← Opening PreparationOct 2025

Clipping · Exhibit · Hybrid Knowledge Retrieval System

Pasted from the desk

Multi-Agent GraphRAG

A multi-agent orchestration framework that combines Neo4j Knowledge Graphs with Vector Databases using LangGraph, enabling intelligent hybrid retrieval that grounds LLM responses in factual enterprise data.

Halftone photograph: wooden card-catalog drawers, one drawer open on a labelled card.
GraphRAG at the card catalog — file photo.
1233a3b44a5673cFig.1.Fig.2.a drum, sectioned

Reference

  1. 1.INTAKE — the user query
  2. 2.KEYER — Text-to-Cypher generation
  3. 3.WIRE WALL — the regulations graph
  4. 4.CARRIAGES — agents, in parallel
  5. 5.WICKET — the self-correcting loop
  6. 6.DELIVERY — answer assembly
  7. 7.GAUGE — +45% retrieval vs vector-only

APPARATUS FOR THE RETRIEVAL OF REGULATIONS. Filed Apr. 2026.

1233a3b44a5673cFig.1.Fig.2.a drum, sectioned

Tech

  • LangGraph
  • Neo4j
  • Knowledge Graphs
  • Vector DB
  • RAG

The line

  1. 01

    Developed a multi-agent orchestration framework using LangGraph to unify structured (Neo4j) and unstructured (Vector DB) data, improving retrieval accuracy by 35%.

  2. 02

    Constructed specialized reasoning agents capable of autonomous Cypher query generation and tool-calling, grounding LLM responses in factual enterprise data.

  3. 03

    Optimized the RAG pipeline by implementing a Global-to-Local retrieval strategy for synthesizing insights across massive document sets.

  4. 04

    Designed a self-correcting fallback mechanism that reroutes ambiguous queries through a broader semantic search layer, minimizing hallucination artifacts to near-zero.

Measurable impact

+35% retrieval accuracy

Repository← Opening Preparation