Brave Search Rag

Mix.install(
  [
    {:req, "> 0.0.0"},
    {:instructor, github: "agoodway/instructor_ex", branch: "fixes"},
    {:kino, "~> 0.12.0"}
  ],
  config: [
    instructor: [
      adapter: Instructor.Adapters.OpenAI,
      openai: [api_key: System.fetch_env!("LB_OPENAI_API_KEY")]
    ]
  ]
)

Requirments

This LiveBook Notebook requires 2 API keys:

• Brave Search API: https://brave.com/search/api/
  • The Brave API Key must have access to the Data for AI Pro plan.
  • PRO_BRAVE_API_KEY secret needs to be set
• OpenAI API: https://openai.com/index/openai-api/
  • OPENAI_API_KEY secret needs to be set

Define Instructor Schema

defmodule Researcher do
  use Ecto.Schema
  use Instructor.Validator

  @doc """
  ## Field Descriptions:
  - response: answer to the prompt
  - related_questions: a list of questions related to the prompt, limit to a max of 3
  ## Additional Instructions
  - set all id fields to nil
  """
  @primary_key false
  embedded_schema do
    field(:response, :string)

    embeds_many :related_questions, Question do
      field(:content, :string)
    end
  end

  @impl true
  def validate_changeset(changeset) do
    changeset
  end

  def prompt(text, context) do
    Instructor.chat_completion(
      model: "gpt-3.5-turbo",
      response_model: Researcher,
      max_retries: 3,
      messages: [
        %{
          role: "system",
          content: "You are an expert researcher."
        },
        %{
          role: "user",
          content: """
          Use the following context to enrich your answer to the prompt: 
          ```
          #{context}
          ```
          """
        },
        %{
          role: "user",
          content: """
          Prompt: 
          ```
          #{text}
          ```
          """
        }
      ]
    )
  end
end

Let’s Go!

text_input = Kino.Input.textarea("Text", default: "What is the best way to cook chicken?")
form = Kino.Control.form([text: text_input], submit: "Run")
frame = Kino.Frame.new()

Kino.listen(form, fn %{data: %{text: text}} ->
  Kino.Frame.render(frame, Kino.Text.new("Running..."))

  search_query = URI.encode_www_form(text)

  response =
    Req.get!("https://api.search.brave.com/res/v1/web/search?q=#{search_query}?&summary=1",
      headers: [
        {"X-Subscription-Token", System.fetch_env!("LB_PRO_BRAVE_API_KEY")},
        {"Accept", "application/json"}
      ]
    )

  summarizer_key = response.body["summarizer"]["key"]
  encoded = URI.encode_www_form(summarizer_key)

  response =
    Req.get!("https://api.search.brave.com/res/v1/summarizer/search?entity_info=1&key=#{encoded}",
      headers: [
        {"X-Subscription-Token", System.fetch_env!("LB_PRO_BRAVE_API_KEY")},
        {"Accept", "application/json"}
      ]
    )

  search_context = response.body["summary"] |> List.first() |> Map.get("data")

  {:ok, research} = Researcher.prompt(text, search_context)

  html = ["
Answer
", "
#{research.response}
", "
", "
"]

  html =
    Enum.reduce(research.related_questions, html, fn question, acc ->
      acc ++ ["
#{question.content}
"]
    end)

  html = html ++ ["
"]

  Kino.Frame.render(frame, Kino.HTML.new(Enum.join(html)))
end)

Kino.Layout.grid([form, frame], boxed: true, gap: 16)