English is the new programming language;
Generative AI is the new compiler;
Python is ... the new byte code.

Installation

pip install pyspark-ai

Configuring OpenAI LLMs

As of June 2023, our extensive testing suggests the most effective utilization with the English SDK and GPT-4.

To use OpenAI's Language Learning Models (LLMs), you can set your OpenAI secret key as the OPENAI_API_KEY environment variable. This key can be found in your OpenAI account. Example:

export OPENAI_API_KEY='sk-...'

By default, the SparkAI instances will use the GPT-4 model. However, you're encouraged to experiment with creating and implementing other LLMs, which can be passed during the initialization of SparkAI instances for various use-cases.

Usage

Initialization

from pyspark_ai import SparkAI

spark_ai = SparkAI()
spark_ai.activate()  # active partial functions for Spark DataFrame

Data Ingestion

If you have set up the Google Python client, you can ingest data via search engine:

auto_df = spark_ai.create_df("2022 USA national auto sales by brand")

Otherwise, you can ingest data via URL:

auto_df = spark_ai.create_df("https://www.carpro.com/blog/full-year-2022-national-auto-sales-by-brand")

Take a look at the data:

auto_df.show(n=5)

rank	brand	us_sales_2022	sales_change_vs_2021
1	Toyota	1849751	-9
2	Ford	1767439	-2
3	Chevrolet	1502389	6
4	Honda	881201	-33
5	Hyundai	724265	-2

Plot

auto_df.ai.plot()

To plot with an instruction:

auto_df.ai.plot("pie chart for US sales market shares, show the top 5 brands and the sum of others")

DataFrame Transformation

auto_top_growth_df=auto_df.ai.transform("brand with the highest growth")
auto_top_growth_df.show()

brand	us_sales_2022	sales_change_vs_2021
Cadillac	134726	14

DataFrame Explanation

auto_top_growth_df.ai.explain()

In summary, this dataframe is retrieving the brand with the highest sales change in 2022 compared to 2021. It presents the results sorted by sales change in descending order and only returns the top result.

DataFrame Attribute Verification

auto_top_growth_df.ai.verify("expect sales change percentage to be between -100 to 100")

result: True

UDF Generation

@spark_ai.udf
def previous_years_sales(brand: str, current_year_sale: int, sales_change_percentage: float) -> int:
    """Calculate previous years sales from sales change percentage"""
    ...
    
spark.udf.register("previous_years_sales", previous_years_sales)
auto_df.createOrReplaceTempView("autoDF")

spark.sql("select brand as brand, previous_years_sales(brand, us_sales, sales_change_percentage) as 2021_sales from autoDF").show()

brand	2021_sales
Toyota	2032693
Ford	1803509
Chevrolet	1417348
Honda	1315225
Hyundai	739045

Cache

The SparkAI supports a simple in-memory and persistent cache system. It keeps an in-memory staging cache, which gets updated for LLM and web search results. The staging cache can be persisted through the commit() method. Cache lookup is always performed on both in-memory staging cache and persistent cache.

spark_ai.commit()

Refer to example.ipynb for more detailed usage examples.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

Licensed under the Apache License 2.0.