minuet-ai.nvim

💃 Dance with Intelligence in Your Code. Minuet AI integrates with nvim-cmp, offering AI completion from popular LLMs including OpenAI, Gemini, Claude, and more.

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Minuet AI is a Neovim plugin that integrates with nvim-cmp to provide AI-powered code completion using multiple AI providers such as OpenAI, Claude, Gemini, Codestral, and Huggingface. It offers customizable configuration options and streaming support for completion delivery. Users can manually invoke completion or use cost-effective models for auto-completion. The plugin requires API keys for supported AI providers and allows customization of system prompts. Minuet AI also supports changing providers, toggling auto-completion, and provides solutions for input delay issues. Integration with lazyvim is possible, and future plans include implementing RAG on the codebase and virtual text UI support.

README:

Minuet AI
Features
Requirements
Installation
Configuration
API Keys
System Prompt
Providers
Commands
- MinuetChangeProvider
- MinuetToggle
FAQ
TODO
Contributing
Acknowledgement

Minuet AI

Minuet AI: Dance with Intelligence in Your Code 💃.

Minuet-ai integrates with nvim-cmp, brings the grace and harmony of a minuet to your coding process. Just as dancers move during a minuet.

Features

AI-powered code completion
Support for multiple AI providers (OpenAI, Claude, Gemini, Codestral, Huggingface, and OpenAI-compatible services)
Customizable configuration options
Streaming support to enable completion delivery even with slower LLMs

Requirements

Neovim 0.10+.
plenary.nvim
nvim-cmp
An API key for at least one of the supported AI providers

Installation

Lazy

specs = {
    {
        'milanglacier/minuet-ai.nvim',
        config = function()
            require('minuet').setup {
                -- Your configuration options here
            }
        end
    },
    { 'nvim-lua/plenary.nvim' },
    { 'hrsh7th/nvim-cmp' },
}


-- If you wish to invoke completion manually,
-- The following configuration binds `A-y` key
-- to invoke the configuration manually.
require('cmp').setup {
    mapping = {
        ["<A-y>"] = require('minuet').make_cmp_map()
        -- and your other keymappings
    },
}

Given the response speed and rate limits of LLM services, we recommend you either invoke minuet completion manually or use a cost-effective model for auto-completion. The author recommends gemini-1.5-flash for auto-completion. You need to add minuet into source of nvim-cmp for auto completion to work.

require('cmp').setup {
    sources = {
        {
            { name = 'minuet' },
            -- and your other sources
        }
    },
    performance = {
        -- It is recommended to increase the timeout duration due to
        -- the typically slower response speed of LLMs compared to
        -- other completion sources. This is not needed when you only
        -- need manual completion.
        fetching_timeout = 2000,
    },
}

If you are using a distribution like lazyvim, see the FAQ section to see how to configure minuet with lazyvim, note that the author does not use lazyvim, the FAQ section does not guarantee to work. PRs are welcome to fix the problem if it exists.

Configuration

Minuet AI comes with the following defaults:

default_config = {
    -- Enable or disable auto-completion. Note that you still need to add
    -- Minuet to your cmp sources. This option controls whether cmp will
    -- attempt to invoke minuet when minuet is included in cmp sources. This
    -- setting has no effect on manual completion; Minuet will always be
    -- enabled when invoked manually.
    enabled = true,
    provider = 'codestral',
    -- the maximum total characters of the context before and after the cursor
    -- 12,800 characters typically equate to approximately 4,000 tokens for
    -- LLMs.
    context_window = 12800,
    -- when the total characters exceed the context window, the ratio of
    -- context before cursor and after cursor, the larger the ratio the more
    -- context before cursor will be used. This option should be between 0 and
    -- 1, context_ratio = 0.75 means the ratio will be 3:1.
    context_ratio = 0.75,
    throttle = 1000, -- only send the request every x milliseconds, use 0 to disable throttle.
    -- debounce the request in x milliseconds, set to 0 to disable debounce
    debounce = 400,
    -- show notification when request is sent or request fails. options:
    -- false to disable notification. Note that you should use false, not "false".
    -- "verbose" for all notifications.
    -- "warn" for warnings and above.
    -- "error" just errors.
    notify = 'verbose',
    -- The request timeout, measured in seconds. When streaming is enabled
    -- (stream = true), setting a shorter request_timeout allows for faster
    -- retrieval of completion items, albeit potentially incomplete.
    -- Conversely, with streaming disabled (stream = false), a timeout
    -- occurring before the LLM returns results will yield no completion items.
    request_timeout = 3,
    -- if completion item has multiple lines, create another completion item only containing its first line.
    add_single_line_entry = true,
    -- The number of completion items (encoded as part of the prompt for the
    -- chat LLM) requested from the language model. It's important to note that
    -- when 'add_single_line_entry' is set to true, the actual number of
    -- returned items may exceed this value. Additionally, the LLM cannot
    -- guarantee the exact number of completion items specified, as this
    -- parameter serves only as a prompt guideline.
    n_completions = 3,
    -- Defines the length of non-whitespace context after the cursor used to
    -- filter completion text. Set to 0 to disable filtering.
    --
    -- Example: With after_cursor_filter_length = 3 and context:
    --
    -- "def fib(n):\n|\n\nfib(5)" (where | represents cursor position),
    --
    -- if the completion text contains "fib", then "fib" and subsequent text
    -- will be removed. This setting filters repeated text generated by the
    -- LLM. A large value (e.g., 15) is recommended to avoid false positives.
    after_cursor_filter_length = 15,
    -- proxy port to use
    proxy = nil,
    provider_options = {
        -- see the documentation in each provider in the following part.
    },
    -- see the documentation in the `System Prompt` section
    default_template = {
        template = '...',
        prompt = '...',
        guidelines = '...',
        n_completion_template = '...',
    },
    default_few_shots = { '...' },
}

API Keys

Minuet AI requires API keys to function. Set the following environment variables:

OPENAI_API_KEY for OpenAI
GEMINI_API_KEY for Gemini
ANTHROPIC_API_KEY for Claude
CODESTRAL_API_KEY for Codestral
HF_API_KEY for Huggingface
Custom environment variable for OpenAI-compatible services (as specified in your configuration)

System Prompt

See prompt for the default system prompt used by minuet.

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_template.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.

Providers

OpenAI

the following is the default configuration for OpenAI:

provider_options = {
    openai = {
        model = 'gpt-4o-mini',
        system = system,
        few_shots = default_few_shots,
        stream = true,
        optional = {
            -- pass any additional parameters you want to send to OpenAI request,
            -- e.g.
            -- stop = { 'end' },
            -- max_tokens = 256,
            -- top_p = 0.9,
        },
    },
}

The following configuration is not the default, but recommended to prevent request timeout from outputing too many tokens.

provider_options = {
    openai = {
        optional = {
            max_tokens = 256,
        },
    },
}

Claude

the following is the default configuration for Claude:

provider_options = {
    claude = {
        max_tokens = 512,
        model = 'claude-3-5-sonnet-20240620',
        system = system,
        few_shots = default_few_shots,
        stream = true,
        optional = {
            -- pass any additional parameters you want to send to claude request,
            -- e.g.
            -- stop_sequences = nil,
        },
    },
}

Codestral

Codestral is a text completion model, not a chat model, so the system prompt and few shot examples does not apply. Note that you should use the CODESTRAL_API_KEY, not the MISTRAL_API_KEY, as they are using different endpoint. To use the Mistral endpoint, simply modify the end_point and api_key parameters in the configuration.

the following is the default configuration for Codestral:

provider_options = {
    codestral = {
        model = 'codestral-latest',
        end_point = 'https://codestral.mistral.ai/v1/fim/completions',
        api_key = 'CODESTRAL_API_KEY',
        stream = true,
        optional = {
            stop = nil, -- the identifier to stop the completion generation
            max_tokens = nil,
        },
    },
}

The following configuration is not the default, but recommended to prevent request timeout from outputing too many tokens.

provider_options = {
    codestral = {
        optional = {
            max_tokens = 256,
            stop = { '\n\n' },
        },
    },
}

Gemini

The following config is the default.

provider_options = {
    gemini = {
        model = 'gemini-1.5-flash-latest',
        system = system,
        few_shots = default_few_shots,
        stream = true,
        optional = {},
    },
}

The following configuration is not the default, but recommended to prevent request timeout from outputing too many tokens. You can also adjust the safety settings following the example:

provider_options = {
    gemini = {
        optional = {
            generationConfig = {
                maxOutputTokens = 256,
            },
            safetySettings = {
                {
                    -- HARM_CATEGORY_HATE_SPEECH,
                    -- HARM_CATEGORY_HARASSMENT
                    -- HARM_CATEGORY_SEXUALLY_EXPLICIT
                    category = 'HARM_CATEGORY_DANGEROUS_CONTENT',
                    -- BLOCK_NONE
                    threshold = 'BLOCK_ONLY_HIGH',
                },
            },
        },
    },
}

OpenAI-compatible

Use any providers compatible with OpenAI's chat completion API.

For example, you can set the end_point to http://localhost:11434/v1/chat/completions to use ollama.

Note that not all openAI compatible services has streaming support, you should change stream=false to disable streaming in case your services do not support it.

The following config is the default.

provider_options = {
    openai_compatible = {
        model = 'llama-3.1-70b-versatile',
        system = default_system,
        few_shots = default_few_shots,
        end_point = 'https://api.groq.com/openai/v1/chat/completions',
        api_key = 'GROQ_API_KEY',
        name = 'Groq',
        stream = true,
        optional = {
            stop = nil,
            max_tokens = nil,
        },
    }
}

OpenAI-FIM-compatible

Use any provider compatible with OpenAI's completion API. This request uses the text completion API, not chat completion, so system prompts and few-shot examples are not applicable.

For example, you can set the end_point to http://localhost:11434/v1/completions to use ollama.

Refer to the Completions Legacy section of the OpenAI documentation for details.

Note that not all openAI compatible services has streaming support, you should change stream=false to disable streaming in case your services do not support it.

provider_options = {
    openai_fim_compatible = {
        model = 'deepseek-coder',
        end_point = 'https://api.deepseek.com/beta/completions',
        api_key = 'DEEPSEEK_API_KEY',
        name = 'Deepseek',
        stream = true,
        optional = {
            stop = nil,
            max_tokens = nil,
        },
    }
}

The following configuration is not the default, but recommended to prevent request timeout from outputing too many tokens.

provider_options = {
    openai_fim_compatible = {
        optional = {
            max_tokens = 256,
            stop = { '\n\n' },
        },
    },
}

Huggingface

Currently only text completion model in huggingface is supported, so the system prompt and few shot examples does not apply.

provider_options = {
    huggingface = {
        end_point = 'https://api-inference.huggingface.co/models/bigcode/starcoder2-3b',
        type = 'completion',
        strategies = {
            completion = {
                markers = {
                    prefix = '<fim_prefix>',
                    suffix = '<fim_suffix>',
                    middle = '<fim_middle>',
                },
                strategy = 'PSM', -- PSM, SPM or PM
            },
        },
        optional = {
            parameters = {
                -- The parameter specifications for different LLMs may vary.
                -- Ensure you specify the parameters after reading the API
                -- documentation.
                stop = nil,
                max_tokens = nil,
                do_sample = nil,
            },
        },
    },
}

Commands

MinuetChangeProvider

This command allows you to change the provider after Minuet has been setup.

MinuetToggle

Enable or disable auto-completion. Note that you still need to add Minuet to your cmp sources. This command controls whether cmp will attempt to invoke minuet when minuet is included in cmp sources. This command has no effect on manual completion; Minuet will always be enabled when invoked manually.

FAQ

Customize `cmp` ui

You can configure the icons of minuet by using the following snippet (referenced from cmp's wiki):

local cmp = require('cmp')
cmp.setup {
  formatting = {
    format = function(entry, vim_item)
      -- Kind icons
      vim_item.kind = string.format('%s %s', kind_icons[vim_item.kind], vim_item.kind) -- This concatenates the icons with the name of the item kind
      -- Source
      vim_item.menu = ({
        minuet = "󱗻"
      })[entry.source.name]
      return vim_item
    end
  },
}

Significant Input Delay When Moving to a New Line

When using Minuet with auto-complete enabled, you may occasionally experience a noticeable delay when pressing <CR> to move to the next line. This occurs because Minuet triggers autocompletion at the start of a new line, while cmp blocks the <CR> key, awaiting Minuet's response.

To address this issue, consider the following solutions:

Unbind the <CR> key from your cmp keymap.
Utilize cmp's internal API to avoid blocking calls, though be aware that this API may change without prior notice.

Here's an example of the second approach using Lua:

local cmp = require 'cmp'
opts.mapping = {
    ['<CR>'] = cmp.mapping(function(fallback)
        -- use the internal non-blocking call to check if cmp is visible
        if cmp.core.view:visible() then
            cmp.confirm { select = true }
        else
            fallback()
        end
    end),
}

Integration with `lazyvim`

{
    'milanglacier/minuet-ai.nvim',
    config = function()
        require('minuet').setup {
            -- Your configuration options here
        }
    end
},
{ 'nvim-lua/plenary.nvim' },
{
    'nvim-cmp',
    opts = function(_, opts)
        -- if you wish to use autocomplete
        table.insert(opts.sources, 1, {
            name = 'minuet',
            group_index = 1,
            priority = 100,
        })

        opts.performance = {
            -- It is recommended to increase the timeout duration due to
            -- the typically slower response speed of LLMs compared to
            -- other completion sources. This is not needed when you only
            -- need manual completion.
            fetching_timeout = 2000,
        }

        opts.mapping = vim.tbl_deep_extend('force', opts.mapping or {}, {
            -- if you wish to use manual complete
            ['<A-y>'] = require('minuet').make_cmp_map(),
            -- You don't need to worry about <CR> delay because lazyvim handles this situation for you.
            ['<CR>'] = nil,
        })
    end,
}

TODO

Implement RAG on the codebase and encode the codebase information into the request to LLM.
Virtual text UI support.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgement

cmp-ai: A large piece of the codebase are based on this plugin.
continue.dev: not a neovim plugin, but I find a lot LLM models from here.

For Tasks:

Click tags to check more tools for each tasks

complete code customize prompts change providers toggle auto-completion integrate with lazyvim

For Jobs:

software developer ai engineer data scientist machine learning engineer code editor plugin developer

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