feat(llm): add Responses API client
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aakashb95
@@ -0,0 +1,209 @@
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require 'openai'
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require 'ruby_llm/tool'
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class Llm::ResponsesClient
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DEFAULT_STORE = false
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DEFAULT_TEXT_FORMAT = { type: 'text' }.freeze
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SYSTEM_ROLES = %w[system developer].freeze
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def initialize(api_key:, api_base: nil, client: nil)
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@client = client || OpenAI::Client.new(access_token: api_key, uri_base: normalized_api_base(api_base))
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end
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def create(model:, messages:, reasoning_effort: nil, schema: nil, tools: [], metadata: {}, store: DEFAULT_STORE, **options)
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payload = build_payload(
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model: model,
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messages: messages,
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reasoning_effort: reasoning_effort,
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schema: schema,
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tools: tools,
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metadata: metadata,
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store: store,
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options: options
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)
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return payload if payload[:error]
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build_response(@client.json_post(path: '/responses', parameters: payload), request_messages: messages)
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end
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def build_payload(model:, messages:, reasoning_effort: nil, schema: nil, tools: [], metadata: {}, store: DEFAULT_STORE, options: {})
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input_messages = conversation_messages(messages)
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return no_conversation_payload(messages) if input_messages.empty?
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payload = {
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model: model,
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input: input_messages,
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store: store,
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text: { format: text_format(schema) }
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}
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instructions = instructions_from(messages)
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payload[:instructions] = instructions if instructions.present?
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payload[:reasoning] = { effort: reasoning_effort } if reasoning_effort.present?
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payload[:tools] = normalize_tools(tools) if tools.present?
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payload[:metadata] = metadata if metadata.present?
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payload.merge!(options.compact)
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payload
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end
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def self.extract_output_text(response)
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Array(response['output']).filter_map do |item|
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next unless item['type'] == 'message'
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Array(item['content']).filter_map do |content|
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content['text'] if content['type'] == 'output_text'
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end.join
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end.join
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end
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def self.extract_function_calls(response)
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Array(response['output']).filter_map do |item|
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next unless item['type'] == 'function_call'
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{
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'id' => item['id'],
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'call_id' => item['call_id'],
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'name' => item['name'],
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'arguments' => parse_arguments(item['arguments']),
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'status' => item['status']
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}
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end
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end
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def self.usage_from(response)
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usage = response['usage'] || {}
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{
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'prompt_tokens' => usage['input_tokens'],
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'completion_tokens' => usage['output_tokens'],
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'total_tokens' => usage['total_tokens'],
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'reasoning_tokens' => usage.dig('output_tokens_details', 'reasoning_tokens')
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}.compact
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end
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def self.parse_arguments(arguments)
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return arguments unless arguments.is_a?(String)
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JSON.parse(arguments)
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rescue JSON::ParserError
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arguments
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end
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private
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def build_response(response, request_messages:)
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{
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message: self.class.extract_output_text(response),
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usage: self.class.usage_from(response),
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response_id: response['id'],
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model: response['model'],
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status: response['status'],
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function_calls: self.class.extract_function_calls(response),
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raw_response: response,
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request_messages: request_messages
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}
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end
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def conversation_messages(messages)
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messages.reject { |message| SYSTEM_ROLES.include?(message[:role].to_s) }.map do |message|
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{
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role: message[:role].to_s,
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content: format_content(message[:content])
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}
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end
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end
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def instructions_from(messages)
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messages.filter_map do |message|
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message[:content] if SYSTEM_ROLES.include?(message[:role].to_s)
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end.join("\n\n")
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end
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def format_content(content)
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return content unless content.is_a?(Array)
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content.map do |part|
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normalized_part = part.deep_symbolize_keys
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case normalized_part[:type]
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when 'text'
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{ type: 'input_text', text: normalized_part[:text] }
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when 'image_url'
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{ type: 'input_image', image_url: normalized_part.dig(:image_url, :url) || normalized_part[:image_url] }
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else
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normalized_part
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end
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end
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end
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def text_format(schema)
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return DEFAULT_TEXT_FORMAT unless schema
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schema_payload = normalize_schema_payload(schema)
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{
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type: 'json_schema',
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name: schema_payload[:name],
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schema: schema_payload[:schema],
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strict: schema_payload[:strict]
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}
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end
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def normalize_schema_payload(schema)
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schema_instance = schema.is_a?(Class) ? schema.new : schema
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raw_schema = schema_instance.respond_to?(:to_json_schema) ? schema_instance.to_json_schema : schema_instance
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raw_schema = raw_schema.deep_symbolize_keys
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schema_def = (raw_schema[:schema] || raw_schema).deep_dup
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strict = raw_schema.key?(:strict) ? raw_schema[:strict] : schema_def.delete(:strict)
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{
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name: sanitize_schema_name(raw_schema[:name] || 'response'),
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schema: schema_def,
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strict: strict.nil? || strict
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}
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end
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def normalize_tools(tools)
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tools.map do |tool|
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if tool.is_a?(Hash)
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normalized_tool = tool.deep_symbolize_keys
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function = normalized_tool[:function]
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next normalized_tool unless function
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next {
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type: 'function',
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name: function[:name],
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description: function[:description],
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parameters: function[:parameters],
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strict: function.fetch(:strict, true)
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}.compact
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end
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tool_instance = tool.is_a?(Class) ? tool.new : tool
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{
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type: 'function',
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name: tool_instance.name,
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description: tool_instance.description,
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parameters: tool_instance.params_schema || RubyLLM::Tool::SchemaDefinition.from_parameters(tool_instance.parameters)&.json_schema,
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strict: true
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}.compact
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end
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end
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def no_conversation_payload(messages)
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{
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error: 'No conversation messages provided',
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error_code: 400,
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request_messages: messages
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}
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end
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def sanitize_schema_name(name)
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sanitized = name.to_s.gsub(/[^a-zA-Z0-9_-]/, '_')
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sanitized.presence || 'response'
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end
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def normalized_api_base(api_base)
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endpoint = api_base.presence || 'https://api.openai.com'
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endpoint = endpoint.chomp('/')
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endpoint.delete_suffix('/v1')
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end
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end
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