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
Source Notes
- 2026-04-07: AI Powered Autonomous Social Video Content Generation and Optimization · ▶ source
- 2026-04-08: Agentic Visual Reasoning Enhancing VLMs for Precise Object Counting an · ▶ source
- 2026-04-17: Bridging the AI Agent Speed Gap Rebuilding Human Centric Web Infrastru · ▶ source
- 2026-04-24: Hermes · ▶ source
- 2026-04-26: DeepSeek · ▶ source