codereview-local 0.1.0

Local RAG-based code review CLI. No API keys. Runs fully on your machine.

codereview

A local, privacy-first code review CLI tool powered by RAG and a local LLM. No API keys. No data leaves your machine.

pip install codereview-local
codereview your_file.py

---

How it works

Most code review tools send your code to a remote API. This one runs entirely on your machine.

It uses a RAG (Retrieval-Augmented Generation) pipeline to intelligently select the most relevant parts of your code before sending them to a local LLM for review. This means it scales to large codebases without hitting context window limits.

your code
    │
    ▼
tree-sitter parses into functions/classes
    │
    ▼
sentence-transformers converts chunks to vectors
    │
    ▼
ChromaDB stores all vectors in memory
    │
    ▼
semantic queries retrieve the most relevant chunks
("security vulnerabilities", "missing error handling", ...)
    │
    ▼
local LLM reviews only what matters
    │
    ▼
actionable feedback printed to terminal

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Features

• Fully local — runs on your machine, no API keys, no data sent anywhere
• RAG pipeline — semantic retrieval finds the most relevant code across your entire project
• AST-based chunking — splits by functions and classes using tree-sitter, not arbitrary character counts
• Multi-query retrieval — five semantic queries cast different nets across your codebase
• Any file type — works on Python, JavaScript, JSX, and anything else
• Directory support — review an entire project at once
• Streaming output — see the review as it generates, token by token
• GPU accelerated — embedding model uses CUDA automatically if available

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Requirements

• Python 3.10+
• Ollama installed and running
• A coding model pulled in Ollama

ollama pull qwen3-coder:latest
# or a smaller/faster option:
ollama pull deepseek-coder:6.7b

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Installation

pip install codereview-local

Or from source:

git clone https://github.com/Muhammad-NSQ/codereview
cd codereview
pip install -e .

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Usage

Review a single file:

codereview path/to/file.py

Review an entire directory:

codereview path/to/project/

Use a different model:

codereview path/to/file.py --model deepseek-coder:6.7b

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Example output

$ codereview app/auth.py

📂 Indexing app/auth.py...
   3 chunks indexed
🔎 Running semantic retrieval...
🤖 Reviewing with LLM...

## Critical Security Issues

**SQL Injection Vulnerability**
- Line 3: Direct string concatenation in SQL query
- Fix: Use parameterized queries: db.query("SELECT * FROM users WHERE id = ?", (id,))

**Hardcoded Credentials**
- Line 2: Database password exposed in plain text
- Fix: Use environment variables or a secrets manager

## Runtime Errors

**Division by Zero**
- Line 12: No check for b == 0 before division
- Fix: Add validation: if b == 0: raise ValueError("Cannot divide by zero")

## Bad Practices

**Resource Leak**
- Line 7: File handle opened but never closed
- Fix: Use context manager: with open(path) as f:

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Tech stack

Component	Library	Purpose
CLI	Typer	Command line interface
AST parsing	tree-sitter	Split code by functions/classes
Embeddings	sentence-transformers	Convert code to vectors
Vector DB	ChromaDB	Store and search embeddings
LLM	Ollama	Local language model inference
HTTP	requests	Talk to Ollama API

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Why RAG for code review?

The naive approach — dump the entire file into the LLM — breaks on large codebases. A 2000-line file with 80 functions easily exceeds most models' context windows.

The RAG approach — index everything, retrieve only what's relevant, send a focused context to the LLM. Five semantic queries target different problem categories:

• Security vulnerabilities and injection attacks
• Missing error handling and uncaught exceptions
• Resource leaks and connection management
• Bad practices and code smells
• Input validation and type safety

All matching chunks from all files share one ChromaDB collection, so the retrieval competes across your entire codebase — not file by file.

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Project structure

codereview/
├── codereview/
│   ├── __init__.py
│   ├── chunker.py      # tree-sitter AST parsing
│   ├── embedder.py     # sentence-transformers embeddings
│   ├── retriever.py    # ChromaDB storage and retrieval
│   ├── reviewer.py     # Ollama LLM integration
│   └── cli.py          # Typer CLI and pipeline orchestration
├── main.py
└── setup.py

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Author

Muhammad — GitHub