Introduction
The English SDK for Apache Spark is an extremely simple yet powerful tool. It takes English instructions and compile them into PySpark objects like DataFrames. Its goal is to make Spark more user-friendly and accessible, allowing you to focus your efforts on extracting insights from your data.
For a more comprehensive introduction and background to our project, we have the following resources:
- Blog Post: A detailed walkthrough of our project.
- Demo Video: 2023 Data + AI summit announcement video with demo.
- Breakout Session: A deep dive into the story behind the English SDK, its features, and future works at DATA+AI summit 2023.
Installation
pyspark-ai can be installed via pip from PyPI:
pip install pyspark-ai
pyspark-ai can also be installed with optional dependencies to enable certain functionality. For example, to install pyspark-ai with the optional dependencies to plot data from a DataFrame:
pip install "pyspark-ai[plot]"
To install all optionall dependencies:
pip install "pyspark-ai[all]"
For a full list of optional dependencies, see Installation and Setup.
Configuring OpenAI LLMs
As of July 2023, we have found that the GPT-4 works optimally with the English SDK. This superior AI model is readily accessible to all developers through the OpenAI API.
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
You can also pass other LLMs to construct the SparkAI instance. For example, by following this guide:
from langchain.chat_models import AzureChatOpenAI
from pyspark_ai import SparkAI
llm = AzureChatOpenAI(
deployment_name=...,
model_name=...
)
spark_ai = SparkAI(llm=llm)
spark_ai.activate() # active partial functions for Spark DataFrame
As per Microsoft's Data Privacy page, using the Azure OpenAI service can provide better data privacy and security.
DataFrame Transformation
Given the following DataFrame df
:
df = spark_ai._spark.createDataFrame(
[
("Normal", "Cellphone", 6000),
("Normal", "Tablet", 1500),
("Mini", "Tablet", 5500),
("Mini", "Cellphone", 5000),
("Foldable", "Cellphone", 6500),
("Foldable", "Tablet", 2500),
("Pro", "Cellphone", 3000),
("Pro", "Tablet", 4000),
("Pro Max", "Cellphone", 4500)
],
["product", "category", "revenue"]
)
You can write English to perform transformations. For example:
df.ai.transform("What are the best-selling and the second best-selling products in every category?").show()
product | category | revenue |
---|---|---|
Foldable | Cellphone | 6500 |
Nromal | Cellphone | 6000 |
Mini | Tablet | 5500 |
Pro | Tablet | 4000 |
df.ai.transform("Pivot the data by product and the revenue for each product").show()
Category | Normal | Mini | Foldable | Pro | Pro Max |
---|---|---|---|---|---|
Cellphone | 6000 | 5000 | 6500 | 3000 | 4500 |
Tablet | 1500 | 5500 | 2500 | 4000 | null |
For a detailed walkthrough of the transformations, please refer to our transform_dataframe.ipynb notebook.
Transform Accuracy Improvement: Vector Similarity Search
To improve the accuracy of transform query generation, you can also optionally enable vector similarity search.
This is done by specifying a vector_store_dir
location for the vector files when you initialize SparkAI. For example:
from pyspark_ai import SparkAI
spark_ai = SparkAI(vector_store_dir="vector_store/") # vector files will be stored in the dir "vector_store"
spark_ai.activate()
Now when you call df.ai.transform as before, the agent will use word embeddings to generate accurate query values.
For a detailed walkthrough, please refer to our vector_similarity_search.ipynb.
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 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
We're delighted that you're considering contributing to the English SDK for Apache Spark project! Whether you're fixing a bug or proposing a new feature, your contribution is highly appreciated.
Before you start, please take a moment to read our Contribution Guide. This guide provides an overview of how you can contribute to our project. We're currently in the early stages of development and we're working on introducing more comprehensive test cases and Github Action jobs for enhanced testing of each pull request.
If you have any questions or need assistance, feel free to open a new issue in the GitHub repository.
Thank you for helping us improve the English SDK for Apache Spark. We're excited to see your contributions!
License
Licensed under the Apache License 2.0.