cocoindex

Topics in the source

• CocoIndex
• Overview
• When to Use This Skill
• Flow Writing Workflow
• Step 1: Understand Requirements
• Step 2: Set Up Dependencies
• Step 3: Set Up Environment
• Step 4: Write the Flow

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: Search documentation: 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" "Ru

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