Https://github. Com/spank-ai-boy/sparknoteai
Here are some of the results:
Note function:

Import function:

Ai assistant:

Ii. Functional characteristics
Spark noteai has the following functional characteristics:
It also has the following structural characteristics:
There are also security mechanisms:
Iii. Deployment
First clone code library:
Gymnasium, glitcone https://github. Com/spank-ai-boy/sparknoteai. Git
Then perform the following command to enter the docker directory:
Cd docker
Create. Env:
. Env. Example. Env
In the . Env document, variables can be modified as appropriate:
== sync, corrected by elderman == @elder man
# app version when built by docker mirror (-build-arg) does not need to be set in . Env
# amage tag references for docker-compose only
app version=latest
== sync, corrected by elderman == @elder man
debug = false
== sync, corrected by elderman == @elder man
postgres user=sarknoteai
postgres password=spanknoteai123
postgres db=sarknoteai
== sync, corrected by elderman == @elder man
redis password=sarknoteai123
== sync, corrected by elderman == @elder man
neo4j password=sarknoteai123
== sync, corrected by elderman == @elder man
# generating method: opensl ran-hex 32
secret key=change-me-in-project
access token expire minutes=10080
== sync, corrected by elderman == @elder man
# generating method: opensl ran-bAss. 64 32
encryption key=change-me-in-project
== sync, corrected by elderman == @elder man
# service-end compatible client versions, multiple commas separated (empty representation of all versions compatible)
compatibble client versions=1. 0. 0, 1. 1. 0
== sync, corrected by elderman == @elder man
admin username=admin
admin password=admin123
admin email=admin@example. Com
Of which secret key and encryption key need to execute the following commands separately to create an opensl ran-bSo, we're going to have to go back to workCase 64 32。
Then docker-compose. Yml
Cp docker-compose. Prod. Ymldocker-compose. Yml
The service is then activated by the following order:
Docker company up-d
Execute the following command to view logs started by docker:
You know, docker copose logs-f
When the following log appears, this indicates the success of the startup (if the hint 80 port is occupied, the image of the 80 port needs to be modified in the docker-compose. Yml):

Experience
Spark noteai currently supports web-end, desktop-end (macos, windows)。
4. 1 home page
Enter ip:port to the front page of the browser, where ip and port are the ip addresses and ports of deployment services。

Enter the . Env configured administrator account code for login, or you can create a new account number. Enter the first page after login is completed:

4. 2 large modeling
In order to use knowledge mapping, autosuming, etc., we need to start with large models。
Enter the settings, select the large model configuration, then click on the new configuration, enter the model's api key, etc. Here i use alibri:

When you click to save, you can see the large configuration on the big model configuration page:

Next in the scene configuration selects the large model that was just added:
The first is a note management configuration:

And then the knowledge map configuration:

Also ai assistant configuration:

When the configuration is complete, you can experience the following functions in turn。
4. 3 notes
First create a look at:

Once you have created the notes, click to save them, you automatically generate a summary, build a knowledge map (incremental) etc.:

4. 4 knowledge mapping
Once the notes are saved, the knowledge map is automatically updated and can be seen on the knowledge map page:

Click on the nodes in the scheme to see the associated notes:

4. 5 import
You can import it by entering a public article on the import page:

4. 6 clients end
Download the client in the list and enter the address of the backend service in the login interface:


Once the login is done, you can experience it:

Summary
This paper presents an open source ai knowledge management tool called spark noteai。
The system integrates large models with smart summary and knowledge mapping visualization, and is designed to help users build structured knowledge systems from fragmented information。
The project supports the local deployment of docker, where users can access the source code through github and direct the configuration of environmental variables for start-up services, leading to the privatization of ai notes。
If everyone's interested, try it
I'm jack bytes
A half-strung ape focused on applying artificial intelligence to everyday life
Usually share technologies like ai, nas, docker, machine skills, open source projects, etc





