gemini.md

Google Gemini Configuration

Use this guide if you want your generated chat server to run on Google Gemini models. This is an optional provider switch; you can keep OpenAI configured and add Gemini alongside it.

Configuration

In config/runtime.exs, ensure your :req_llm config includes a Google API key. If you already have other providers configured, keep them and add the Google key.

config :req_llm,
  google_api_key: System.fetch_env!("GOOGLE_API_KEY"),
  # Optional: keep this if your app also uses OpenAI models.
  openai_api_key: System.get_env("OPENAI_API_KEY")

Chat Component

In

• lib/your_app/chat/message/changes/respond.ex
• lib/your_app/chat/conversation/changes/generate_name.ex

If you want Gemini for chat generation, set the model to a Google model spec:

model: "google:gemini-2.5-pro"

If you prefer OpenAI (or another provider), keep your existing model: value.

Embeddings

create lib/your_app/google_ai_embedding_model.ex

defmodule YourApp.GoogleAiEmbeddingModel do
  use AshAi.EmbeddingModel

  @impl true
  def dimensions(_opts), do: 3072

  @impl true
  def generate(texts, _opts) do
    parts = Enum.map(texts, fn t -> %{text: t} end)
    api_key = System.fetch_env!("GOOGLE_API_KEY")

    headers = [
      {"x-goog-api-key", "#{api_key}"},
      {"Content-Type", "application/json"}
    ]

    body = %{
      "content" => %{parts: parts},
      "model" => "models/gemini-embedding-001"
    }

    response =
      Req.post!(
        "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent",
        json: body,
        headers: headers
      )

    case response.status do
      200 ->
        {:ok, [response.body["embedding"]["values"]]}

      _status ->
        {:error, response.body}
    end
  end
end

and in your vectorize block change:

embedding_model YourApp.OpenAiEmbeddingModel

with:

embedding_model YourApp.GoogleAiEmbeddingModel