Untitled notebook

Mix.install([
  {:zoi, "0.8.1"},
  {:req_llm, "~> 1.0.0"},
  {:kino, "~> 0.17.0"}
])

Section

Mix.Dep.Fetcher.al

# List OpenAI Models
{:ok, models} = ReqLLM.Provider.Registry.list_models(:openai)

# List Google Models
{:ok, models} = ReqLLM.Provider.Registry.list_models(:google)

{:ok, openai_model} =
  ReqLLM.Provider.Registry.get_model(:openai, "gpt-5")

{:ok, openai_model} =
  ReqLLM.Provider.Registry.get_model(:openai, "gpt-5")

{:ok, google_model} =
  ReqLLM.Provider.Registry.get_model(:google, "gemini-2.5-flash")

System.fetch_env!("LB_GOOGLE_API_KEY")

ReqLLM.put_key(:openai_api_key, System.fetch_env!("LB_OPENAI_API_KEY"))
ReqLLM.put_key(:google_api_key, System.fetch_env!("LB_GOOGLE_API_KEY"))

ReqLLM.generate_text!(openai_model, "Who are you?")

ReqLLM.generate_text!(google_model, "Who are you?")

output_frame = Kino.Frame.new()
Kino.render(output_frame)

{:ok, response} =
  ReqLLM.stream_text(
    openai_model,
    "Write a short markdown-formatted report on the history of the Shure SM7B microphone.",
    provider_options: [reasoning_effort: :low]
  ) |> IO.inspect()

response
|> ReqLLM.StreamResponse.tokens()
|> IO.inspect()
|> Enum.reduce("", fn new_values, accumulator ->
  accumulator = accumulator <> new_values

  Kino.Frame.render(output_frame, Kino.Markdown.new(accumulator))

  accumulator
end)

ReqLLM.Providers.OpenAI.supported_provider_options()

output_frame = Kino.Frame.new()
Kino.render(output_frame)


schema = [
  language_name: [type: :string, required: true, doc: "Language name in English"],
  language_code: [type: :string, required: true, doc: "ISO-639 language code"],
  language_family:  [type: :string, required: true, doc: "Language family"]
]

context = ReqLLM.Context.new([
  ReqLLM.Context.system("You are a helpful language analysis assistant. Given a text you will analyse it and respond according to schema"),
  ReqLLM.Context.user("Jag skulle vilja ha en kaffe")
])

{:ok, response} = ReqLLM.stream_object(openai_model, 
  context, 
  schema,
  provider_options: [response_format: %{
    type: "json_schema",
    json_schema: %{
      name: "language_analysis",
      schema: schema
    }
  }])
response
|> ReqLLM.StreamResponse.tokens()
|> Enum.reduce("", fn new_values, accumulator ->
  new_values |> IO.inspect()
  accumulator = accumulator <> new_values

  Kino.Frame.render(output_frame, Kino.Markdown.new(accumulator))

  accumulator
end)