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Console-ai is a Node.js module that boosts console functionality with a new method, console.ai(). It harnesses OpenAI's language models to convert error logs into readable formats, detailing potential causes and solutions. By replacing the standard consol
Console-ai is a Node.js module that enhances console functionality with a new method, console.ai()
. It utilizes OpenAI's language models to translate error logs into a more readable format, providing potential causes and solutions. By replacing the standard console.log()
with console.ai()
, developers can gain insights into complex issues and expedite the debugging process. The original error message can be logged, if you want ensuring data integrity. As such, Console-ai is a potent tool that not only enhances debugging, but also offers insights into application behavior, thereby improving development efficiency.
Use the package manager npm to install Console-ai.
npm install console-ai
Console-ai uses the powerful package called PolyFact to generate AI responses. To use it, you need to get a PolyFact token.
Follow these steps to get your PolyFact token:
Go to app.polyfact.com.
Connect with GitHub.
Copy the token.
Then, you need to export the PolyFact token in your environment:
export POLYFACT_TOKEN=<your_polyfact_token>
First, import and initialize the module:
import { extendConsole } from "console-ai";
extendConsole();
Now, you can replace your usual console.log()
or console.error()
calls with console.ai()
:
try {
// code that might throw an error
} catch (e) {
console.ai(e);
}
Converts error messages into a more understandable format.
Outlines potential causes of an error.
Suggests possible solutions.
Ensures original error messages are logged.
The extendConsole()
function can take an AILoggerOptions
object:
extendConsole({
prompt?: string;
sections?: ErrorSection[];
showOriginalError?: boolean;
showResultWithJsonFormat?: boolean;
});
Where:
prompt
is a string that instructs the OpenAI model how to format the output.
sections
is an array of ErrorSection
enums. It determines the sections of the output (default is all sections).
showOriginalError
is a boolean that indicates whether to log the original error (default is true
).
showResultWithJsonFormat
is a boolean that specifies whether to show the formatted error message in JSON format (default is false
).
You can customize the output of error information using the sections
array, which accepts values of the ErrorSection
enum. Each enum value corresponds to a different aspect of the error information:
ErrorSection.Error
: This displays the actual error message or code.
ErrorSection.Location
: This shows the file, function, or location where the error occurred.
ErrorSection.Summary
: This provides a brief explanation or summary of the error.
ErrorSection.Causes
: This lists the possible causes that may have led to the error.
ErrorSection.Solutions
: This suggests potential solutions or fixes for the error.
By default, all sections will be displayed if the sections
array is not specified. However, you can choose to display only certain sections by explicitly specifying them in the sections
array.
Here is an example on how to do this:
typescriptCopy code
const sections: ErrorSection[] = [ErrorSection.Error, ErrorSection.Location];
In this case, the output will only display the error message/code and its location. Any section not included in the sections
array will not be displayed in the output. Adjust this array to suit the error information needs of your application.
Please make sure to update tests as appropriate.
If you want to contact me you can reach me at kevin@polyfact.com.
FAQs
Console-ai is a Node.js module that boosts console functionality with a new method, console.ai(). It harnesses OpenAI's language models to convert error logs into readable formats, detailing potential causes and solutions. By replacing the standard consol
The npm package console-ai receives a total of 1 weekly downloads. As such, console-ai popularity was classified as not popular.
We found that console-ai demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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