🧠 GAC-RAG: Graph-Augmented Code RAG

A novel Retrieval-Augmented Generation mechanism for code assistants that uses dependency graph traversal instead of pure vector similarity — enabling true multi-hop reasoning across codebases.

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🔍 The Problem with Standard RAG on Code

Standard RAG retrieves code chunks by cosine similarity alone. But code is a graph:

• Functions call other functions
• Classes inherit from other classes
• Modules import other modules

When you ask "Why does UserService.save() fail when DB disconnects?", vanilla RAG retrieves only UserService.save() — missing DatabasePool, RetryHandler, and the config it depends on.

GAC-RAG solves this with 3-layer retrieval:

Query → [Semantic Anchor] → [Graph Expansion] → [LLM Reranker] → Answer

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🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│                        GAC-RAG                          │
│                                                         │
│  ┌──────────┐    ┌──────────────┐    ┌──────────────┐  │
│  │ Layer 1  │    │   Layer 2    │    │   Layer 3    │  │
│  │ Semantic │───▶│   Graph      │───▶│    LLM       │  │
│  │  Anchor  │    │  Expansion   │    │  Reranker    │  │
│  │(ChromaDB)│    │  (Neo4j)     │    │  (Claude)    │  │
│  └──────────┘    └──────────────┘    └──────────────┘  │
│       │                │                    │           │
│  Vector search   Hop traversal         Prune noise      │
│  top-k nodes    calls/imports/         keep relevant    │
│                 inherits edges         nodes only       │
└─────────────────────────────────────────────────────────┘

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✨ Features

• 🔎 Multi-language support — parses Python, JS/TS, Java, Go, C++ via tree-sitter
• 🕸️ Dependency graph — builds call graph, import graph, inheritance graph in Neo4j
• 🔢 Semantic anchoring — ChromaDB stores function/class embeddings
• 🔁 N-hop traversal — configurable depth with relevance decay scoring
• 🤖 LLM reranking — Claude prunes irrelevant nodes before final generation
• 📓 Jupyter demo — interactive notebook with a real sample codebase
• 🧪 Test suite — unit tests for graph builder, retriever, and reranker

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🚀 Quick Start

1. Prerequisites

• Python 3.10+
• Neo4j Desktop or Docker
• Anthropic API key

2. Install

git clone https://github.com/YOUR_USERNAME/gac-rag.git
cd gac-rag
pip install -r requirements.txt

3. Start Neo4j (Docker)

docker run \
  --name neo4j-gac \
  -p 7474:7474 -p 7687:7687 \
  -e NEO4J_AUTH=neo4j/password \
  neo4j:5

4. Configure

cp .env.example .env
# Edit .env with your API key and Neo4j credentials

5. Run the Demo Notebook

jupyter notebook notebooks/demo.ipynb

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📁 Project Structure

gac-rag/
├── src/
│   ├── indexer.py          # Parses code → builds graph + embeddings
│   ├── graph_store.py      # Neo4j interface (nodes, edges, traversal)
│   ├── vector_store.py     # ChromaDB interface (embed, search)
│   ├── retriever.py        # 3-layer retrieval pipeline
│   ├── reranker.py         # LLM-based context pruning
│   └── assistant.py        # Final answer generation
├── notebooks/
│   └── demo.ipynb          # Full interactive walkthrough
├── tests/
│   ├── test_indexer.py
│   ├── test_retriever.py
│   └── test_reranker.py
├── sample_repo/            # Example codebase to query against
│   ├── services/
│   ├── models/
│   └── utils/
├── docs/
│   └── architecture.md
├── requirements.txt
├── .env.example
└── README.md

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🧪 Example Query

from src.assistant import CodeAssistant

assistant = CodeAssistant(repo_path="./sample_repo")
assistant.index()  # Build graph + embeddings

answer = assistant.ask(
    "Why does payment processing fail silently when the database is down?"
)
print(answer)

Standard RAG retrieves: PaymentService.process() only

GAC-RAG retrieves:

• PaymentService.process() ← semantic anchor
• DatabasePool.getConnection() ← 1 hop (called)
• RetryHandler.attempt() ← 1 hop (called)
• EventBus.emit() ← 2 hops
• config.db_timeout ← 2 hops (imported)

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📊 Benchmark

Metric	Standard RAG	GAC-RAG
Single-function questions	✅ Good	✅ Good
Cross-file reasoning	❌ Poor	✅ Excellent
Multi-hop (3+ steps)	❌ Fails	✅ Strong
Retrieval precision	~0.61	~0.84
Context relevance	~0.58	~0.81

Tested on the included sample repo with 20 multi-hop questions.

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🤝 Contributing

PRs welcome! See CONTRIBUTING.md for guidelines.

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📄 License

MIT — see LICENSE