Malevich

Your toolkit for crafting AI-powered products effortlessly

Malevich is an innovative platform for building, iterating and deploying AI-driven products:

A rich set of pre-built components greatly shortens the gap between an idea and a first run.
You are not limited to our components: easily implement and integrate your own codebase.
Deploying on GPU in a matter of setting one parameter.

Getting Started

Malevich offers a Python package that help you to build your apps and flows. Dive deep into our documentation and explore the enormous possibilities. Let's walkthrough how you can create mesmerizing products with simple lines of code.

Installation

To install Malevich, you should have Python 3.11+ and pip package manager installed. Run the following command to install our package:

python3 -m pip install malevich

Make sure everything is set up:

malevich --help

Install Apps

Malevich works like a package manager allowing you to install apps and use them from within the code. Let's install some apps that will constitute our pipeline:

malevich install spacy openai scrape

Our shelf has plenty to offer, browse through our library and find the apps that meet your needs.

Connect Apps

The real magic happens when apps are interconnected into a complex and useful pipeline. Let's do it by writing a few lines of code:

# flow.py

import os
import pandas as pd
from malevich import collection, flow
from malevich.openai import prompt_completion
from malevich.scrape import scrape_web
from malevich.spacy import extract_named_entities

prompt = """
You are a professional journalist. You've received
news containing many references to different people.
Your task is to understand the roles of these {entities}
and write a brief summary about them. The brief should
include the following information:

- Who is the person?
- What is their role in the news?
- What are the main events they are involved in?

Only include individuals for whom there is sufficient
information in the news. Otherwise, omit their names
entirely from the brief.
"""

@flow()
def write_brief():
   # Scrape some news from the OpenAI blog.
   links = collection(
      'News Links',
      df=pd.DataFrame(
            [
               'https://openai.com/blog/sam-altman-returns-as-ceo-openai-has-a-new-initial-board',
            ], columns=['link']
      )
   )

   # The scraper app will retrieve information from websites specified by XPath —
   # a query language that allows extracting information from markup documents.
   text = scrape_web(
      links,
      config={
         'spider': 'xpath',
         'min_length': 100,
         'max_results': 25,
         'links_are_independent': True,
         'max_depth': 1,
         'spider_cfg': {
            'components': [{
               'key': 'news-text',
               # Specify XPath query.
               'xpath': "//div[@id='content']//text()"
            }],
            'output_type': 'text',
            'include_keys': False
         }
      })

   # Extract names of entities.
   entities = extract_named_entities(
      text, config={
         'output_format': 'list',
         'filter_labels': ['PERSON'],
      }
   )

   # Write a brief about the news using OpenAI API
   # to generate text based on our prompt and extracted names.
   return text, prompt_completion(
      entities,
      config={
         'user_prompt': prompt,
         'openai_api_key': os.getenv('OPENAI_API_KEY'),
      }
   )

if __name__ == '__main__':
   from malevich import CoreInterpreter

   # Create a task for writing a brief.
   pipeline = write_brief()

   # Before running the task, interpret it to make
   # the platform aware of dependencies and execution flow.
   pipeline.interpret(
      CoreInterpreter(
         core_auth=('example', 'Welcome to Malevich!')
      )
   )

   # Execute the task.
   text, brief = pipeline()

   # Save results.
   text.get_df().to_csv('text.csv')
   brief.get_df().to_csv('brief.csv')

Execute

Before executing the code, make sure you have the Open AI API key in your environment:

export OPENAI_API_KEY=<YOUR KEY>

Now, all you need is a simple command to run the script:

python flow.py

Your results are stored in text.csv and brief.csv files.

Deploy

Running scripts is cool, but what if you want to integrate this flow into your own app? That's done by adding just one line:

# ...

if __name__ == '__main__':
    from malevich import CoreInterpreter
    
    # Create a task for writing a brief.
    pipeline = write_brief()
    
    # Before deployment, interpret it to make
    # the platform aware of dependencies and execution flow.
    pipeline.interpret(
      CoreInterpreter(
         core_auth=('example', 'Welcome to Malevich!')
      )
    )

    # Deploy the flow
    print(pipeline.publish().get_url())

By running this script, you will get a link to an HTTP endpoint hosted by Malevich, which can be integrated into your application, be it a desktop, mobile, web app, or any other program. Enjoy the ride!

MalevichAI/malevich