AI Data Pipeline

A structured workflow for transforming raw data into AI-ready formats, encompassing ingestion, processing, storage, and model integration. Key components include data sourcing, cleaning, feature extraction, and model deployment.

• Knowledge Graph Integration: Knowledge Graph construction from documents via llm-rag for enhanced semantic search and context-aware queries
• Cocoindex Framework: Real-time knowledge graph builder using LLM-driven entity/relationship extraction from markdown documents, stored in Neo4j
• Pipeline Components:
  • Document ingestion (e.g., markdown collections)
  • LLM-based entity/relationship extraction
  • Graph database population (Neo4j)
  • RAG system integration for query augmentation
• Project Example: Cocoindex tutorial demonstrates end-to-end implementation with video guide

2026 04 14 Cocoindex channel and knowledge Graphs for LLM RAG