To read text from a Notion database and perform natural language processing analysis.
To achieve functionality similar to flomo, I have created a database using Notion, where I have recorded my thoughts and insights over the years, accumulating a rich corpus of language. However, the random roaming feature of flomo did not meet my needs, so I decided to develop a small tool that integrates with the Notion API and performs NLP analysis.
Now, the tool can:
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Output intuitive and visually appealing word cloud images.
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Generate thematic summaries of your Notion notes.
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Support multiple languages. I have added stopword lists for several languages including Chinese, English, Russian, French, Japanese, and German. Users can also customize their own stopword lists.
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Support multiple tasks. Users can configure multiple databases and corresponding filtering and sorting conditions to create rich analysis tasks.
For example, I have added the following tasks:
- 🤔 Reflections from the past year
- 🚩 Optimization of annual summaries for the current year
⚠️ Self-admonitions from all time periods
I am pleased to share this tool and hope it can be helpful to you. 😆
flowchart TB
A1[(Get personal access token from note-taking software)]
B2[Customize NLP module parameters]
B3[Customize visualization module parameters]
B1[Configure API key and corresponding database ID]
C1((Run task))
D1([Read rich text via API])
D2([Segment/Clean/Build word-sentence mapping])
D3[/Calculate TF-IDF/]
E1{{Markdown of keywords and source sentences}}
E2{{Word cloud with multiple color styles}}
A1 -- Configure_task_parameters --> Parameter_types
subgraph Parameter_types
B1
B2
B3
end
B1 & B2 & B3 --> C1
C1 --> Calculation_module
subgraph Calculation_module
D1 --> D2 --> D3
end
Calculation_module --> Visualization_module
subgraph Visualization_module
E1
E2
end
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Windows System
Download the latest release of the
Windowsversionzipfile, extract it, and double-clickstart.batto follow the script prompts and start experiencing. -
Linux System
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Method 1: Download the latest release of the
Linuxversionzipfile, extract it, open the terminal in the current directory, and enter./notion-nlp-linux --helpto view the command details. -
Method 2: Install the package from
PyPIto thePythonenvironment.pip install notion-nlp
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- Configuration file reference config.sample.yaml (hereinafter config, please rename to
config.yamlas your own configuration file)
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In notion integrations create a new integration, get your own token and fill in the token in the config.yaml file afterwards.
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If you open the notion database page in your browser or click on the share copy link, you will see the database id in the address link (similar to a string of jumbles) and fill in the database_id under the task of config.
- The task's extra is used to filter and sort the database, see notion filter API for format and content, the config.sample.yaml file already provides 2 configurations.
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Select "Run all tasks" through Windows interactive script
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Call using terminal or Python code after installing the package from PyPI
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Run from command line
python3.8 -m notion_nlp run-all-tasks --config-file /path/to/your/config/file
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Run from Python code
from notion_nlp import run_all_tasks config_file = "./notion-nlp-dataset/configs/config.yaml" run_all_tasks(config_file)
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Select "Run specified task" through Windows interactive script
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Call using terminal or Python code after installing the package from PyPI
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In the
run_taskcommand, you can specify the task in several ways, including:task: an instance ofTaskParams;task_json: a JSON string representing the task information;task_name: the name of the task.
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If
config_fileexists, you can usetask_nameto specify the task. Note that the task needs to be activated, otherwise an exception will be thrown. Ifconfig_filedoes not exist, you need to provide atokenand eitherTaskParamsortask_json.-
With an existing
configfile, pass intask name/task json/task parameter class-
Run from command line
# Option 1 python3.8 -m notion_nlp run-task --task-name task_1 --config-file /path/to/your/config/file # Option 2 python3.8 -m notion_nlp run-task --task-json '{"name": "task_1", "database_id": "your_database_id"}' --config-file /path/to/your/config/file
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Run from Python code
from notion_nlp import run_task task_name = "task_1" database_id = "your_database_id" config_file="./notion-nlp-dataset/configs/config.yaml" # Option 1 run_task(task_name=task_name, config_file=config_file) # Option 2 (not recommended for Python code) import json task_info = {"name": task_name, "database_id": database_id} run_task(task_json=json.dumps(task_info, ensure_ascii=False), config_file=config_file) # Option 3 (recommended) from notion_nlp.parameter.config import TaskParams task = TaskParams(name=task_name, database_id=database_id) run_task(task=task, config_file=config_file)
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Without a
configfile, pass intokenandtask json/task parameter class-
Run from command line
# Option 1 python3.8 -m notion_nlp run-task --task-json '{"name": "task_1", "database_id": "your_database_id"}' --token 'your_notion_integration_token'
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Run from Python code
from notion_nlp import run_task task_name = "task_1" database_id = "your_database_id" notion_token = "your_notion_integration_token" # Option 1 (not recommended for Python code) import json task_info = {"name": task_name, "database_id": database_id} run_task(task_json=json.dumps(task_info, ensure_ascii=False), token=notion_token) # Option 2 (recommended) from notion_nlp.parameter.config import TaskParams task = TaskParams(name=task_name, database_id=database_id) run_task(task=task, token=notion_token)
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-
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- Add a text file in the stopwords directory with the suffix
stopwords.txt, such ascustom.stopwords.txt. Each stopword should be on a separate line in the file.
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Welcome to fork and add new features/fix bugs.
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After cloning the project, use the
create_python_env_in_new_machine.shscript to create a Poetry virtual environment. -
After completing the code development, use the invoke command to perform a series of formatting tasks, including black/isort tasks added in task.py.
invoke check
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After submitting the formatted changes, run unit tests to check coverage.
poetry run tox
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The word segmentation tool has two built-in options: jieba/
pkuseg. (Considering adding language analysis to automatically select the most suitable word segmentation tool for that language.)- jieba is used by default.
pkuseg cannot be installed with poetry and needs to be installed manually with pip. In addition, this library is slow and requires high memory usage. It has been tested that a VPS with less than 1G memory needs to load virtual memory to use it.
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The analysis method using tf-idf is too simple. Consider integrating the API of LLM (such as openAI GPT-3) for further analysis.
- scikit-learn - https://github.com/scikit-learn/scikit-learn
- Alir3z4/stop-words - https://github.com/Alir3z4/stop-words
- MIT License
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The MIT License is a permissive open-source software license. This means that anyone is free to use, copy, modify, and distribute your software, as long as they include the original copyright notice and license in their derivative works.
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However, the MIT License comes with no warranty or liability, meaning that you cannot be held liable for any damages or losses arising from the use or distribution of your software.
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By using this software, you agree to the terms and conditions of the MIT License.
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- See more at my HomePage
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