Module: Legion::LLM::API::Namespaces::OpenAI::Responses

Extended by:
Legion::Logging::Helper
Defined in:
lib/legion/llm/api/namespaces/openai/responses.rb

Class Method Summary collapse

Class Method Details

.build_output_tool_calls(pipeline_response) ⇒ Object 



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# File 'lib/legion/llm/api/namespaces/openai/responses.rb', line 305

def self.build_output_tool_calls(pipeline_response)
  tools_data = pipeline_response.respond_to?(:tools) ? pipeline_response.tools : nil
  return [] unless tools_data.is_a?(Array) && !tools_data.empty?

  tools_data.filter_map do |tc|
    name  = tc.respond_to?(:name) ? tc.name : (tc[:name] || tc['name'])
    args  = tc.respond_to?(:arguments) ? tc.arguments : (tc[:arguments] || tc['arguments'] || {})
    tc_id = tc.respond_to?(:id) ? tc.id : (tc[:id] || tc['id'] || "call_#{SecureRandom.hex(8)}")
    next unless name

    { type: 'function_call', id: "fc_#{SecureRandom.hex(12)}", call_id: tc_id,
      name: name.to_s, arguments: args.is_a?(String) ? args : Legion::JSON.dump(args), status: 'completed' }
  end
end

.build_tool_declarations(tools) ⇒ Object 



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# File 'lib/legion/llm/api/namespaces/openai/responses.rb', line 190

def self.build_tool_declarations(tools)
  return [] unless tools.is_a?(Array) && !tools.empty?

  tools.filter_map do |tool|
    fn = nil
    t  = tool.respond_to?(:transform_keys) ? tool.transform_keys(&:to_sym) : tool
    fn = t[:function] || t
    fn = fn.transform_keys(&:to_sym) if fn.respond_to?(:transform_keys)
    next unless fn[:name].to_s.length.positive?

    Legion::LLM::Types::ToolDefinition.build(
      name:        fn[:name].to_s,
      description: fn[:description].to_s,
      parameters:  fn[:parameters] || {},
      source:      { type: :client, executable: true }
    )
  rescue StandardError => e
    tool_name = fn.is_a?(Hash) ? fn[:name] : nil
    Legion::Logging::Helper.log.warn("[llm][api][namespaces][openai][responses] build_tool failed name=#{tool_name} error=#{e.message}")
    nil
  end
end

.build_usage(tokens) ⇒ Object 



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# File 'lib/legion/llm/api/namespaces/openai/responses.rb', line 320

def self.build_usage(tokens)
  i = extract_token(tokens, :input_tokens)
  o = extract_token(tokens, :output_tokens)
  { input_tokens: i, output_tokens: o, total_tokens: i + o }
end

.call_executor(executor, upstream_body: nil) ⇒ Object 



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# File 'lib/legion/llm/api/namespaces/openai/responses.rb', line 297

def self.call_executor(executor, upstream_body: nil, &)
  if upstream_body && executor.respond_to?(:call_responses)
    executor.call_responses(body: upstream_body, stream: true, &)
  else
    executor.call_stream(&)
  end
end

.extract_token(tokens, key) ⇒ Object 



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# File 'lib/legion/llm/api/namespaces/openai/responses.rb', line 326

def self.extract_token(tokens, key)
  return 0 if tokens.nil?

  if tokens.is_a?(Hash)
    v = tokens[key] || tokens[key.to_s]
    return v.to_i unless v.nil?

    alt = key == :input_tokens ? :input : :output
    v2  = tokens[alt] || tokens[alt.to_s]
    return v2.to_i unless v2.nil?

    return 0
  end

  method_name = { input_tokens: :input_tokens, output_tokens: :output_tokens,
                  input: :input_tokens, output: :output_tokens }[key]
  return tokens.public_send(method_name).to_i if method_name && tokens.respond_to?(method_name)

  0
end

.flush_pending_tool_calls(messages, pending) ⇒ Object 



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# File 'lib/legion/llm/api/namespaces/openai/responses.rb', line 177

def self.flush_pending_tool_calls(messages, pending)
  return if pending.empty?

  messages << {
    role:       'assistant',
    content:    '',
    tool_calls: pending.map do |tc|
      { id: tc[:id], type: 'function', function: { name: tc[:name], arguments: tc[:arguments] } }
    end
  }
  pending.clear
end

.format_response(pipeline_response, request_id:, model:) ⇒ Object 



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# File 'lib/legion/llm/api/namespaces/openai/responses.rb', line 213

def self.format_response(pipeline_response, request_id:, model:)
  routing       = pipeline_response.routing || {}
  tokens        = pipeline_response.tokens || {}
  raw_msg       = pipeline_response.message
  content       = raw_msg.is_a?(Hash) ? (raw_msg[:content] || raw_msg['content']).to_s : raw_msg.to_s
  resolved_model = (routing[:model] || routing['model'] || model).to_s
  tool_calls = build_output_tool_calls(pipeline_response)

  output = [*tool_calls, {
    type:    'message',
    id:      "msg_#{SecureRandom.hex(12)}",
    role:    'assistant',
    content: [{ type: 'output_text', text: content }],
    status:  'completed'
  }]

  { id: request_id, object: 'response', created_at: Time.now.to_i,
    model: resolved_model, output: output, usage: build_usage(tokens), status: 'completed' }
end

.normalize_input_array(input) ⇒ Object

— Support methods —



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# File 'lib/legion/llm/api/namespaces/openai/responses.rb', line 147

def self.normalize_input_array(input)
  messages = []
  pending_tool_calls = []

  input.each do |item|
    item = item.transform_keys(&:to_sym) if item.respond_to?(:transform_keys)
    case item[:type]&.to_s
    when 'function_call'
      pending_tool_calls << {
        id:        item[:call_id] || item[:id],
        name:      item[:name].to_s,
        arguments: item[:arguments].is_a?(String) ? item[:arguments] : Legion::JSON.dump(item[:arguments] || {})
      }
    when 'function_call_output'
      flush_pending_tool_calls(messages, pending_tool_calls)
      messages << { role: 'tool', tool_call_id: item[:call_id], content: item[:output].to_s }
    else
      flush_pending_tool_calls(messages, pending_tool_calls)
      role = item[:role]&.to_s
      next unless role

      content = item[:content]
      content = content.to_s if content && !content.is_a?(Array)
      messages << { role: role, content: content }.compact
    end
  end
  flush_pending_tool_calls(messages, pending_tool_calls)
  messages
end

.registered(app) ⇒ Object

rubocop:disable Metrics/AbcSize,Metrics/MethodLength



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# File 'lib/legion/llm/api/namespaces/openai/responses.rb', line 17

def self.registered(app) # rubocop:disable Metrics/AbcSize,Metrics/MethodLength
  log.debug('[llm][api][namespaces][openai][responses] registering routes')

  # rubocop:disable Metrics/BlockLength
  app.post '/v1/responses' do
    require_llm!
    body = parse_request_body
    request_id = "resp_#{SecureRandom.hex(16)}"

    input = body[:input]
    messages = case input
               when Array
                 Responses.normalize_input_array(input)
               when String
                 [{ role: 'user', content: input }]
               else
                 return openai_error('input is required (string or array)',
                                     type: 'invalid_request_error', status_code: 400)
               end

    messages = [{ role: 'system', content: body[:instructions].to_s }] + messages if body[:instructions]

    model       = body[:model] || Legion::LLM::Settings.value(:default_model) || 'default'
    streaming   = body[:stream] == true
    tool_decls  = Responses.build_tool_declarations(body[:tools])

    log.info("[llm][api][namespaces][openai][responses] action=accepted request_id=#{request_id} model=#{model} stream=#{streaming}")

    inference_request = Legion::LLM::Inference::Request.build(
      id:       request_id,
      messages: messages,
      routing:  { model: model },
      tools:    tool_decls,
      caller:   build_server_caller(source: 'openai_responses', path: request.path, env: env),
      stream:   streaming,
      cache:    { strategy: :default, cacheable: true }
    )
    executor = Legion::LLM::Inference::Executor.new(inference_request)

    if streaming
      content_type 'text/event-stream'
      headers 'Cache-Control' => 'no-cache', 'Connection' => 'keep-alive', 'X-Accel-Buffering' => 'no'
      stream do |out|
        Responses.stream_response(out, executor, request_id: request_id, model: model, upstream_body: body)
      rescue StandardError => e
        handle_exception(e, level: :error, handled: false, operation: 'llm.api.namespaces.openai.responses.stream', request_id: request_id)
        out << "event: error\ndata: #{Legion::JSON.dump({ type: 'server_error', message: e.message })}\n\n"
      end
    else
      pipeline_response = executor.call_responses(body: body, stream: false)
      response_body     = Responses.format_response(pipeline_response, request_id: request_id, model: model)
      log.info("[llm][api][namespaces][openai][responses] action=complete request_id=#{request_id}")
      content_type :json
      status 200
      Legion::JSON.dump(response_body)
    end
  rescue Legion::LLM::AuthError => e
    handle_exception(e, level: :error, handled: true, operation: 'llm.api.namespaces.openai.responses.auth')
    openai_error(e.message, type: 'authentication_error', status_code: 401)
  rescue Legion::LLM::RateLimitError => e
    handle_exception(e, level: :warn, handled: true, operation: 'llm.api.namespaces.openai.responses.rate_limit')
    openai_error(e.message, type: 'rate_limit_error', code: 'rate_limit_exceeded', status_code: 429)
  rescue Legion::LLM::ProviderDown, Legion::LLM::ProviderError => e
    handle_exception(e, level: :error, handled: true, operation: 'llm.api.namespaces.openai.responses.provider')
    openai_error(e.message, type: 'server_error', status_code: 502)
  rescue StandardError => e
    handle_exception(e, level: :error, handled: false, operation: 'llm.api.namespaces.openai.responses')
    openai_error(e.message, type: 'server_error', status_code: 500)
  end
  # rubocop:enable Metrics/BlockLength

  app.get '/v1/responses/:id' do
    log.debug("[llm][api][namespaces][openai][responses] action=retrieve id=#{params[:id]}")
    openai_error("Response '#{params[:id]}' not found", type: 'invalid_request_error',
                                                        code: 'response_not_found', status_code: 404)
  end

  app.delete '/v1/responses/:id' do
    log.debug("[llm][api][namespaces][openai][responses] action=delete id=#{params[:id]}")
    content_type :json
    Legion::JSON.dump({ id: params[:id], object: 'response', deleted: true })
  end

  app.post '/v1/responses/:id/cancel' do
    log.debug("[llm][api][namespaces][openai][responses] action=cancel id=#{params[:id]}")
    openai_error("Response '#{params[:id]}' not found or already completed",
                 type: 'invalid_request_error', status_code: 404)
  end

  app.get '/v1/responses/:id/input_items' do
    log.debug("[llm][api][namespaces][openai][responses] action=input_items id=#{params[:id]}")
    content_type :json
    Legion::JSON.dump({ object: 'list', data: [], has_more: false })
  end

  app.post '/v1/responses/:id/input_tokens/count' do
    body  = parse_request_body
    input = body[:input]
    model = body[:model] || params[:id]
    messages = case input
               when Array  then Responses.normalize_input_array(input)
               when String then [{ role: 'user', content: input }]
               else []
               end
    result = Legion::LLM::TokenEstimation.estimate(messages: messages, model: model.to_s)
    content_type :json
    Legion::JSON.dump(result)
  rescue StandardError => e
    handle_exception(e, level: :error, handled: false, operation: 'llm.api.namespaces.openai.responses.count_tokens')
    openai_error(e.message, type: 'server_error', status_code: 500)
  end

  app.post '/v1/responses/:id/compact' do
    log.debug("[llm][api][namespaces][openai][responses] action=compact id=#{params[:id]}")
    openai_error("Response '#{params[:id]}' not found", type: 'invalid_request_error', status_code: 404)
  end

  # Legacy alias — preserved for clients using the pre-namespace path.
  app.post '/api/llm/inference/v1/responses' do
    log.debug('[llm][api][namespaces][openai][responses] action=legacy_alias forwarding to /v1/responses handler')
    call env.merge('PATH_INFO' => '/v1/responses')
  end

  log.debug('[llm][api][namespaces][openai][responses] routes registered')
rescue StandardError => e
  handle_exception(e, level: :error, handled: false, operation: 'llm.api.namespaces.openai.responses.register')
end

.sse(name, payload) ⇒ Object 



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# File 'lib/legion/llm/api/namespaces/openai/responses.rb', line 347

def self.sse(name, payload)
  "event: #{name}\ndata: #{Legion::JSON.dump(payload)}\n\n"
end

.stream_response(out, executor, request_id:, model:, upstream_body: nil) ⇒ Object 



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# File 'lib/legion/llm/api/namespaces/openai/responses.rb', line 233

def self.stream_response(out, executor, request_id:, model:, upstream_body: nil)
  created_at = Time.now.to_i
  seq        = 0
  base_resp  = { id: request_id, object: 'response', created_at: created_at,
                 status: 'in_progress', model: model, output: [], usage: nil }

  out << sse('response.created',       { type: 'response.created',       sequence_number: seq += 1, response: base_resp })
  out << sse('response.in_progress',   { type: 'response.in_progress',   sequence_number: seq += 1, response: base_resp })

  msg_id = "msg_#{SecureRandom.hex(12)}"
  out << sse('response.output_item.added',  { type: 'response.output_item.added',  sequence_number: seq += 1, output_index: 0,
                                              item: { id: msg_id, type: 'message', role: 'assistant', content: [], status: 'in_progress' } })
  out << sse('response.content_part.added', { type: 'response.content_part.added', sequence_number: seq += 1, output_index: 0,
                                              content_index: 0, item_id: msg_id,
                                              part: { type: 'output_text', text: '', annotations: [] } })

  full_text = +''
  pipeline_response = call_executor(executor, upstream_body: upstream_body) do |chunk|
    text = chunk.respond_to?(:content) ? chunk.content.to_s : chunk.to_s
    next if text.empty?

    full_text << text
    out << sse('response.output_text.delta', { type: 'response.output_text.delta', sequence_number: seq += 1,
                                               output_index: 0, content_index: 0, item_id: msg_id, delta: text })
  end

  routing        = pipeline_response.routing || {}
  tokens         = pipeline_response.tokens  || {}
  resolved_model = (routing[:model] || routing['model'] || model).to_s
  usage          = build_usage(tokens)
  function_calls = build_output_tool_calls(pipeline_response)

  out << sse('response.output_text.done',   { type: 'response.output_text.done',   sequence_number: seq += 1,
                                              output_index: 0, content_index: 0, item_id: msg_id, text: full_text })
  out << sse('response.content_part.done',  { type: 'response.content_part.done',  sequence_number: seq += 1,
                                              output_index: 0, content_index: 0, item_id: msg_id,
                                              part: { type: 'output_text', text: full_text, annotations: [] } })

  completed_item = { id: msg_id, type: 'message', role: 'assistant', status: 'completed',
                     content: [{ type: 'output_text', text: full_text, annotations: [] }] }
  out << sse('response.output_item.done', { type: 'response.output_item.done', sequence_number: seq += 1,
                                            output_index: 0, item: completed_item })

  function_calls.each_with_index do |fc, idx|
    oi = idx + 1
    out << sse('response.output_item.added',
               { type: 'response.output_item.added', sequence_number: seq += 1, output_index: oi,
item: fc.merge(status: 'in_progress', arguments: '') })
    out << sse('response.function_call_arguments.delta',
               { type: 'response.function_call_arguments.delta', sequence_number: seq += 1, output_index: oi, item_id: fc[:id],
delta: fc[:arguments] })
    out << sse('response.function_call_arguments.done',
               { type: 'response.function_call_arguments.done',  sequence_number: seq += 1, output_index: oi, item_id: fc[:id],
arguments: fc[:arguments] })
    out << sse('response.output_item.done',
               { type: 'response.output_item.done',              sequence_number: seq += 1, output_index: oi, item: fc })
  end

  out << sse('response.completed', { type: 'response.completed', sequence_number: seq + 1,
                                     response: { id: request_id, object: 'response', created_at: created_at,
                                                 status: 'completed', model: resolved_model,
                                                 output: [completed_item, *function_calls], usage: usage } })
end