prompt.md

FIM LLM Prompt Structure

The prompt sent to the FIM LLM follows this structure:

provider_options = {
    openai_fim_compatible = {
        template = {
            prompt = function(context_before_cursor, context_after_cursor) end,
            suffix = function(context_before_cursor, context_after_cursor) end,
        }
    }
}

The template contains two main functions:

• prompt: return language and the indentation style, followed by the context_before_cursor verbatim.
• suffix: return context_after_cursor verbatim.

Both functions can be customized to provide additional context to the LLM. The suffix function can be disabled by setting suffix = false, which will result in only the prompt being included in the request.

Note: for Ollama users: Do not include special tokens (e.g., <|fim_begin|>) within the prompt or suffix functions, as these will be automatically populated by Ollama. If your use case requires special tokens not covered by Ollama's default template, first set suffix = false and then incorporate the special tokens within the prompt function.

Chat LLM Prompt Structure

Default Template

{{{prompt}}}\n{{{guidelines}}}\n{{{n_completion_template}}}

Default Prompt

You are the backend of an AI-powered code completion engine. Your task is to provide code suggestions based on the user's input. The user's code will be enclosed in markers:

• <contextAfterCursor>: Code context after the cursor
• <cursorPosition>: Current cursor location
• <contextBeforeCursor>: Code context before the cursor

Note that the user's code will be prompted in reverse order: first the code after the cursor, then the code before the cursor.

Default Guidelines

Guidelines:

1. Offer completions after the <cursorPosition> marker.
2. Make sure you have maintained the user's existing whitespace and indentation. This is REALLY IMPORTANT!
3. Provide multiple completion options when possible.
4. Return completions separated by the marker <endCompletion>.
5. The returned message will be further parsed and processed. DO NOT include additional comments or markdown code block fences. Return the result directly.
6. Keep each completion option concise, limiting it to a single line or a few lines.
7. Create entirely new code completion that DO NOT REPEAT OR COPY any user's existing code around <cursorPosition>.

Default n_completions template

8. Provide at most %d completion items.

Default Few Shots Examples

local default_few_shots = {
    {
        role = 'user',
        content = [[
# language: python
<contextAfterCursor>

fib(5)
<contextBeforeCursor>
def fibonacci(n):
    <cursorPosition>]],
    },
    {
        role = 'assistant',
        content = [[
    '''
    Recursive Fibonacci implementation
    '''
    if n < 2:
        return n
    return fib(n - 1) + fib(n - 2)
<endCompletion>
    '''
    Iterative Fibonacci implementation
    '''
    a, b = 0, 1
    for _ in range(n):
        a, b = b, a + b
    return a
<endCompletion>
]],
    },
}

Default Chat Input Example

The chat input represents the final prompt delivered to the LLM for completion. Its template follows a structured format similar to the system prompt and can be customized as follows:

The chat input template follows a structure similar to the system prompt and can be customized using the following format:

{{{language}}}\n{{{tab}}}\n<contextAfterCursor>\n{{{context_after_cursor}}}\n<contextBeforeCursor>\n{{{context_before_cursor}}}<cursorPosition>

Components:

• language: The programming language user is working on
• tab: The user's indentation style used by the user
• context_before_cursor and context_after_cursor: Represent the text content before and after the cursor position

Each subcomponent must be defined by a function that takes two parameters (context_before_cursor, context_after_cursor) and returns a string value.

Customization

You can customize the template by encoding placeholders within triple braces. These placeholders will be interpolated using the corresponding key-value pairs from the table. The value can be either a string or a function that takes no arguments and returns a string.

Here's a simplified example for illustrative purposes (not intended for actual configuration):

system = {
    template = '{{{assistant}}}\n{{{role}}}'
    assistant = function() return 'you are a helpful assistant' end,
    role = "you are also a code expert.",
}

Note that n_completion_template is a special placeholder as it contains one %d which will be encoded with config.n_completions, if you want to customize this template, make sure your prompt also contains only one %d.

Similarly, few_shots can be a table in the following form or a function that takes no argument and returns a table in the following form:

{
    { role = "user", content = "something" },
    { role = "assistant", content = "something" }
    -- ...
    -- You can pass as many turns as you want
}

Below is an example to configure the prompt based on filetype:

require('minuet').setup {
    provider_options = {
        openai = {
            system = {
                prompt = function()
                    if vim.bo.ft == 'tex' then
                        return [[your prompt for completing prose.]]
                    else
                        return require('minuet.config').default_system.prompt
                    end
                end,
            },
            few_shots = function()
                if vim.bo.ft == 'tex' then
                    return {
                        -- your few shots examples for prose
                    }
                else
                    return require('minuet.config').default_few_shots
                end
            end,
        },
    },
}

There's no need to replicate unchanged fields. The system will automatically merge modified fields with default values using the tbl_deep_extend function.