The Tool Desk
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What the two technologies actually do
Vector databases retrieve semantic similarity
A vector database stores an embedding—a numerical representation of text, code, images or other content—alongside the source record. At query time, the search system embeds the user’s question and returns chunks whose vectors are nearest to it. This works well when the user paraphrases the wording in a document or asks a broad question such as “How do we handle an employee relocation?” even though the source uses different terms.
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Vector retrieval is strongest with unstructured material: policies, tickets, meeting notes, manuals and narrative reports. Its limitation is that it can only retrieve concepts the embedding model can represent. Google Cloud’s semantic-search documentation makes the same boundary explicit: semantic search cannot discover meaning the embedding model fails to understand. Similarity also does not prove that two names refer to the same person, that one event preceded another, or that several facts belong to one authorized business context.
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Lexical search preserves exact terms
Traditional full-text retrieval matches tokens, fields and linguistic signals. It is better than semantic similarity for product codes, contract numbers, error strings, dates, legal clauses, uncommon names and specialist jargon. Microsoft Learn describes hybrid search as one request configured for both full-text and vector queries; the result lists can be merged with Reciprocal Rank Fusion (RRF).
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For enterprise search, lexical and vector retrieval are complements rather than competing replacements. Lexical search protects exact identifiers, while vectors recover paraphrases and related wording. A system that omits either mode will produce avoidable misses.
Knowledge graphs model entities and relationships
A knowledge graph represents entities—people, teams, customers, products, controls, systems, documents and events—as nodes connected by typed relationships. Examples include reports to, owns, depends on, implements, mentioned in and supersedes. Each edge can carry dates, confidence, provenance and access-control attributes.
Graph retrieval first links a query’s words to canonical entities, disambiguates names and then follows relevant paths. That makes it useful for questions such as “Which services depend on the payment gateway owned by the European platform team?” or “Show every control affected by the new retention policy and the evidence for each one.” The answer may require several documents that share no close semantic wording but are connected through people, systems or dependencies.
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Google Cloud describes this pattern as linking three enterprise pillars—people, content and interactions—while also supporting entity disambiguation, relationship understanding and access-control checks. A graph therefore provides context and structure; it does not automatically replace document retrieval. The source passages still need to be returned so a language model can cite and quote evidence.
GraphRAG combines both retrieval modes
GraphRAG uses vector or lexical search to find initial evidence, maps that evidence to graph entities, traverses selected relationships, and retrieves additional passages attached to the resulting subgraph. The final context contains both semantically relevant text and structurally related evidence. AWS describes this as a hybrid graph-and-vector pattern for agentic retrieval and enterprise knowledge management.
The graph can be built before queries (an indexed knowledge graph), during ingestion, or partly at query time. Pre-building reduces query work but introduces update lag. Query-time extraction can reflect fresh content but increases latency and model cost. In either case, GraphRAG is an architecture, not a single database product.
How the approaches compare
| Dimension | Vector-only retrieval | Lexical plus vector hybrid | Graph-only retrieval | GraphRAG (graph plus vector) |
|---|---|---|---|---|
| Best query shape | Direct semantic lookup and paraphrased questions | General enterprise search mixing natural language and exact terms | Questions about known entities and explicit paths | Cross-document, multi-hop and relationship-heavy questions |
| Primary data fit | Mostly unstructured documents | Unstructured documents with important identifiers | Stable, modeled entities and relationships | Documents plus connected entities, events and dependencies |
| Strength | Fast semantic recall | Broad recall plus exact-token precision | Deterministic paths and explainable relationships | Context that is both semantically and structurally relevant |
| Typical weakness | Misses exact terms and explicit multi-hop links | More ranking and tuning work than one index | Expensive modeling when the ontology is immature | Extraction, entity resolution, synchronization and latency complexity |
| Governance focus | Document-level ACL filtering and source provenance | ACL filtering across both result lists and fusion | Edge and node ownership, temporal validity and path authorization | All of these, including permissions on expanded evidence |
| Operational profile | Embedding refreshes and index maintenance | Two indexes, fusion tuning and observability | Ontology, ingestion and graph-update pipelines | Coordinated vector, lexical and graph pipelines |
Which architecture fits your questions?
Choose vector retrieval when the corpus is broad and mostly unstructured
Start with vectors when users ask “find information about X,” summarize a policy, locate similar incidents or search across many document types without a stable entity model. This is the shortest route to useful semantic recall. Keep the original files, page or section boundaries, timestamps and permissions as metadata so every chunk can be traced back to an authorized source.
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Choose lexical retrieval for identifiers and compliance language
If users routinely enter SKUs, ticket IDs, API names, regulation numbers, dates or exact contractual phrases, lexical signals are essential. Add field weighting, filters and synonyms before assuming an embedding model will learn every acronym. Hybrid ranking is usually safer than trying to force exact matching into a vector-only index.
Add a graph for repeated relationship and multi-hop work
A graph earns its complexity when the same classes of questions recur: ownership, reporting lines, application dependencies, control-to-evidence mapping, supplier networks, product lineage, or “what changed because of this event?” Measure the unanswered or poorly answered queries first. If the failure is merely an absent synonym or bad chunk, improve lexical/vector retrieval; if the answer requires following several verified relationships, model those relationships.
Use GraphRAG when evidence is distributed across connected sources
GraphRAG is appropriate when no single passage answers the question and the relevant evidence is connected by entities. For example, a risk review might need a control document, the system implementing that control, the owner’s team and the latest audit finding. Graph traversal supplies the connections; vector and lexical search supply the text.
A practical enterprise design
Layer 1: ingestion and canonical metadata
Ingest files, wikis, tickets, databases and event feeds into a common record format. Preserve source URI, title, section, author, creation and modification times, jurisdiction, retention state and access-control identifiers. Chunk documents at semantic boundaries, but retain parent-document and section links so retrieved text can be expanded when context is needed.
Layer 2: lexical and vector indexes
Create a full-text index and an embedding index over the same records. Normalize identifiers for lexical matching; choose an embedding model that supports the languages and content types in your corpus. At query time, run both searches, apply security filters before results reach the model, and fuse the ranked lists. Microsoft’s RRF description is a useful conceptual model: a document that ranks well in either list can receive a combined score without requiring the two score scales to be identical.
Layer 3: entity extraction and resolution
Extract candidate people, organizations, systems, products, controls, locations, dates and events from documents. Resolve aliases to canonical IDs using authoritative directories and reference data, not string similarity alone. Store confidence and provenance for each link. When two employees share a name, retain the ambiguity until department, email domain, time period or another trusted attribute resolves it.
Layer 4: relationship and provenance graph
Represent relationships with direction, type, validity interval and source evidence. “Service A depends on Service B” is different from “Service A mentioned Service B”; the distinction matters for impact analysis. Attach the document span that supports each edge so an answer can explain why the relationship exists and when it was true.
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Layer 5: query planning and controlled traversal
Classify each question as semantic lookup, exact lookup, entity lookup, aggregation or multi-hop reasoning. Use vector and lexical retrieval for the first two classes. For graph questions, identify seed entities, set a maximum hop count, restrict relationship types and apply time and permission filters. Retrieve source passages for every selected node or edge. Do not expose a graph path merely because it is connected; require evidence and authorization.
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Give the language model a compact context containing passages, entity IDs, relationship types, timestamps and source links. Instruct it to distinguish directly supported facts from inferences and to say when evidence conflicts or is missing. Log the retrieved IDs, graph paths, model version and policy decision so an auditor can reproduce the result.
Governance, permissions and freshness
Enforce authorization during retrieval
Filtering only after generation is unsafe: a model may summarize a restricted passage that was present in its context. Apply document ACLs to lexical and vector candidates before fusion. Graph expansion must check permissions on nodes, edges and the underlying evidence; a user who can see a project does not necessarily have access to every person, contract or incident connected to it.
Track freshness as separate service-level measures
Record the delay from source change to lexical availability, embedding availability and graph availability separately. A graph rebuilt nightly can be accurate yet stale for an incident system that changes hourly. Incremental edge updates, event-driven embedding refreshes and a visible “last updated” timestamp are preferable to claiming real-time data without measurement.
Make provenance and temporal validity first-class
Store who asserted a fact, which source supported it, when it became valid and when it expired. Keep superseded relationships rather than silently overwriting them. Answers about ownership, policy or compliance should be evaluated against the relevant date, not only the latest graph state.
Cost, latency and operating trade-offs
There is no universal price advantage. Vector systems incur embedding generation, storage and nearest-neighbor-search costs. Hybrid systems add a full-text index, fusion logic and more tuning. Graph systems add ontology design, entity resolution, edge extraction, graph storage and update pipelines. GraphRAG adds query planning, traversal limits, provenance handling and often extra model calls.
Benchmark your own workload using p50 and p95 latency, recall of known answers, citation precision, authorization errors, ingestion delay and cost per answered query. Test cold-start and peak-load behavior, not only a small hand-picked set. A graph that improves difficult questions but makes common lookups ten times slower may belong on a routed path rather than in every query.
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What the available evidence says
A February 2024 report from TechTarget Enterprise Strategy Group, cited by Neo4j, said 26% of surveyed organizations with AI roadmaps already had generative-AI models in production and 66% planned deployment within the following 12 months. That adoption context explains why search teams are adding structure, but it is not evidence that one retrieval architecture wins universally.
AWS Prescriptive Guidance cites linked AWS and arXiv benchmarks reporting a 7–35% retrieval-accuracy improvement for GraphRAG over vector-only approaches. Treat that range as a benchmark summary under particular datasets and configurations, not as a guaranteed production uplift. The HybridRAG paper reports that combining vector-database and knowledge-graph context outperformed VectorRAG and GraphRAG individually at both retrieval and generation stages in its evaluations; results can vary with graph quality, chunking, model choice and query mix.
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The graph is technically large but practically empty
If extraction produces generic “related to” edges, traversal adds noise. Define a small ontology around decisions users actually make, require typed relationships and retain evidence spans. Expand the schema only after measuring a recurring unanswered question.
Entity resolution silently joins the wrong records
Names, abbreviations and reused project codes create false paths. Use canonical identifiers, authoritative directories, confidence thresholds and an exception queue for ambiguous matches. Never let a low-confidence alias drive an access decision.
Vector results look relevant but omit the decisive clause
Revisit chunk boundaries, parent-document expansion, metadata filters and lexical fusion. Preserve tables, headings and negations; a chunk that loses its “not,” date or exception is semantically misleading.
Graph traversal leaks information
Apply ACL checks at every expansion step and test indirect paths. Red-team queries that start with an authorized entity and attempt to reach restricted owners, incidents or contracts through shared relationships.
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Expose per-source timestamps, alert when embedding or graph lag exceeds its service target, and support temporary fallback to the authoritative source for high-risk workflows.
A staged adoption plan
- Baseline search: instrument current queries, collect relevance judgments and measure exact-term misses, semantic misses and multi-hop failures.
- Hybrid foundation: deploy full-text plus vector retrieval over a permission-aware document index, with RRF or another tested fusion method.
- Failure-driven graph pilot: select one relationship-heavy workflow, define its entities and edge types, and attach every edge to source evidence.
- Route selectively: send graph-shaped queries to controlled traversal while keeping ordinary lookups on the faster hybrid path.
- Evaluate continuously: compare recall, citation precision, latency, freshness, authorization accuracy and cost against the baseline.
- Scale only proven patterns: automate extraction and incremental updates after the pilot demonstrates value and operational ownership.
Bottom line for enterprise teams
Use lexical plus vector hybrid retrieval as the default foundation for a mixed enterprise corpus. Add a knowledge graph when users need verified entities, permissions-aware relationships or multi-hop answers that document similarity cannot supply. Use GraphRAG to combine the two selectively, with explicit controls for provenance, freshness, traversal depth and authorization. The right decision follows your query failures and governance requirements—not the popularity of a database category.
Frequently Asked Questions
Is GraphRAG always better than vector RAG?
No. GraphRAG can improve relationship-heavy and multi-hop questions, but it adds extraction, entity-resolution and synchronization work. For direct document lookups, a well-tuned lexical-plus-vector system is usually simpler and faster.
Do we need a knowledge graph for internal company search?
Not necessarily. Start with permission-aware full-text and vector retrieval. A graph becomes justified when repeated searches require ownership, dependency, reporting-line or evidence paths that are not explicit in one document.
Can a vector database and knowledge graph share the same data?
Yes. Store common source IDs and metadata, attach graph entities and edges to the originating passages, and let the query planner combine vector, lexical and graph results while applying the same authorization policy.
How should we measure whether adding a graph worked?
Create a representative query set and compare multi-hop recall, citation precision, authorization accuracy, p95 latency, ingestion lag and cost per answer before and after the graph pilot. Separate gains on relationship questions from unchanged performance on ordinary lookups.
What is the biggest security mistake in GraphRAG?
Applying permissions only after the language model receives context. Authorization must filter lexical and vector candidates and every graph node, edge and evidence passage during retrieval.
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