Knowledge Graphs for Grounded Retrieval

Vector search finds passages that look like the question. A knowledge graph finds the facts that answer it, along with where they came from. This guide walks through the full lifecycle: deciding whether you need a graph, extracting entities and relationships from documents, keeping the graph accurate, and combining graph and vector retrieval in one pipeline.

What you will be able to do

• Decide between relational, vector, and graph stores for a given question type.
• Design a graph schema that stays small enough to maintain.
• Build an extraction pipeline with review steps that keep noise out.
• Write hybrid retrieval that combines graph traversal with semantic search.
• Return answers with provenance a reader can verify.

Inside the guide

1. 01

   What a graph is good at

   Multi-hop questions, provenance, and consistency across sources.

2. 02

   Schema before data

   Node and edge types, properties, and the identity problem.

3. 03

   Extraction pipelines

   Turning documents into candidate facts and deciding which ones to keep.

4. 04

   Entity resolution

   Merging duplicates without merging strangers.

5. 05

   Hybrid retrieval

   Graph traversal, vector search, and reranking in one request.

6. 06

   Keeping the graph true

   Change detection, re-extraction, and expiring stale facts.

7. 07

   Serving and explaining answers

   Citations, paths, and confidence that a human can check.

From the guide

A graph is worth its cost when the question has a shape the data does not. 'Which suppliers of our top-margin products are in regions with new export rules?' spans three systems and two hops. No single passage contains the answer, so a system that only finds passages cannot produce it.

Who it is for

• Engineers running retrieval systems that need traceable answers.
• Data teams consolidating facts spread across many sources.
• Anyone evaluating whether 'GraphRAG' is worth the operational cost.