Stockfish Chatbot with a Mistral LLM
Last Updated: 09/01/25
Live demo below! Example Prompt: “What does the safe_destination do in bitboard.cpp?”
Note: The backend server uses a free tier on Render, which shuts down when it’s not in use. So the first prompt may take 30-60 seconds to return. After the first response, future responses should only take a few seconds.
Introduction
I’ve always been fascinated by chess engines—especially Stockfish, one of the strongest open-source chess engines in the world. As powerful as it is, diving into the Stockfish codebase can be overwhelming. It’s highly optimized, written in C++, and has a ton of subtle logic. For developers and contributors, it can sometimes feel like spelunking through a labyrinth.
That got me thinking: what if we could talk to Stockfish’s source code the same way we talk to ChatGPT? What if a developer could ask “Where is the UCI loop implemented?” or “Show me how evaluation is computed”, and immediately get code snippets and explanations?
That’s the idea behind my project: a Stockfish Chatbot powered by an LLM.
Large Language Models (LLMs) like ChatGPT are great at explaining concepts, but they don’t always have access to the exact source code you care about. If you ask about Stockfish, you might get a decent explanation, but not the actual function definitions or line numbers from the codebase.
That’s where Retrieval-Augmented Generation (RAG) comes in. RAG is a technique where you retrieve relevant documents from a knowledge base (in this case, the Stockfish source code) and then feed those into the LLM as context before it generates an answer. The result is that instead of vague descriptions, the chatbot can show you precise code snippets from Stockfish, explain them, and let you copy them directly.
This project is a proof-of-concept RAG system tailored to Stockfish. It indexes the entire source code, lets you query it in natural language, and provides highlighted, copyable code snippets along with explanations.
The Vision
This project started as a way for me to contribute something meaningful to the open-source community. I’ve been interested in Stockfish for a while, and I wanted to create a tool that could improve the quality of life for developers working on the engine.
The basic concept is simple:
Break Stockfish’s source code into chunks.
Store those chunks alongside vector embeddings.
When a developer asks a question, retrieve the most relevant chunks.
Feed them into a language model (LLM) that can provide context-aware answers.
In short: a chatbot that understands Stockfish’s source code.
Chunking the Source Code
To make this possible, I wrote a script to chunk the Stockfish source code into manageable pieces. Each chunk is embedded using vector representations, which lets me compare semantic similarity between a question and parts of the codebase.
This retrieval-augmented approach means the LLM doesn’t need to memorize all of Stockfish—it can just focus on the snippets that matter for the current question.
Backend: From Hugging Face to Mistral
Backend: From Hugging Face to Mistral
One of the first decisions I had to make was how to actually run an LLM for this project. Since I wanted the chatbot to be deployed and accessible online, I needed something with an API I could call, rather than a model that only ran locally.
At first, I experimented with some free models on Hugging Face. I spun up my own spaces and tried out a couple of options:
Flan-T5 (Base) – around 250 million parameters. It ran relatively quickly (responses came back in a few seconds), but the output quality was terrible. The answers were shallow, off-topic, or completely wrong most of the time.
Phi-2 – about 2.7 billion parameters. This was stronger than Flan, but still not great, and running it on Hugging Face’s free CPU-only tier was painfully slow. Sometimes it would take up to 10 minutes just to generate a response. Clearly not usable for an interactive chatbot.
I quickly discovered that all the good open models were hidden behind one of three walls:
No API access.
GPU-only hosting requirements.
Paid tiers (which didn’t make sense for a proof-of-concept project).
That’s when I found Mistral. Their models weigh in at 7 billion parameters, which puts them in a completely different league compared to the smaller Hugging Face freebies I had tested. More importantly, Mistral provides a free API tier for experimentation, which meant I could deploy my project without worrying about hosting or hardware.
The performance difference was night and day. Mistral was fast, accurate, and much better at reasoning over technical material like Stockfish’s C++ code. Exactly what I needed.
To make everything work in production, I built a lightweight Express.js backend container that called the Mistral API, and deployed it on Render using their free tier.
Frontend: React + GitHub Pages
For the frontend, I used React because it’s lightweight, flexible, perfect for quickly spinning up a UI, and I have experience with it. I styled the chatbot to display syntax-highlighted code blocks, so when the LLM serves a Stockfish snippet, it’s easy to read and copy.
The frontend is deployed on GitHub Pages, which provides free hosting directly from the repository. This setup makes it accessible to anyone with just a browser.
The frontend calls the backend deployed on Render whenever the user submits a question. That backend then reaches out to Mistral’s API, retrieves the answer, combines it with any relevant Stockfish code snippets, and sends it back to the frontend to display.
Proof of Concept (and What’s Next)
Right now, this is very much a proof of concept. It works: you can ask questions about the Stockfish engine and get answers with relevant code snippets. But there’s still a lot of polish to add:
Improving chunking strategies for better retrieval.
Enhancing the UI for usability.
Adding caching or rate limiting for production use.
Experimenting with other LLMs as they become available.
Even in this early state, though, I’m excited about the potential. A tool like this could save Stockfish developers hours of time digging through code, while also serving as an educational resource for anyone curious about how one of the world’s strongest chess engines works.
Closing Thoughts
This project represents something I’ve been wanting to do for a while: combine my interest in open-source software with cutting-edge AI tools to build something useful. It’s not perfect yet, but it’s a step toward making complex codebases more approachable through natural language.
