Which vector databases can I use with CocoIndex?

CocoIndex supports a wide range of targets including Postgres with pgvector, Qdrant, LanceDB, Neo4j, and Kuzu.

How does the CocoIndex skill help Claude assist me?

The skill provides Claude with domain-specific knowledge about the CocoIndex API, flow writing patterns, and environment configuration to help you build production-ready data pipelines faster.

Does this skill support incremental data updates?

Yes, incremental processing is a core feature, ensuring that only new or modified data is processed during pipeline updates to save time and compute costs.

Can I use local LLM models for processing?

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

What is CocoIndex used for in AI development?

CocoIndex is used to build data transformation pipelines (flows) that extract data, transform it using LLMs or embedding models, and index it into vector or graph databases for AI applications.

CocoIndex Data Pipelines

Name: CocoIndex Data Pipelines
Author: cocoindex-io

bycocoindex-io

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데이터 과학 및 ML

Builds ultra-performant real-time ETL pipelines for AI indexing, vector search, and incremental data processing.

소개

CocoIndex is a specialized framework designed for creating sophisticated data transformation flows that bridge the gap between raw data sources and AI-ready databases. This skill empowers Claude to architect indexing flows that extract data from sources like S3 or Postgres, apply complex transformations such as chunking and embedding, and export results to vector or graph databases. With a focus on incremental processing, it ensures that only changed data is processed, providing a high-performance solution for building live search indexes and knowledge graphs.

주요 기능

Built-in LLM functions for text chunking and embeddings
Incremental processing to sync only changed data
Multi-source support for S3, Azure, local files, and databases
Real-time monitoring via the CocoInsight interface
32 GitHub stars
Exports to Qdrant, Neo4j, pgvector, and LanceDB

사용 사례

Building an automated vector search index for enterprise document sets
Developing custom LLM extraction pipelines for structured data processing
Creating a live-updating knowledge graph from unstructured data

소개

주요 기능

Built-in LLM functions for text chunking and embeddings
Incremental processing to sync only changed data
Multi-source support for S3, Azure, local files, and databases
Real-time monitoring via the CocoInsight interface
32 GitHub stars
Exports to Qdrant, Neo4j, pgvector, and LanceDB

사용 사례

Building an automated vector search index for enterprise document sets
Developing custom LLM extraction pipelines for structured data processing
Creating a live-updating knowledge graph from unstructured data