Use Case: RAG Pipeline

Standard RAG retrieves flat chunks and trusts the LLM to figure out the rest. Trusted RAG runs on a world model — every fact has a confidence score, every source has provenance, and retrieval follows relationship paths rather than approximate vector similarity.

LLMs are strong at language. They are weak at multi-hop reasoning over connected facts — finding what links entity A to entity B through three degrees of relationship. That's a traversal problem. It requires world model infrastructure, not a vector index.

Build a retrieval-augmented generation pipeline that combines vector similarity, graph traversal, and full-text search — all operating on the same spatial-temporal world model.

The problem#

Standard RAG (vector search → LLM) misses structural context:

• Related entities that aren't in the retrieved chunks
• Relationship paths between concepts
• Community structure and importance rankings

Why graph-powered RAG#

ArcFlow combines three retrieval strategies in one engine:

1. Vector search — semantic similarity over embeddings
2. Graph traversal — follow relationships for structured context
3. Full-text search — keyword matching with BM25 scoring

No separate databases. No orchestration layer. One query engine.

Implementation#

import { open } from '@ozinc/arcflow'
 
const db = open('./rag-graph')
 
async function graphRAG(query: string, queryEmbedding: number[]) {
  // 1. Vector search: find semantically similar documents
  const similar = db.query(
    "CALL algo.vectorSearch('doc_index', $vec, 5)",
    { vec: JSON.stringify(queryEmbedding) }
  )
 
  // 2. Graph expansion: follow relationships from retrieved docs
  const context: string[] = []
  for (const row of similar.rows) {
    const title = String(row.get('title'))
    context.push(`Document: ${title}`)
 
    // Get mentioned entities
    const entities = db.query(
      "MATCH (d:Document {title: $t})-[:MENTIONS]->(e) RETURN e.name, labels(e)",
      { t: title }
    )
    for (const e of entities.rows) {
      context.push(`  Mentions: ${e.get('name')} (${e.get('labels(e)')})`)
    }
  }
 
  // 3. Full-text search: keyword-based retrieval
  const keywords = db.query(
    "CALL db.index.fulltext.queryNodes('doc_text', $q)",
    { q: query }
  )
 
  // 4. Graph algorithms: importance ranking
  const ranked = db.query("CALL algo.pageRank()")
 
  // 5. Assemble context for LLM
  return {
    vectorResults: similar.rows.map(r => r.toObject()),
    graphContext: context,
    keywordResults: keywords.rows.map(r => r.toObject()),
    topEntities: ranked.rows.slice(0, 10).map(r => r.toObject()),
  }
}

Built-in GraphRAG#

ArcFlow includes a built-in Trusted GraphRAG pipeline:

const context = db.query("CALL algo.graphRAGTrusted()")

This combines vector retrieval, graph traversal, and trust scoring in a single call.

See Also#

• Trusted RAG — full Trusted RAG page with confidence scoring, observation classes, and provenance
• Vector Search — vector index creation, k-NN search, hybrid search
• RAG Pipeline Guide — step-by-step implementation guide
• Graph Algorithms — algo.graphRAG(), algo.graphRAGTrusted(), algo.graphRAGContext()
• Knowledge Management — persistent world model as RAG substrate