How Graph RAG Combines Knowledge Graphs With LLMs To Structure Text Into Entities And Relationships, Enabling Explainable Reasoning And More Accurate Insights

What is RAG, Knowledge Graphs, and Graph RAG

To understand Graph RAG, we need to define its constituent parts:

Retrieval-Augmented Generation (RAG):
RAG refers to a family of techniques where, instead of relying solely on what the Language Model “knows” (during training), you retrieve relevant information from external sources, and augment the prompt / input to the LLM with that information. This helps with freshness, domain-specificity, reducing hallucinations, and grounding.
Knowledge Graph (KG):
A knowledge graph is a structured representation of information in which entities (nodes) are connected by relationships (edges). Entities might be people, places, concepts, objects; relationships might represent “works_for”, “located_in”, “depends_on”, etc. Knowledge graphs encode the semantics explicitly and also allow multi-hop reasoning (following chains of relationships).
Graph RAG (Graph-based Retrieval-Augmented Generation):
This is the hybrid paradigm where RAG is not just using unstructured text + vectors, but also structured knowledge (from a KG). Graph RAG uses the KG to support or filter retrieval, to provide context, to perform reasoning over entities & relations, and to make the output of the LLM more explainable (because you can show which entities/relationships contributed to the answer). Sometimes called “GraphRAG”, “Graph-RAG”, “GRAG”, etc.

Why Combine Knowledge Graphs with LLMs / RAG

Here are the benefits and motivations for combining KGs + RAG + LLMs:

Improved Accuracy & Less Hallucination
Since the LLM can draw on concrete, structured facts (entities and relationships) from a KG, it’s less likely to hallucinate false facts or make up relationships.
Explainable Reasoning / Traceability
Because the KG has explicit structure, you can trace why a certain entity was chosen, or how a chain of relationships led to a conclusion. This is especially important in regulated domains (healthcare, law, finance).
Multi-Hop Reasoning
Many complex queries require following multiple relationships. For example: “Which suppliers in Europe provide lithium-ion batteries from manufacturers with ISO certification X?” That involves product → supplier → location → certification relationships. A KG supports multi-hop queries; pure vector search may miss or degrade on these.
Combining Structured & Unstructured Data
Unstructured text (documents, reports) contains rich content; structured KG provides schema, consistency, relations. Graph RAG allows combining both.
Domain Adaptability and Updating Knowledge
Because adding new entities or relations to a KG or updating existing ones can be more precise than retraining an LLM. Also helps with freshness.

Architectural Patterns / How Graph RAG Works

There are a few patterns by which Graph RAG can be implemented. These differ in how retrieval is done, how the KG is built/maintained, and how the LLM uses the KG information.

Pattern	Key steps	Strengths	Weaknesses
Vector based + KG filtering	First retrieve via vectors (from embeddings) relevant text / entities, then use KG to filter or expand result via relationships.	Fast retrieval, good recall, KG adds precision.	KG must be well built; filtering might remove useful info; combining might be complex.
Prompt-to-query / KG query	Use the LLM (or a preprocessor) to translate natural language query into queries in graph query languages (e.g. SPARQL, Cypher). Then retrieve subgraph, feed to LLM for answer generation.	Very precise; full use of relationships; explainable.	Requires well-defined schema, potentially more latency; translating to query reliably can be hard.
Hybrid Graph + Text (Mixed Context)	Build a KG from entities/relations extracted from text; store text units and graph structures. At query time, combine subgraph retrieval + relevant document text + context to feed to LLM.	Best of both worlds. Enables multi-hop reasoning + context from unstructured data.	More complex indexing and infrastructure; costs for graph construction; handling large graphs and embeddings.

Neo4j + LangChain is a common infrastructure combo for implementing Graph RAG in practice.

How to Build Graph RAG: Step-By-Step (with Code Examples)

Below is a conceptual / partial working example to show how you might build Graph RAG in Python using existing tools (e.g. LangChain, Neo4j, OpenAI). You can adapt according to your domain.

Setup & Dependencies

Building / Extracting the Knowledge Graph

Given a corpus of documents (or CSVs, or JSON etc.):

Entity Extraction
Use an LLM or other NLP tool (Named Entity Recognition + Linking) to find entities in text.
Relation Extraction
For pairs of entities in the same document or sentence, extract relationships via the LLM or relation extraction models.
Store Triples in Graph DB

# Suppose you have extracted triples of the form (entity_a, relation, entity_b)

triples = [

("Supplier X", "provides", "Lithium-ion Battery"),

("Supplier X", "located_in", "Germany"),

# etc.

]

# Connect to Neo4j and add nodes & relationships
uri = “bolt://localhost:7687”
user = “neo4j”
password = “password”driver = GraphDatabase.driver(uri, auth=(user, password))def add_triple(tx, h, r, t):
tx.run(“””
MERGE (a:Entity {name: $h})
MERGE (b:Entity {name: $t})
MERGE (a)-[:REL {label: $r}]->(b)
“””, h=h, r=r, t=t)with driver.session() as session:
for (h, r, t) in triples:
session.write_transaction(add_triple, h, r, t)

Embed Entity Descriptions / Node Text for Vector Retrieval
For each entity, you might store a description (maybe the sentences or paragraphs where it appears). Compute embeddings for those descriptions.

Query / Retrieval Pipeline

When a user asks a query (natural language), you can combine vector search + graph traversal + prompt to LLM.

def graph_rag_answer(question: str):

# 1. Retrieve related entities via vector search

results = emb_index.similarity_search(question, k=5)

entities = [res.metadata["entity"] for res in results]

# 2. Fetch subgraph from the KG around those entities
with driver.session() as session:
# For simplicity, fetch direct neighbors
query = “””
MATCH (e:Entity)-[r]-(n)
WHERE e.name IN $entities
RETURN e.name AS head, type(r) AS relation, n.name AS tail, labels(n) as tail_labels
LIMIT 20
“””
rec = session.run(query, entities=entities)
subgraph_triples = [(r[“head”], r[“relation”], r[“tail”]) for r in rec]# 3. Format subgraph into a textual context
context = “”
for (h, r, t) in subgraph_triples:
context += f”{h} {r} {t}.\n”# 4. Build prompt combining question + context
prompt = f”””You are an expert system. Use the following facts to answer the question.Facts:
{context}Question: {question}Answer:
“””
# 5. Call LLM
response = openai.ChatCompletion.create(
model=“gpt-4”, # or GPT-3.5 etc.
messages=[
{“role”:“system”, “content”:“You are a precise and factual assistant.”},
{“role”:“user”, “content”: prompt}
]
)
return response.choices[0].message.content

# Example:
ans = graph_rag_answer(“Which German suppliers provide lithium-ion batteries?”)
print(ans)

This is a simplified version. In a production setup, you might:

Use multi-hop graph traversal (neighbors of neighbors, following certain relation types)
Use ranking or filtering on edges/nodes (e.g. drop weak relations, filter by properties)
Use prompt templates or more complex chain of thought in LLM
Combine document text for more context when entity descriptions are not enough

Hybrid Retrieval & Filtering

Often you don’t want to rely solely on the KG or solely on text. Many Graph RAG systems use hybrid retrieval:

First vector search for relevant documents or entity descriptions
Use graph query to pull related entities/relations
Possibly re-rank
Provide both document text + KG context as input to LLM

For example, Neo4j + LangChain tutorial outlines using Neo4j as vector store + KG store.

Real-World Implementations & Research

Some projects and papers illustrate how Graph RAG is used or improved:

Neo4j tutorial: building a GraphRAG system using Neo4j as knowledge graph + vector store + LangChain + OpenAI, for answering both structured & unstructured queries in a synthetic DevOps environment.
“Enhancing RAG-based application accuracy by constructing and leveraging knowledge graphs” (LangChain + Neo4j) shows combining graph data + vector search to improve depth and context of retrieved info.
KET-RAG paper: This is about reducing the indexing cost of Graph RAG by building a skeleton graph plus a lighter bipartite graph to balance cost vs retrieval performance.
GNN-RAG: A method where a Graph Neural Network is used over KG subgraphs to retrieve answer candidates; shortest paths are verbalized then fed into LLM for final answer; claims strong performance, especially on multi‐entity and multi-hop questions.
Think-on-Graph 2.0 (ToG-2): A hybrid retrieval method that tightly couples retrieving graph info and unstructured contexts.

Practical Challenges & Considerations

While Graph RAG is powerful, there are caveats and practical trade-offs to be aware of:

Graph Construction Cost & Quality
Extracting entities and relations reliably from text is not trivial. Errors in entity recognition, relation extraction, or linking can introduce noise. Maintaining and updating the KG is also work.
Schema / Ontology Design
To make the KG useful, you need a good schema (what kinds of entities, relation types, attributes). If the schema is too flat / generic, you lose the benefit. If too complex, it becomes brittle.
Scalability
Large graphs can become expensive or slow to traverse, especially for multi-hop or large neighborhood queries. Efficient indexing, caching, pruned traversal, vector embeddings of node descriptions, etc. are needed.
Latency
Graph queries + LLM calls + vector retrieval can add up. Balancing freshness vs performance is important.
Prompt Engineering & Integration
How you represent the extracted graph facts in the prompt, how you decide what to include or omit, how you ensure LLM uses them rather than ignoring them — all require careful design.
Explainability vs. Overwhelming Detail
You want explainability, but showing too many nodes/edges can overwhelm the user or make the answer verbose.
Cost
Embedding many texts/entities, storing and traversing large graphs, making many LLM API calls – all have financial costs.

Code Example: More Complete GraphRAG Flow

Here’s a more end-to-end sketch of a Graph RAG pipeline. This is more illustrative than production-ready, but shows the main components.

import openai

from langchain.embeddings import OpenAIEmbeddings

from langchain.vectorstores import FAISS

from neo4j import GraphDatabase

import json

import time

openai.api_key = “YOUR_KEY”# ———— Build / Load Graph + Embedding Store —————-driver = GraphDatabase.driver(“bolt://localhost:7687”, auth=(“neo4j”,“password”))def extract_entities_relations(text: str):
“””Use LLM to extract entity/relation triples from text”””
prompt = f”””
You are an assistant. Extract the entities and relationships from the following text. \
Return a JSON list of triples of the form {{ “head”:…, “relation”:…, “tail”:… }}.Text: \”\”\”{text}\”\”\”
“””
resp = openai.ChatCompletion.create(
model=“gpt-4”,
messages=[{“role”:“system”,“content”:“You extract entities/relations.”},
{“role”:“user”,“content”:prompt}],
temperature=0
)
# parse response
triples = json.loads(resp.choices[0].message.content)
return triplesdef store_triples(triples):
with driver.session() as session:
for t in triples:
h, r, ta = t[“head”], t[“relation”], t[“tail”]
session.run(“””
MERGE (a:Entity {name: $h})
MERGE (b:Entity {name: $ta})
MERGE (a)-[rel:RELATION {name: $r}]->(b)
“””, h=h, r=r, ta=ta)

# Suppose you have a corpus
documents = [
“Supplier X located in Germany provides lithium-ion batteries to Company Y.”,
“Company Y has ISO 9001 certification.”,
# etc.
]

entity_descs = {} # map from entity name to description

for doc in documents:
triples = extract_entities_relations(doc)
store_triples(triples)
# update description
for t in triples:
for ent in [t[“head”], t[“tail”]]:
if ent not in entity_descs:
entity_descs[ent] = doc
else:
entity_descs[ent] += ” “ + doc

# Build vector store on entity descriptions
emb = OpenAIEmbeddings()
texts = list(entity_descs.values())
ents = list(entity_descs.keys())
vs = FAISS.from_texts(texts, emb, metadatas=[{“entity”: e} for e in ents])

# ———— Query Side —————-

def get_subgraph(entities, depth=1):
“””Fetch nodes & edges up to a certain depth around entities”””
with driver.session() as session:
query = “””
MATCH (e:Entity)-[r*1..$depth]-(n)
WHERE e.name IN $entities
RETURN DISTINCT e, relationships(r) as rels, n
LIMIT 50
“””
rec = session.run(query, entities=entities, depth=depth)
triples = []
for record in rec:
# process record to pull head, relation, tail
# simplified for illustration
pass
return triples

def graph_rag(question: str):
# 1. Vector search for entities
results = vs.similarity_search(question, k=5)
entities = [res.metadata[“entity”] for res in results]

# 2. Get subgraph
sub_triples = get_subgraph(entities, depth=2)

# 3. Format context
context_facts = “\n”.join([f”{h} {r} {t}.” for (h, r, t) in sub_triples])

# 4. Retrieve text docs (optionally) related
# (could do another vector search over original docs)

# 5. Build prompt
prompt = f”””
You are a helpful assistant. Use the following facts to answer the question:

Facts:
{context_facts}

Question: {question}

Answer:
“””
resp = openai.ChatCompletion.create(
model=“gpt-4”,
messages=[
{“role”:“system”, “content”:“You are precise, factual, and reasoning.”},
{“role”:“user”, “content”: prompt}
],
temperature=0
)
return resp.choices[0].message.content

# Example
print(graph_rag(“Does any supplier in Germany with ISO certification supply lithium-ion batteries?”))

Comparison: Graph RAG vs Pure Vector RAG vs Pure KGQA

To put things in perspective:

Approach	Pure Vector RAG	Pure KGQA (knowledge graph only)	Graph RAG / Hybrid
Handles unstructured text well?	Yes (retrieves docs / passages)	Not directly; KG must have structured data	Yes, since you combine KG + docs or entity descriptions
Enables multi-hop reasoning?	Weak or approximation via multiple retrievals	Strong (you can traverse edges)	Strong
Explainability / traceability	Low: you see docs but less structure of “why”	High: you see paths / relations used	High
Freshness / updating	Dependent on how you update the vector store/documents	Updating KG is more structured but needs manual / tooling work	Needs work in both halves
Implementation complexity	Moderate	High (graph schema, relation extraction, query design)	Higher (you need to build/maintain both)

Emerging Research & Advanced Methods

Think-on-Graph 2.0 (ToG-2): Hybrid, tightly coupling structured graph retrieval and unstructured context retrieval in an iterative way. This helps deepen context via KG while using documents to enrich entity contexts.
KET-RAG: A strategy to reduce the cost of indexing large collections by building a “skeleton” graph plus a lightweight bipartite graph for filtering. Helps maintain retrieval/generation quality while lowering infrastructure cost.
GNN-RAG: Uses Graph Neural Networks over KG subgraphs to assist with retrieval of candidate answers, then uses LLMs to verbalize reasoning. This architecture shows improved performance on standard KGQA benchmarks.

Use Cases

Graph RAG is particularly helpful in several domains:

Supply Chain / Procurement QA: e.g., “Which suppliers in Europe that deliver battery packs have passed compliance checks?” KG helps express relations like compliance, location, products.
Enterprise Knowledge Management: combining internal documents, organizational structure, project dependencies.
Healthcare & Life Sciences: mapping diseases, treatments, studies, adverse effect relations; answering multi-aspect clinical questions.
Legal / Regulatory Compliance: tracing laws, regulations, cross references, precedents.
Technical Support & Diagnostics: dependency graphs (e.g., microservices, tasks), root-cause analysis.

Tips & Best Practices for Graph RAG

Define a clear ontology/schema early: What entity types and relation types matter for your domain? This guides extraction and usage.
Use LLMs to assist extraction but validate: Entity / relation extraction via LLMs is powerful, but be ready to clean up, de-duplicate, resolve ambiguity.
Hybrid retrieval: Use both vector search (for broad context, richness) + graph queries (for structure, precision).
Prune irrelevant parts of the graph: For each query you might retrieve too much; efficient filtering helps (relation type filtering, hop count, metadata).
Design prompt templates that incorporate graph facts clearly: Make it easy for LLMs to consume entity-relationship statements, possibly with labels.
Track provenance: record which triples / which nodes contributed to answers. This aids explainability and debugging.
Monitor performance & latency: Graphs can get large; optimize indexing, caching, precomputation.

Example Scenario in Depth: Supply Chain Query

Let’s walk through a more elaborate example: You have a procurement department. Data sources include CSVs with supplier info, product catalogs, compliance reports, and unstructured text documents (reports, emails). You want to build a system to answer: “Which suppliers in Europe supply lithium-ion batteries and have passed safety compliance XY, and what is their risk level based on recent complaints?”

Steps:

Data Ingestion & Extraction
- Ingest structured data: supplier lists, catalogs, compliance status.
- Read unstructured reports (emails, news) and use LLM to extract: supplier name, product, compliance info, complaint incidents.
Build KG
- Entities: Supplier, Product, ComplianceStandard, Location, ComplaintIncident
- Relations: supplies, located_in, passed_compliance, has_complaint, note_severity, etc.
Embed Entities/Descriptions
- For each supplier, product, etc., get description (structured + unstructured content linked to them) => embeddings.
User Query Pipeline
- Vector search: find entities whose descriptions best match “lithium-ion batteries”, “compliance XY”, “complaints”.
- Graph traversal: around those suppliers, check location = Europe; compliance standard passed; check linked complaint nodes (e.g. count, severity).
- Construct subgraph / set of relevant facts.
Answer Generation
- Feed the facts + question into LLM, along with maybe document passages.
- Provide answer and also trace: “Supplier A (located_in Germany) supplies lithium-ion batteries, passed compliance XY; has 2 complaints in last year of moderate severity” etc.
Explainability
- Provide paths from query to answer: supplier → supplies → product; supplier → passed_compliance → compliance standard; supplier → has_complaint → incident.

This kind of scenario shows the power of Graph RAG in enabling nuanced, relational, explainable insight.

Limitations & Open Problems

Graph RAG is an active research area. Some limitations and open issues:

Reliability of extraction: mis‐extracted relations or missing entities can mislead reasoning.
KG updates & versioning: Ensuring that the graph stays in sync with evolving data sources; dealing with temporal aspects.
Scaling to large corpora: both in terms of text and graph size; indexing, query latencies, memory.
Balancing completeness vs. noise: including too many graph nodes/edges may introduce irrelevant or misleading context.
LLM’s usage of provided facts: sometimes LLMs ignore or misinterpret the provided KG context. Making sure prompts force usage / penalize hallucination.
Benchmarking & evaluation metrics: how to measure the impact of Graph RAG vs other methods (precision, recall, reasoning diagnostics, explanation quality).

Conclusion

Graph RAG represents a powerful evolution in how we combine large language models with structured knowledge. By structuring text into entities and relationships, a knowledge graph provides a skeleton of facts that can be queried, traversed, explained; while LLMs bring flexibility, language understanding, natural language generation, and the ability to consume unstructured text.

When you combine the two:

You get more accurate insights, especially in domains requiring relational reasoning and multi-step inference.
You improve explainability, because you can point to which entities/relations/facts contributed to an answer.
You reduce hallucination and error, by grounding the reasoning in concrete graph‐structured knowledge.
You can integrate structured and unstructured data sources, using the KG as a semantic backbone and text for detail.

However, to realize these benefits, it is not enough to just “add a graph”. Success depends on:

Good entity / relation extraction
A well-designed ontology/schema
Efficient indexing and retrieval (vectors + graph queries)
Prompt engineering that ensures the LLM uses the KG facts
Monitoring quality, explainability, and performance

As research like KET-RAG, GNN-RAG, ToG-2 shows, there are promising algorithmic improvements in cost-efficiency, reasoning depth, and hybrid architectures. For practitioners, Graph RAG opens up a path to build systems that are both powerful and trustworthy.