Vector Databases and Knowledge Graphs for RAG in 2026: When to Use Each, Together, or Neither

TL;DR: Vector Databases vs Knowledge Graphs for RAG in 2026

If you only have time for one move in 2026: start with a vector DB plus a reranker. Add a knowledge graph or GraphRAG-style index when you have multi-hop questions, entity disambiguation pain, or strict compliance requirements.

Vector Databases in 2026: How They Work and What Changed

A vector database stores fixed-length numerical embeddings produced by an encoder model and indexes them for fast approximate nearest neighbor (ANN) search. A query is embedded with the same encoder, and the database returns the top-K closest vectors by cosine similarity, dot product, or L2 distance, depending on configuration.

Modern vector databases also ship:

• Metadata filters for structured constraints like tenant_id, date ranges, or document type.
• Hybrid search combining dense embedding similarity with sparse keyword scoring (BM25, SPLADE).
• Built-in rerankers or first-class reranker integrations (Cohere Rerank 3, BGE rerankers, voyage-rerank).
• Multi-tenancy with namespace isolation and per-tenant scaling.
• Real-time upserts with sub-second visibility into newly indexed documents.

Six vector databases worth evaluating in 2026

Pick based on deployment constraints (managed vs self-host, on-prem vs cloud), filter complexity, hybrid-search needs, and whether the team already runs a managed search stack like Elasticsearch or OpenSearch (both now competent vector engines).

What changed for vector databases between 2025 and 2026

• Hybrid search is the default. Pure dense retrieval lost ground to dense + sparse + reranker pipelines on most public RAG benchmarks (BEIR, MTEB-RAG slices, MIRACL).
• Filters got cheaper. Qdrant’s payload indexes, Weaviate’s ACORN, and Pinecone serverless materially reduced the cost of high-cardinality metadata filtering.
• Built-in reranker hooks shipped. Several DBs added first-class reranker steps so RAG teams stop building one-off rerank services.
• Multi-vector and ColBERT-style late interaction. Qdrant and Vespa popularized multi-vector indexing for token-level scoring, which improves recall on long documents.

Knowledge Graphs in 2026: How They Work and Where They Shine

A knowledge graph (KG) is a graph database whose nodes are entities and edges are typed relationships. Example: (Patient)-[DIAGNOSED_WITH]->(Condition)-[TREATED_BY]->(Drug). Queries are written in Cypher, Gremlin, or SPARQL, and traversal returns explicit paths with provable provenance.

Knowledge graphs excel at:

• Multi-hop questions. “Find drugs that treat a condition the patient has, that do not interact with anything in their current medication list.”
• Entity disambiguation. Two documents about “Apple” disambiguated to the company vs the fruit by linked entities.
• Compliance lookups. Regulated industries need explainable retrieval paths, not just top-K vector hits.
• Tool selection in agent stacks. The graph encodes which tools to invoke for which entities.

Common 2026 graph stores

GraphRAG, LightRAG, and HippoRAG

These are retrieval frameworks, not databases. They sit on top of one or more underlying stores:

• Microsoft GraphRAG. Builds entity and community graphs from a corpus, summarises communities, and uses both vector and graph retrieval to answer queries.
• LightRAG. A lighter alternative that constructs a dual-level retrieval over entities and chunks, popular for cost-sensitive deployments.
• HippoRAG. Uses personalised PageRank over a knowledge graph plus passage embeddings, taking inspiration from hippocampal indexing.

All three combine vector retrieval with graph structure. They usually do not remove the need for embeddings and vector retrieval, only the specific deployment shape: framework-managed indexes, embedded stores like LanceDB, or a separate vector database. They change how you build and query graph plus vector indexes together.

Vector Database vs Knowledge Graph: Direct Comparison

When to Choose a Vector Database

Pick a vector DB as your primary retriever when:

• Your corpus is mostly unstructured text, code, image, or audio.
• Queries are open-ended and look like natural-language questions.
• You need sub-50 ms p95 retrieval at scale.
• You can tolerate fuzzy results and lean on a reranker plus eval suite for quality.
• You do not have the budget or stable schema to build a knowledge graph.

Common 2026 use cases:

• Documentation chatbots and customer-support assistants over unstructured tickets.
• Internal search across Slack, Notion, Jira, and meeting transcripts.
• Code retrieval for coding agents and IDE assistants.
• Multimedia search across images, audio, and video embeddings.
• Fraud, recommendation, and personalization signals over user-event embeddings.

When to Choose a Knowledge Graph

Pick a knowledge graph as the primary retriever when:

• Your data is naturally structured: drugs, regulations, products, supply chains, identity graphs.
• Questions are multi-hop and require following typed relationships.
• You need explainability for compliance, audit, or clinical use.
• The schema is stable enough to justify extraction or curation.
• You already have entity-extraction pipelines feeding a master data store.

Common 2026 use cases:

• Healthcare and pharma decision support over patients, drugs, and conditions.
• Financial-services KYC, AML, and regulatory lookups.
• Enterprise master-data management and product taxonomy queries.
• Agent tool selection where the graph encodes which APIs match which entities.
• Government and legal research over statutes and case law.

When to Use Both Together

Hybrid vector + graph patterns are now standard in 2026:

1. Vector for recall, graph for precision. Vector retrieval surfaces candidate documents. A graph traversal then constrains them to entities connected by required relationships.
2. Graph for structure, vector on graph nodes. Each graph node carries an embedding. Retrieval starts with vector similarity over node embeddings, then expands via traversal.
3. GraphRAG-style community summaries. Build entity graphs from a corpus, summarise communities, then retrieve community summaries plus passages.
4. LightRAG dual-level retrieval. Cheap to build, retrieves at both entity and chunk granularity.
5. Agent-router pattern. The LLM chooses between vector and graph tools per question.

The risk is operational: two indexes, two refresh pipelines, and two eval streams to monitor. Most teams adopt the hybrid only after the vector-only baseline plateaus.

How to Evaluate Retrieval Quality with Future AGI

FAGI does not ship a vector database or knowledge graph. It instruments and evaluates whichever retriever you pick. Wrap retrievals with traceAI for tracing, then score with fi.evals for faithfulness, groundedness, and context-recall metrics. The Agent Command Center at /platform/monitor/command-center exposes these signals in production.

from fi.evals import evaluate

def retrieve(query: str) -> list[str]:
    """Return top-K chunks from your vector DB or graph retriever."""
    raise NotImplementedError

def call_llm(query: str, context: str) -> str:
    """Replace with your LLM client (OpenAI, Anthropic, LiteLLM, etc.)."""
    raise NotImplementedError

def answer(query: str) -> dict:
    chunks = retrieve(query)
    context = "\n".join(chunks)
    response = call_llm(query, context)
    scores = {
        "faithfulness": evaluate("faithfulness", output=response, context=context),
        "context_recall": evaluate("context_recall", output=response, context=context, expected=query),
    }
    return {"response": response, "scores": scores}

For a stricter judge over RAG outputs:

from fi.opt.base import Evaluator
from fi.evals.metrics import CustomLLMJudge
from fi.evals.llm import LiteLLMProvider

retrieval_judge = Evaluator(
    metric=CustomLLMJudge(
        prompt="Score 0-1: does the answer use only the supplied context, with all entities grounded?",
        provider=LiteLLMProvider(model="gpt-5-2025-08-07"),
    ),
)

Decision Matrix

Related Reading

• Best Vector Databases for RAG 2026
• Agentic RAG Systems
• RAG Architecture in LLMs
• Best RAG Evaluation Tools 2026
• Advanced Chunking Techniques for RAG

References

• Pinecone docs and serverless
• Weaviate docs
• Qdrant docs
• Milvus docs
• Chroma docs
• LanceDB docs
• Neo4j docs and GDS library
• Memgraph docs
• Kuzu docs
• Microsoft GraphRAG paper and GitHub
• LightRAG paper and GitHub
• HippoRAG paper
• Future AGI ai-evaluation (Apache 2.0)