This content originally appeared on DEV Community and was authored by Nikhil Agrawal
Let's be honest. Slapping a vector search on top of an LLM is the "hello world" of GenAI. It's a great start, but it breaks down fast when faced with real-world, interconnected data. You can't answer multi-hop questions, and you're constantly fighting to give the LLM enough context.
We hit that wall. So we re-architected. Here's the pattern we implemented: Knowledge Graph RAG (KG-RAG). It's less about a single tool and more about orchestrating specialized data stores.
The Stack:
- Source of Truth: MongoDB
- Vector Store (Semantic Search): Weaviate
- Graph Store (Context & Relationships): Neo4j
- LLM/Embeddings: Google Gemini
The Problem with Vector-Only RAG:
A query like "What did team A conclude about the feature B launch?" is hard. Vector search might find docs about team A and docs about feature B, but it struggles to guarantee the retrieved context contains team A's conclusions about feature B.
The KG-RAG Solution:
We built a ChatService orchestrator that follows a "Graph-First" retrieval pattern.
Here's the pseudo-code for the backend logic:
`async function handleQuery(userQuery) {
// 1. Identify entities in the query (e.g., "team A", "feature B")
const entities = extractEntities(userQuery);
// 2. Query the Knowledge Graph for context and specific chunk IDs
// CYPHER: MATCH (t:Team {name:"team A"})-[]->(d:Doc)-[]->(c:Chunk)
// WHERE (d)-[:DISCUSSES]->(:Feature {name:"feature B"}) RETURN c.id
const contextualChunkIds = await neo4j.run(cypherQuery, { entities });
// 3. Perform a filtered vector search in Weaviate
// This is way more accurate than a broad search.
const relevantChunks = await weaviate.search({
vector: embed(userQuery),
filters: { chunkId: { $in: contextualChunkIds } }
});
// 4. Synthesize prompt and generate with LLM
const prompt = createPrompt(userQuery, relevantChunks);
const answer = await gemini.generate(prompt);
// 5. Also, return the graph data from step 2 for UI visualization
return { chatAnswer: answer, graphData: getGraphFrom(contextualChunkIds) };
}`
This pattern is a game-changer. It grounds the LLM in structured, factual relationships before feeding it semantic context, dramatically improving accuracy.
The best part? The UI is now a dual-pane view: a standard chatbot on the left and an interactive 3D graph on the right, both powered by the same API call. Clicking a node on the graph fires a new query. It's the interactive dev loop we've always wanted for data.
Stop building basic RAG toys. Start thinking about orchestration.
This content originally appeared on DEV Community and was authored by Nikhil Agrawal
Nikhil Agrawal | Sciencx (2025-08-14T08:49:27+00:00) Your RAG is Basic. Here’s the KG-RAG Pattern We Used to Build a Real AI Agent.. Retrieved from https://www.scien.cx/2025/08/14/your-rag-is-basic-heres-the-kg-rag-pattern-we-used-to-build-a-real-ai-agent/
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