Traditional RAG (Retrieval-Augmented Generation) works by embedding texts into high-dimensional vectors and then retrieving the most similar ones when a user asks a question. It’s effective for small and isolated chunks of knowledge. But as the knowledge base grows complex — especially when the content is interconnected like custom SDK codes or component dependencies — the retrieval becomes fragmented. RAG starts to lose context across related but separate documents.

This is where Graph RAG enters.

Unlike traditional databases built on rigid rows and columns, Neo4j (the graph database that powers Graph RAG) stores data as nodes and edges — each representing entities and their relationships. It’s not just about “what exists,” but “how things are connected.”

This structure is key. Instead of just pulling the top N most similar chunks from a vector search, Graph RAG can follow relational paths between concepts — ensuring connected parts are retrieved together.

For example, Let’s say your knowledge base has these facts:

• Alice likes apples and pears.
• Bob only likes pears.
• Pears grow on trees.

If you use a normal RAG, and you ask, “Who likes fruits that grow on trees?”, it might only match “Alice likes apples” or “Bob likes pears” based on text similarity — missing the deeper connection that pears grow on trees.

Graph RAG store like

(Alice) —[LIKES]→ (Apples)  
(Alice) —[LIKES]→ (Pears)  
(Bob) —[LIKES]→ (Pears)  
(Pears) —[GROW_ON]→ (Trees)

Now, when the query is about “fruits that grow on trees,” it can traverse these links and return both Alice and Bob — because it reasons through relationships, not just word overlap.

Below shows the general data flow, and how to enhance my own AI system with proprietary data:

                 ┌──────────────────────────┐
                 │         User Query        │
                 │  e.g. “How does my SDK    │
                 │   handle sensor fusion?”  │
                 └────────────┬──────────────┘
                              │
                              ▼
                    ┌────────────────┐
                    │ Query Embedder │ ← Embedding model
                    │ (e.g. SBERT)   │ from Hugging Face
                    └────────────────┘
                              │
                              ▼
              ┌──────────────────────────────┐
              │ 1️⃣ Vector Search (FAISS)     │
              │ Finds semantically similar    │
              │ code/doc chunks → node IDs    │
              └─────────────┬────────────────┘
                            │
                            ▼
        ┌────────────────────────────────────────┐
        │ 2️⃣ Graph Database (Neo4j)              │
        │ Nodes = entities (modules, sensors)    │
        │ Edges = relationships (CALLS, USES)    │
        │ Query with Cypher to traverse neighbors│
        └───────────────┬────────────────────────┘
                        │
                        ▼
         ┌────────────────────────────────┐
         │ 3️⃣ Context Assembler           │
         │ Pulls subgraph: relevant nodes │
         │ + connected facts, summaries   │
         └──────────────┬─────────────────┘
                        │
                        ▼
         ┌───────────────────────────────────────┐
         │ 4️⃣ LLM Inference Layer                │
         │ Local LLaMA / Mistral / GPT API       │
         │ Input = compact, structured context    │
         │ Output = reasoning / summary answer    │
         └──────────────┬────────────────────────┘
                        │
                        ▼
              ┌────────────────────────────┐
              │ 5️⃣ Output Interface        │
              │ REST API (FastAPI) or chat │
              │ UI displays LLM response   │
              └────────────────────────────┘

Graph RAG transforms knowledge retrieval from flat similarity to structured reasoning.
It combines embeddings (for understanding meaning) and graphs (for understanding structure).

Here is a minimal, intuitive blueprint to start experimenting.

# Step 1: Import core tools
from sentence_transformers import SentenceTransformer
import faiss
from neo4j import GraphDatabase

# Step 2: Create embeddings for your text/code chunks
docs = ["Alice likes apples", "Bob likes pears", "Pears grow on trees"]
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode(docs)

# Step 3: Build a FAISS index for vector search
index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(embeddings)

# Step 4: Create Neo4j graph for relationships
driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))

with driver.session() as session:
    session.run("""
        CREATE (a:Person {name:'Alice'})
        CREATE (b:Person {name:'Bob'})
        CREATE (p:Fruit {name:'Pears'})
        CREATE (ap:Fruit {name:'Apples'})
        CREATE (t:Tree {name:'Tree'})
        CREATE (a)-[:LIKES]->(p)
        CREATE (a)-[:LIKES]->(ap)
        CREATE (b)-[:LIKES]->(p)
        CREATE (p)-[:GROW_ON]->(t)
    """)

# Step 5: Query: semantic + relational retrieval
query = "Who likes fruits that grow on trees?"
q_emb = model.encode([query])
D, I = index.search(q_emb, k=2)  # top 2 similar docs

# Use Neo4j traversal to connect the pieces
with driver.session() as session:
    result = session.run("""
        MATCH (p:Person)-[:LIKES]->(f:Fruit)-[:GROW_ON]->(t:Tree)
        RETURN p.name AS person, f.name AS fruit
    """)
    for record in result:
        print(record["person"], "likes", record["fruit"], "that grow on trees.")