2026 RAG Selection Guide: How to Choose Between Vector, Graph, and Vectorless

Why Traditional Vector RAG Is Failing

Vector RAG became the default because it is fast, cheap, and works for simple queries: documents are chunked, embeddings are generated, and similarity search retrieves the most relevant chunk. However, three failure modes appear:

It lacks a notion of "relationship"; semantic similarity cannot capture cross‑references such as "Section 4 cites Appendix C".

Chunking destroys document structure—tables, headings, and footnotes are split, breaking the meaning of numeric data.

For complex queries the accuracy collapses. In Diffbot’s benchmark, once a query involves more than five entities, accuracy drops to 0 % for schema‑bound queries.

GraphRAG: When Relationships Are the Answer

GraphRAG adds a knowledge‑graph layer on top of vector search, representing entities (people, companies, concepts, regulations) as nodes and their relationships as edges. This enables queries that require traversing connections rather than mere similarity.

GRAPHRAG ARCHITECTURE
Documents
   ↓
Entity Extraction (LLM identifies: people, orgs, concepts)
   ↓
Relationship Extraction (LLM identifies: who connects to what and how)
   ↓
Community Detection (Leiden algorithm groups related entities)
   ↓
Community Summaries (LLM summarizes each cluster)
   ↓
Knowledge Graph (nodes + edges stored in graph database)

Key advantages reported:

In enterprise settings GraphRAG achieves 72–83 % comprehensiveness and a 3.4× accuracy boost.

Indexing a typical corpus costs $20–500; LazyGraphRAG (2025) reduces this to 0.1 % of full GraphRAG by delaying summarization to query time, at the cost of an extra 2–8 seconds per query.

Vectorless RAG: When Structure Beats Similarity

Vectorless RAG discards vector databases entirely. The flagship framework, PageIndex (released Sep 2025 by VectifyAI’s Mingtian Zhang and Yu Tang, >23 000 ★ on GitHub), lets the LLM navigate a tree‑structured representation of the document.

VECTORLESS RAG ARCHITECTURE (PageIndex)
Document Ingestion:
PDF/Doc → Tree Indexing (preserves natural hierarchy)
        → Chapter → Section → Subsection → Table cells

NO chunking. NO embeddings. NO vector database.

Tree example:
├── Chapter 1: Revenue
│   ├── 1.1 Q1 Results
│   │   ├── Table: Revenue by Region
│   │   └── Footnotes: Currency adjustments
│   └── 1.2 Q2 Results
└── Chapter 2: Expenses

Query Time:
User Query → LLM inspects Table of Contents tree
          → LLM reasons: "Revenue figures are in Chapter 1"
          → LLM navigates to Chapter 1.1, retrieves context
          → LLM generates answer with exact citations
          → If incomplete → LLM iterates further

Performance on FinanceBench:

PageIndex‑driven Mafin 2.5 reaches 98.7 % accuracy.

Traditional Vector RAG ≈ 50 %.

GPT‑4o without RAG ≈ 31 %.

Perplexity ≈ 45 %.

The three drivers of this gap are:

Cross‑reference following – the tree tracks "see Appendix G" which vector similarity cannot.

Structure preservation – table headers, footnotes, and cell relationships remain intact.

Multi‑step reasoning – iterative navigation extracts data from multiple chapters.

Deep Comparison and Decision Framework

GraphRAG, Vectorless RAG, and Vector RAG solve different problem classes:

GraphRAG excels in large, relationship‑dense corpora (e.g., competitive intelligence, regulatory compliance, research synthesis, medical knowledge graphs).

Vectorless RAG shines when precise answers depend on internal document structure (financial filings, legal contracts, technical specifications).

Vector RAG remains optimal for massive, unstructured text collections, simple semantic search, high‑throughput low‑latency queries, and early‑stage prototypes.

2026 introduces Adaptive RAG: a query‑complexity classifier routes each request to the appropriate pipeline.

User Query → Complexity Classifier
          ↓
Simple? → Vector RAG (fast, cheap)
Complex? → GraphRAG or Vectorless RAG (accurate)
Relationship? → GraphRAG
Structured doc? → Vectorless RAG

This routing is already deployed in production; teams combine pipelines rather than committing to a single one, achieving higher accuracy at lower overall cost.

Conclusion

Teams that experience failures with RAG often used Vector RAG for queries it cannot answer. Understanding the three distinct retrieval paradigms—Vector (optimistic similarity), Graph (pre‑built relational mapping), and Vectorless (LLM‑driven structural navigation)—allows engineers to build Adaptive RAG systems that select the right tool for each query, turning retrieval from a bottleneck into a reliable component.