Running Your Own Private Librarian: Building a Calibre-Integrated Book Recommender with Ollama
Hey there, fellow bookworms & tech enthusiasts! Ever stared at your massive Calibre library, filled with hundreds, maybe thousands, of ebooks, & felt that familiar pang of "what on earth do I read next?" Yeah, me too. It's the paradox of choice, right? You've got this incredible, personally curated digital library, but finding the perfect next read can feel like searching for a specific needle in a haystack the size of a small country.
What if I told you that you could build your own, hyper-personalized book recommender? One that lives right on your computer, understands the nuances of your library, & doesn't send your reading data off to some corporate server in the cloud. We're talking about a system that's all yours, powered by the magic of local large language models (LLMs).
Honestly, it's not as complicated as it sounds. In this post, I'm going to walk you through how to create a Calibre-integrated book recommender using a pretty cool tool called Ollama. We'll be diving into the nitty-gritty of how to pull your book data from Calibre, use an LLM to understand what your books are about, & then build a simple system to get some surprisingly good recommendations. Let's get started.
The Big Idea: Your Books, Your AI, Your Rules
Here's the thing about most recommendation algorithms: they're black boxes. You don't really know why they're suggesting a particular book, & they're often based on what's popular, not necessarily what's a good fit for you.
Our approach is going to be different. We're going to build a system that's based on the content of your books. We'll be using a technique called "semantic search," which is a fancy way of saying we're going to find books that are similar in meaning, not just in keywords. This is where Ollama comes in.
Ollama, for those who haven't heard of it, is a fantastic tool that lets you run powerful open-source LLMs right on your own computer. Think of it as having your own private ChatGPT, but without the privacy concerns. It's free, it's open-source, & it's surprisingly easy to set up.
Calibre, as you probably already know, is the undisputed champion of ebook management. It's a free & open-source powerhouse that lets you organize, convert, & edit your ebooks. But what many people don't realize is that Calibre also has a powerful database under the hood, & we can tap into that to get the data we need for our recommender.
So, the plan is this:
- Extract Your Book Data from Calibre: We'll use a neat little trick to get a list of all your books, along with their titles, authors, & descriptions.
- Turn Your Books into "Embeddings": This is where the magic happens. We'll use Ollama to create "vector embeddings" for each of your books. These are basically numerical representations of your books' content, & they're what will allow us to find similar books.
- Build a Simple Recommender: We'll write a Python script that takes a book you like as input, finds its embedding, & then searches for the most similar embeddings in your library.
It's a pretty cool project, & by the end, you'll have a much deeper understanding of how LLMs & recommender systems work. Plus, you'll have a killer tool to help you rediscover the gems hiding in your own library.
Step 1: Getting Your House in Order - Setting up Ollama & Python
First things first, we need to get our tools ready. This is the "mise en place" of our coding adventure.
Installing Ollama:
Getting Ollama up & running is a breeze. Just head over to their website (ollama.com) & download the installer for your operating system. Once it's installed, you'll want to pull a model. I'd recommend starting with a smaller, more general-purpose model like
. You can do this by opening your terminal & running: