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Connect a knowledge graph to retrieval-augmented generation (RAG) by using the graph as a relationship-aware retrieval layer, not as a replacement for vector search. A production pipeline ingests documents, extracts entities and relationships with provenance, stores them with source text, builds vector and full-text indexes, retrieves seed nodes with hybrid search, expands only the graph paths needed for the question, and gives the resulting subgraph and evidence excerpts to the language model.
What a knowledge graph adds to RAG
Standard RAG usually retrieves text chunks that resemble a question. That works well for definitions and single-document lookups, but similarity alone can miss the relationship that determines an answer. A knowledge graph represents entities as nodes and their connections as typed edges, so the system can answer questions involving ownership, dependencies, timelines, memberships, or several linked facts.
The graph should complement, rather than replace, semantic and lexical retrieval. Vector search finds paraphrases and conceptually similar passages. Full-text search finds exact names, identifiers, error codes, and rare terms. Graph traversal then joins the relevant entities and checks the required relationship pattern. The language model receives both the structured path and the supporting source text, which makes the answer easier to ground and audit.
- Vector retrieval handles semantic similarity and paraphrased questions.
- Full-text retrieval handles exact terms, product codes, names, and quoted language.
- Graph traversal handles constraints and multi-hop relationships.
- Provenance preserves which document, passage, or record supports each node and edge.
The six-stage connection pattern
1. Ingest documents and structured sources
Collect the documents, database records, tickets, manuals, or APIs that are in scope. Normalize encoding, remove duplicate versions where appropriate, and assign a stable source identifier to every item. Keep the original text or a retrievable pointer; a graph fact without evidence is difficult to verify.
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Record source metadata such as title, author, publication or update time, access permissions, and document version. If the same document is reprocessed, use an idempotent key so the pipeline updates the existing record instead of creating duplicate nodes.
2. Chunk text and extract graph structure
Split long documents into coherent chunks, normally around headings, paragraphs, or list boundaries rather than arbitrary character cuts. For each chunk, extract candidate entities, relationship statements, and claims. Store the extraction confidence and the chunk identifier with every assertion so uncertain model output can be reviewed or filtered.
Entity resolution is essential. “International Business Machines,” “IBM,” and an internal customer ID may refer to one entity; an identical name can also refer to several different entities. Resolve against authoritative identifiers where available, retain aliases, and send ambiguous matches to a review queue instead of merging them automatically.
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Create stable node labels such as Person, Organization, Product, Project, and Document. Give each node a canonical identifier and human-readable properties. Use typed relationships such as OWNS, DEPENDS_ON, AUTHORED, or LOCATED_IN. Attach temporal fields when a relationship can change.
Represent evidence explicitly. A relationship can carry the source document ID, chunk ID, extraction method, confidence, and valid-from or valid-to dates. An alternative is an intermediate claim node that links a subject, predicate, object, and evidence record. Claim nodes are useful when several documents disagree or when one passage supports multiple interpretations.
4. Build retrieval indexes
Create a vector index over chunk text, entity descriptions, or both. Create a full-text index for names, aliases, identifiers, and exact phrases. A hybrid retriever combines the two signals before graph expansion. Index configuration is part of the runtime setup; the Neo4j GraphRAG documentation identifies a graph driver, a retriever, and an LLM as the basic components, with indexes prepared before querying.
5. Resolve each question
Classify the question’s intent and identify entities, dates, and constraints. Retrieve a small set of seed chunks or nodes with vector, keyword, or hybrid search. Translate the question into a constrained graph query when relationships determine the answer. Expand only the relevant edge types and depth, and retain the paths that led to each result.
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6. Assemble context, generate, and evaluate
Serialize the selected nodes, relationship paths, source excerpts, and provenance into a compact context block. Instruct the model to answer only from that context, distinguish direct evidence from inference, and return source identifiers or citations. Measure retrieval recall, answer faithfulness, latency, and cost with representative questions before enabling production traffic.
Design the graph model before extracting data
A small, stable schema is easier to retrieve than an unrestricted collection of model-generated labels. Start with the questions the application must answer and define the minimum entities and relationships required for them.
| Design element | Recommended practice | Why it matters for RAG |
|---|---|---|
| Entity types | Use a controlled set of labels with clear definitions. | Consistent labels make filters and Cypher patterns reliable. |
| Identifiers | Prefer authoritative IDs; retain aliases and normalized names. | Prevents duplicate nodes and improves seed matching. |
| Relationships | Use specific, directional verbs and document their meaning. | Limits traversal to paths that answer the question. |
| Temporal data | Store effective dates, observed dates, or version fields. | Prevents current and historical facts from being mixed. |
| Provenance | Keep source ID, chunk ID, offsets, confidence, and extraction time. | Enables citations, conflict handling, and audits. |
| Permissions | Propagate document access controls to nodes, edges, and chunks. | Stops retrieval from exposing restricted evidence. |
Do not create an edge merely because two entities appear in the same paragraph. Require an extracted predicate or a deterministic rule, and retain the sentence that supports it. If a relationship is inferred rather than stated, mark it as inferred and give it a separate confidence or derivation field.
Build the ingestion and enrichment workflow
- Register each source. Assign a stable document ID, version, checksum, and access policy.
- Parse and clean. Extract text from files or records, preserve headings and tables where they carry meaning, and remove navigation or boilerplate that would pollute retrieval.
- Chunk semantically. Keep a chunk’s heading path, page or section location, and neighboring chunk references.
- Extract candidates. Identify entities, aliases, relationships, dates, quantities, and claims. Save the model prompt or extraction version for reproducibility.
- Resolve entities. Match candidates to existing IDs using deterministic keys first, then similarity and business rules. Flag uncertain matches.
- Write graph records. Upsert nodes and relationships with provenance rather than inserting duplicates on every run.
- Embed searchable content. Generate embeddings for chunks and, when useful, entity descriptions or claim text. Store the embedding model and version.
- Refresh indexes. Update vector and full-text indexes after graph writes, then run a smoke query for known entities and phrases.
Microsoft’s documented GraphRAG workflow follows this general shape: it loads documents, chunks them, extracts graph structure and claims, embeds chunks and entities, detects communities, and generates reports. Community detection and reports are particularly useful when users ask for a thematic or organization-wide summary rather than a single fact.
Illustrative graph write
The following Cypher-style pattern shows the important fields; adapt labels and property names to your schema.
MERGE (s:Person {id: $subject_id})
ON CREATE SET s.name = $subject_name
MERGE (o:Organization {id: $object_id})
ON CREATE SET o.name = $object_name
MERGE (s)-[r:WORKS_FOR {claim_id: $claim_id}]->(o)
SET r.source_id = $source_id,
r.chunk_id = $chunk_id,
r.confidence = $confidence,
r.observed_at = datetime($observed_at);Use a claim identifier when multiple documents can support the same pair of nodes. That lets you retain conflicting or time-bounded assertions instead of silently overwriting them.
Choose the right retrieval mode
| Mode | Best for | Typical flow | Main limitation |
|---|---|---|---|
| Vector | Paraphrases, conceptual questions, and natural-language descriptions. | Embed the question, retrieve nearest chunks or entities, then optionally expand the graph. | May miss exact identifiers or return text that is similar but not causally related. |
| Full-text or BM25 | Exact names, error codes, SKUs, legal phrases, and rare terms. | Search analyzed terms and aliases, then map hits to graph nodes. | Misses synonyms and wording that differs substantially from the source. |
| Hybrid | General-purpose production questions containing both concepts and exact terms. | Combine semantic and lexical scores, select seeds, then traverse. | Requires score normalization and tuning. |
| Cypher or graph traversal | Questions whose answer depends on relationships, constraints, or multi-hop paths. | Bind recognized entities and parameters, match bounded patterns, return paths and evidence. | Needs reliable entity resolution and a safe query-generation policy. |
Hybrid retrieval is a practical default. Use lexical signals to anchor names and identifiers, semantic signals to catch paraphrases, and graph expansion to connect the results. Keep the original scores and retrieval method in tracing data so you can diagnose why a passage was selected.
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Translate questions into safe graph queries
Text2Cypher systems translate a natural-language question into a graph query, but generated queries must be constrained. Provide the model with the allowed labels, relationship types, property names, and parameter formats. Reject write clauses, unrestricted procedures, cross-tenant matches, and patterns that exceed configured limits.
- Detect intent. Distinguish lookup, comparison, aggregation, timeline, explanation, and global-summary questions.
- Extract entities and filters. Normalize names and dates, and resolve them to IDs where possible.
- Select a retrieval plan. Choose vector, full-text, hybrid, direct Cypher, or a combination based on the intent and available anchors.
- Bind parameters. Pass values separately from the query text; do not concatenate user input into executable Cypher.
- Bound the pattern. Set maximum hop count, relationship types, node count, and execution time.
- Return evidence. Include node IDs, path relationships, source IDs, chunk IDs, and excerpts in the result.
- Validate the result. Check that required entities were found and that every generated claim has supporting provenance.
A question such as “Which services depend on the payment gateway used by project Atlas?” may need two stages: find the project and its gateway, then traverse DEPENDS_ON relationships from services to that gateway. A single similarity search can retrieve the words “payment gateway” without proving the dependency path.
Control traversal depth and context size
Unbounded expansion creates noisy subgraphs, slow queries, and expensive prompts. Set a default depth, such as one or two hops, and allow deeper traversal only for intents that require it. Rank paths by relationship specificity, seed confidence, recency, and evidence quality. Deduplicate nodes and excerpts before serialization.
For each selected path, include the subject, relationship type, object, relevant properties, and one or more supporting excerpts. Truncate long properties and keep a hard token budget for the context block. If the graph returns more candidates than fit, ask a second retrieval step to rerank or summarize evidence before generation; do not let the model choose from an unbounded dump.
Assemble a grounded prompt
A useful context block separates structured facts from source text and makes provenance visible:
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- project: Atlas (id=project-17)
- service: Billing API (id=service-42)
RELATIONSHIPS
- Billing API --DEPENDS_ON--> Payment Gateway
source=doc-88, chunk=doc-88#sec-3, observed=2025-11-04
SOURCE EXCERPTS
[doc-88#sec-3] “The Billing API sends all card authorization requests through the Payment Gateway.”Prompt instructions should tell the model to answer from the supplied context, cite the source IDs, state when evidence is missing or conflicting, and avoid presenting an inferred relationship as a quoted fact. Keep the user question separate from retrieved content, and delimit retrieved text so instructions embedded in a document cannot override the system’s rules.
Runtime components and framework choices
The runtime has three essential roles: a graph driver to connect to the database, a retriever to run vector, full-text, hybrid, or graph queries, and an LLM to interpret the question and generate the grounded response. Neo4j’s user guide describes these as the basic quickstart components and documents custom Cypher retrieval, related-entity traversal, and Text2Cypher patterns.
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| Option | Strengths | Trade-offs to assess | Good fit |
|---|---|---|---|
| Neo4j self-managed or AuraDB with its GraphRAG Python package | Direct graph-model control, Cypher, configurable indexes, and a dedicated GraphRAG integration. | Operational responsibility varies by deployment; entity extraction and resolution remain your responsibility. | Teams that need explicit graph modeling and multi-hop queries. |
| Microsoft GraphRAG workflow | Document-to-graph extraction, claims, community detection, and generated reports for local and global questions. | Extraction, embedding, and report generation can increase processing and model costs; tune the workflow to your corpus. | Applications needing thematic or corpus-wide summaries. |
| AWS reference architecture | Connects data sources, parsing, SageMaker, Bedrock or LangChain, a graph database, graph analytics, and GraphRAG applications. | More managed services mean more integration points, policies, and cloud-specific operations. | Organizations already standardized on AWS services. |
| Azure AI Foundry and Azure AI Search around a graph layer | Managed model, orchestration, retrieval, and evaluation components can surround the graph. | Portability, service limits, and combined indexing costs require architectural review. | Teams using Azure governance and managed RAG operations. |
Compare these approaches on graph-model control, entity-resolution quality, multi-hop recall, latency, indexing and LLM cost, operational burden, cloud portability, observability, and citation support. A framework does not compensate for weak identifiers or missing provenance.
Evaluate retrieval and answer quality
Build a test set from real user questions, including exact-name lookups, paraphrases, multi-hop questions, conflicting documents, and questions with no answer in the corpus. Store the expected entities, relationships, evidence passages, and acceptable answer conditions.
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- Seed recall: whether the correct entity or chunk appears among initial retrieval results.
- Path recall: whether the required relationship chain is returned.
- Evidence precision: how much of the supplied context is relevant.
- Answer faithfulness: whether every material statement is supported by retrieved evidence.
- Abstention quality: whether the system says evidence is insufficient instead of guessing.
- Latency: time spent in embedding, search, traversal, reranking, and generation.
- Cost: embedding refreshes, graph queries, reranking, and model tokens.
Run evaluations after schema, extraction, embedding, or prompt changes. Keep traces containing the question, retrieval mode, generated query, parameters, returned IDs, traversal depth, prompt size, answer, and citations. This makes a wrong answer diagnosable rather than merely observable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common failure modes and fixes
The graph contains duplicates
Symptom: one person or product appears as several nodes and paths disagree. Fix: enforce canonical IDs, normalize aliases, add uniqueness constraints where supported, and reprocess unresolved candidates through a review workflow.
Vector results are relevant but cannot answer the question
Symptom: retrieved passages mention the right topic but do not establish the requested relationship. Fix: use the passages to identify seed nodes, then run a bounded traversal over the relationship types that encode the question.
Cypher generation is unsafe or expensive
Symptom: generated queries scan the whole graph, attempt writes, or time out. Fix: expose a read-only schema, bind parameters, validate generated statements, enforce hop and row limits, add indexes, and fall back to a predefined query template when intent is known.
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Answers cite nodes but not evidence
Symptom: the response names an entity without showing which document supports the claim. Fix: make source and chunk IDs mandatory fields in retrieval results and require the generator to cite them.
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Security, governance, and operations
Apply document permissions during ingestion and again during retrieval. A graph edge can reveal confidential information even when the source paragraph is not returned, so authorization must cover nodes, relationships, claims, and derived summaries. Store tenant or access-group identifiers on evidence records and include them in every query predicate.
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Protect personal and regulated data in both the graph and vector index. Encrypt connections, restrict write credentials, log administrative changes, and define retention rules for source text, embeddings, and model traces. Treat extracted relationships as data products with owners, refresh schedules, and quality thresholds.
Monitor index freshness, extraction failure rates, unresolved-entity queues, query timeouts, traversal sizes, citation coverage, and abstention rates. Alert when a source update causes an unusual drop in seed recall or when a model change increases unsupported answers.
A practical rollout plan
- Start with one question family. Choose a relationship-heavy use case with authoritative source documents and measurable answers.
- Define the minimum schema. Specify entities, predicates, IDs, temporal rules, provenance, and permissions before bulk extraction.
- Build a small gold set. Have domain experts label expected nodes, paths, and evidence for representative questions.
- Implement hybrid seeds. Add vector and full-text indexes, then compare retrieval results on the gold set.
- Add bounded traversal. Implement read-only Cypher templates or constrained Text2Cypher for the selected intents.
- Ground generation. Serialize paths and excerpts with source IDs and require citations or an explicit insufficient-evidence response.
- Measure and iterate. Tune extraction, entity resolution, ranking, depth, and prompt budgets using recall, faithfulness, latency, and cost.
- Expand cautiously. Add new entity types or global community summaries only after the initial workflow is observable and reliable.
When to use a graph, vectors, or both
Use vector-only RAG when the application mainly retrieves self-contained passages and relationship structure is not central. Add full-text search when exact identifiers or specialized terminology matter. Add a knowledge graph when users ask who-is-connected-to-whom, which systems depend on another system, what changed over time, or which multi-step path satisfies several constraints.
For most enterprise document collections, the strongest default is hybrid retrieval followed by a narrowly scoped graph expansion. It preserves the recall of semantic and lexical search while adding the explicit relationships that a language model cannot safely infer from proximity alone. The graph’s value comes from accurate modeling, entity resolution, provenance, and disciplined traversal—not from selecting a fashionable framework.
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