How to Chat With Your Codebase Locally and Privately, No Code Leaves Your Machine

2026-06-26 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cybersecurity & Data Privacy · Depth: Intermediate, long

Summary

A guide details how to build a local, private AI assistant for codebases, addressing common issues with cloud-based tools such as hallucinations and proprietary code exposure. This solution indexes an entire repository, providing answers grounded in actual code without transmitting any data off-machine. A critical element for its effectiveness is "structure-aware chunking," which splits code by natural boundaries like functions or classes, rather than fixed-size blocks, to maintain context. The article outlines two implementation paths: utilizing existing open-source tools like Continue, which integrates with editors like VS Code and JetBrains, or constructing a custom pipeline for greater control over chunking and indexing. The setup involves installing Ollama to run local models, pulling a dedicated embedding model like nomic-embed-text, and selecting a code-focused chat model (e.g., qwen2.5-coder:14b or qwen3-coder:30b) based on hardware capabilities (16GB memory for 14B model, 24GB GPU for 30-33B model). While local inference can be slower, it offers near-zero latency and ensures privacy for sensitive projects.

Key takeaway

For AI Engineers or Machine Learning Engineers working with proprietary or sensitive code, building a local AI assistant is crucial to avoid data exposure and improve accuracy. You should prioritize structure-aware code chunking, splitting by functions or classes, as this significantly enhances model performance. Consider using Ollama with nomic-embed-text and a suitable code model (e.g., qwen2.5-coder:14b) via tools like Continue, or build a custom RAG pipeline, to ensure your code remains private and responses are grounded in your actual repository.

Key insights

Local, private AI code assistants prevent hallucinations and data leaks by indexing code with structure-aware chunking.

Principles

• Code chunking must follow natural boundaries.
• Retrieval Augmented Generation (RAG) grounds AI answers in real code.
• Local models offer privacy for sensitive code.

Method

Install Ollama, pull nomic-embed-text and a code-focused chat model. Choose between using an existing tool like Continue or building a custom RAG pipeline with a vector database, ensuring structure-aware code chunking.

In practice

• Use Ollama to run local LLMs.
• Configure Continue with local models for editor integration.
• Implement function-aware chunking for custom RAG.

Topics

• Local LLMs
• Codebase AI Assistant
• Retrieval-Augmented Generation
• Structure-aware Chunking
• Ollama
• Code Privacy
• Continue

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.