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GraphRAG

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When to use a graph instead of vectors

Vector RAG is great for semantic similarity: “find documents about X”. It struggles with relationship reasoning:
  • “Which projects does Alice’s direct reports work on?”
  • “How many bugs has team B closed in the last 30 days?”
  • “Trace this transaction’s path through the ledger.”
Those questions need a graph database. GraphRAG generates a Cypher query from the natural-language question, runs it against Neo4j or Memgraph, and returns the rows. Combine the two via HybridRetriever for the best coverage.

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Architecture

                              ┌──────────────────────────────┐
   user question  ──────────▶│  GraphRAGRetriever            │
                              │   1. get live schema (labels, │
                              │      relationship types,      │
                              │      property keys)           │
                              │   2. LLM ➜ Cypher              │
                              │   3. ensure LIMIT              │
                              │   4. run Cypher                │
                              └──────────┬───────────────────┘
                                         │
                                         ▼
                              ┌──────────────────────────────┐
                              │       CypherStore             │
                              │   neo4j-driver or compatible  │
                              └──────────────────────────────┘

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CypherStore

Low-level interface for any graph DB that speaks the Cypher / Bolt protocol. 
interface CypherStore {
  readonly providerId: string;
  connect(): Promise<void>;
  runCypher(cypher: string, params?: Record<string, unknown>): Promise<CypherRecord[]>;
  getSchema(): Promise<CypherSchema>;
  close(): Promise<void>;
}

interface CypherRecord {
  values: Record<string, unknown>;   // column name -> value
}

interface CypherSchema {
  nodeLabels: string[];
  relationshipTypes: string[];
  propertyKeys?: string[];
}

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Neo4jCypherStore

import { Neo4jCypherStore } from "@agentium/core";

const store = new Neo4jCypherStore({
  uri: process.env.NEO4J_URI ?? "bolt://localhost:7687",
  username: process.env.NEO4J_USER ?? "neo4j",
  password: process.env.NEO4J_PASSWORD ?? "neo4j",
  database: "neo4j",                   // optional, default is "neo4j"
});
await store.connect();
Requires: npm install neo4j-driver (already an optional peer dep of @agentium/core). Defaults pull from NEO4J_URI, NEO4J_USER, NEO4J_PASSWORD environment variables.

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MemgraphCypherStore

Memgraph speaks the same Bolt protocol as Neo4j, so the adapter just sub-classes: 
import { MemgraphCypherStore } from "@agentium/core";

const store = new MemgraphCypherStore({
  uri: process.env.MEMGRAPH_URI ?? "bolt://localhost:7687",
  username: process.env.MEMGRAPH_USER,
  password: process.env.MEMGRAPH_PASSWORD,
});
Differences from Neo4j: Memgraph defaults to no auth (username: ""). It’s faster for streaming analytics and ships some unique procedures (e.g. mgps.*).

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runCypher(cypher, params?)

Execute any Cypher query, return rows as CypherRecord[]: 
const rows = await store.runCypher(
  "MATCH (p:Person {name: $name})-[:KNOWS]->(other) RETURN other.name AS knows",
  { name: "Alice" },
);
for (const r of rows) console.log(r.values.knows);
Always parameterize. Never string-interpolate user input into Cypher.

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getSchema()

Returns the live schema by calling db.labels(), db.relationshipTypes(), db.propertyKeys(): 
const schema = await store.getSchema();
console.log(schema.nodeLabels);          // ["Person", "Project"]
console.log(schema.relationshipTypes);   // ["KNOWS", "WORKS_ON"]
console.log(schema.propertyKeys);        // ["name", "email", "title"]
This is what GraphRAGRetriever reads to ground the LLM’s Cypher generation.

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GraphRAGRetriever

LLM-to-Cypher with schema-aware prompting. 
import { GraphRAGRetriever, Neo4jCypherStore, openai } from "@agentium/core";

const store = new Neo4jCypherStore({ /* ... */ });
await store.connect();

const retriever = new GraphRAGRetriever({
  store,
  model: openai("gpt-4o"),
  maxRecords: 25,             // default 25 - auto-appends LIMIT 25 if missing
  systemPrompt: undefined,    // optional override - see below
});

const result = await retriever.retrieve("Who works on the Atlas project?");
console.log(result.cypher);   // "MATCH (p:Person)-[:WORKS_ON]->(:Project {name: 'Atlas'}) RETURN p.name LIMIT 25"
console.log(result.text);     // "name=Alice\nname=Bob"

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retrieve(query) return shape

interface GraphRAGResult {
  cypher: string;          // the Cypher the model generated (post-LIMIT-injection)
  records: CypherRecord[]; // raw rows from the DB
  text: string;            // plain-text rendering of records (great as RAG context)
}

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Default system prompt

You convert a natural-language question into a Cypher query that runs against a Neo4j-compatible graph.
Rules:
1. Use ONLY the labels and relationship types listed in the schema.
2. Return only Cypher - no prose, no markdown fences.
3. Always end with a LIMIT clause (default 25 rows).
4. Prefer MATCH ... RETURN over WRITE operations.
Pass systemPrompt to override for domain-specific instructions (e.g. “use the German names for nodes” or “always project full names + emails”).

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Safety: automatic LIMIT

If the model omits LIMIT, GraphRAGRetriever appends one with the configured maxRecords. This is the difference between a runaway query and a useful one. For destructive operations (CREATE, DELETE, MERGE, SET), the default system prompt says “prefer MATCH … RETURN”. For an extra hard fence, run a Cypher parser yourself or use a read-only Neo4j user.

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Markdown fence stripping

Models sometimes wrap their Cypher in cypher ... fences despite being told not to. The retriever strips that automatically.

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HybridRetriever

Compose vector search + graph search + optional rerank into a single retrieval pipeline. 
import {
  GraphRAGRetriever,
  HybridRetriever,
  InMemoryVectorStore,
  Neo4jCypherStore,
  OpenAIEmbedding,
  openai,
  CohereReranker,
} from "@agentium/core";

const vector = new InMemoryVectorStore(new OpenAIEmbedding());
await vector.upsert("notes", { id: "1", content: "Alice manages Bob and Carol." });
await vector.upsert("notes", { id: "2", content: "Project Atlas is shipping in Q4." });

const graphStore = new Neo4jCypherStore({ /* ... */ });
await graphStore.connect();
const graph = new GraphRAGRetriever({ store: graphStore, model: openai("gpt-4o") });

const hybrid = new HybridRetriever({
  vector: { store: vector, collection: "notes", topK: 10 },
  graph: { retriever: graph },
  rerank: new CohereReranker(),
  topK: 5,
});

const results = await hybrid.retrieve("Who works on Atlas?");
for (const r of results) {
  console.log(`[${r.source}] ${r.score.toFixed(3)}  ${r.content}`);
}

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Algorithm

  1. Parallel fan-out: vector search + graph retrieval run concurrently.
  2. Score normalization: vector cosine scores (0–1) and graph row-rank scores (1 - rank/N) are unioned.
  3. Reciprocal Rank Fusion: combine the two ranked lists with k=60 (the standard RRF constant). Each result keeps the better of its vector or graph rank, plus a small bonus for appearing in both.
  4. Optional rerank: if a Reranker is configured, the top topK * 3 fused results are passed to it for a final reorder.

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HybridResult

interface HybridResult {
  source: "vector" | "graph"; // which retriever surfaced this
  id: string;                  // unique id, prefixed with v: or g:
  content: string;
  score: number;               // RRF or rerank score
}

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Config

interface HybridRetrieverConfig {
  vector?: { store: VectorStore; collection: string; topK?: number };
  graph?: { retriever: GraphRAGRetriever };
  rerank?: Reranker;
  topK?: number;     // default 10
}
Either vector or graph may be omitted — HybridRetriever becomes a one-side pipeline. Useful when you want to keep the rerank-and-fuse boilerplate but only have one source.

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Performance characteristics

StepLatency
store.getSchema() (cached at the DB level)~5ms
LLM Cypher generation (GPT-4o-mini)~600ms
runCypher (small Neo4j)~10ms
RRF fusion< 1ms
Rerank (Cohere, 30 candidates)~200ms
Total Hybrid retrieve(): ~800ms for typical queries.

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Best practices

  • Cache the schema at startup if your graph schema is stable. Read it once and pass it into GraphRAGRetriever via a custom systemPrompt.
  • Use a read-only DB user. The LLM should never have credentials that can DROP nodes.
  • Cap costs. Set maxRecords low (10-25) — the LLM can ask for more by paginating in subsequent calls if it really needs them.
  • Log all generated Cypher. Add a hook that emits result.cypher to your observability stack so you can spot prompt regressions.
  • Pair with vector for breadth. Pure GraphRAG misses semantic matches that aren’t in the graph. Pure vector misses relationship answers. Hybrid is almost always the right choice in production.

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See also