Your RAG is Basic. Here's the KG-RAG Pattern We Used to Build a Real AI Agent.

Nikhil Agrawal — Thu, 14 Aug 2025 08:49:27 +0000

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.

I spent hours debugging a None error in my RAG chatbot. The fix was one line.

Nikhil Agrawal — Mon, 07 Jul 2025 07:30:22 +0000

I was so sure my Gemini API key was invalid.
I'm building a RAG chatbot with Flask, Weaviate, and Gemini. The main API for summarizing docs worked perfectly. The chatbot API? RuntimeError: Gemini model is not initialized.
I checked my .env file a dozen times. I generated new API keys. I put print() statements everywhere. The initialization code was definitely running and succeeding at startup! So why was my model None when the API route was called?
Turns out, it was a classic Flask circular dependency issue.

My app/init.py was doing this (the wrong way):
from .routes import main # <-- Imports routes at the top

def create_app():
app = Flask(name)
#...
init_gemini_model(app) # <-- Initializes the model
app.register_blueprint(main)
return app

The problem is that Python imports routes before create_app is ever called. So my RAG service was importing the GEMINI_MODEL when it was still None.
The one-line fix was to change the import structure (the right way):

def create_app():
app = Flask(name)
#...
init_gemini_model(app)

from . import routes # <-- Import INSIDE the function
app.register_blueprint(routes.main)
return app

By importing the routes after the client is initialized, the circular dependency is broken. The error vanished.
It's a huge reminder that sometimes the bug isn't in your fancy AI logic, but in the fundamentals of the framework you're using. Hope this saves someone else a few hours of head-scratching!

DEV Community: Nikhil Agrawal

Your RAG is Basic. Here's the KG-RAG Pattern We Used to Build a Real AI Agent.

I spent hours debugging a None error in my RAG chatbot. The fix was one line.