TimelineBuilder

Table of Content

Setup: how to set up for this repo
Importers: how to create LifeLog entries from several data sources.
- Downloading Digital Data
- Running the importers
Sample Dataset: a sampled set of anonymized data for testing
Data Visualization: a ReactJS-based visualization frontend of the personal timeline
Question Answering: a LLM-based QA engine over the personal timeline
TimelineQA: a synthetic benchmark for evaluating personal timeline QA systems

General Setup

Step 0: Create environment

Install Docker Desktop from this link.
Follow install steps and use the Desktop app to start the docker engine.
Install git-lfs and clone the repo. You may need a conda env to do that:

conda create -n personal-timeline python=3.10
conda activate personal-timeline

conda install -c conda-forge git-lfs
git lfs install

git clone https://github.com/facebookresearch/personal-timeline
cd personal-timeline

Run init script (needs python)

sh src/init.sh

This will create a bunch of files/folders/symlinks needed for running the app. This will also create a new directory under your home folder ~/personal-data, the directory where your personal data will reside.

Step 1: Setting up

For Data Ingestion

Ingestion configs are controlled via parameters in conf/ingest.conf file. The configurations are defaulted for optimized processing and don't need to be changed. You can adjust values for these parameters to run importer with a different configuration.

For Data visualization

To set up a Google Map API (free), follow these instructions.

Copy the following lines to env/frontend.env.list:

GOOGLE_MAP_API=<the API key goes here>

To embed Spotify, you need to set up a Spotify API (free) following here. You need to log in with a Spotify account, create a project, and show the secret.

Copy the following lines to env/frontend.env.list:

SPOTIFY_TOKEN=<the token goes here>
SPOTIFY_SECRET=<the secret goes here>

For Question-Answering

Set up an OpenAI API following these instructions.

Copy the following line to env/frontend.env.list:

OPENAI_API_KEY=<the API key goes here>

Digital Data Importers

Downloading your personal data

We currently support 9 data sources. Here is a summary table:

Digital Services	Instructions	Destinations	Use cases
Apple Health	Link	personal-data/apple-health	Exercise patterns, calorie counts
Amazon	Link	personal-data/amazon	Product recommendation, purchase history summarization
Amazon Kindle	Link	personal-data/amazon-kindle	Book recommendation
Spotify	Link	personal-data/spotify	Music / streaming recommendation
Venmo	Link	personal-data/venmo	Monthly spend summarization
Libby	Link	personal-data/libby	Book recommendation
Google Photos	Link	personal-data/google_photos	Food recommendation, Object detections, and more
Google Location	Link	personal-data/google-timeline/Location History/Semantic Location History	Location tracking / visualization
Facebook posts	Link	personal-data/facebook	Question-Answering over FB posts / photos

If you have a different data source not listed above, follow the instructions here to add this data source to the importer.

GOOGLE PHOTOS and GOOGLE TIMELINE

You can download your Google photos and location (also Gmail, map and google calendar) data from Google Takeout.
The download from Google Takeout would be in multiple zip files. Unzip all the files.
For Google photos, move all the unzipped folders inside ~/personal-data/google_photos/. There can be any number of sub-folders under google_photos.
For Google locations, move the unzipped files to personal-data/google-timeline/Location History/Semantic Location History.

FACEBOOK DATA

Go to Facebook Settings
Click on Download your information and download FB data in JSON format
Unzip the downloaded file and copy the directory posts sub-folder to ~/personal-data/facebook. The posts folder would sit directly under the Facebook folder.

APPLE HEALTH

Go to the Apple Health app on your phone and ask to export your data. This will create a file called iwatch.xml and that's the input file to the importer.
Move the downloaded file to this ~/personal-data/apple-health

AMAZON

Request your data from Amazon here: https://www.amazon.com/gp/help/customer/display.html?nodeId=GXPU3YPMBZQRWZK2 They say it can take up to 30 days, but it took about 2 days. They'll email you when it's ready.

They separate Amazon purchases from Kindle purchases into two different directories.

The file you need for Amazon purchases is Retail.OrderHistory.1.csv The file you need for Kindle purchases is Digital Items.csv

Move data for Amazon purchases to ~/personal-data/amazon folder and of kindle downloads to ~/personal-data/amazon-kindle folder

VENMO

Download your data from Venmo here -- https://help.venmo.com/hc/en-us/articles/360016096974-Transaction-History
Move the data into ~/personal-data/venmo folder.

LIBBY

Download your data from Libby here -- https://libbyapp.com/timeline/activities. Click on Actions then Export Timeline
Move the data into ~/personal-data/libby folder.

SPOTIFY

Download your data from Spotify here -- https://support.spotify.com/us/article/data-rights-and-privacy-settings/ They say it can take up to 30 days, but it took about 2 days. They'll email you when it's ready.
Move the data into ~/personal-data/spotify folder.

Running the code

Now that we have all the data and setting in place, we can either run individual steps or the end-to-end system. This will import your photo data to SQLite (this is what will go into the episodic database), build summaries and make data available for visualization and search.

Running the Ingestion container will add two types of file to ~/personal-data/app_data folder

Import your data to an SQLite DB named raw_data.db
Export your personal data into csv files such as books.csv, exercise.csv, etc.

Option 1:

To run the pipeline end-to-end (with frontend and QA backend), simply run

docker-compose up -d --build

Option 2:

You can also run ingestion, visualization, and the QA engine separately. To start data ingestion, use

docker-compose up -d backend --build

Check progress

Once the docker command is run, you can see running containers for backend and frontend in the docker for Mac UI. Copy the container Id for ingest and see logs by running the following command:

docker logs -f <container_id>

Visualization of the personal timeline

To start the visualization frontend:

docker-compose up -d frontend --build

Running the Frontend will start a ReactJS UI at http://localhost:3000. See here for more details.

We provide an anonymized digital data dataset for testing the UI and QA system, see here for more details.

Question Answer over the personal timeline

The QA engine is based on PostText, a QA system for answering queries that require computing aggregates over personal data.

PostText Reference --- https://arxiv.org/abs/2306.01061:

@article{tan2023posttext,
      title={Reimagining Retrieval Augmented Language Models for Answering Queries},
      author={Wang-Chiew Tan and Yuliang Li and Pedro Rodriguez and Richard James and Xi Victoria Lin and Alon Halevy and Scott Yih},
      journal={arXiv preprint:2306.01061},
      year={2023},
}

To start the QA engine, run:

docker-compose up -d qa --build

The QA engine will be running on a flask server inside a docker container at http://localhost:8085.

See here for more details.

There are 3 options for the QA engine.

ChatGPT: uses OpenAI's gpt-3.5-turbo API without the personal timeline as context. It answers world knowledge question such as what is the GDP of US in 2021 but not personal questions.
Retrieval-based: answers question by retrieving the top-k most relevant episodes from the personal timeline as the LLM's context. It can answer questions over the personal timeline such as show me some plants in my neighborhood.
View-based: translates the input question to a (customized) SQL query over tabular views (e.g., books, exercise, etc.) of the personal timeline. This QA engine is good at answering aggregate queries (how many books did I purchase?) and min/max queries (when was the last time I travel to Japan).

Example questions you may try:

Show me some photos of plants in my neighborhood
Which cities did I visit when I traveled to Japan?
How many books did I purchase in April?

TimelineQA: a benchmark for Question Answer over the personal timeline

TimelineQA is a synthetic benchmark for accelerating progress on querying personal timelines. TimelineQA generates lifelogs of imaginary people. The episodes in the lifelog range from major life episodes such as high school graduation to those that occur on a daily basis such as going for a run. We have evaluated SOTA models for atomic and multi-hop QA on the benchmark.

Please check out the TimelineQA github repo and the TimelineQA paper --- https://arxiv.org/abs/2306.01061:

@article{tan2023timelineqa,
  title={TimelineQA: A Benchmark for Question Answering over Timelines},
  author={Tan, Wang-Chiew and Dwivedi-Yu, Jane and Li, Yuliang and Mathias, Lambert and Saeidi, Marzieh and Yan, Jing Nathan and Halevy, Alon Y},
  journal={arXiv preprint arXiv:2306.01069},
  year={2023}
}

License

The codebase is licensed under the Apache 2.0 license.

Contributing

See contributing and the code of conduct.

Contributor Attribution

We'd like to thank the following contributors for their contributions to this project:

Tripti Singh
- Design and implementation of the sqlite DB backend
- Designing a pluggable data import and enrichment layer and building the pipeline orchestrator.
- Importers for all six data sources
- Generic csv and json data sources importer with instructions
- Dockerization
- Contributing in Documentation
Wang-Chiew Tan
- Implementation of the PostText query engine
Pierre Moulon for providing open-sourcing guidelines and suggestions