cyyrus 0.14.0

Transform Unstructured Data into Usable Datasets

🚧🚨 Please note that Cyyrus is currently in active development as we prepare for its public release 🚧🚨

Why

World has plenty of data, but most of it is trapped in formats that are difficult to utilize. We’re talking about messy relational databases, unstructured text, audio, video, even the latent space of LLMs. It's not a goldmine; it's a landfill. And we're spending millions trying to clean it up.

Introducting Cyyrus

DataOps today looks a mix of ClickOps, CryOps and PrayOps. You're either clicking your life away, crying over broken pipelines, or praying your dataset doesn't implode. Fun times, right?

Cyyrus aims to do for datasets, what Terraform did for Infrastructure. It transforms unstructured data into usable datasets.

Task	Description	Status	Future Work
Parsing	Parse unstructured data sources (PDF, images)	Beta	Add support for more document types, audio, and video
Generation	Generate data using Language Models	Added OpenAI integration	Implement deterministic and rule-based generators
Extraction	Seed data from existing datasets (CSV, JSON, etc.)	In development	Coming Soon
Scraping	Collect and seed data from web sources	In development	Coming Soon
Labelling	Enrich image datasets for detection, classification, and segmentation	To Be Determined	Coming Soon

Cyyrus does't make assumptions about your data because it doesn't have to. It handles it all. You can create datasets by composing these tasks into one another, enabling declarative dataset generation pipelines. Think of them like Lego bricks.

Sure but doesn't X do this already? Yeah, maybe. The market is saturated with products like these but these tools don't solve data silos; they create new ones.

Cyyrus doesn't bundle an analytics product, it plans to charge for transformations and checkpoints, not data storage so it benefits with data movement. Put simply, the more your data moves, the happier we are. Not the other way round.

We've put together a neat documentation. Check them out (Shoutout to our hyper-cafinated devs prompting cursor day in and out :D).

Quick Start

We built Cyyrus so that it doesn’t need a manual to operate, but here’s one anyway. Let's get you upto speed. You'd be a pro in no time.

grug dev express lane

We get it. This is cool. But Writing SCHEMA? Manually? In 2024? Bruh, are you serious?

You know what's fun? Literally anything else. You know what's not? Staring at a CLI, questioning your life choices as you painstakingly create yet another YAML file.

So here's the deal: We made a notebook that does the heavy lifting for you. Why? Because we're not sadists, and you're not a masochist (we hope).

Incase we got your hopes high, spoiler alert, this isn't perfect. But gives you a great start. We have custom components. You'd need to know which buttons to smash, and you're golden. Hell, we even made guides for the button-smashing part.

The notebook is so good, you might actually enjoy creating schemas. (Don't worry, we won't tell anyone.) Go ahead. Click it. Or stick around for the rest of the quickstart.

Installation

Alright, baby steps. Install Cyyrus with pip:

pip install cyyrus

Configuration

With Cyyrus, you can configure your schema using a simple YAML file. This YAML defines the structure, properties, and types of the dataset. Think of it as poor man's HCL.

1. Define Your Tasks: First step is to define the tasks that will be used in the dataset generation process. These tasks can include parsing, extraction, or something else.

   tasks:
       # Define the invoice parsing task
       invoice_parsing:
           task_type: parsing
           task_properties:
               directory: experimental/sample
               file_type: pdf
               max_depth: 5
               parsed_format: base64
   
       # Define the customer info extraction task
       extract_customer_info:
           task_type: generation
           task_properties:
               model: gpt-4o-mini
               prompt: Extract customer info from the given invoice
               response_format: customer_info
               api_key: $OPENAI_API_KEY
   

2. Define Your Types: Once the tasks are defined, the next step is to define the types that will be used in the dataset. These types can include objects, arrays, or any other data type.

   types:
       customer_info:
           type: object
           properties:
               customer_name:
                   type: string  # What do we call this hooman?
               customer_address:
                   type: string  # Where do we find them?
               invoice_id:
                   type: string  # The unique identifier for their financial pain
               total_amount:
                   type: float   # How much do they owe us for our digital crack?
   

3. Define Your Columns: Columns are the attributes of the dataset.

   columns:
       # Define the parsed invoice column
       parsed_invoice:
           task_id: invoice_parsing # Associate a task_id with the column
   
       # Define the customer info column
       customer_info:
           task_id: extract_customer_info
           task_input: [parsed_invoice]
           # Define the input for the task.
   
       # Define the invoice items column
       invoice_items:
           task_id: extract_invoice_items
           task_input: [parsed_invoice]
   
       # Define the invoice qna column
       invoice_qna:
           task_id: create_invoice_qna
           task_input: [invoice_items, customer_info]
           # Ensures the task is executed after the task_input is available
   

4. Define Your Datasets: Datasets are defined by the metadata, splits, attributes, and shuffle properties.

       # Define the properties of the dataset
   dataset:
       # Define the metadata of the dataset
       metadata:
           name: Invoice Dataset
           description: Dataset containing the invoice data
           tags: [invoice, financial, document]
           license: CC-BY-NC-SA
           languages: [en]
   
       # Define how to shuffle the dataset
       shuffle:
           seed: 42
   
       # Define the splits of the dataset
       splits:
           train: 0.8
           test: 0.2
           seed: 42
   
       # Define the attributes of the dataset
       attributes:
           required_columns: [invoice_items, customer_info]
           unique_columns: []
           flatten_columns: [invoice_items, invoice_qna]
           exclude_columns: [parsed_invoice]
           nulls: include
   

   

Generation

Now comes the exciting part. With our schema in place, we’re ready to generate data.

cyrus run --schema-path path/to/your/schema.yaml --env-path path/to/your/.env

Cyrus processes each column defined in your schema, handling dependencies, types, error cases, and one-to-many mappings. You’ll see progress bars and logs for each step:

2024-08-26 16:01:14,096 - cyrus.composer.core - INFO - Preparing column: parsed_invoice
2024-08-26 16:01:14,097 - cyrus.composer.core - INFO - Executing task: TaskType.PARSING
100%|█████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.09s/it]
2024-08-26 16:01:22,191 - cyrus.composer.core - INFO - Preparing column: customer_info
2024-08-26 16:01:22,191 - cyrus.composer.core - INFO - Executing task: TaskType.GENERATION
100%|█████████████████████████████████████████████████| 11/11 [00:44<00:00,  4.03s/it]
...

Preview

And just like that you unf*ucked unstructured data. Pretty, ain't it?

Exports

But we’re not done yet! After generation, you’ll have the option to export your dataset:

Ready to export the dataset? [y/N]: y
Enter the export directory [/Users/Code/cyrus]: export
Enter the export format (huggingface, json, csv, pickle, parquet) [huggingface]: json
Enter a name for your dataset (How about: Pierce_Macadamia ?) [Pierce_Macadamia]: invoice

Choose your flavor - JSON, CSV, pickle, parquet - Cyrus has got you covered.

Publishing

Cyrus is best buddies with Hugging Face, and it’s ready to help you publish your dataset.

Do you want to publish the dataset? [Y/N]: y
HF TOKEN found in environment. Use 'hf_PT...NFTJu'? [Y/N]: y
Enter the repository identifier: wizenheimer/invoice-dataset
Keep the dataset private? [Y/N]: y

Cyrus handles the upload process, creating the necessary formats and uploading to Hugging Face:

2024-08-26 16:05:35,604 - cyrus.composer.core - INFO - Publishing dataset to Hugging Face: wizenheimer/invoice-dataset
Creating parquet from Arrow format: 100%|████████████████| 1/1 [00:00<00:00, 152.07ba/s]
Uploading the dataset shards: 100%|███████████████████| 1/1 [00:03<00:00,  3.04s/it]
Creating parquet from Arrow format: 100%|████████████████| 1/1 [00:00<00:00, 255.36ba/s]
Uploading the dataset shards: 100%|███████████████████| 1/1 [00:01<00:00,  1.60s/it]
2024-08-26 16:05:42,223 - cyrus.composer.core - INFO - Dataset successfully published to wizenheimer/invoice-dataset
2024-08-26 16:05:42,224 - cyrus.cli.main - INFO - Published dataset to None. Happy sharing!
2024-08-26 16:05:42,224 - cyrus.cli.main - INFO - Dataset published successfully!

And there you have it, folks! your dataset is generated, exported, and published to Hugging Face

Documentation

We've put together a neat documentation. Check out docs.

Feedback

We're here to give developers what they really need, not what looks good in a TechCrunch headline. We've been there. We've felt the pain, and yes, we've even built some of those well-intentioned but ultimately inadequate tools ourselves. Now, we're channeling that into building Cyyrus.

The current Cyyrus package is experimental and built over the weekend to understand if terraforming data ops is viable. Does our approach resonate with you? ? Or do you think we're completely off base?

Don't hold back - we love to talk, and more importantly, we love to listen. Lessgo.

Contributing

Wanna lend us a hand? Great! There are many ways you can make Cyyrus awesome.

1. Improving the docs: Got a knack for explaining stuff? This is your time to shine! Everything is written in [Markdown] to facilitate the process of contributing.
2. Implementing new tasks: Wanna see a new Task? We’re all ears! We love expanding Cyyrus to cover as many use cases as possible.
3. Callout our flaws: Spotted a Bug? Awesome. Make some noise. Help us notice. Tell us all about it! What happened? What did you expect to happen instead?
4. Rev us up: Got a Feature Idea? Awesome! Tell us all about it! Paint us a picture, why it’s useful, and how you think it should work.

Head over to contributor's guide to know how to get started. We absolutely love pull requests!

Adjacent Work

Current tooling around running evaluation, performing finetuning are broken. They are built by optimists, dreamers, and in many cases, brilliant engineers. But they're building tools for a world that doesn't exist - a world where data comes pre-cleaned, perfectly labeled, and ready for AI consumption.

Data is unstructured and messy. These $10/month tools? They're useless for 80% of your data. Sure, the tool costs $10/month. But what about the army of data scientist you need to make your data "tool-ready”.

Cyyrus plans to introduce components which makes existing tools "data-ready". Think react.email for last mile data transformation.

Credits

Created by engineers who have an irrational belief that data transformation doesn't need to be a clusterfuck as it is today.

Terraform made infrastructure sexy. They believe a tool like Cyyrus can make data transformation sexy as well. With their half built, barely functional MVP, they're curious if they can make others believe as well.