Completion Request Options

Configure how completion requests are made to the LLM provider by customizing headers, prompts, and other options to fine-tune the behavior of code completions.

Custom Headers for LLM Requests

You can add custom headers to the provider's completion requests. For example, if you select OpenAI as your provider, you can add a custom header to the OpenAI completion requests made by Monacopilot.

copilot.complete({
    options: {
        headers: {
            'X-Custom-Header': 'custom-value',
        },
    },
});

Custom Prompt

You can customize the prompt used for generating completions by providing a customPrompt function in the options parameter of the copilot.complete method. This allows you to tailor the AI's behavior to your specific needs.

Usage

copilot.complete({
    options: {
        customPrompt: metadata => ({
            system: 'Your custom system prompt here',
            user: 'Your custom user prompt here',
        }),
    },
});

The system and user prompts in the customPrompt function are optional. If you omit either the system or user prompt, the default prompt for that field will be used. Example of customizing only the system prompt:

copilot.complete({
    options: {
        customPrompt: metadata => ({
            system: 'You are an AI assistant specialized in writing React components, focusing on creating clean...',
        }),
    },
});

Parameters

The customPrompt function receives a completionMetadata object, which contains information about the current editor state and can be used to tailor the prompt.

Completion Metadata

Property	Type	Description
language	string	The programming language of the code.
cursorPosition	{ lineNumber: number; column: number }	The current cursor position in the editor.
filename	string or undefined	The name of the file being edited. Only available if you have provided the filename option in the registerCompletion function.
technologies	string[] or undefined	An array of technologies used in the project. Only available if you have provided the technologies option in the registerCompletion function.
relatedFiles	object[] or undefined	An array of objects containing the path and content of related files. Only available if you have provided the relatedFiles option in the registerCompletion function.
textAfterCursor	string	The text that appears after the cursor.
textBeforeCursor	string	The text that appears before the cursor.
editorState	object	An object containing the completionMode property.

The editorState.completionMode can be one of the following:

Mode	Description
insert	Indicates that there is a character immediately after the cursor. In this mode, the AI will generate content to be inserted at the cursor position.
complete	Indicates that there is a character after the cursor but not immediately. In this mode, the AI will generate content to complete the text from the cursor position.
continue	Indicates that there is no character after the cursor. In this mode, the AI will generate content to continue the text from the cursor position.

For additional completionMetadata needs, please open an issue.

The customPrompt function should return an object with two properties:

Property	Type	Description
system	string or undefined	A string representing the system prompt for the model.
user	string or undefined	A string representing the user prompt for the model.

Example

Here's an example of a custom prompt that focuses on generating React component code:

const customPrompt = ({textBeforeCursor, textAfterCursor}) => ({
    system: 'You are an AI assistant specialized in writing React components. Focus on creating clean, reusable, and well-structured components.',
    user: `Please complete the following React component:

    ${textBeforeCursor}
    // Cursor position
    ${textAfterCursor}

    Use modern React practices and hooks where appropriate. If you're adding new props, make sure to include proper TypeScript types. Please provide only the completed part of the code without additional comments or explanations.`,
});

copilot.complete({
    options: {customPrompt},
});

By using a custom prompt, you can guide the model to generate completions that better fit your coding style, project requirements, or specific technologies you're working with.