cocoindexlisted

# CocoIndex ## Overview CocoIndex is an ultra-performant real-time data transformation framework for AI with incremental processing. This skill enables building **indexing flows** that extract data from sources, apply transformations (chunking, embedding, LLM extraction), and export to targets (vector databases, graph databases, relational databases). **Core capabilities:** 1. **Write indexing flows** - Define ETL pipelines using Python 2. **Create custom functions** - Build reusable transformation logic 3. **Operate flows** - Run and manage flows using CLI or Python API **Key features:** - Incremental processing (only processes changed data) - Live updates (continuously sync source changes to targets) - Built-in functions (text chunking, embeddings, LLM extraction) - Multiple data sources (local files, S3, Azure Blob, Google Drive, Postgres) - Multiple targets (Postgres+pgvector, Qdrant, LanceDB, Neo4j, Kuzu) **For detailed documentation:** <https://cocoindex.io/docs/> **Search documentation:** <https://cocoindex.io/docs/search?q=url%20encoded%20keyword> ## When to Use This Skill Use when users request: - "Build a vector search index for my documents" - "Create an embedding pipeline for code/PDFs/images" - "Extract structured information using LLMs" - "Build a knowledge graph from documents" - "Set up live document indexing" - "Create custom transformation functions" - "Run/update my CocoIndex flow" ## Flow Writing Workflow ### Step 1: Understand Requirements As