
palimpzest
A System for Optimized Semantic Computation
Stars: 144

Palimpzest (PZ) is a tool for managing and optimizing workloads, particularly for data processing tasks. It provides a CLI tool and Python demos for users to register datasets, run workloads, and access results. Users can easily initialize their system, register datasets, and manage configurations using the CLI commands provided. Palimpzest also supports caching intermediate results and configuring for parallel execution with remote services like OpenAI and together.ai. The tool aims to streamline the workflow of working with datasets and optimizing performance for data extraction tasks.
README:
Our full documentation is the definitive resource for learning how to use PZ. It contains all of the installation and quickstart materials on this page, as well as user guides, full API documentation, and much more.
You can find a stable version of the PZ package on PyPI here. To install the package, run:
$ pip install palimpzest
Alternatively, to install the latest version of the package from this repository, you can clone this repository and run the following commands:
$ git clone [email protected]:mitdbg/palimpzest.git
$ cd palimpzest
$ pip install .
We are actively hacking on PZ and would love to have you join our community
Our Discord server is the best place to:
- Get help with your PZ program(s)
- Give feedback to the maintainers
- Discuss the future direction(s) of the project
- Discuss anything related to data processing with LLMs!
We are eager to learn more about your workloads and use cases, and will take them into consideration in planning our future roadmap.
The easiest way to get started with Palimpzest is to run the quickstart.ipynb
jupyter notebook. We demonstrate the full workflow of working with PZ, including registering a dataset, composing and executing a pipeline, and accessing the results.
To run the notebook, you can use the following command:
$ jupyter notebook
And then access the notebook from the jupyter interface in your browser at localhost:8888
.
For eager readers, the code in the notebook can be found in the following condensed snippet. However, we do suggest reading the notebook as it contains more insight into each element of the program.
import palimpzest as pz
# define the fields we wish to compute
email_cols = [
{"name": "sender", "type": str, "desc": "The email address of the sender"},
{"name": "subject", "type": str, "desc": "The subject of the email"},
{"name": "date", "type": str, "desc": "The date the email was sent"},
]
# lazily construct the computation to get emails about holidays sent in July
dataset = pz.Dataset("testdata/enron-tiny/")
dataset = dataset.sem_add_columns(email_cols)
dataset = dataset.sem_filter("The email was sent in July")
dataset = dataset.sem_filter("The email is about holidays")
# execute the computation w/the MinCost policy
config = pz.QueryProcessorConfig(policy=pz.MinCost(), verbose=True)
output = dataset.run(config)
# display output (if using Jupyter, otherwise use print(output_df))
output_df = output.to_df(cols=["date", "sender", "subject"])
display(output_df)
Below are simple instructions to run PZ on a test data set of enron emails that is included with the system.
To run the provided demos, you will need to download the test data. Due to the size of the data, we are unable to include it in the repository. You can download the test data by running the following command from a unix terminal (requires wget
and tar
):
chmod +x testdata/download-testdata.sh
./testdata/download-testdata.sh
Set your OpenAI (or Together.ai) api key at the command line:
# set one (or both) of the following:
export OPENAI_API_KEY=<your-api-key>
export TOGETHER_API_KEY=<your-api-key>
Now you can run the simple test program with:
$ python demos/simple-demo.py --task enron --dataset testdata/enron-eval-tiny --verbose
If you would like to cite our work, please use the following citation:
@inproceedings{palimpzestCIDR,
title={Palimpzest: Optimizing AI-Powered Analytics with Declarative Query Processing},
author={Liu, Chunwei and Russo, Matthew and Cafarella, Michael and Cao, Lei and Chen, Peter Baile and Chen, Zui and Franklin, Michael and Kraska, Tim and Madden, Samuel and Shahout, Rana and Vitagliano, Gerardo},
booktitle = {Proceedings of the {{Conference}} on {{Innovative Database Research}} ({{CIDR}})},
date = 2025,
}
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