LlamaFS

Inspiration

Open your ~/Downloads directory. Or your Desktop. It's probably a mess...

There are only two hard things in Computer Science: cache invalidation and naming things.

What it does

LlamaFS is a self-organizing file manager. It automatically renames and organizes your files based on their content and well-known conventions (e.g., time). It supports many kinds of files, including images (through Moondream) and audio (through Whisper).

LlamaFS runs in two "modes" - as a batch job (batch mode), and an interactive daemon (watch mode).

In batch mode, you can send a directory to LlamaFS, and it will return a suggested file structure and organize your files.

In watch mode, LlamaFS starts a daemon that watches your directory. It intercepts all filesystem operations and uses your most recent edits to proactively learn how you rename file. For example, if you create a folder for your 2023 tax documents, and start moving 1-3 files in it, LlamaFS will automatically create and move the files for you!

Uh... Sending all my personal files to an API provider?! No thank you!

It also has a toggle for "incognito mode," allowing you route every request through Ollama instead of Groq. Since they use the same Llama 3 model, the perform identically.

How we built it

We built LlamaFS on a Python backend, leveraging the Llama3 model through Groq for file content summarization and tree structuring. For local processing, we integrated Ollama running the same model to ensure privacy in incognito mode. The frontend is crafted with Electron, providing a sleek, user-friendly interface that allows users to interact with the suggested file structures before finalizing changes.

It's extremely fast! (by LLM standards)! Most file operations are processed in <500ms in watch mode (benchmarked by AgentOps). This is because of our smart caching that selectively rewrites sections of the index based on the minimum necessary filesystem diff. And of course, Groq's super fast inference API. 😉
It's immediately useful - It's very low friction to use and addresses a problem almost everyone has. We started using it ourselves on this project (very Meta).

What's next for LlamaFS

Find and remove old/unused files
We have some really cool ideas for - filesystem diffs are hard...

Installation

Prerequisites

Before installing, ensure you have the following requirements:

Python 3.10 or higher
pip (Python package installer)

Installing

To install the project, follow these steps:

Clone the repository:

git clone https://github.com/iyaja/llama-fs.git

Navigate to the project directory:
```
cd llama-fs
```
Install requirements
```
pip install -r requirements.txt
```
Update your .env Copy .env.example into a new file called .env. Then, provide the following API keys:
- Groq: You can obtain one from here.
- AgentOps: You can obtain one from here.

Groq is used for fast cloud inference but can be replaced with Ollama in the code directly (TODO.)

AgentOps is used for logging and monitoring and will report the latency, cost per session, and give you a full session replay of each LlamaFS call.

(Optional) Install moondream if you want to use the incognito mode
```
ollama pull moondream
```

Usage

To serve the application locally using FastAPI, run the following command

   fastapi dev server.py

This will run the server by default on port 8000. The API can be queried using a curl command, and passing in the file path as the argument. For example, on the Downloads folder:

   curl -X POST http://127.0.0.1:8000/batch \
    -H "Content-Type: application/json" \
    -d '{"path": "/Users/<username>/Downloads/", "instruction": "string", "incognito": false}'

iyaja / llama-fs

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