You ever open a 50-page PDF and think, “Yeah, I’ll just Ctrl+F my way through this mess”? Yeah, same. That frustration is actually what pushed me to build DocuLens, my open-source, AI-powered document analyzer.
Instead of scrolling endlessly, you can just ask your documents questions like, “Hey, where does this paper talk about cloud computing?” And boom, it pulls up the exact snippets you need. Let me take you through how I built it, why it works the way it does, and the little design choices that made it fun.
It’s basically a Python project that lets you throw in a bunch of text documents and it parses, analyzes, and even answers questions about them. Think of it as your nerdy friend who not only reads all your docs but also remembers the interesting bits and gives you smart summaries whenever you ask.
The Big Idea
We all deal with information overload. Articles, PDFs, notes, research papers, even random .txt files floating around on our laptops. But digging through them to find “that one definition” is painful.
So I thought, what if I could build a mini tool that organizes these docs into a library, analyzes them for keywords, and lets me query them like I’m talking to a search engine?
The Workflow
Here’s what the Document Analyzer does under the hood, step by step:
- Collects all your documents: I made a documents/folder where you can dump .txt files.
- Creates a library: this is where LLMware organizes and indexes everything.
- Parses + Analyzes: word count, character count, keyword spotting, and previews.
- Lets you Query: you can ask stuff like “machine learning” or “cloud computing”, and it fetches relevant snippets.
- Shows you stats: how many docs, how big your library is, and other geeky numbers.
- (Bonus): Lists available LLMware models you can experiment with.
It’s like a pipeline: dump -> organize -> analyze -> query -> summarize.
Document Analysis: The Fun Part
The script goes through each document and does a little detective work:
- Word + character counts: quick way to size up a file.
- Keyword hunting: I picked some techy keywords like AI, technology, computing, machine learning. If the doc mentions them, it flags them.
- Summary previews: it grabs the first few sentences so you instantly know what the doc is about without opening it.
📖 Analyzing: future_of_AI.txt
📊 Word count: 1450
📊 Character count: 8320
🔑 Keywords found: AI, machine learning
📝 Preview: Artificial Intelligence is reshaping industries worldwide. From healthcare to finance...
It makes it feel as if your documents are talking back to you.
Querying Your Docs
This is where it gets really cool. Once your library is built, you can ask it questions.
I set up a few sample queries like:
“artificial intelligence”
“cloud computing”
“technology trends”
And the analyzer actually surfaces passages from your docs that match.
So instead of manually searching 10 different files, you just query the library and get back smart snippets. It’s not ChatGPT-level conversation (yet), but it’s super handy for quick lookups.
Imagine being a student with 20 readings due tomorrow (rip), this thing can save you hours.
Playing with Models
Another thing I added was the ability to list available LLMware models. Just to see what’s out there.
It prints out the first 5 models from the catalog with their names and families, so if you’re in the mood to experiment, you can try different ones.
Why I Built It
For me, this project was less about reinventing the wheel and more about learning how to glue together LLMs and document workflows.
I wanted to:
- Understand how libraries like LLMware manage text.
- Build a tool that feels useful right out of the box.
- Get comfortable mixing “classic” text processing (counts, keywords) with “AI-powered” querying.
And honestly? I learned way more by making this than I would’ve by just following a tutorial.
Final Thoughts
At its heart, this Document Analyzer is simple: it reads, it analyzes, it helps you find stuff faster. But it’s also a stepping stone, proof that with the right tools, you can go from raw text files to something that feels a lot smarter in just a few lines of Python.
If you’ve been curious about playing with LLMs but don’t want to dive straight into building a chatbot, this kind of project is the perfect starter. You’ll learn about parsing, querying, model catalogs, and you’ll end up with something you can actually use.
You can find it here: https://github.com/HavelCS/llmware-document-analyzer1
So yeah, that’s the journey. Hope it inspires you to build your own quirky AI-powered tools. Because honestly? That’s half the fun.
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