gemini.lua

local openai = require("codecompanion.adapters.openai")

---@class Gemini.Adapter: CodeCompanion.Adapter
return {
  name = "gemini",
  formatted_name = "Gemini",
  roles = {
    llm = "assistant",
    user = "user",
  },
  opts = {
    stream = true,
  },
  features = {
    text = true,
    tokens = true,
    vision = true,
  },
  url = "https://generativelanguage.googleapis.com/v1beta/openai/chat/completions",
  env = {
    api_key = "GEMINI_API_KEY",
  },
  headers = {
    Authorization = "Bearer ${api_key}",
    ["Content-Type"] = "application/json",
  },
  handlers = {
    --- Use the OpenAI adapter for the bulk of the work
    setup = function(self)
      return openai.handlers.setup(self)
    end,
    tokens = function(self, data)
      return openai.handlers.tokens(self, data)
    end,
    form_parameters = function(self, params, messages)
      return openai.handlers.form_parameters(self, params, messages)
    end,
    form_messages = function(self, messages)
      return openai.handlers.form_messages(self, messages)
    end,
    chat_output = function(self, data)
      return openai.handlers.chat_output(self, data)
    end,
    inline_output = function(self, data, context)
      return openai.handlers.inline_output(self, data, context)
    end,
    on_exit = function(self, data)
      return openai.handlers.on_exit(self, data)
    end,
  },
  schema = {
    ---@type CodeCompanion.Schema
    model = {
      order = 1,
      mapping = "parameters",
      type = "enum",
      desc = "The model that will complete your prompt. See https://ai.google.dev/gemini-api/docs/models/gemini#model-variations for additional details and options.",
      default = "gemini-2.0-flash",
      choices = {
        "gemini-2.5-pro-exp-03-25",
        "gemini-2.0-flash",
        "gemini-2.0-pro-exp-02-05",
        "gemini-1.5-flash",
        "gemini-1.5-pro",
        "gemini-1.0-pro",
      },
    },
    ---@type CodeCompanion.Schema
    maxOutputTokens = {
      order = 2,
      mapping = "parameters",
      type = "integer",
      optional = true,
      default = nil,
      desc = "The maximum number of tokens to include in a response candidate. Note: The default value varies by model",
      validate = function(n)
        return n > 0, "Must be greater than 0"
      end,
    },
    ---@type CodeCompanion.Schema
    temperature = {
      order = 3,
      mapping = "parameters",
      type = "number",
      optional = true,
      default = nil,
      desc = "Controls the randomness of the output.",
      validate = function(n)
        return n >= 0 and n <= 2, "Must be between 0 and 2"
      end,
    },
    ---@type CodeCompanion.Schema
    topP = {
      order = 4,
      mapping = "parameters",
      type = "integer",
      optional = true,
      default = nil,
      desc = "The maximum cumulative probability of tokens to consider when sampling. The model uses combined Top-k and Top-p (nucleus) sampling. Tokens are sorted based on their assigned probabilities so that only the most likely tokens are considered. Top-k sampling directly limits the maximum number of tokens to consider, while Nucleus sampling limits the number of tokens based on the cumulative probability.",
      validate = function(n)
        return n > 0, "Must be greater than 0"
      end,
    },
    ---@type CodeCompanion.Schema
    topK = {
      order = 5,
      mapping = "parameters",
      type = "integer",
      optional = true,
      default = nil,
      desc = "The maximum number of tokens to consider when sampling",
      validate = function(n)
        return n > 0, "Must be greater than 0"
      end,
    },
    ---@type CodeCompanion.Schema
    presencePenalty = {
      order = 6,
      mapping = "parameters",
      type = "number",
      optional = true,
      default = nil,
      desc = "Presence penalty applied to the next token's logprobs if the token has already been seen in the response",
    },
    ---@type CodeCompanion.Schema
    frequencyPenalty = {
      order = 7,
      mapping = "parameters",
      type = "number",
      optional = true,
      default = nil,
      desc = "Frequency penalty applied to the next token's logprobs, multiplied by the number of times each token has been seen in the response so far.",
    },
  },
}