document-processing

Document processing involves automated extraction, structuring, and analysis of information from unstructured documents to enable efficient retrieval, reasoning, and knowledge management.

Key Applications:

Entity and relationship extraction using LLM for semantic understanding
Knowledge Graph construction from document collections
Enhanced rag (Retrieval-Augmented Generation) systems via graph-based semantic search
Real-time knowledge graph updates from document streams
Agentic RAG systems enhanced with knowledge graphs for dynamic agent reasoning (Cole Medin’s RAG 2.0)
Graph RAG: Emerging technique using knowledge graphs for structured data querying, offering flexibility over vector search (no requirement for same model for embedding/retrieval) as demonstrated in 2026 04 14 IBM Explainer creating GraphRAG

Recent Implementation:

Cocoindex channel and Cocolndex framework for LLM-driven knowledge graph construction:
- Processes markdown documents to extract entities/relationships via LLM
- Builds graph in Neo4j for real-time rag augmentation
- Demonstrated in YouTube tutorial

2026 04 14 Cocoindex channel and knowledge Graphs for LLM RAG

2026 04 14 IBM Explainer creating GraphRAG

Source Notes