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rag-graph

GraphRAG

Instead of flat chunks, documents are mined for entities and relationships which form a Neo4j knowledge graph. Queries are translated into Cypher traversals that hop across relationships — answering questions a vector search alone never could.

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Interactive Architecture diagram
KNOWLEDGE-GRAPH BUILDGRAPH-GUIDED RETRIEVALExtractMiningBuildBuildTraverseCollectVerbalizeGenerate
Diagnostics Dashboard

Stage-by-Stage Data Flow Explorer

Select a phase from the controller below, then click individual step nodes to view their technical role, inputs, outputs, and mockup diagnostics data stream.

Phase Summary:

Indexing

An LLM extracts entities and relationships from each document and writes them into a graph.

Click a Node to Inspect:
[ROLE]:Reads source documents from files, repositories, or URLs, parsing the binary content and encoding it into standard, clean UTF-8 text passages.
[TECH STACK]:PyPDF2 / Docx2txt / LangChain WebBaseLoader / PDFPlumber
[INPUT]:Raw binary data stream (PDF, DOCX, TXT, HTML, JSON)
[OUTPUT]:Normalized string representing document plaintext content with structure metadata
[RAW DATA STREAM]:
> INGEST_STREAM: "financial_report_2026.pdf" (Size: 2.4 MB)
> DECODING_META: { mime: "application/pdf", pages: 12 }
> READOUT: "Ragiment Corp Annual Report 2026. EBITDA grew 18% to $4.6M. Product lines expanded by..."

Best suited for

Multi-hop, relational questions whose answers are spread across many documents.

Corpus
Relational
Queries
Multi-hop · analysis
Infra
Neo4j graph
Latency
Higher (traversal)

Complexity

High

LLM-driven entity and relationship extraction, a graph database, and query-to-Cypher translation add real operational weight at both ingest and query time.

Relevance today

Rising fast in enterprise settings — Microsoft's GraphRAG popularized it — wherever relational reasoning over connected data matters.

Where it's used

Financial & legal analysis

Trace ownership, obligations, and dependencies across filings and contracts.

Scientific & medical literature

Connect entities — genes, drugs, conditions — across thousands of papers.

Compliance & data lineage

Answer “how does A affect C through B?” across an enterprise's records.

Why it matters

  • Answers relational, multi-hop questions that flat vector search structurally cannot.
  • Traversal paths make every answer explainable and auditable.
  • Entities are deduplicated, so all facts about one entity are unified across the corpus.

Trade-offs & considerations

  • Graph construction is LLM-heavy — ingestion is slower and more expensive than embedding.
  • Requires a graph database (Neo4j) and well-tuned entity-extraction prompts.
  • Overkill for simple lookup Q&A, where Standard RAG is cheaper and faster.