How to Perform AI-Powered Code Optimization with Code-Graph-RAG: A Complete Guide

Code-Graph-RAG combines deterministic property graphs with semantic embeddings to find, analyze, and automatically optimize code using LLMs.

AI-powered code optimization requires more than feeding raw files to a language model. The vitali87/code-graph-rag repository solves this with a two-layer architecture: a deterministic graph that maps exact symbols and relationships, plus a vector store that surfaces semantically similar patterns. This guide walks through the complete workflow—from graph construction to automated patch application—using the actual source code implementation as implemented in vitali87/code-graph-rag.

The Two-Layer Architecture for AI Code Optimization

Code-Graph-RAG separates deterministic structure from semantic similarity. This separation ensures reproducible results while enabling AI-driven insights.

Layer	Responsibility	Key File
Property Graph	Maps functions, classes, and their relationships with sorted, reproducible queries	codebase_rag/graph_query.py
Vector Store	Caches semantic embeddings for similarity search across code fragments	codebase_rag/embedder.py, codebase_rag/vector_store.py

The graph layer uses Cypher-style queries to resolve exact symbols. The embedding layer retrieves analogous patterns for LLM context. Together, they feed a structured optimization prompt that produces reviewable, automatically applicable patches.

Step 1: Build and Load the Code Graph

Graph construction starts with tree-sitter parsers that handle C, C++, C#, Go, Java, JavaScript, Python, Rust, and more. The codebase_rag/graph_updater.py module extracts nodes (functions, classes, modules) and edges (calls, imports, references), then serializes to JSON.

Load an existing graph with GraphLoader:

from codebase_rag.graph_loader import load_graph

graph = load_graph("/path/to/my_repo.graph.json")

The loader in codebase_rag/graph_loader.py indexes nodes by id, label, and property for fast lookups without reloading the full structure on every query.

Step 2: Locate Your Optimization Target

Use deterministic queries in codebase_rag/graph_query.py to pinpoint exact symbols. The resolve function finds functions by qualified name, while callers and callees map dependency relationships:

from codebase_rag.graph_query import resolve

# Find the exact function node to optimize

func_rows = resolve(graph.load, project_name="my_repo", target="utils.cleanup")
target = func_rows[0]          # deterministic, sorted result

source = target["source"]      # raw source code string

All query results are sorted for reproducibility—critical for CI/CD integration where identical inputs must yield identical outputs.

Step 3: Retrieve Semantically Similar Patterns

The embedding layer in codebase_rag/embedder.py manages caching and model selection. The _cache_namespace ensures embeddings are isolated by provider and model, preventing cross-contamination:

from codebase_rag.embedder import get_embedding_cache

cache = get_embedding_cache()
embedding = cache.get(source) or cache.put(source, cache.get(source))

Query the vector store (backed by Qdrant) for nearest neighbors:

from codebase_rag.vector_store import vector_store

similar = vector_store.nearest(embedding, k=5)

These similar snippets provide the LLM with proven patterns from your own codebase, reducing hallucinated optimizations.

Step 4: Construct the Optimization Prompt

Combine the target function with retrieved examples into a structured prompt. The embedding layer supports OpenAI and UnixCoder models; configure your provider in codebase_rag/config.py:

prompt = f"""\
You are an expert Python engineer. Optimize the following function for speed
and readability while preserving its behaviour. Also, keep the same
public API.

Original:
{source}

Similar patterns (for inspiration):
{chr(10).join([s['source'] for s in similar])}
"""

Send to your LLM through the project's client wrapper:

from codebase_rag.llm import ask_llm

optimised_code = ask_llm(prompt)

Step 5: Apply the Patch Automatically

The editing pipeline in codebase_rag/editing/patcher.py and codebase_rag/editing/transaction.py translates LLM suggestions into concrete source changes. PatchTransaction manages atomicity, allowing rollback if verification fails:

from codebase_rag.editing.patcher import apply_patch

apply_patch(
    file_path=target["path"],
    old_source=source,
    new_source=optimised_code,
    description="AI‑driven optimisation of utils.cleanup"
)

The patcher uses AST-aware diffs to minimize merge conflicts and preserve formatting outside the optimized region.

Complete AI-Powered Optimization Workflow

Here's the full integration as implemented in vitali87/code-graph-rag:

from codebase_rag.graph_loader import load_graph
from codebase_rag.graph_query import resolve
from codebase_rag.embedder import get_embedding_cache
from codebase_rag.vector_store import vector_store
from codebase_rag.llm import ask_llm
from codebase_rag.editing.patcher import apply_patch

# 1. Load the graph

graph = load_graph("/path/to/my_repo.graph.json")

# 2. Resolve target function

func_rows = resolve(graph.load, project_name="my_repo", target="utils.cleanup")
target = func_rows[0]
source = target["source"]

# 3. Get embedding (cached)

cache = get_embedding_cache()
embedding = cache.get(source) or cache.put(source, cache.get(source))

# 4. Find similar patterns

similar = vector_store.nearest(embedding, k=5)

# 5. Prompt LLM for optimization

prompt = f"""\
You are an expert Python engineer. Optimize the following function for speed
and readability while preserving its behaviour. Also, keep the same
public API.

Original:
{source}

Similar patterns (for inspiration):
{chr(10).join([s['source'] for s in similar])}
"""
optimised_code = ask_llm(prompt)

# 6. Apply patch

apply_patch(
    file_path=target["path"],
    old_source=source,
    new_source=optimised_code,
    description="AI‑driven optimisation of utils.cleanup"
)

Why Deterministic Graphs Matter for CI/CD

Deterministic outputs distinguish Code-Graph-RAG from naive RAG approaches. The codebase_rag/graph_query.py module sorts all query results, and the embedding cache uses namespaced keys. Repeated runs with identical inputs produce identical retrieval sets—essential for:

• Reproducible optimization reports
• Automated regression testing of the optimization pipeline itself
• Safe integration into build systems without nondeterministic failures

Key Files for AI-Powered Code Optimization

Component	File	Purpose
Graph construction	codebase_rag/graph_updater.py	Parses source, extracts symbols, writes JSON graph
Graph access	codebase_rag/graph_loader.py	Lazy loading, indexed lookups
Deterministic queries	codebase_rag/graph_query.py	resolve, callers, callees, test_reachable
Embeddings	codebase_rag/embedder.py	EmbeddingCache, provider selection, batching
Vector search	codebase_rag/vector_store.py	Qdrant interface for nearest-neighbor queries
Patch application	codebase_rag/editing/patcher.py	apply_patch, AST-aware diffs
Transaction safety	codebase_rag/editing/transaction.py	PatchTransaction for atomic changes
Configuration	codebase_rag/config.py	Model providers, vector DB settings
CLI entry	main.py	Top-level orchestration

Summary

• Code-Graph-RAG enables AI-powered code optimization through a deterministic property graph paired with semantic embeddings
• The graph layer (graph_query.py) provides exact symbol resolution with sorted, reproducible results
• The embedding layer (embedder.py, vector_store.py) retrieves semantically similar patterns for LLM context
• The editing pipeline (patcher.py, transaction.py) converts LLM suggestions into verified, atomic source patches
• Cache namespacing and sorted queries ensure deterministic, CI-safe optimization workflows

Frequently Asked Questions

What programming languages does Code-Graph-RAG support?

Code-Graph-RAG supports C, C++, C#, Go, Java, JavaScript, Python, Rust, and additional languages through tree-sitter parsers as implemented in codebase_rag/graph_updater.py. The modular parser architecture allows extension to new languages by adding the corresponding tree-sitter grammar.

How does the deterministic query engine ensure reproducible results?

The codebase_rag/graph_query.py module sorts all Cypher-style query results before returning them. Combined with the _cache_namespace isolation in codebase_rag/embedder.py, this guarantees that identical inputs produce identical retrieval sets across repeated runs—critical for CI/CD integration.

Can I use a different embedding model or vector database?

Yes. The codebase_rag/embedder.py module supports pluggable providers including OpenAI and UnixCoder through configuration in codebase_rag/config.py. The codebase_rag/vector_store.py interface abstracts the underlying database; Qdrant is the default implementation but compatible alternatives can be substituted.

What happens if the LLM generates invalid or broken code?

The codebase_rag/editing/patcher.py module parses the LLM response into structured Patch objects, and codebase_rag/editing/transaction.py wraps changes in PatchTransaction for atomic application. Failed patches can be rejected or rolled back before reaching your codebase, with the description field tracking each optimization attempt for audit purposes.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}