fetch_hive

gem "fetch_hive", "~> 0.2.6"

require "fetch_hive"

client = FetchHive::Client.new(api_key: ENV["FETCH_HIVE_API_KEY"])

result = client.invoke_prompt(
  deployment: "my-prompt",
  inputs: { name: "Alice", topic: "machine learning" },
  metadata: {}
)
puts result["response"]

client.invoke_prompt_stream(deployment: "my-prompt", inputs: { name: "Alice" }) do |chunk|
  case chunk["type"]
  when "response" then print chunk["response"]
  when "usage"    then puts "\nUsage: #{chunk['usage']}"
  end
end

run = client.invoke_workflow(
  deployment: "my-workflow",
  inputs: { customer_id: "42" },
  metadata: {}
)
puts run["status"], run["output"]

run = client.invoke_workflow(
  deployment: "my-workflow",
  inputs: { customer_id: "42" },
  async_mode: true,
  callback_url: "https://example.com/webhook"
)
puts "Queued: #{run['run_id']}"

reply = client.invoke_agent(
  agent: "my-agent",
  message: "What is the weather in London?",
  metadata: {}
)
puts reply["response"]

client.invoke_agent_stream(
  agent: "my-agent",
  message: "What is the weather in London?",
  thread_id: "session-abc123"  # optional — persist conversation history
) do |chunk|
  case chunk["type"]
  when "response" then print chunk["response"]
  when "tool"     then puts "\nCalling tool: #{chunk['tool']}"
  when "usage"    then puts "\nUsage: #{chunk['usage']}"
  end
end

result = client.invoke_agent(
  agent: "vision-agent",
  message: "Describe this image",
  image_urls: ["https://example.com/photo.jpg"]
)
puts result["response"]

client = FetchHive::Client.new  # picks up FETCH_HIVE_API_KEY automatically