Engineering

GraphRAG vs. Traditional RAG

Why knowledge-graph-driven RAG outperforms vector-only retrieval on multi-hop reasoning, provenance, and hallucination resistance.

What Traditional RAG Gets Right

Standard vector RAG (retrieval-augmented generation) is the default pattern in almost every LLM application today. It works like this: chunk your documents, embed them into a dense vector space, and at query time retrieve the top-k most similar chunks by cosine distance. Then feed those chunks into the LLM context window.

This is elegant, fast, and surprisingly effective for single-hop questions — questions whose answer lives in one contiguous block of text. "What is the refund policy?" or "How do I reset my password?" — vector RAG handles these beautifully.

Where it breaks

Multi-hop reasoning: If the answer requires connecting two facts that live in different chunks, vector similarity alone cannot bridge them.
Provenance blindness: The LLM sees chunks but not where they came from. It cannot trace a claim back to its original source document or section.
Entity drift: Dense embeddings conflate similar-sounding but semantically different entities. "Apple" the fruit and "Apple" the company can end up in the same neighborhood.
Context window limits: Even with perfect retrieval, stuffing too many chunks into the prompt wastes tokens and degrades answer quality.

How GraphRAG Changes the Game

GraphRAG replaces (or augments) the flat chunk index with a structured knowledge graph. Documents are parsed into entities, relationships, and claims. Every node and edge is typed. Every fact is connected to its source. The graph itself becomes the retrieval substrate.

Structured retrieval

Instead of retrieving raw text chunks, GraphRAG retrieves subgraphs: connected patterns of entities and relationships that directly answer the query. A question like "Which board members invested in climate startups after 2020?" becomes a graph traversal — not a hope that two unrelated chunks happen to land in the same top-k window.

Provenance as first-class citizen

Every edge carries provenance metadata: source document, section, confidence score, and extraction timestamp. The LLM can cite its sources explicitly. Users can audit the chain of evidence behind every claim.

Entity disambiguation

Entities are canonicalized. "Apple Inc.", "Apple Computer", and "AAPL" all resolve to the same node. The model never conflates the company with the fruit because they are distinct nodes in the graph.

Seedthink's Hybrid Approach

Seedthink does not choose between graph and vector — it uses both, layered:

Layer 1

Knowledge Graph

All verified facts live as typed entities and relationships. Structured queries, reasoning chains, and provenance are handled natively here.

Layer 2

Verification Engine

Before any fact enters the graph, it passes multi-model consensus checks and source validation. Nothing unverified gets stored. Nothing uncertain gets cited.

Layer 3

Vector Memory

Semantic embeddings power fast approximate retrieval for fuzzy matching, paraphrase detection, and similarity-based recall when exact graph traversal misses.

Layer 4

Distillation Loop

Verified graph knowledge is distilled into training data, continuously improving Seedthink's own models so the graph gets smarter as it grows.

The Bottom Line

If your AI only retrieves chunks, it can only answer questions that fit inside one chunk. If your AI reasons over a verified knowledge graph, it can follow chains of evidence, cite sources, and answer questions no single document could answer alone.

That is the difference between a search engine and a growing intelligence.