I will optimize enterprise rag systems with advanced graphrag and neo4j

Naive vector-search RAG architectures fail on multi-document cross-referencing, relational tables, and global semantic aggregation. When information chunks are retrieved devoid of structural context, LLMs generate hallucinations or fragmented outputs.

I reconstruct flawed retrieval pipelines by implementing advanced hierarchical indexing, hybrid search mechanisms, and explicit knowledge graph mapping (GraphRAG) via Neo4j.

Engineering Focus

• Knowledge Graph Extraction: Utilizing models to extract and map unstructured enterprise data into deterministic entity-relation graphs inside Neo4j for multi-hop query routing.
• Context-Preserving Chunking: Structuring content with parent-child token relations to maintain original semantic continuity prior to embedding generation.
• Hybrid Retrieval & Reranking: Interlocking sparse keyword layers (BM25) with dense vector layers, optimized via Cohere/BGE rerankers for precise context density.
• Multi-Modal Document Parsing: Building custom pipeline middleware to extract, structure, and vectorize data embedded within complex PDF schemas and financial tables.