Does CocoIndex support incremental updates?

Yes, CocoIndex features a high-performance incremental processing engine that only processes changed data, significantly reducing computation time and API costs.

Which databases can I export data to using this skill?

CocoIndex supports multiple targets including Postgres with pgvector, Qdrant, LanceDB, Neo4j, and Kuzu.

What is CocoIndex used for?

CocoIndex is used to create data transformation pipelines (flows) that extract data from various sources, apply AI transformations like embedding or chunking, and export them to vector or graph databases.

Can I use local LLM models with CocoIndex?

Yes, CocoIndex supports local models via Ollama, as well as major providers like OpenAI, Anthropic, Gemini, and Voyage.

CocoIndex AI Data Framework

Name: CocoIndex AI Data Framework
Author: pur3v4d3r

bypur3v4d3r

•

Data Science & ML

Builds high-performance, incremental AI data transformation pipelines for vector databases and knowledge graphs.

About

CocoIndex is a specialized toolkit for developers creating real-time data transformation flows tailored for AI applications. It simplifies the development of ETL pipelines—handling tasks like text chunking, embedding, and LLM-based extraction—while leveraging incremental processing to sync only changed data. By connecting various sources like S3 or local files to advanced targets such as Qdrant, LanceDB, or Neo4j, this skill allows users to build robust RAG systems, knowledge graphs, and live document indexes with minimal configuration and maximum efficiency.

Key Features

1 GitHub stars
Incremental processing for efficient data synchronization
Native integration with vector and graph database targets
Multi-source support including S3, Google Drive, and SQL databases
Extensible architecture for custom transformation functions
Built-in AI functions for embeddings and LLM extraction

Use Cases

Building a vector search index for RAG applications
Setting up live document-to-embedding pipelines for codebases
Creating automated knowledge graphs from technical documentation

About

Key Features

1 GitHub stars
Incremental processing for efficient data synchronization
Native integration with vector and graph database targets
Multi-source support including S3, Google Drive, and SQL databases
Extensible architecture for custom transformation functions
Built-in AI functions for embeddings and LLM extraction

Use Cases

Building a vector search index for RAG applications
Setting up live document-to-embedding pipelines for codebases
Creating automated knowledge graphs from technical documentation