# `Arcana.Ask`
[🔗](https://github.com/georgeguimaraes/arcana/blob/main/lib/arcana/ask.ex#L1)

RAG (Retrieval Augmented Generation) question answering.

This module handles the core ask workflow:
1. Search for relevant context chunks
2. Build a prompt with the context
3. Call the LLM for an answer

## Usage

    {:ok, answer, context} = Arcana.ask("What is X?",
      repo: MyApp.Repo,
      llm: "openai:gpt-4o-mini"
    )

# `ask`

Asks a question using retrieved context from the knowledge base.

Performs a search to find relevant chunks, then passes them along with
the question to an LLM for answer generation.

## Options

  * `:repo` - The Ecto repo to use (required)
  * `:llm` - Any type implementing the `Arcana.LLM` protocol (required)
  * `:limit` - Maximum number of context chunks to retrieve (default: 5)
  * `:source_id` - Filter context to a specific source
  * `:threshold` - Minimum similarity score for context (default: 0.0)
  * `:mode` - Search mode: `:semantic` (default), `:fulltext`, or `:hybrid`
  * `:collection` - Filter to a specific collection
  * `:collections` - Filter to multiple collections
  * `:prompt` - Custom prompt function `fn question, context -> system_prompt_string end`

## Examples

    # Basic usage
    {:ok, answer, context} = Arcana.ask("What is Elixir?",
      repo: MyApp.Repo,
      llm: "openai:gpt-4o-mini"
    )

    # With custom prompt
    {:ok, answer, _} = Arcana.ask("Summarize the docs",
      repo: MyApp.Repo,
      llm: my_llm,
      prompt: fn question, context ->
        "Be concise. Question: #{question}"
      end
    )

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