← Back to BlogGraphRAG Explained: When Vector Search Needs a Knowledge Graph
A clear briefing on : why flat chunk retrieval fails multi-hop questions, how entities and relations help, hybrid patterns with classic RAG, and when Compile Vision recommends a graph layer.
GraphRAG adds structure (entities and relations) on top of document retrieval.
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Classic RAG excels when the answer sits in a few similar passages. It struggles when the question jumps across people, projects, policies, and dates that never co-occur in one chunk.
(and related knowledge-graph retrieval patterns) extract entities and relationships, then retrieve along those links before the LLM writes an answer. You trade indexing complexity for better multi-hop reasoning.
Use this briefing to decide whether vectors alone are enough, or whether your corpus needs a graph layer that Compile Vision can design with your existing RAG stack.
When chunks stay flat
Vector search returns neighbors in embedding space. ” needs three documents that never share wording, top-k chunks often miss the path.
Teams then overstuff context, raise k, or blame the model, when the retrieval shape was wrong for the question.
When links carry the answer
A knowledge graph stores entities (people, products, clauses) and edges (owns, depends-on, supersedes). Retrieval can walk those edges, then pull the supporting passages.
The LLM still writes the final prose. The graph’s job is to assemble the right evidence set for questions that are relational by nature.
A practical shape
Ingest documents as usual. Run entity/relation extraction (LLM-assisted or rules) into a graph store. Keep chunk embeddings for local semantic lookup. At query time, detect whether the question is relational; if so, expand via the graph, then rank passages for generation.
”, and agents when tools or live APIs are required.
Cost and quality gates matter. Extraction errors poison the graph. Start with a bounded domain, org chart + product catalog + policy set, before boiling the ocean wiki.
How Compile Vision helps
We extend production RAG with graph extraction jobs, hybrid routers, eval questions that require multi-hop proof, and UIs that show why an answer’s path was chosen.
If you already read our RAG and CAG briefings, is the next fork: same grounding goal, richer retrieval topology.
Key takeaways
Vector RAG is excellent for local semantic matches.
Multi-hop and relational questions often need entity links.
combines graph expansion with passage grounding.
Start narrow; dirty extractions hurt more than missing edges.
Compile Vision builds hybrid RAG + graph routers with evals.
Graph-augmented retrieval patterns
Teams use several related shapes under the GraphRAG umbrella.
| Approach | How it works | Best for | Differs by |
|---|---|---|---|
| Entity-linked RAG | Extract entities from the query, match graph nodes, pull linked chunk IDs, then generate. | Org, product, and policy corpora with clear nouns. | Lighter than community summarization; strong for ownership questions. |
| Community / summary GraphRAG | Cluster the graph, pre-summarize communities, retrieve summaries then details. | Broad “what’s going on in X domain?” questions. | Heavier index build; great for exploratory briefs. |
| Hybrid vector + graph router | Classifier chooses vector-only, graph walk, or both based on question type. | Production copilots with mixed question traffic. | Best cost/quality balance when tuned with evals. |
| Agentic graph tools | An agent calls graph query tools iteratively before answering. | Complex investigations with unknown hop depth. | Flexible but needs strict tool rails and budgets. |
Tools & stack options
Practical pieces teams use to prototype and ship.
| Tool | Role |
|---|---|
| Microsoft GraphRAG | Reference pipeline for graph + community summaries |
| Neo4j / Amazon Neptune | Graph stores for entities and relations |
| LlamaIndex PropertyGraph | Python orchestration for graph RAG |
| LangChain / LangGraph | Routers and agentic retrieval loops |
| pgvector / Pinecone / Weaviate | Chunk embeddings beside the graph |
| spaCy / LLM extractors | Entity and relation extraction jobs |
| Ragas / custom eval suites | Multi-hop answer and citation scoring |
| Compile Vision RAG console | Path visualization and operator review |
What you can build
- Policy copilots that track superseding clauses across years
- Product support that links parts, manuals, and tickets
- Org knowledge bots that answer ownership and dependency questions
- Research assistants that walk citation and concept graphs
- Hybrid CAG + GraphRAG for stable cores plus long-tail links
- Audit UIs that show the exact graph path behind an answer
Architecture & diagrams
GraphRAG hybrid retrieval
Vectors and graph walks cooperate before generation.
Index-time graph build
Documents become chunks plus a typed knowledge graph.
Essential reading
References & further reading
Sources matter as much as the briefing itself. Start with the primary reference, then dig into the supporting links.
Open reference implementation and papers.
- 2Compile Vision: RAG explained for builders
Foundation patterns before adding a graph layer.
- 3Compile Vision: Production RAG pipelines
Ops, tenancy, and eval habits GraphRAG still needs.
- 4Neo4j GraphRAG patterns
Vendor perspective on graph + LLM retrieval.
Learning / build steps
- 1
Map question types
Local fact vs relational / multi-hop
- 2
Extract graph
Entities, relations, provenance to chunks
- 3
Hybrid retrieve
Vector + graph walk + rerank
- 4
Generate + cite
Answer with linked sources
- 5
Eval paths
Score multi-hop suites separately
Need GraphRAG on your company knowledge?
Compile Vision designs hybrid retrieval with vectors, graphs, and eval suites so multi-hop questions stop failing silently. Share your corpus shape and we will propose an architecture.
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