Automates real-time data transformation and indexing workflows via Python API or CLI.
npx skills add https://github.com/89jobrien/steve --skill cocoindex
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
This Skill enables building indexing flows for AI data processing, embedding, and exporting to various vector databases and knowledge graphs.
Create a basic CocoIndex flow with a simple source and export to a vector store.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
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.
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