How to Use Code-Graph-RAG for AI-Powered Code Optimization

Code-Graph-RAG parses your codebase into a Memgraph knowledge graph and uses UniXcoder embeddings to enable natural language queries for identifying refactoring opportunities, dead code removal, and performance optimizations through an interactive CLI workflow.

Code-Graph-RAG (CGR) is an open-source intelligence layer that transforms static codebases into queryable knowledge graphs. Available in the vitali87/code-graph-rag repository, this tool combines Tree-sitter parsing with graph-based retrieval to deliver AI-powered code optimization without requiring manual static analysis expertise.

Core Architecture and Components

The system operates through five interconnected modules defined in the source tree.

Parser and Graph Builder

The codebase_rag/graph_loader.py module orchestrates repository ingestion by traversing the filesystem and invoking Tree-sitter to generate language-agnostic ASTs. For incremental updates, codebase_rag/graph_updater.py modifies the Memgraph instance without requiring full reloads.

Semantic Embedding Engine

Located in codebase_rag/embedder.py, this component generates UniXcoder embeddings for every code symbol. These vectors enable intent-based retrieval, allowing queries like "find all functions that sort a list" to return semantically relevant results even when keywords differ.

Query Interface and CLI

The codebase_rag/cli.py module implements the cgr command-line interface, while cgr/__init__.py exposes the Python SDK. Both interfaces translate natural language into Cypher queries executed against the graph engine.

Real-time Synchronization

The realtime_updater.py daemon monitors filesystem events and triggers codebase_rag/graph_updater.py to keep the Memgraph database synchronized with live code changes.

Getting Started with Code-Graph-RAG

Begin by installing the package and ensuring Memgraph is accessible on your system.

Loading Your Codebase

Execute the load command to parse and index your repository:

cgr load --path /path/to/your/repo

This command invokes codebase_rag/graph_loader.py to walk the directory structure, extract functions, classes, methods, and modules using Tree-sitter, and persist the relationships into Memgraph.

Querying via Natural Language

Use the CLI for ad-hoc investigations:

cgr ask "Which functions have a cyclomatic complexity > 10?"

Or integrate the Python SDK into your automation scripts:

from cgr import GraphClient

client = GraphClient()
result = client.ask("Find all classes that inherit from BaseRepository")
print(result)

Running Optimization Workflows

Trigger the AI-powered optimization analysis with:

cgr optimise --review

This enters an interactive session where the system presents refactoring suggestions—such as loop unrolling, dead-code elimination, or complexity reduction—based on patterns identified in docs/guide/code-optimization.md. Review each suggestion individually before applying.

To commit approved changes without interactivity:

cgr optimise --apply

Enabling Real-time Updates

Maintain graph currency during active development:

cgr watch --path /path/to/your/repo

The watcher process monitors file modifications and delegates incremental updates to codebase_rag/graph_updater.py, ensuring subsequent optimization queries reflect the latest codebase state.

Key Implementation Files

File	Responsibility
codebase_rag/cli.py	Main entry point for the cgr command interface
codebase_rag/graph_loader.py	Walks repositories and builds the initial knowledge graph via Tree-sitter
codebase_rag/graph_updater.py	Handles incremental graph updates for real-time synchronization
codebase_rag/embedder.py	Generates UniXcoder embeddings for semantic code search
realtime_updater.py	Filesystem watcher that triggers graph updates on code changes
docs/guide/code-optimization.md	Defines optimization patterns and best-practice heuristics

Summary

• Install and load: Use cgr load to initialize the Memgraph knowledge graph from your repository via Tree-sitter parsing.
• Query naturally: Leverage UniXcoder embeddings through cgr ask or the Python SDK to search code by intent rather than pattern.
• Optimize interactively: Execute cgr optimise --review to evaluate AI-generated refactoring suggestions before applying them.
• Stay synchronized: Run cgr watch to maintain real-time consistency between your codebase and the knowledge graph.

Frequently Asked Questions

What programming languages does Code-Graph-RAG support?

Code-Graph-RAG supports any language compatible with Tree-sitter, including Python, JavaScript, TypeScript, Go, Rust, and C++. The parser in codebase_rag/graph_loader.py uses Tree-sitter's language-agnostic AST generation to extract symbols uniformly across these languages.

How does the semantic search understand developer intent?

The system utilizes UniXcoder embeddings generated in codebase_rag/embedder.py to capture the semantic meaning of code blocks. This allows queries like "functions that handle authentication" to return contextually relevant results even if the function names and comments do not contain the exact query keywords.

Can I automate optimization without interactive review?

Yes. While cgr optimise --review provides an interactive approval workflow, you can apply all suggested optimizations non-interactively using cgr optimise --apply. This enables integration into CI/CD pipelines for automated code improvement workflows.

How does real-time synchronization affect performance?

The realtime_updater.py daemon uses efficient filesystem watching to trigger incremental updates through codebase_rag/graph_updater.py only when files change. This ensures the Memgraph database remains current without requiring full repository reloads, maintaining query performance during active development.

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