This Python script is a note summarizer that processes daily, weekly, and meeting notes. It uses the OpenAI API (gpt4o-mini) to summarize notes and identify tasks. It can also extract action items and add them to the Apple Reminders App. Last but not least; it's able to extract learnings from a separate file and autogenerate tags for each of the learnings. Learnings are stored individually in a separate folder. The script accepts several command-line arguments to customize its behavior.
Based on timestamped notes in a single file it can generate
- A daily summary, including tags and actionable items
- A weekly summary (including accomplishments and learnings)
- Extract meeting notes
- Extract action items and add them to the Apple Reminders App.
- Extract learnings and generate tags for each of the learnings
Please note
This script has been entirely created by CodeLLama, GPT-4o and Claude 3.5 Sonnet.
This script allows me to summarize my day, identify action items and add them to my reminders, so I can keep track of what I need to do.
I use jrnl (https://jrnl.sh/) to write daily notes during my work day, it's easy and entirely terminal based, which allows for quickly jotting down ideas, notes, quotes and report meetings. I have configured an alias for jrnl, called daily which opens neovim and adds everything I jot down to a single file prefixed with a timestamp. This is the jrnl config I use:
1 │ colors:
2 │ body: none
3 │ date: black
4 │ tags: yellow
5 │ title: cyan
6 │ default_hour: 9
7 │ default_minute: 0
8 │ editor: nvim -f
9 │ encrypt: false
10 │ highlight: true
11 │ indent_character: "|"
12 │ journals:
13 │ daily:
14 │ journal: ~/Documents/notes/jrnl/daily.md
15 │ default:
16 │ journal: ~/Documents/notes/jrnl/journal.md
19 │ idea:
20 │ journal: ~/Documents/notes/jrnl/ideas.md
23 │ linewrap: 79
24 │ tagsymbols: "#@"
25 │ template: false
26 │ timeformat: "%F %r"
27 │ version: v4.1The zsh-alias to quickly start a daily note;
alias daily="jrnl daily"
At the end of the day the file containing notes would look like this:
[2024-05-18 08:45:32 AM] started the day by reviewing the sprint backlog. Identified key tasks to tackle using Trello.
[2024-05-18 11:12:09 AM] debugged the API integration issues with the new payment gateway.
[2024-05-18 12:30:44 PM] had a quick sync with the UX team to finalize the design for the new dashboard feature.
[2024-05-18 02:15:20 PM] attended the bi-weekly planning meeting. Discussed the priorities for the next sprint.
[2024-05-18 03:47:18 PM] researched optimization techniques for the search algorithm. Found some promising approaches using Elasticsearch.
[2024-05-18 04:25:51 PM] created a prototype for the new notification system. Need to test it with real user data next week.
[2024-05-18 05:08:33 PM] updated the project documentation to reflect the recent changes in the architecture.
[2024-05-18 06:22:54 PM] set up automated tests for the new microservices. This should help catch issues earlier in the development cycle.
[2024-05-18 06:45:10 PM] ended the day by reviewing PRs and providing feedback to the team.
[2024-05-19 09:10:13 AM] started the morning with a team stand-up meeting. Discussed blockers and progress.
[2024-05-19 10:55:48 AM] worked on refactoring the legacy codebase to improve maintainability.
[2024-05-19 12:15:30 PM] joined a webinar on the latest trends in AI and machine learning. Took notes for later review.
If you'd use the script on this journal like this:
python main.py --skip-reminders --dry-run
This would be the generated output:
[08:45:32 AM] Reviewed sprint backlog and identified key tasks using Trello.
[11:12:09 AM] Debugged API integration issues with new payment gateway.
[12:30:44 PM] Synced with UX team to finalize dashboard feature design.
[02:15:20 PM] Discussed next sprint priorities in bi-weekly planning meeting.
[03:47:18 PM] Researched search algorithm optimization techniques using Elasticsearch.
[04:25:51 PM] Created prototype for new notification system; planning to test with real user data next week.
[05:08:33 PM] Updated project documentation to reflect recent architecture changes.
[06:22:54 PM] Set up automated tests for new microservices to catch issues earlier in development.
[06:45:10 PM] Reviewed PRs and provided feedback to team.
#sprint_backlog #api_integration #ux_design #sprint_planning #search_optimization #notification_system #project_documentation #automated_testing #code_review
- Test the new notification system with real user data next week.
The items extracted to action items will be added to Apple Reminders with a deadline of next morning 0900.
- Python 3.x
- OpenAI API key
- Apple Reminders
- Clone the repository.
- Install the required Python packages:
pip install -r requirements.txt. - Configure the script by editing the
config.yamlfile.
Copy config.template.yml to config.yml and setup the correct values.
Here is a brief explanation of each configuration item:
-
daily_notes_file: This is the path to the file where your daily notes are stored. The script will read from this file when processing daily notes. For example,"~/Documents/notes/jrnl/daily.md". -
daily_output_dir: This is the directory where the script will save the processed daily notes. For example,"~/Documents/notes/daily". -
weekly_output_dir: This is the directory where the script will save the processed weekly notes. For example,"~/Documents/notes/Weekly". -
meeting_notes_output_dir: This is the directory where the script will save the processed meeting notes. For example,"~/Documents/notes/meetingnotes". -
base_url: This is the (optional) base URL for the OpenAI API. The script will send requests to this URL to interact with the API. For example,"https://api.openai.com/v1/". -
api_key: This is your OpenAI API key. The script will use this key to authenticate with the OpenAI API. -
model: This is the model that the script will use for the OpenAI API. For example,"gpt-4o". -
learnings_file: this is the path to the file where your learnings are stored. The script will read from this file when processing learnings. For example,"~/Documents/notes/learnings.md". -
learnings_output_dir: this is the directory where the script will save the processed learnings. For example,"~/Documents/notes/learnings".
Here is a brief explanation of each argument:
-
--config: This argument is used to specify the path to the configuration file. The default value isconfig.yaml. -
--process-learnings: If this argument is provided, the script will process learnings from the file that is configured in theconfig.yamlfile :learnings_file -
--weekly: If this argument is provided, the script will process weekly notes. -
--dry-run: If this argument is provided, the script will not write to files or create reminders. It's useful for testing the script without making changes. -
--skip-reminders: If this argument is provided, the script will not create reminders. -
--replace-summary: If this argument is provided, the script will replace the existing summary instead of appending to it. -
--meetingnotes: If this argument is provided, the script will generate and save meeting notes. -
--date: Optional date string to force meetingnotes or daily notes to be processed for a specific date. The format should beYYYY-MM-DD. In case of weekly notes this value reflect the start date
Here is an example of how to run the script with some arguments:
python main.py --config my_config.yaml --weekly --dry-runIn this example, the script will process weekly notes using the configuration file my_config.yaml, and it will not write to files or create reminders because the --dry-run argument is provided.
You can set up a cron job to run this script at the end of each day. Here’s how to add a cron job on a Unix-based system:
crontab -eAdd this line to the file:
0 18 * * * /usr/bin/python3 /path/to/the/script.pyThis cron job will run the script daily at 6 PM.
- Add mail functionality to send the summary and action items to an email address.
- Add support for a vector search API to search for related notes in order to link them