llm

defmodule LLM do
  @moduledoc """
  Lightweight Elixir client for LLM APIs.

  Supports 4 adapters covering 99% of providers:
  - **OpenAI Chat Completions** — `/v1/chat/completions` (also works with Ollama, Groq, OpenRouter, DeepSeek, xAI, etc.)
  - **OpenAI Responses** — `/v1/responses`
  - **Anthropic Messages** — `/v1/messages`
  - **Gemini** — `/v1beta/models/{model}:generateContent`

  ## Quick Start

      # Simple text generation
      {:ok, response} = LLM.generate("What is Elixir?",
        provider: :openai,
        model: "gpt-4"
      )
      response.message.content  #=> "Elixir is a..."

      # Streaming
      {:ok, stream} = LLM.stream("Tell me a story",
        provider: :anthropic,
        model: "claude-sonnet-4-5-20250514"
      )
      {:ok, response} = LLM.Stream.collect(stream, on_chunk: &IO.write/1)

      # Using provider modules
      {:ok, response} = LLM.generate("Hello",
        provider: LLM.Provider.OpenAI,
        model: "gpt-4"
      )

      # With explicit API key
      {:ok, response} = LLM.generate("Hello",
        provider: {LLM.Provider.OpenAI, api_key: "sk-..."},
        model: "gpt-4"
      )

      # Custom provider (runtime)
      {:ok, response} = LLM.generate("Hello",
        provider: %{
          adapter: LLM.Adapter.Anthropic,
          base_url: "https://my-proxy.com",
          api_key: "sk-ant-..."
        },
        model: "claude-sonnet-4-5-20250514"
      )

      # With tools
      {:ok, response} = LLM.generate("Read mix.exs",
        provider: :openai,
        model: "gpt-4",
        tools: [MyApp.ReadFileTool]
      )

  ## Configuration

      # config/config.exs
      config :llm, :providers,
        openai: [api_key: "sk-..."],
        anthropic: [api_key: "sk-ant-..."]

      # Or at runtime
      LLM.put_key(:openai, "sk-...")

  ## Provider

  A provider can be:
  - An atom preset (`:openai`, `:anthropic`, `:gemini`, `:openrouter`, `:openai_responses`)
  - A provider module (`LLM.Provider.OpenAI`, `LLM.Provider.Anthropic`, etc.)
  - A tuple `{module, opts}` for runtime API key (`{LLM.Provider.OpenAI, api_key: "sk-..."}`)
  - A map with `:adapter`, `:base_url`, and optionally `:api_key`
  """

  @type stream_option ::
          {:provider, atom() | map() | module()}
          | {:model, String.t()}
          | {:max_tokens, non_neg_integer()}
          | {:temperature, float()}
          | {:thinking, atom() | map()}
          | {:tools, [module() | LLM.Tool.t() | {module(), map()}]}
          | {:auto_tools, boolean()}
          | {:max_rounds, non_neg_integer()}
          | {:on_chunk, (LLM.Stream.chunk_type() -> any())}
          | {:on_message, (LLM.Message.t() -> any())}
          | {:system, String.t()}
          | {:messages, [LLM.Message.t()]}

  @doc """
  Stream a prompt, returning a stream handle.

  Returns `{:ok, stream}` on success, `{:error, reason}` on failure.

  The stream can be consumed with `LLM.Stream.next/1` and `LLM.Stream.collect/2`.
  """
  @spec stream(String.t() | LLM.Context.t(), [stream_option]) ::
          {:ok, LLM.Stream.t()} | {:error, term()}
  def stream(prompt, opts \\ []) do
    context = to_context(prompt, opts)
    LLM.Stream.start(context, opts)
  end

  @doc """
  Generate text (non-streaming). Returns the final response.

  Automatically collects the stream and executes tool calls if present.
  """
  @spec generate(String.t() | LLM.Context.t(), [stream_option]) ::
          {:ok, LLM.Response.t()} | {:error, term()}
  def generate(prompt, opts \\ []) do
    with {:ok, s} <- stream(prompt, opts) do
      LLM.Stream.collect(s, opts)
    end
  end

  @doc """
  Generate text, raising on error.
  """
  @spec generate!(String.t() | LLM.Context.t(), [stream_option]) :: LLM.Response.t()
  def generate!(prompt, opts \\ []) do
    case generate(prompt, opts) do
      {:ok, response} -> response
      {:error, reason} -> raise "LLM generation failed: #{inspect(reason)}"
    end
  end

  @doc """
  List available provider presets.
  """
  @spec providers() :: [atom()]
  def providers, do: LLM.Provider.Resolver.list_providers()

  @doc """
  List available models from a provider.

  Returns `{:ok, models}` on success, where `models` is a list of model info maps.
  Returns `{:error, reason}` on failure.

  ## Options

    * `:provider` - provider preset atom, module, or config map (defaults to `:openai`)

  ## Examples

      {:ok, models} = LLM.models(provider: :openai)
      {:ok, models} = LLM.models(provider: :anthropic)

  """
  @spec models(keyword()) :: {:ok, [LLM.Adapter.model_info()]} | {:error, term()}
  def models(opts \\ []) do
    provider = LLM.Provider.Resolver.resolve(opts[:provider] || :openai)

    try do
      provider.adapter.list_models(provider)
    rescue
      UndefinedFunctionError -> {:error, :not_supported}
    end
  end

  @doc """
  Store an API key at runtime.
  """
  @spec put_key(atom(), String.t()) :: :ok
  def put_key(provider_name, api_key) do
    Process.put({__MODULE__, :provider_key, provider_name}, api_key)
    :ok
  end

  @doc """
  Get an API key, checking process dictionary first, then application config.
  """
  @spec get_key(atom()) :: String.t() | nil
  def get_key(provider_name) do
    case Process.get({__MODULE__, :provider_key, provider_name}) do
      nil ->
        case Application.get_env(:llm, :providers, %{}) do
          config when is_list(config) ->
            config[provider_name][:api_key]

          config when is_map(config) ->
            config[provider_name][:api_key]

          _ ->
            nil
        end

      key ->
        key
    end
  end

  # --- Private ---

  defp to_context(prompt, opts) when is_binary(prompt) do
    tools = normalize_tools(opts[:tools] || [])
    previous_messages = opts[:messages] || []

    %LLM.Context{
      system: opts[:system],
      messages: previous_messages ++ [LLM.Message.new(prompt)],
      tools: tools,
      provider_state: %{}
    }
  end

  defp to_context(%LLM.Context{} = ctx, opts) do
    tools = normalize_tools(opts[:tools] || [])
    %{ctx | tools: tools ++ ctx.tools}
  end

  defp normalize_tools(tools) do
    Enum.map(tools, &LLM.Tool.normalize/1)
  end
end