I need a knowledge graph that can be queried by a Large Language Model.

Yes. What you want is essentially a GraphRAG architecture: a knowledge graph acts as structured memory, while an LLM uses graph traversal + semantic retrieval to assemble the context needed to answer questions.

A strong production architecture would look like:

                    ┌──────────────────┐
                    │   Source data    │
                    │ PDFs / DB / APIs │
                    └────────┬─────────┘
                             │
                             ▼
                  ┌─────────────────────┐
                  │ Entity/relationship │
                  │     extraction      │
                  │       (LLM)         │
                  └──────────┬──────────┘
                             │
                             ▼
              ┌─────────────────────────────┐
              │       Knowledge Graph       │
              │                             │
              │ Person ──WORKS_FOR──> Org  │
              │   │                         │
              │   └──LOCATED_IN──> City     │
              └─────────────┬───────────────┘
                            │
             ┌──────────────┴──────────────┐
             ▼                             ▼
      Graph traversal                 Vector search
       / Cypher / GQL                 / embeddings
             │                             │
             └──────────────┬──────────────┘
                            ▼
                    ┌──────────────┐
                    │  Retriever   │
                    └──────┬───────┘
                           ▼
                    ┌──────────────┐
                    │     LLM      │
                    └──────┬───────┘
                           ▼
                    Answer + sources

My recommended stack

For a new system, I'd strongly consider:

• Neo4j as the graph database.
• Neo4j GraphRAG for Python as the retrieval/orchestration layer.
• An LLM for entity/relation extraction and answer generation.
• Embeddings stored alongside the graph or in a vector index.
• Original document/chunk references attached to graph facts so answers can be traced back to evidence.

Neo4j's current GraphRAG package explicitly supports a driver → retriever → LLM → GraphRAG pipeline, including vector retrieval and multiple LLM providers.

For example, conceptually your graph might contain:

(:Person {
    id: "person:123",
    name: "Jane Smith"
})

(:Company {
    id: "company:456",
    name: "Acme Corp"
})

(:Document {
    id: "doc:789",
    source: "...",
    title: "Annual Report 2025"
})

(:Person)-[:WORKS_FOR {
    since: 2021,
    source: "doc:789"
}]->(:Company)

Then the LLM doesn't need to memorize the information. It can ask the retrieval layer something like:

"What companies has Jane Smith worked for, and when?" The retrieval system can traverse:

MATCH (p:Person {name: "Jane Smith"})-[r:WORKS_FOR]->(c:Company)
RETURN p.name, r.since, c.name
ORDER BY r.since

and feed the resulting structured facts—and ideally their source passages—into the LLM.

Don't make it only a graph

This is an important design decision.

I'd build a hybrid GraphRAG system, rather than expecting the LLM to answer everything through graph queries.

Use:

Graph retrieval for questions involving relationships:

"Which customers are connected to projects using technology X?" Vector retrieval for questions involving concepts or passages:

"What does our policy say about employee relocation?" Graph + vector retrieval for complex questions:

"Which customers affected by the 2025 pricing change are also using product X, and what concerns did they raise?" This is close to the approach Microsoft describes for GraphRAG: its indexing pipeline extracts entities, relationships and claims, creates communities/summaries, and also embeds text; its query layer supports local, global, DRIFT, and basic/vector search.

The key part: design the graph schema first

I would not start by dumping documents into an LLM and letting it invent arbitrary nodes and relationships.

Define a domain schema such as:

Person
Organization
Product
Project
Location
Document
Event
Claim

with controlled relationships:

WORKS_FOR
OWNS
MANAGES
USES
LOCATED_IN
PART_OF
DEPENDS_ON
MENTIONS
SUPPORTED_BY
CONTRADICTS

Then constrain extraction to that schema.

Neo4j's current knowledge-graph builder follows essentially this pattern: documents are chunked, a schema grounds the extracted node/relationship types, entities and relations are extracted, and the resulting graph can be pruned against the schema.

Give every fact provenance

This is arguably the most important production feature.

Don't store merely:

Alice --WORKS_FOR--> Acme

Store something closer to:

Alice
   │
   │ WORKS_FOR
   │
   ▼
Acme

Evidence:
  document_id = 8472
  chunk_id = 193
  source_url = ...
  extracted_at = ...
  confidence = ...

That lets the LLM answer:

Jane Smith works for Acme Corp. and also give you:

Source: Annual Report 2025, page 42. It makes the system substantially easier to audit and debug.

How the LLM should query it

There are two good patterns.

1. Controlled retrieval API

The safest approach is to expose tools such as:

search_entities(name, type)
get_entity(id)
get_neighbors(id, relationship_types)
find_path(source, target)
search_documents(query)
get_evidence(fact_id)

The LLM calls these tools rather than having unrestricted access to the database.

2. Text-to-Cypher / text-to-GQL

For more flexible analytical questions:

User question
      ↓
LLM
      ↓
Generate Cypher/GQL
      ↓
Validate query
      ↓
Execute against graph
      ↓
Return structured results
      ↓
LLM synthesizes answer

This is powerful, but don't execute arbitrary LLM-generated database queries without validation and permissions.

Neo4j specifically supports the Text-to-Cypher pattern as part of its GraphRAG ecosystem.

If your data starts as documents

I'd build the ingestion pipeline like this:

PDF / HTML / Word / database
             ↓
         text extraction
             ↓
          chunking
             ↓
      entity extraction
             ↓
    relationship extraction
             ↓
       entity resolution
             ↓
      schema validation
             ↓
       graph insertion
             ↓
       embeddings/index
             ↓
       provenance links

Entity resolution is particularly important.

For example, these should become the same entity:

"IBM"
"International Business Machines"
"IBM Corp."
"International Business Machines Corporation"

rather than four separate companies.

One alternative worth knowing about

Microsoft's GraphRAG project is useful if your primary problem is turning a large corpus of unstructured documents into a graph-oriented retrieval system. It automatically extracts entities/relationships and builds hierarchical communities and summaries.

However, Microsoft currently describes the project as largely being in maintenance mode, so I would be cautious about making it the core long-term database layer.

For a system where you own the knowledge graph and expect to evolve its schema, I'd lean toward Neo4j + a custom/Neo4j GraphRAG retrieval layer.

A good target architecture

                 ┌─────────────────────┐
                 │      LLM / Agent     │
                 └──────────┬──────────┘
                            │
                     tool calls / RAG
                            │
                 ┌──────────▼──────────┐
                 │   Retrieval Layer   │
                 │                     │
                 │ Graph │ Vector │ KV │
                 └───┬─────┬──────┬────┘
                     │     │      │
             ┌───────▼─┐ ┌─▼────┐ │
             │ Neo4j   │ │Vector│ │
             │   KG    │ │Index │ │
             └─────────┘ └──────┘ │
                                  │
                           ┌──────▼──────┐
                           │ Source data │
                           └─────────────┘

If you tell me what your source data is (documents, database records, APIs, etc.) and roughly how large it is, I can design the actual schema, ingestion pipeline, retrieval API, and LLM query flow for your use case—including a concrete Neo4j schema and Python implementation.

Neo4j's current GraphRAG package explicitly supports a driver → retriever → LLM → GraphRAG pipeline, including vector retrieval and multiple LLM providers.

For example, conceptually your graph might contain:

• Knowledge Graph (KG) integration for LLMs bridges the gap between static training data and dynamic, fact-based retrieval, often called GraphRAG.
• Graph Database / Store: Neo4j, NebulaGraph, or Amazon Neptune to store nodes and edges.
• LLM Orchestration Framework: LlamaIndex or LangChain to parse, extract, and query the graph.

1. Define the Domain and Schema: Establish the entity types (N o d e cap N o d e𝑁𝑜𝑑𝑒) and relationships (E d g e cap E d g e𝐸𝑑𝑔𝑒) relevant to your data.
2. Choose a Storage Backend: Select a graph database like Neo4j for native graph traversal and querying.
3. Extract Entities and Relations: Use an LLM or an NLP pipeline (such as spaCy or LlamaIndex's SimpleGraphRAG ) to process unstructured text into triples:(S u b j e c t,P r e d i c a t e,O b j e c t).
4. Populate the Graph: Ingest the extracted triples into your graph database using Cypher or a similar query language.
5. Connect to the LLM: Implement a retrieval mechanism where the LLM translates natural language queries into graph queries (e.g., Cypher) or performs graph-augmented vector search.

If you'd like, let me know:

• What kind of data you are working with (documents, SQL database, APIs)?
• Your preferred tech stack (Python, LangChain, Neo4j, etc.)?

I can help you write a starter script or design a custom schema for your project.

You can build an LLM-queryable knowledge graph by pairing a graph database like Neo4j or Amazon Neptune with an orchestration framework that supports GraphRAG or Text-to-Cypher agents.

To set this up effectively, you need a structured workflow that bridges natural language and structured graph data without relying purely on brittle syntax generation.

Core Architecture Patterns

• Text-to-Query (Agentic approach): The LLM reads the graph schema (node labels, properties, and relationship types) and dynamically writes database queries (like Cypher or SPARQL) to fetch exact answers.
• Graph Embedding / Semantic Search: Convert graph node descriptions or subgraphs into vector embeddings, matching user queries semantically before traversing adjacent nodes for deeper context.
• Model Context Protocol (MCP): Expose your graph database as a tool using an MCP server, allowing an autonomous LLM agent to inspect schemas and run exploratory queries safely.

Recommended Toolchain

Watch this walkthrough on connecting a graph database directly to an LLM for natural language chats:

47s Graph DB to LLM: Chat with Structured Data (NO Cypher/SQL)1.4K views  · 10 months ago YouTube · InfoQ

If you want to tailor this, tell me:

• What source data are you using (SQL databases, PDFs, APIs, or raw text)?
• Which LLM framework or ecosystem are you building with (LangChain, LlamaIndex, or custom agents)?

I can help you design the precise schema and retrieval pipeline.

Yes. What you want is essentially a GraphRAG architecture: a knowledge graph stores entities, relationships, facts, and provenance; an LLM translates natural-language questions into graph/vector retrieval operations; the retrieved subgraph is then supplied to the LLM as grounded context.

Microsoft’s GraphRAG approach explicitly extracts entities, relationships, and claims from documents, builds communities/summaries, and supports local, global, and hybrid retrieval.

Recommended architecture

                    ┌─────────────────────┐
                    │   Source Data       │
                    │ PDFs / DB / APIs     │
                    │ emails / webpages    │
                    └──────────┬──────────┘
                               │
                         ingestion
                               ▼
                    ┌─────────────────────┐
                    │ Entity / Relation   │
                    │ Extraction (LLM)    │
                    └──────────┬──────────┘
                               │
                               ▼
              ┌────────────────────────────────┐
              │       Knowledge Graph           │
              │                                │
              │ Person ──WORKS_FOR──> Company  │
              │   │                 │           │
              │   └─AUTHORED──> Document        │
              │                       │          │
              │                 SUPPORTS        │
              │                       ▼          │
              │                     Claim        │
              └───────────────┬────────────────┘
                              │
                    ┌─────────┴──────────┐
                    │                   │
               Graph search        Vector search
                 Cypher              embeddings
                    │                   │
                    └─────────┬─────────┘
                              ▼
                     ┌─────────────────┐
                     │ Context Builder │
                     └────────┬────────┘
                              ▼
                         ┌─────────┐
                         │   LLM   │
                         └────┬────┘
                              ▼
                    Answer + citations

1. Use a real graph database

For a production system, I'd strongly consider Neo4j. Its GraphRAG tooling supports graph retrieval, vector retrieval, entity-based retrieval, cluster/global retrieval, and natural-language-to-Cypher querying.

An example graph might look like:

(:Person {
    id: "person_123",
    name: "Jane Smith"
})

(:Company {
    id: "company_456",
    name: "Acme Corp"
})

(:Document {
    id: "doc_789",
    title: "Annual Report 2026",
    source: "...",
    page: 42
})

(:Claim {
    text: "Acme Corp acquired XYZ in 2025",
    confidence: 0.94
})

With relationships:

(Jane)-[:WORKS_FOR]->(Acme)
(Acme)-[:ACQUIRED]->(XYZ)
(Claim)-[:ABOUT]->(Acme)
(Claim)-[:SUPPORTED_BY]->(Document)

The provenance relationship is extremely important. Don't just store:

Acme --ACQUIRED--> XYZ

Store where that assertion came from:

Acme
  │
  └── ACQUIRED ──> XYZ
                     ▲
                     │
                  supported by
                     │
                 Document 789
                     │
                  page 42

That lets the LLM answer and explain why it believes the answer.

2. Give the LLM a controlled query interface

Don't give the LLM unrestricted database access.

Instead:

User question
      ↓
LLM
      ↓
Intent + entities
      ↓
Query planner
      ↓
Cypher / graph retrieval
      ↓
Validated query
      ↓
Knowledge graph

For example:

"Which companies acquired businesses founded by former Acme employees?" The LLM could generate something conceptually like:

MATCH
  (employee:Person)-[:WORKED_FOR]->(acme:Company {name: "Acme"}),
  (employee)-[:FOUNDED]->(target:Company),
  (acquirer:Company)-[:ACQUIRED]->(target)
RETURN employee, target, acquirer

Then your application validates the generated query, executes it, and gives the results back to the LLM.

Neo4j explicitly documents this natural-language → graph-query pattern as one of its GraphRAG approaches.

3. Don't make it graph-only

I'd recommend a hybrid graph + vector architecture:

For example:

Question: "What are the major risks associated with Acme's European expansion?"

Vector search might find relevant documents.

Graph retrieval might discover:

Acme
 ├── expanding_to → Germany
 ├── expanding_to → France
 ├── acquired → EuroCo
 ├── regulated_by → EU
 └── has_risk → Currency Risk
                    │
                    └── discussed_in → Risk Report

The combination is much more powerful than either method alone. Neo4j's current GraphRAG documentation describes precisely this combination of vector and structural graph retrieval.

4. Add provenance to every important fact

I'd make provenance a first-class part of the schema:

(:Claim)
    ├── [:ABOUT] → (:Entity)
    ├── [:SUPPORTED_BY] → (:Document)
    ├── [:EXTRACTED_FROM] → (:Chunk)
    ├── [:ASSERTED_AT] → timestamp
    ├── confidence
    └── extraction_model

This gives you the ability to tell the LLM:

{
  "claim": "Acme acquired XYZ in 2025",
  "source": "annual_report_2025.pdf",
  "page": 42,
  "confidence": 0.94
}

rather than simply dumping graph nodes into the prompt.

5. Consider GraphRAG rather than building everything yourself

Microsoft's GraphRAG pipeline already implements much of the indexing methodology:

Documents
   ↓
Text units
   ↓
Entity extraction
   ↓
Relationship extraction
   ↓
Claim extraction
   ↓
Community detection
   ↓
Community summaries
   ↓
Embeddings
   ↓
Query

It provides local search for questions about specific entities and global search for questions about themes across a corpus.

One caveat: Microsoft's current repository describes GraphRAG as a research project that is largely in maintenance mode, so I would treat it as an architectural reference/tool rather than automatically making it the foundation of a new production platform.

A stack I'd use today

For a new system, my default would be:

                 ┌──────────────────┐
                 │   Your documents  │
                 └────────┬─────────┘
                          │
                    extraction
                          │
                          ▼
                 ┌──────────────────┐
                 │      Neo4j       │
                 │                  │
                 │ Graph + vectors  │
                 │ + full text      │
                 └────────┬─────────┘
                          │
                ┌─────────┴─────────┐
                │                   │
          Graph retrieval     Vector retrieval
                │                   │
                └─────────┬─────────┘
                          ▼
                    Context builder
                          │
                          ▼
                       LLM/API
                          │
                          ▼
                   Answer + sources

Neo4j also has a first-party Python GraphRAG package, including retrievers and LLM integration, which makes this architecture relatively straightforward to prototype.

The key design decision

There are actually three different things people mean when they say "knowledge graph for an LLM":

1. A graph database that the LLM can query → Neo4j + NL→Cypher.
2. A graph extracted automatically from documents for RAG → GraphRAG.
3. A formal semantic knowledge base → RDF/OWL/SPARQL, potentially with an ontology/reasoner.

If your goal is "I have a large collection of documents/data and I want an LLM to answer questions by reasoning over the relationships in that data," I'd choose #1 + #2 together: a persistent graph database plus GraphRAG-style retrieval.

If you tell me what your source data is (PDFs, database tables, APIs, web pages, etc.), the approximate size, and the kinds of questions the LLM needs to answer, I can design the actual schema, ingestion pipeline, retrieval strategy, and sample implementation for you.

For a production system, I'd strongly consider Neo4j. Its GraphRAG tooling supports graph retrieval, vector retrieval, entity-based retrieval, cluster/global retrieval, and natural-language-to-Cypher querying.

An example graph might look like:

(:Person {
    id: "person_123",
    name: "Jane Smith"
})

(:Company {
    id: "company_456",
    name: "Acme Corp"
})

(:Document {
    id: "doc_789",
    title: "Annual Report 2026",
    source: "...",
    page: 42
})

(:Claim {
    text: "Acme Corp acquired XYZ in 2025",
    confidence: 0.94
})

With relationships:

(Jane)-[:WORKS_FOR]->(Acme)
(Acme)-[:ACQUIRED]->(XYZ)
(Claim)-[:ABOUT]->(Acme)
(Claim)-[:SUPPORTED_BY]->(Document)

The provenance relationship is extremely important. Don't just store:

Acme --ACQUIRED--> XYZ

Store where that assertion came from:

Acme
  │
  └── ACQUIRED ──> XYZ
                     ▲
                     │
                  supported by
                     │
                 Document 789
                     │
                  page 42

That lets the LLM answer and explain why it believes the answer.

2. Give the LLM a controlled query interface

Don't give the LLM unrestricted database access.

Instead:

User question
      ↓
LLM
      ↓
Intent + entities
      ↓
Query planner
      ↓
Cypher / graph retrieval
      ↓
Validated query
      ↓
Knowledge graph

For example:

"Which companies acquired businesses founded by former Acme employees?" The LLM could generate something conceptually like:

MATCH
  (employee:Person)-[:WORKED_FOR]->(acme:Company {name: "Acme"}),
  (employee)-[:FOUNDED]->(target:Company),
  (acquirer:Company)-[:ACQUIRED]->(target)
RETURN employee, target, acquirer

Then your application validates the generated query, executes it, and gives the results back to the LLM.

Neo4j explicitly documents this natural-language → graph-query pattern as one of its GraphRAG approaches.

3. Don't make it graph-only

I'd recommend a hybrid graph + vector architecture:

For example:

Question: "What are the major risks associated with Acme's European expansion?"

Vector search might find relevant documents.

Graph retrieval might discover:

Acme
 ├── expanding_to → Germany
 ├── expanding_to → France
 ├── acquired → EuroCo
 ├── regulated_by → EU
 └── has_risk → Currency Risk
                    │
                    └── discussed_in → Risk Report

The combination is much more powerful than either method alone. Neo4j's current GraphRAG documentation describes precisely this combination of vector and structural graph retrieval.

4. Add provenance to every important fact

I'd make provenance a first-class part of the schema:

(:Claim)
    ├── [:ABOUT] → (:Entity)
    ├── [:SUPPORTED_BY] → (:Document)
    ├── [:EXTRACTED_FROM] → (:Chunk)
    ├── [:ASSERTED_AT] → timestamp
    ├── confidence
    └── extraction_model

This gives you the ability to tell the LLM:

{
  "claim": "Acme acquired XYZ in 2025",
  "source": "annual_report_2025.pdf",
  "page": 42,
  "confidence": 0.94
}

rather than simply dumping graph nodes into the prompt.

5. Consider GraphRAG rather than building everything yourself

Microsoft's GraphRAG pipeline already implements much of the indexing methodology:

Documents
   ↓
Text units
   ↓
Entity extraction
   ↓
Relationship extraction
   ↓
Claim extraction
   ↓
Community detection
   ↓
Community summaries
   ↓
Embeddings
   ↓
Query

It provides local search for questions about specific entities and global search for questions about themes across a corpus.

One caveat: Microsoft's current repository describes GraphRAG as a research project that is largely in maintenance mode, so I would treat it as an architectural reference/tool rather than automatically making it the foundation of a new production platform.

A stack I'd use today

For a new system, my default would be:

                 ┌──────────────────┐
                 │   Your documents  │
                 └────────┬─────────┘
                          │
                    extraction
                          │
                          ▼
                 ┌──────────────────┐
                 │      Neo4j       │
                 │                  │
                 │ Graph + vectors  │
                 │ + full text      │
                 └────────┬─────────┘
                          │
                ┌─────────┴─────────┐
                │                   │
          Graph retrieval     Vector retrieval
                │                   │
                └─────────┬─────────┘
                          ▼
                    Context builder
                          │
                          ▼
                       LLM/API
                          │
                          ▼
                   Answer + sources

Neo4j also has a first-party Python GraphRAG package, including retrievers and LLM integration, which makes this architecture relatively straightforward to prototype.

The key design decision

There are actually three different things people mean when they say "knowledge graph for an LLM":

Setting up a knowledge graph (KG) for a Large Language Model involves choosing how you want to store the graph, how the LLM will query or interact with it, and how you plan to extract the data.

Here is a clean, structured blueprint to get a production-ready LLM-queried knowledge graph up and running.

Phase 1: Choose Your Core Infrastructure

You need a database that handles graph structures natively or an orchestration framework that bridges text to graph.

• Graph Databases (Storage Layer):
  • Neo4j : The industry standard for property graphs; excellent Cypher query language support and native LLM integration packages.
  • Amazon Neptune / Memgraph: Great if you are locked into AWS or require high-throughput, real-time memory graph operations.
• Orchestration & Frameworks (Query & Retrieval Layer):
  • Microsoft GraphRAG: Best for deep corpus-wide sensemaking, global summarization, and community-based querying over static documents.
  • Graphiti / LightRAG: Ideal if your data changes frequently and you need a temporal-aware or lightweight dynamic context graph.
  • LlamaIndex / LangChain: Ideal for building custom ingestion and hybrid vector-graph query pipelines.

Phase 2: Pick Your Query Pattern

How an LLM actually extracts value from a knowledge graph generally falls into three design patterns:

1. Text-to-Cypher / Text-to-Query: The LLM translates a natural language question directly into a graph query language (like Cypher for Neo4j), executes it, and turns the structured JSON result into a human-readable answer.
2. GraphRAG (Global/Local Retrieval): Instead of writing direct queries, the LLM traverses pre-clustered communities of nodes and edges, retrieving narrative summaries or localized subgraphs to inject into its context window.
3. Agentic Tool-Use: You expose the graph database as an explicit "tool" or function for an agentic LLM (using frameworks like LangGraph or AutoGen), allowing the model to multi-step search, pivot across relationships, and self-correct its traversal if a node turns up empty.

Phase 3: Step-by-Step Implementation Blueprint

1. Define a Loose Schema First: Do not build a massive enterprise ontology on day one. Define core entity types (e.g., Person, Organization, Concept ) and primary relationships (e.g., WORKS_FOR, RELATED_TO).
2. Ingest and Extract: Feed your unstructured text, PDFs, or APIs through an LLM extraction pipeline. Use libraries like Neo4j LangChain/LlamaIndex integrations to parse chunks into (Entity) -> [Relationship] -> (Entity) triplets.
3. Establish Entity Resolution: Ensure that "Microsoft", "MSFT", and "Microsoft Inc." map to the same node, rather than fracturing your graph.
4. Expose via API/Tool: Wrap your graph query functions in a clean API or LLM tool-calling schema so the model can invoke it dynamically.

To help narrow down the exact tech stack you should use, tell me:

• What is your data source (static PDFs, a live relational database, or real-time event streams)?
• Do you prefer a managed cloud service or a self-hosted/local open-source setup?
• Are you leaning toward Text-to-Cypher generation or a GraphRAG summarization approach?

Yes. What you want is essentially a Knowledge Graph + GraphRAG layer: the LLM should not just retrieve similar text chunks; it should be able to traverse entities, relationships, attributes, and source documents before generating an answer.

A good production architecture is:

                 ┌─────────────────────┐
                 │   Source Data       │
                 │ PDFs / DB / APIs    │
                 │ Docs / Web / CSV    │
                 └──────────┬──────────┘
                            │
                     Entity / Relation
                       Extraction
                            │
                            ▼
                 ┌─────────────────────┐
                 │   Knowledge Graph   │
                 │                     │
                 │ Entity ──REL──> Entity
                 │    │                │
                 │    └── Property     │
                 │                     │
                 │ + source/provenance │
                 └──────────┬──────────┘
                            │
              ┌─────────────┼─────────────┐
              │             │             │
          Vector         Graph         Full-text
          Search         Search         Search
              │             │             │
              └─────────────┼─────────────┘
                            ▼
                   ┌─────────────────┐
                   │   Retriever /   │
                   │   GraphRAG      │
                   └────────┬────────┘
                            │
                     relevant context
                            ▼
                   ┌─────────────────┐
                   │      LLM        │
                   │                 │
                   │ answer +        │
                   │ citations       │
                   └─────────────────┘

What I'd recommend

For a new system, I'd strongly consider Neo4j + its GraphRAG Python package + your LLM.

Neo4j's current GraphRAG tooling supports vector, full-text, hybrid, Cypher-based, and natural-language-to-Cypher retrieval. Its Text2Cypher retriever can translate a user's question into a Cypher query against the knowledge graph.

The important distinction is that you don't want the LLM directly "querying the graph" with unrestricted access. Instead, give it controlled retrieval tools such as:

search_entities(name)
get_entity(id)
find_relationships(entity_id)
traverse(entity_id, relationship_type, depth)
search_documents(query)
execute_cypher(read_only_query)

Then the LLM can reason over the results.

Example

Suppose your graph contains:

Acme Corp
   │
   ├── manufactures ──> Product X
   │                         │
   │                         ├── uses ──> Component A
   │                         │
   │                         └── regulated_by ──> FDA
   │
   └── acquired ──> Beta Corp
                         │
                         └── owns ──> Patent 123

The user asks:

"Which products manufactured by companies Acme acquired use components regulated by the FDA?" A vector database might retrieve documents mentioning Acme, FDA, and components.

A graph can actually traverse:

Acme
  → acquired
    → Beta Corp
      → owns
        → ...

and separately:

Acme/Beta
  → manufactures
    → Product
      → uses
        → Component
          → regulated_by
            → FDA

The LLM gets the resulting subgraph and explains the answer.

That's where knowledge graphs become particularly useful for LLMs: multi-hop reasoning over connected facts.

Microsoft's GraphRAG research similarly uses extracted entities and relationships, community detection, summaries, and graph-aware retrieval rather than relying solely on vector similarity.

The graph schema matters enormously

I would structure each fact approximately like this:

(:Entity {
    id,
    name,
    type,
    description
})

(:Document {
    id,
    title,
    uri,
    date
})

(:Chunk {
    id,
    text,
    embedding
})

with relationships such as:

(Entity)-[:RELATION {
    confidence,
    valid_from,
    valid_to,
    source_id
}]->(Entity)

(Document)-[:CONTAINS]->(Chunk)

(Chunk)-[:MENTIONS]->(Entity)

For example:

(:Person {name: "Alice"})
    -[:WORKS_FOR {since: 2023, source: "doc-17"}]->
(:Company {name: "Acme"})

Provenance is critical. Every important relationship should be traceable back to the document/chunk/source from which it was derived. That lets the LLM say why it believes something rather than merely asserting it.

I'd use hybrid retrieval

Don't make this "graph vs vector."

Use:

                    User question
                         │
             ┌───────────┴───────────┐
             ▼                       ▼
        Vector search            Entity search
             │                       │
             └───────────┬───────────┘
                         ▼
                  Graph traversal
                         │
             ┌───────────┴───────────┐
             ▼                       ▼
        Relevant facts          Source chunks
             │                       │
             └───────────┬───────────┘
                         ▼
                        LLM

Neo4j explicitly supports combinations such as vector + graph retrieval and hybrid retrieval, including retrieval that expands from matched nodes into their surrounding graph.

If your data starts as documents

I'd build the ingestion pipeline like this:

Documents
   ↓
chunking
   ↓
LLM extraction
   ↓
entities + relationships + claims
   ↓
entity resolution / deduplication
   ↓
schema validation
   ↓
knowledge graph
   ↓
embeddings

Neo4j's current Knowledge Graph Builder follows essentially this pattern: document loading, text splitting, optional embeddings, schema construction, entity/relation extraction, and graph pruning. Structured LLM output can also be used to make extraction more reliable.

One important design decision

There are actually two different things people mean by "an LLM-queryable knowledge graph":

1. Semantic GraphRAG

The LLM asks:

"What is relevant to this question?" The system finds entities, relationships, documents, and neighboring facts.

Best for:

• research
• unstructured documents
• complex questions
• discovering connections
• summarization

2. Natural-language graph querying

The LLM translates:

"Which customers purchased Product X in 2025?" into something like:

MATCH (c:Customer)-[:PURCHASED]->(p:Product)
WHERE p.name = 'Product X'
  AND ...
RETURN c

This is much better for:

• structured business data
• analytics
• exact counts
• filtering
• aggregations
• deterministic answers

In practice, I'd implement both. Neo4j's current tooling supports Text2Cypher alongside other retrieval strategies.

A practical stack

If I were building this today, my default stack would be:

You can also use Microsoft's GraphRAG methodology, although its current repository describes itself as largely in maintenance mode; for a new production application, I'd lean toward a maintained graph database/GraphRAG framework rather than building directly around that research repository.

The key idea

Don't make the architecture:

LLM → vector DB → text → answer

Make it:

LLM
 │
 ▼
Query planner
 │
 ├── semantic retrieval
 ├── entity lookup
 ├── graph traversal
 ├── Cypher query
 └── source retrieval
       │
       ▼
   evidence set
       │
       ▼
      LLM
       │
       ▼
 answer + provenance

That gives you a queryable, inspectable memory layer rather than just a document-search system.

If you tell me what kind of data you want the graph to contain (e.g. company documents, scientific literature, customer data, code, legal documents, or a mix), I can design the actual schema, ingestion pipeline, retrieval strategy, and API for it.

Acme/Beta
  → manufactures
    → Product
      → uses
        → Component
          → regulated_by
            → FDA

The LLM gets the resulting subgraph and explains the answer.

That's where knowledge graphs become particularly useful for LLMs: multi-hop reasoning over connected facts.

Microsoft's GraphRAG research similarly uses extracted entities and relationships, community detection, summaries, and graph-aware retrieval rather than relying solely on vector similarity.

The graph schema matters enormously

I would structure each fact approximately like this:

Connecting a knowledge graph (KG) to a Large Language Model—often referred to as GraphRAG or hybrid retrieval—is one of the best ways to eliminate hallucinations, handle multi-hop reasoning, and give your LLM a structured "memory".

To build or query a knowledge graph with an LLM, you typically choose between three implementation paths depending on your stack and goals:

1. Ready-to-Use Frameworks (Fastest to Implement)

If you want to ingest documents, automatically extract entities/relationships using an LLM, and query them without managing a heavy database infrastructure from scratch:

• Microsoft GraphRAG: An open-source package that automates the extraction of a knowledge graph from raw text, performs hierarchical community summarization, and allows global/local queries via LLMs.
• LlamaIndex (Graph Store integrations): Offers robust abstractions to construct property graphs or semantic graphs out of unstructured documents and query them natively using PropertyGraphIndex.
• LangChain: Provides graph transformers (like LLMGraphTransformer ) to parse text into graph documents and query tools for Neo4j, NetworkX, and other backends.

2. Graph Databases with Native LLM Support (Best for Enterprise & Scale)

If you need high-performance querying, persistent storage, and transactional safety:

• Neo4j: The industry standard for property graphs. Neo4j provides native plugins and Python libraries (neo4j-graphrag ) that translate natural language queries into Cypher or perform hybrid vector + graph searches.
• Amazon Neptune: Fully managed graph database on AWS with built-in integrations for LangChain and vector embeddings for semantic graph searching.
• Ontotext GraphDB / Stardog: Best if you require semantic web standards (RDF, OWL, SPARQL) and explicit logical reasoning layers.

3. Lightweight / Developer-First Memory Graphs

• Zep: An open-source graph-based memory layer designed specifically for LLM and AI agent applications, automatically maintaining user and entity relationships over long conversations.

To help narrow down the best architecture for your project, tell me:

• Are you querying unstructured documents (PDFs, text files) or structured data (SQL databases, APIs)?
• Do you prefer a local open-source Python library (like LlamaIndex/Microsoft GraphRAG) or a managed database (like Neo4j)?
• Is this for an AI chatbot/agent memory or a document search engine?

Yes. If your goal is “an LLM can ask questions over a structured body of knowledge, follow relationships, and produce grounded answers,” I’d build a Knowledge Graph + GraphRAG layer, rather than giving the LLM direct access to a raw graph.

A strong current architecture is Neo4j + an LLM + vector/full-text retrieval + Cypher. Neo4j’s official GraphRAG package now supports knowledge-graph construction, vector retrieval, hybrid retrieval, and Text-to-Cypher.

Recommended architecture

                    ┌─────────────────────┐
                    │   Source Material   │
                    │ PDFs / DB / APIs /  │
                    │     Documents       │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │   KG Construction   │
                    │                     │
                    │ Entity extraction  │
                    │ Relation extraction│
                    │ Entity resolution   │
                    │ Schema validation   │
                    └──────────┬──────────┘
                               │
                               ▼
                 ┌──────────────────────────┐
                 │       Knowledge Graph    │
                 │                          │
                 │ Person ──WORKS_FOR──► Org│
                 │   │                      │
                 │   └──LOCATED_IN──► Place │
                 │                          │
                 │ + provenance + metadata  │
                 └────────────┬─────────────┘
                              │
                ┌─────────────┴─────────────┐
                │                           │
                ▼                           ▼
       ┌─────────────────┐        ┌─────────────────┐
       │ Vector Search   │        │ Graph / Cypher  │
       │                 │        │                 │
       │ semantic        │        │ exact relations │
       │ retrieval       │        │ multi-hop       │
       └────────┬────────┘        └────────┬────────┘
                │                           │
                └─────────────┬─────────────┘
                              ▼
                    ┌─────────────────────┐
                    │   LLM / GraphRAG    │
                    │                     │
                    │ question → retrieve │
                    │ context → answer    │
                    └──────────┬──────────┘
                               ▼
                         User's answer

Why not just put everything into a vector database?

Because a knowledge graph gives the LLM something vectors don't naturally represent:

• Entities: people, companies, products, concepts, locations, etc.
• Relationships: WORKS_FOR, OWNS, DEPENDS_ON, LOCATED_IN, etc.
• Multi-hop reasoning: A → B → C → D.
• Constraints: dates, types, ownership, status, permissions.
• Provenance: where did this fact come from?
• Exact queries: “Which companies acquired subsidiaries of X between 2020 and 2024?”
• Semantic retrieval: “Find things related to this concept even if the wording differs.”

The best systems generally combine graph retrieval and vector retrieval, rather than choosing one.

The graph schema matters a lot

I'd start with an explicit ontology rather than letting an LLM invent arbitrary nodes and relationships.

For example:

(:Person)
(:Organization)
(:Product)
(:Document)
(:Concept)
(:Location)

(:Person)-[:WORKS_FOR]->(:Organization)
(:Organization)-[:OWNS]->(:Organization)
(:Organization)-[:PRODUCES]->(:Product)
(:Product)-[:DEPENDS_ON]->(:Product)
(:Document)-[:MENTIONS]->(:Person)
(:Document)-[:MENTIONS]->(:Organization)
(:Concept)-[:RELATED_TO]->(:Concept)

Then put provenance on relationships/facts where appropriate:

(:Organization)-[
    :ACQUIRED {
        date: "...",
        source: "...",
        confidence: 0.96
    }
]->(:Organization)

I'd also preserve the original document/chunk → extracted fact connection. That becomes extremely valuable when the LLM needs to explain why it believes something.

Neo4j's current KG Builder pipeline explicitly supports document loading, chunking, schema construction, entity/relation extraction, graph pruning, writing, and entity resolution.

How the LLM should query it

Don't make the LLM blindly generate arbitrary database queries.

Give it controlled retrieval tools such as:

search_entities(query)
search_documents(query)
find_relationships(entity, relationship_type)
run_graph_query(query)
get_neighbors(entity, depth)

For questions that require exact graph traversal, an LLM can translate natural language into Cypher:

User:
Which suppliers provide components used by products
manufactured by Acme?

        ↓

LLM

        ↓

MATCH (acme:Organization {name:"Acme"})
      -[:PRODUCES]->(product:Product)
      -[:USES_COMPONENT]->(component:Component)
      <-[:SUPPLIES]-(supplier:Organization)
RETURN supplier.name, component.name

Neo4j's current GraphRAG package includes a Text2CypherRetriever specifically for translating natural-language questions into Cypher and executing those queries against the knowledge graph.

For less structured questions, use semantic retrieval:

question
   ↓
embedding
   ↓
vector search
   ↓
relevant entities / chunks
   ↓
expand graph around them
   ↓
LLM

And ideally use a hybrid approach where the LLM can choose between vector retrieval and graph/Cypher retrieval. Neo4j's current package provides vector, hybrid, Cypher, and tool-based retrievers for this pattern.

A practical stack

I'd use:

Neo4j currently provides an official neo4j-graphrag Python package for exactly this kind of application, including KG construction and retrieval.

The important part: don't make it “LLM → database”

I'd make the architecture:

                 ┌──────────────┐
                 │     User     │
                 └──────┬───────┘
                        │
                        ▼
                 ┌──────────────┐
                 │     LLM      │
                 │  orchestrator│
                 └──────┬───────┘
                        │
              ┌─────────┼─────────┐
              ▼         ▼         ▼
          Vector      Graph     Metadata
          Search      Search     Search
              │         │         │
              └─────────┼─────────┘
                        ▼
                 ┌──────────────┐
                 │   Evidence   │
                 │ + provenance │
                 └──────┬───────┘
                        ▼
                 ┌──────────────┐
                 │     LLM      │
                 │  generation  │
                 └──────┬───────┘
                        ▼
                   Answer +
                   citations

That separation is important because it lets you audit what the LLM actually retrieved instead of treating the model's internal reasoning as the knowledge source.

If you're starting from documents

The end-to-end pipeline I'd build is:

Documents → chunks → entities/relations → entity resolution → Neo4j → embeddings → hybrid retrieval → LLM

Neo4j's current KG Builder follows essentially this construction pattern, including entity resolution to merge similar entities.

If you tell me what your knowledge is about (e.g. company data, technical documentation, research papers, healthcare, product catalogs, internal business data) and roughly how much data you have, I can design the actual schema, ingestion pipeline, retrieval strategy, and LLM query interface for it.

The graph schema matters a lot

I'd start with an explicit ontology rather than letting an LLM invent arbitrary nodes and relationships.

For example:

A knowledge graph for an LLM is usually built as a structured retrieval layer between your data sources and the model. The LLM does not typically “query the graph directly” in the same way a database client does; instead, you build a system where the LLM translates questions into graph queries, retrieves relevant subgraphs, and uses those facts as grounded context. Knowledge-graph RAG approaches are increasingly used for this because they preserve relationships that chunk-based retrieval often loses.

A typical architecture looks like this:

                 Documents / APIs / Databases
                           |
                           v
                 Entity + Relation Extraction
                           |
                           v
                 Knowledge Graph Construction
                           |
        +------------------+------------------+
        |                                     |
        v                                     v
 Graph Database                         Vector Index
 (facts, relations)                     (semantic search)
        |                                     |
        +------------------+------------------+
                           |
                           v
                  LLM Retrieval Agent
                           |
                 Query Planning / Reasoning
                           |
                           v
                     LLM Response

1. Choose a graph model

A common representation is a property graph:

(Node)          (Relationship)          (Node)

Alice -------- works_at --------> OpenAI
  |                                  |
  |                                  |
knows                              founded_by
  |                                  |
  v                                  v
Bob ---------------------------> Greg

Example triples:

(Alice, works_at, OpenAI)
(OpenAI, founded_by, Greg)
(Alice, located_in, San Francisco)

You usually store:

• Entities

• people

• companies

• products

• documents

• concepts

• events

• Relationships

• works_for

• owns

• created

• depends_on

• located_in

• contradicts

• Attributes

• timestamps

• confidence scores

• provenance

• source documents

2. Pick a graph database

Common choices:

• Neo4j — property graph model, Cypher query language, popular for LLM applications.
• Amazon Neptune — managed graph database supporting RDF and property graphs.
• TigerGraph — designed for large-scale graph analytics.
• ArangoDB — supports graph, document, and key-value models.

For an LLM assistant, a property graph is often easier because relationships map naturally to reasoning paths.

3. Ingest knowledge

Your pipeline might look like:

PDFs
Web pages
Databases
APIs
Emails
Code repositories

        |
        v

LLM extraction

        |
        v

Entities + Relationships

        |
        v

Knowledge Graph

Example extraction:

Input:

"Tesla acquired SolarCity in 2016 for approximately $2.6 billion." Generated graph:

Tesla
 |
 | acquired
 |
SolarCity

properties:
date = 2016
value = 2.6B USD
source = document_123
confidence = 0.92

4. Add an LLM query layer

The LLM needs tools/functions such as:

Entity lookup

User:

"Who founded companies acquired by Tesla?" LLM generates:

MATCH
(founder)<-[:FOUNDED_BY]-(company)<-[:ACQUIRED]-(Tesla)
RETURN founder, company

Graph returns:

SolarCity
  founded_by:
    Lyndon Rive
    Peter Rive

The LLM turns that into an answer.

5. Use graph + vector retrieval together

A strong design is hybrid retrieval:

Vector search answers:

"Find documents related to battery technology."

Graph traversal answers:

"Which companies supply batteries to Tesla suppliers?" The graph provides structure; vectors provide fuzzy semantic matching.

Modern KG-RAG systems commonly combine semantic retrieval with graph expansion to improve multi-hop reasoning.

6. Recommended production stack

A practical stack:

7. Important design choices

Store provenance

Every fact should remember where it came from:

Fact:
  Apple acquired Beats
  Source:
    SEC filing
  Confidence:
    0.98
  Extracted:
    2026-08-29

This lets the LLM cite evidence and avoid hallucinating.

Store uncertainty

Do not force every extraction to be true:

(Person A)-[possibly_related_to]->(Company B)

confidence = 0.63

Create graph summaries

Large graphs are too big to send to an LLM. Build:

• entity summaries
• community summaries
• relationship paths
• relevant subgraphs

This is the approach behind many graph-based RAG systems.

8. Minimal implementation example

A simple flow:

question = "What products depend on Company X?"

entities = llm.extract_entities(question)

subgraph = graph.query("""
MATCH (company {name:$name})
      -[:PROVIDES]->(product)
RETURN product
""", entities)

context = format_graph(subgraph)

answer = llm.generate(
    prompt=f"""
    Answer using only this knowledge:

    {context}

    Question:
    {question}
    """
)

9. A good first version

For a first implementation, I would build:

1. Neo4j property graph
2. Document ingestion pipeline
3. LLM entity/relation extraction
4. Vector embeddings for documents
5. LLM agent with two tools - search_documents()

• query_graph()

6. Answer generation with citations

That gives you a knowledge graph that an LLM can actually reason over rather than just a static database.

question = "What products depend on Company X?"

entities = llm.extract_entities(question)

subgraph = graph.query("""
MATCH (company {name:$name})
      -[:PROVIDES]->(product)
RETURN product
""", entities)

context = format_graph(subgraph)

answer = llm.generate(
    prompt=f"""
    Answer using only this knowledge:

    {context}

    Question:
    {question}
    """
)

9. A good first version

For a first implementation, I would build:

1. Neo4j property graph
2. Document ingestion pipeline
3. LLM entity/relation extraction
4. Vector embeddings for documents
5. LLM agent with two tools - search_documents()

• query_graph()

6. Answer generation with citations

That gives you a knowledge graph that an LLM can actually reason over rather than just a static database.

To make a knowledge graph (KG) queryable by a Large Language Model, you need a setup where the LLM can either translate natural language into a database query (like Cypher or SQL) or use graph traversal tools to pull relevant context.

Here is a practical, step-by-step architecture and toolkit to build one:

• Architecture Options: Choose between text-to-query (LLM writes graph queries) or graph RAG (retrieval-augmented generation via vector + graph search).
• Database Layer: Select a graph database that natively supports vector embeddings or Cypher querying.
• Ingestion/Extraction Layer: Use tools to parse your raw documents, extract entities and relationships, and load them into the database.
• LLM Orchestration Layer: Connect your LLM framework to the graph database using specialized toolkits.

1. The Modern Architecture Options

• Graph RAG (Retrieval-Augmented Generation): Best for general Q&A. Instead of making the LLM write complex graph queries, you store text chunks, entity embeddings, and relationships together. When a user asks a question, the system retrieves subgraphs or connected neighborhoods and feeds them into the LLM context window.
• Text-to-Cypher / Text-to-Query: Best for precise, structured data retrieval (e.g., "Find all employees who report to X"). The LLM is prompted with the graph schema and translates the user's natural language question into a Cypher (for Neo4j) or Gremlin query, executes it, and summarizes the result.

2. Recommended Tech Stack & Tools

• Neo4j + LangChain/LlamaIndex: The industry standard. Neo4j supports vector indexing alongside a property graph model. Both LangChain and LlamaIndex have built-in modules for Text-to-Cypher and Graph RAG.
• NetworkX: Great if you want a lightweight, in-memory Python graph that an LLM can query or traverse using custom Python function-calling/tools.
• NebulaGraph / AWS Neptune: Heavy-duty distributed graph databases if you are dealing with massive, enterprise-scale datasets.
• GraphRAG by Microsoft: An open-source toolkit specifically designed to extract a hierarchical knowledge graph from raw text using an LLM and perform community-based summarization queries.

3. Implementation Workflow

To help narrow down the best approach, tell me:

• What is your data source (PDFs, SQL databases, APIs, raw text)?
• Do you prefer a managed cloud service or an open-source/local setup?
• Are you looking for a Graph RAG setup or precise Text-to-Cypher data lookups?