgres.

Can I use LLMs to extract and enrich data before exporting to a knowledge graph?▼

Yes, you can use LLMs for data extraction and enrichment by configuring your API keys within the CocoIndex flow, allowing you to transform raw text into structured relationships for knowledge graph exports.

What's the best way to automate incremental data transformation for vector databases?▼

Automating incremental data transformation for vector databases is handled by running CocoIndex flows via the Python API or CLI, which process only new or updated source data to efficiently update your target stores.

Do I need to install specific libraries to run data ingestion and transformation flows?▼

You need to install the CocoIndex core library to run data ingestion and transformation flows, plus optional extras like lancedb or embeddings packages depending on your specific pipeline requirements.

How does CocoIndex handle custom functions in an ETL data flow?▼

CocoIndex handles custom functions by allowing you to create reusable transform flows within your pipeline, enabling you to apply specialized data transformation logic before exporting to your target database.

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