# `Arcana.Agent.Answerer`
[🔗](https://github.com/georgeguimaraes/arcana/blob/main/lib/arcana/agent/answerer.ex#L1)

Behaviour for answer generation in the Agent pipeline.

The answerer generates the final response based on the question and
retrieved context chunks.

## Built-in Implementations

- `Arcana.Agent.Answerer.LLM` - Uses your LLM to generate answers (default)

## Implementing a Custom Answerer

    defmodule MyApp.TemplateAnswerer do
      @behaviour Arcana.Agent.Answerer

      @impl true
      def answer(question, chunks, _opts) do
        context = Enum.map_join(chunks, "
", & &1.text)
        answer = "Based on " <> Integer.to_string(length(chunks)) <> " sources:

" <> context
        {:ok, answer}
      end
    end

## Using a Custom Answerer

    Agent.new(question, repo: repo, llm: llm)
    |> Agent.search()
    |> Agent.answer(answerer: MyApp.TemplateAnswerer)

## Using an Inline Function

    Agent.answer(ctx,
      answerer: fn question, chunks, opts ->
        llm = Keyword.fetch!(opts, :llm)
        prompt = build_my_prompt(question, chunks)
        Arcana.LLM.complete(llm, prompt, [], [])
      end
    )

# `answer`

```elixir
@callback answer(
  question :: String.t(),
  chunks :: [map()],
  opts :: keyword()
) :: {:ok, String.t()} | {:error, term()}
```

Generates an answer based on the question and context chunks.

## Parameters

- `question` - The user's original question
- `chunks` - List of context chunks retrieved by search
- `opts` - Options passed to `Agent.answer/2`, including:
  - `:llm` - The LLM function (for LLM-based answerers)
  - `:prompt` - Custom prompt function `fn question, chunks -> prompt end`
  - Any other options passed to `Agent.answer/2`

## Returns

- `{:ok, answer}` - The generated answer string
- `{:error, reason}` - On failure

---

*Consult [api-reference.md](api-reference.md) for complete listing*