GraphRAG vs Vector RAG: Which Retrieval Method is Best?

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GraphRAG vs Vector RAG: Which Retrieval Method is Best?

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GraphRAG vs Vector RAG: Which Retrieval Method is Best?

Janvi Kumari Last Updated : 29 Jun, 2026

10 min read

GraphRAG and Vector RAG address different retrieval needs. Vector RAG splits documents into chunks, embeds them, retrieves semantically similar passages, and sends them to an LLM. It is simple, fast to build, and works best when answers sit within one or two relevant chunks.

GraphRAG adds structure by extracting entities, relationships, and communities, making it stronger for multi-hop reasoning, explainability, and corpus-wide synthesis across connected ideas. In this article, a practical comparison of GraphRAG and Vector RAG, we’ll break down where each approach fits best.

Table of contents

Definitions and Architecture

How Retrieval Works at Query Time

Hands-on: Build Vector RAG and GraphRAG from Start to End

When to Use Vector RAG, GraphRAG, or Hybrid RAG

Performance, Cost, and Maintenance Trade-offs

Limitations and Failure Modes

Conclusion

Frequently Asked Questions

Definitions and Architecture

Vector RAG works by splitting documents into small text chunks. Each chunk is converted into an embedding and stored in a vector database. When a user asks a question, the question is also converted into an embedding. The system then finds the most similar chunks and sends them to the LLM to generate an answer.

Vector RAG is simple, fast, and easy to update. It works well for direct factual questions. But it stores meaning mostly through embeddings and text, not through explicit entities or relationships. Because of this, it can struggle with questions that need connections across multiple chunks.

GraphRAG adds more structure. It extracts entities, relationships, claims, and communities from the documents. It then builds a graph that shows how different pieces of information are connected.

This makes GraphRAG better for relationship-based questions, multi-step reasoning, and broad understanding across a large set of documents. The tradeoff is that it takes more effort and cost to build because it needs graph construction, community detection, and summarization.

In practice, many systems use both. Vector search quickly finds relevant text, while graph retrieval adds connected context and better reasoning.

How Retrieval Works at Query Time

The biggest difference between Vector RAG and GraphRAG becomes clear at query time. In Vector RAG, the query is treated as a semantic search problem. The user question is converted into an embedding. The system compares this query embedding with stored chunk embeddings. It retrieves the closest chunks and sends them to the LLM. The LLM then answers using only those chunks as context. This works well when the answer is directly available in a small set of similar passages.

GraphRAG handles the query differently. It first tries to understand whether the question is local or global. A local question is about a specific entity, event, customer, product, or document. A global question asks for themes, patterns, risks, summaries, or relationships across the corpus.

This means Vector RAG retrieves by similarity, while GraphRAG retrieves by structure and meaning together. Vector RAG is faster and easier when the question is narrow. GraphRAG is stronger when the answer depends on connections across many documents. A hybrid system can use both paths. It can first retrieve relevant chunks through vector search, then expand the context using graph relationships. This gives the LLM both textual evidence and structured grounding.

Hands-on: Build Vector RAG and GraphRAG from Start to End

In this hands-on section, we will build both Vector RAG and GraphRAG on the same small corpus. The goal is simple. We want to show how Vector RAG retrieves similar text chunks, while GraphRAG retrieves entities, relationships, and connected context. We will use Python, SentenceTransformers for embeddings, FAISS for vector search, and NetworkX for graph storage and traversal. SentenceTransformers supports encoding text into embeddings, FAISS is built for efficient vector similarity search, and NetworkX stores graphs as nodes and edges with attributes.

First, install the required libraries.

pip install sentence-transformers faiss-cpu networkx pandas numpy

Now create a small demo corpus. This corpus is intentionally small so the difference is easy to show.

docs = [ { "id": "doc1", "text": "NourishCo is facing rising logistics costs in its North region. The operations team believes the issue is linked to poor demand forecasting.", }, { "id": "doc2", "text": "The North region uses Vendor A for cold chain delivery. Vendor A has repeated delivery delays during high-demand weeks.", }, { "id": "doc3", "text": "The analytics team proposed a machine learning forecasting model to reduce stockouts and improve supply planning.", }, { "id": "doc4", "text": "The finance team is concerned that Vendor A delays are increasing working capital pressure because inventory buffers are rising.", }, { "id": "doc5", "text": "The leadership team wants an AI roadmap that connects demand forecasting, logistics optimization, and vendor performance monitoring.", }, ]

Now define a simple chunking function. In this demo, each document is already short, so we will treat each document as one chunk.

chunks = []

for doc in docs: chunks.append({ "chunk_id": doc["id"], "text": doc["text"], })

print(chunks)

Now build the Vector RAG index.

from sentence_transformers import SentenceTransformer import faiss import numpy as np

model = SentenceTransformer("all-MiniLM-L6-v2")

texts = [chunk["text"] for chunk in chunks] embeddings = model.encode(texts, convert_to_numpy=True)

dimension = embeddings.shape[1] index = faiss.IndexFlatL2(dimension) index.add(embeddings)

print("Vector index created with", index.ntotal, "chunks")

Now create a Vector RAG retrieval function.

def vector_rag_search(query, top_k=3): query_embedding = model.encode([query], convert_to_numpy=True)

distances, indices = index.search(query_embedding, top_k)

results = []

for idx in indices[0]: results.append(chunks[idx])

return results

Test the Vector RAG pipeline

query = "Why are logistics costs rising in the North region?"

vector_results = vector_rag_search(query)

for result in vector_results: print(result["chunk_id"], ":", result["text"])

This retrieves chunks that are semantically close to the question. It should return documents about North region, logistics costs, Vendor A, and delays. This is useful when the answer is present in one or two similar chunks.

Now let us build the GraphRAG version. In a production system, entities and relationships are usually extracted with an LLM or an information extraction model. For this hands-on demo, we will manually define them so the flow is easy to understand and explain.

import networkx as nx

G = nx.Graph()

entities = [ "NourishCo", "North Region", "Logistics Costs", "Demand Forecasting", "Vendor A", "Delivery Delays", "Analytics Team", "ML Forecasting Model", "Stockouts", "Supply Planning", "Finance Team", "Working Capital Pressure", "Inventory Buffers", "Leadership Team", "AI Roadmap", "Logistics Optimization", "Vendor Performance Monitoring", ]

G.add_nodes_from(entities)

relationships = [ ("NourishCo", "North Region", "operates in"), ("North Region", "Logistics Costs", "has issue"), ("Logistics Costs", "Demand Forecasting", "linked to"), ("North Region", "Vendor A", "uses"), ("Vendor A", "Delivery Delays", "causes"), ("Delivery Delays", "Logistics Costs", "increases"), ("Analytics Team", "ML Forecasting Model", "proposed"), ("ML Forecasting Model", "Demand Forecasting", "improves"), ("ML Forecasting Model", "Stockouts", "reduces"), ("ML Forecasting Model", "Supply Planning", "improves"), ("Finance Team", "Working Capital Pressure", "concerned about"), ("Vendor A", "Working Capital Pressure", "contributes to"), ("Inventory Buffers", "Working Capital Pressure", "increase"), ("Delivery Delays", "Inventory Buffers", "increase"), ("Leadership Team", "AI Roadmap", "wants"), ("AI Roadmap", "Demand Forecasting", "includes"), ("AI Roadmap", "Logistics Optimization", "includes"), ("AI Roadmap", "Vendor Performance Monitoring", "includes"), ]

for source, target, relation in relationships: G.add_edge(source, target, relation=relation)

print( "Graph created with", G.number_of_nodes(), "nodes and", G.number_of_edges(), "edges", )

Now create a function to inspect graph neighbors.

def get_graph_context(entity, depth=1): if entity not in G: return []

context = [] visited = set([entity]) frontier = [entity]

for _ in range(depth): next_frontier = []

for node in frontier: for neighbor in G.neighbors(node): edge_data = G.get_edge_data(node, neighbor) relation = edge_data["relation"]

context.append({ "source": node, "relation": relation, "target": neighbor, })

if neighbor not in visited: visited.add(neighbor) next_frontier.append(neighbor)

frontier = next_frontier

return context

Test the graph retrieval

graph_results = get_graph_context("Vendor A", depth=2)

for item in graph_results: print(item["source"], "--", item["relation"], "--", item["target"])

This gives connected context. It does not just retrieve similar chunks. It shows how Vendor A connects to delivery delays, logistics costs, inventory buffers, and working capital pressure.

Now we create a simple GraphRAG query function. For the demo, we will map query keywords to entities.

def detect_entity(query): query_lower = query.lower()

entity_map = { "vendor": "Vendor A", "logistics": "Logistics Costs", "north": "North Region", "forecasting": "Demand Forecasting", "working capital": "Working Capital Pressure", "financial pressure": "Working Capital Pressure", "roadmap": "AI Roadmap", }

for keyword, entity in entity_map.items(): if keyword in query_lower: return entity

return None

def graph_rag_search(query, depth=2): entity = detect_entity(query)

if not entity: return []

return get_graph_context(entity, depth=depth)

Test GraphRAG

query = "How is Vendor A connected to financial pressure?"

graph_context = graph_rag_search(query)

for item in graph_context: print(item["source"], "--", item["relation"], "--", item["target"])

Now compare both methods on the same query.

query = "How is Vendor A connected to financial pressure?"

print("VECTOR RAG RESULTS")

vector_results = vector_rag_search(query)

for result in vector_results: print("-", result["text"])

print("\nGRAPHRAG RESULTS")

graph_context = graph_rag_search(query)

for item in graph_context: print("-", item["source"], item["relation"], item["target"])

The Vector RAG output will return the most similar text chunks. It may find the finance document and the Vendor A document. GraphRAG will show the relationship chain more clearly. It can show that Vendor A causes delivery delays, delivery delays increase inventory buffers, and inventory buffers increase working capital pressure.

Now add a simple answer generator. This version does not require an LLM API. It creates a readable answer fr

[truncated for AI cost control]