ReqLLM • Getting Started

Section

Mix.install([
  {:req_llm, github: "agentjido/req_llm"},
  {:kino, "~> 0.14.2"}
])

Setup

API Key Configuration

Enter your API keys for the providers you want to use. At least one provider key is required.

Tip: You can also add keys to Livebook secrets as OPENAI_API_KEY, ANTHROPIC_API_KEY, or GROQ_API_KEY to avoid re-entering them each time. Livebook exposes these secrets to the notebook with an LB_ prefix.

openai_key_input = Kino.Input.password("OpenAI API Key")
anthropic_key_input = Kino.Input.password("Anthropic API Key (optional)")
groq_key_input = Kino.Input.password("Groq API Key (optional)")

Kino.Layout.grid([openai_key_input, anthropic_key_input, groq_key_input], columns: 1)

available_models = [
  {"openai:gpt-5", "OpenAI GPT-5"},
  {"openai:gpt-5-mini", "OpenAI GPT-5 Mini"},
  {"anthropic:claude-sonnet-4-6", "Anthropic Claude Sonnet 4.6"},
  {"anthropic:claude-haiku-4-5", "Anthropic Claude Haiku 4.5"},
  {"groq:openai/gpt-oss-120b", "Groq GPT-OSS 120B"},
  {"groq:openai/gpt-oss-20b", "Groq GPT-OSS 20B"}
]

model_input = Kino.Input.select("Select Model", available_models)

provider_configurations = [
  {"openai", :openai, "OPENAI_API_KEY", openai_key_input},
  {"anthropic", :anthropic, "ANTHROPIC_API_KEY", anthropic_key_input},
  {"groq", :groq, "GROQ_API_KEY", groq_key_input}
]

key_statuses =
  Map.new(provider_configurations, fn {provider_name, provider, env_var, input} ->
    livebook_key = System.get_env("LB_" <> env_var)

    key_status =
      cond do
        is_binary(livebook_key) and livebook_key != "" ->
          ReqLLM.put_key(ReqLLM.Keys.config_key(provider), livebook_key)
          {:ok, :livebook}

        true ->
          case ReqLLM.Keys.get(provider) do
            {:ok, _key, source} ->
              {:ok, source}

            {:error, _reason} ->
              input_key = Kino.Input.read(input)

              if is_binary(input_key) and input_key != "" do
                ReqLLM.put_key(ReqLLM.Keys.config_key(provider), input_key)
                {:ok, :manual}
              else
                :missing
              end
          end
      end

    {provider_name, key_status}
  end)

selected_model = Kino.Input.read(model_input)
provider = selected_model |> String.split(":") |> List.first()

key_status = Map.get(key_statuses, provider, :missing)
has_key? = match?({:ok, _source}, key_status)

key_source =
  case key_status do
    {:ok, :livebook} -> "Livebook secret"
    {:ok, :system} -> "environment variable"
    {:ok, :application} -> "application configuration"
    {:ok, :manual} -> "manual input"
    _ -> "unknown"
  end

if has_key? do
  Kino.Markdown.new("""
  ✅ **Configuration Complete**

  Provider: `#{provider}`  
  Model: `#{selected_model}`  
  Key source: #{key_source}
  """)
else
  Kino.Markdown.new("""
  ⚠️  **Missing API Key**

  Please enter the API key for **#{provider}** above, or add it to Livebook secrets as `#{String.upcase(provider)}_API_KEY`.
  """)
end

1) Basic Text Generation

Simple, synchronous text generation. The model returns complete text after processing.

prompt_input = Kino.Input.textarea("Enter your prompt", default: "Explain functional programming in one sentence.")

prompt = Kino.Input.read(prompt_input)

text = ReqLLM.generate_text!(selected_model, prompt)

Kino.Markdown.new("""
**Response:**

#{text}
""")

2) Streaming Text

Tokens arrive in real-time as the model generates them. Perfect for chat interfaces.

stream_prompt_input = Kino.Input.textarea("Enter your streaming prompt", default: "Write a 3-4 sentence upbeat intro to Elixir.")

stream_prompt = Kino.Input.read(stream_prompt_input)

{:ok, response} = ReqLLM.stream_text(selected_model, stream_prompt)

response.stream
|> Stream.filter(&(&1.type == :content))
|> Enum.each(fn chunk ->
  IO.write(chunk.text)
end)

:ok

3) Structured Object Generation

Generate validated, type-safe data structures from prompts using schemas.

object_prompt_input = Kino.Input.textarea("Enter prompt for object generation", default: "Create a profile for a software engineer named Alice who is 32 years old")

object_prompt = Kino.Input.read(object_prompt_input)

schema = [
  name: [type: :string, required: true, doc: "Person's name"],
  age: [type: :pos_integer, required: true, doc: "Person's age"],
  occupation: [type: :string, doc: "Person's occupation"],
  location: [type: :string, doc: "Person's location"]
]

{:ok, response} = ReqLLM.generate_object(selected_model, object_prompt, schema)

object = ReqLLM.Response.object(response)

json_output = Jason.encode!(object, pretty: true)

Kino.Markdown.new("""
**Generated Object:**

   `#{json_output}`

**Fields:**
- **Name:** #{object["name"]}
- **Age:** #{object["age"]}
- **Occupation:** #{object["occupation"] || "Not specified"}
- **Location:** #{object["location"] || "Not specified"}
""")

4) Function Calling / Tools

Enable models to call functions and use tools to perform actions.

tools_prompt_input = Kino.Input.textarea("Enter prompt for tool calling", default: "What's the weather in Paris in Celsius? What time is it?")

tools_prompt = Kino.Input.read(tools_prompt_input)

weather_tool = ReqLLM.tool(
  name: "get_weather",
  description: "Get the current weather for a location",
  parameter_schema: [
    location: [type: :string, required: true, doc: "City name or location"],
    unit: [type: :string, default: "fahrenheit", doc: "Temperature unit (celsius or fahrenheit)"]
  ],
  callback: fn args ->
    location = args["location"] || args[:location]
    unit = args["unit"] || args[:unit] || "fahrenheit"
    temp = if unit == "celsius", do: "22°C", else: "72°F"
    {:ok, "The weather in #{location} is #{temp}, sunny with clear skies."}
  end
)

time_tool = ReqLLM.tool(
  name: "get_time",
  description: "Get the current time",
  parameter_schema: [],
  callback: fn _args ->
    time = DateTime.utc_now() |> DateTime.to_string()
    {:ok, "The current UTC time is #{time}"}
  end
)

tools = [weather_tool, time_tool]

{:ok, response} = ReqLLM.generate_text(selected_model, tools_prompt, tools: tools)

tool_calls = ReqLLM.Response.tool_calls(response)

output = if tool_calls != [] do
  results = Enum.map(tool_calls, fn tool_call ->
    %{name: name, arguments: arguments} = ReqLLM.ToolCall.to_map(tool_call)
    {:ok, result} = ReqLLM.ToolCall.execute(tool_call, tools)
    "**#{name}** (#{inspect(arguments)})\n→ #{result}"
  end)
  Enum.join(results, "\n\n")
else
  ReqLLM.Response.text(response)
end

Kino.Markdown.new("""
**Tool Calls:**

#{output}
""")

5) Embeddings

Convert text into vector representations for similarity search and semantic analysis.

embedding_text_input = Kino.Input.textarea("Enter text to embed", default: "Elixir is a dynamic, functional programming language")

embedding_text = Kino.Input.read(embedding_text_input)

embedding_model = "openai:text-embedding-3-small"

{:ok, embedding} = ReqLLM.Embedding.embed(embedding_model, embedding_text)

dimensions = length(embedding)
preview = Enum.take(embedding, 8)

Kino.Markdown.new("""
**Embedding Generated:**

- **Dimensions:** #{dimensions}
- **First 8 values:** `#{inspect(preview)}`
- **Total values:** #{dimensions} floating point numbers

Embeddings can be used for:
- Semantic similarity comparisons
- Document search and retrieval
- Clustering related content
- Recommendation systems
""")

← Previous Page V2 Migration Audit

Next Page → ReqLLM • Image Generation Comparison