Module: Legion::LLM::API::Translators::OpenAIResponse

Extended by:
Legion::Logging::Helper
Defined in:
lib/legion/llm/api/translators/openai_response.rb

Constant Summary collapse

FINISH_REASON_MAP = 
{
  'stop'           => 'stop',
  'length'         => 'length',
  'tool_calls'     => 'tool_calls',
  'content_filter' => 'content_filter'
}.freeze

Class Method Summary collapse

Class Method Details

.base64_float32_vector(vector) ⇒ Object

OpenAI encoding_format: 'base64' — the vector as one base64 string of little-endian float32 words ('g' is little-endian). pack('g*') yields the binary words; base64-ENCODING that binary is [binary].pack('m0') (m0 = base64 without line breaks; unpack1('m0') would DECODE, and String has no #pack).



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# File 'lib/legion/llm/api/translators/openai_response.rb', line 176

def base64_float32_vector(vector)
  [vector.pack('g*')].pack('m0')
end

.build_tool_calls(pipeline_response) ⇒ Object 



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# File 'lib/legion/llm/api/translators/openai_response.rb', line 197

def build_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.each_with_index.filter_map do |tc, idx|
    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

    {
      id:       tc_id,
      type:     'function',
      index:    idx,
      function: {
        name:      name.to_s,
        arguments: args.is_a?(String) ? args : Legion::JSON.dump(args)
      }
    }
  end
end

.content_looks_like_tool_json?(content) ⇒ Boolean

Heuristic: does the content look like a bare JSON object that is tool-call arguments (e.g. "...", "limit": 300)?

Returns:

  • (Boolean) 


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# File 'lib/legion/llm/api/translators/openai_response.rb', line 247

def content_looks_like_tool_json?(content)
  stripped = content.to_s.strip
  return false unless stripped.start_with?('{"') && stripped.end_with?('}')

  parsed = Legion::JSON.parse(stripped, symbolize_names: false)
  parsed.is_a?(Hash) && parsed.keys.any?
rescue Legion::JSON::ParseError, StandardError
  false
end

.embedding_token_count(entries, input_texts) ⇒ Object

Prefers the provider's token counts (carried under :tokens on each embed result); falls back to the input word count when no entry carries a count.



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# File 'lib/legion/llm/api/translators/openai_response.rb', line 238

def embedding_token_count(entries, input_texts)
  counts = Array(entries).filter_map { |e| e.is_a?(Hash) ? (e[:tokens] || e['tokens']) : nil }
  return counts.sum.to_i unless counts.empty?

  Array(input_texts).sum { |t| t.to_s.split.size }
end

.extract_token_count(tokens, key) ⇒ Object 



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# File 'lib/legion/llm/api/translators/openai_response.rb', line 225

def extract_token_count(tokens, key)
  return nil if tokens.nil?
  return tokens[key] || tokens[key.to_s] if tokens.is_a?(Hash)

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

  nil
end

.format_chat_completion(pipeline_response, model:, request_id: nil, include_reasoning: false) ⇒ Object 



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# File 'lib/legion/llm/api/translators/openai_response.rb', line 23

def format_chat_completion(pipeline_response, model:, request_id: nil, include_reasoning: false)
  request_id ||= SecureRandom.uuid
  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']) : raw_msg.to_s
  stop_reason = pipeline_response.stop&.dig(:reason)&.to_s
  tool_calls = build_tool_calls(pipeline_response)
  resolved_model = (routing[:model] || routing['model'] || model).to_s

  log.debug("[llm][translator][openai_response] action=format_chat_completion request_id=#{request_id} model=#{resolved_model}")

  finish_reason = tool_calls.empty? ? map_finish_reason(stop_reason) : 'tool_calls'

  # When tool calls are present and content is just JSON arguments
  # (e.g. vLLM/qwen forced tool choice), clear the content field
  # so the client sees only structured tool_calls.
  content = nil if tool_calls.any? && content_looks_like_tool_json?(content)

  message_body = { role: 'assistant', content: content }
  message_body[:tool_calls] = tool_calls unless tool_calls.empty?

  # Include reasoning/thinking content in the response when requested.
  # Uses the `reasoning_content` field convention from OpenAI's reasoning models.
  if include_reasoning && pipeline_response.respond_to?(:thinking) && pipeline_response.thinking
    thinking_data = pipeline_response.thinking
    reasoning_text = if thinking_data.is_a?(Hash)
                       thinking_data[:content] || thinking_data['content'] || thinking_data[:text] || thinking_data['text']
                     elsif thinking_data.respond_to?(:content)
                       thinking_data.content
                     elsif thinking_data.respond_to?(:text)
                       thinking_data.text
                     else
                       thinking_data.to_s
                     end
    message_body[:reasoning_content] = reasoning_text.to_s unless reasoning_text.to_s.empty?
  end

  {
    id:      "chatcmpl-#{request_id.delete('-')}",
    object:  'chat.completion',
    created: Time.now.to_i,
    model:   resolved_model,
    choices: [
      {
        index:         0,
        message:       message_body,
        finish_reason: finish_reason
      }
    ],
    usage:   {
      prompt_tokens:     extract_token_count(tokens, :input),
      completion_tokens: extract_token_count(tokens, :output),
      total_tokens:      (extract_token_count(tokens, :input).to_i + extract_token_count(tokens, :output).to_i)
    }
  }
end

.format_embeddings(entries, model:, input_texts: nil, encoding_format: 'float') ⇒ Object

Parameters:

  • entries (Array) —

    one element per input item, in input order. Each element is a Hash carrying the raw float vector under :vector (and the provider token count under :tokens when present) or a bare Array of floats.

  • encoding_format (String) (defaults to: 'float') —

    'float' (default) emits raw floats; 'base64' emits a base64 string of little-endian float32 words.



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# File 'lib/legion/llm/api/translators/openai_response.rb', line 147

def format_embeddings(entries, model:, input_texts: nil, encoding_format: 'float')
  base64 = encoding_format.to_s == 'base64'
  data = Array(entries).each_with_index.map do |entry, index|
    vector = entry.is_a?(Hash) ? (entry[:vector] || entry['vector']) : entry
    vector = [] unless vector.is_a?(Array)
    {
      object:    'embedding',
      embedding: base64 ? base64_float32_vector(vector) : vector,
      index:     index
    }
  end
  prompt_tokens = embedding_token_count(entries, input_texts)

  {
    object: 'list',
    data:   data,
    model:  model.to_s,
    usage:  {
      prompt_tokens: prompt_tokens,
      total_tokens:  prompt_tokens
    }
  }
end

.format_model_object(id, created: nil, owned_by: 'legion', limits: nil) ⇒ Object 



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# File 'lib/legion/llm/api/translators/openai_response.rb', line 180

def format_model_object(id, created: nil, owned_by: 'legion', limits: nil)
  obj = {
    id:       id.to_s,
    object:   'model',
    created:  created || Time.now.to_i,
    owned_by: owned_by
  }
  if limits.is_a?(Hash)
    if limits[:context_window]
      obj[:context_window] = limits[:context_window]
      obj[:context_size] = limits[:context_window]
    end
    obj[:max_output_tokens] = limits[:max_output_tokens] if limits[:max_output_tokens]
  end
  obj
end

.format_stream_chunk(delta_text, model:, request_id:, finish_reason: nil, usage: nil) ⇒ Object 



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# File 'lib/legion/llm/api/translators/openai_response.rb', line 81

def format_stream_chunk(delta_text, model:, request_id:, finish_reason: nil, usage: nil)
  choice = { index: 0, delta: {}, finish_reason: finish_reason }
  choice[:delta][:content] = delta_text if delta_text && !delta_text.empty?

  chunk = {
    id:      "chatcmpl-#{request_id.delete('-')}",
    object:  'chat.completion.chunk',
    created: Time.now.to_i,
    model:   model.to_s,
    choices: [choice]
  }
  chunk[:usage] = usage if usage
  chunk
end

.format_stream_delta_chunk(delta, model:, request_id:, finish_reason: nil) ⇒ Object 



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# File 'lib/legion/llm/api/translators/openai_response.rb', line 125

def format_stream_delta_chunk(delta, model:, request_id:, finish_reason: nil)
  {
    id:      "chatcmpl-#{request_id.delete('-')}",
    object:  'chat.completion.chunk',
    created: Time.now.to_i,
    model:   model.to_s,
    choices: [
      {
        index:         0,
        delta:         delta,
        finish_reason: finish_reason
      }
    ]
  }
end

.format_stream_tool_call_chunk(tool_call, model:, request_id:, index:) ⇒ Object 



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# File 'lib/legion/llm/api/translators/openai_response.rb', line 96

def format_stream_tool_call_chunk(tool_call, model:, request_id:, index:)
  fn = tool_call.is_a?(Hash) ? (tool_call[:function] || tool_call['function'] || {}) : {}
  name = tool_call.respond_to?(:name) ? tool_call.name : (tool_call[:name] || tool_call['name'] || fn[:name] || fn['name'])
  args = if tool_call.respond_to?(:arguments)
           tool_call.arguments
         else
           tool_call[:arguments] || tool_call['arguments'] || fn[:arguments] || fn['arguments'] || {}
         end
  tc_id = tool_call.respond_to?(:id) ? tool_call.id : (tool_call[:id] || tool_call['id'] || "call_#{SecureRandom.hex(8)}")

  format_stream_delta_chunk(
    {
      tool_calls: [
        {
          index:    index,
          id:       tc_id,
          type:     'function',
          function: {
            name:      name.to_s,
            arguments: args.is_a?(String) ? args : Legion::JSON.dump(args)
          }
        }
      ]
    },
    model:      model,
    request_id: request_id
  )
end

.map_finish_reason(stop_reason) ⇒ Object 



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# File 'lib/legion/llm/api/translators/openai_response.rb', line 219

def map_finish_reason(stop_reason)
  return 'stop' if stop_reason.nil? || stop_reason.to_s.empty?

  FINISH_REASON_MAP.fetch(stop_reason.to_s, 'error')
end