Class: Ask::Providers::OpenAI

Inherits:
Ask::Provider
  • Object
show all
Includes:
LLM::ProviderConfig, LLM::SSEBuffer
Defined in:
lib/ask/provider/openai.rb

Overview

OpenAI API provider. Also handles all OpenAI-compatible providers (OpenRouter, DeepSeek, Azure, XAI, Perplexity, GPUStack, etc.) via base_url override.

Direct Known Subclasses

OpenAICompatible

Class Method Summary collapse

Instance Method Summary collapse

Methods included from LLM::SSEBuffer

#each_sse_event, #init_sse_buffer

Constructor Details

#initialize(config = {}) ⇒ OpenAI

Returns a new instance of OpenAI.



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# File 'lib/ask/provider/openai.rb', line 12

def initialize(config = {})
  @provider_keys = extract_provider_keys(config)
  config = normalize_config(config)
  super(config)
  @http = build_http
end

Class Method Details

.capabilities ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 75

def capabilities
  {
    chat: true, streaming: true, tool_calls: true, vision: true,
    thinking: true, structured_output: true, embed: true,
    transcribe: true, paint: true, moderate: true,
    prompt_caching: true
  }
end

.configuration_options ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 84

def configuration_options; %i[api_key base_url organization_id project_id]; end

.configuration_requirements ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 85

def configuration_requirements; %i[api_key]; end

.slug ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 73

def slug; "openai"; end

Instance Method Details

#api_base ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 19

def api_base
  @config.base_url || "https://api.openai.com/v1"
end

#build_request(messages, model:, tools: nil, temperature: nil, stream: nil, schema: nil, **params) ⇒ Object

--- Config transformation contract ---



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# File 'lib/ask/provider/openai.rb', line 90

def build_request(messages, model:, tools: nil, temperature: nil, stream: nil, schema: nil, **params)
  payload = { model:, messages: format_messages(messages), stream: stream || false }
  payload[:temperature] = temperature if temperature
  payload[:tools] = format_tools(tools) if tools&.any?
  if schema
    payload[:response_format] = {
      type: "json_schema",
      json_schema: { name: "response", schema:, strict: true }
    }
  end
  payload.merge(params)
end

#chat(messages, model:, tools: nil, temperature: nil, stream: nil, schema: nil, **params, &block) ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 32

def chat(messages, model:, tools: nil, temperature: nil, stream: nil, schema: nil, **params, &block)
  msgs = messages.is_a?(Ask::Conversation) ? messages.to_a : messages

  # Separate provider tools from regular tools
  regular_tools, provider_tools = split_tools(tools)

  if provider_tools.any?
    # Use the Responses API when provider tools are involved
    responses_chat(msgs, model:, regular_tools:, provider_tools:,
                   temperature:, stream:, schema:, **params, &block)
  else
    payload = build_request(msgs, model:, tools: regular_tools,
                            temperature:, stream:, schema:, **params)
    stream ? chat_stream(payload, model, &block) : chat_nonstream(payload, model)
  end
end

#embed(texts, model:) ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 49

def embed(texts, model:)
  texts = Array(texts)
  response = @http.post("embeddings") { |r| r.body = { model:, input: texts } }
  raise LLM::HTTP.map_error(response.status, response.body, provider: "OpenAI") unless response.success?

  embeddings = response.body["data"].map { |d| d["embedding"] }
  Ask::Result.success(embeddings.one? ? embeddings.first : embeddings)
end

#extract_responses_provider_results(output, provider_tools) ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 289

def extract_responses_provider_results(output, provider_tools)
  results = {}
  provider_tool_names = provider_tools.map(&:name)

  output.each do |item|
    case item["type"]
    when "web_search_call"
      result_item = output.find { |o| o["type"] == "web_search_result" && o["id"] == item["id"] }
      if result_item
        results[item["id"]] = {
          provider_executed: true,
          tool_name: "web_search",
          message: result_item.to_s,
          status: "success"
        }
      end
    when "file_search_call"
      result_item = output.find { |o| o["type"] == "file_search_result" && o["id"] == item["id"] }
      if result_item
        results[item["id"]] = {
          provider_executed: true,
          tool_name: "file_search",
          message: result_item.to_s,
          status: "success"
        }
      end
    when "function_call"
      # Regular tool call — handled elsewhere
    end
  end
  results
end

#extract_responses_tool_calls(output) ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 322

def extract_responses_tool_calls(output)
  output.select { |o| o["type"] == "function_call" }.map do |fc|
    { id: fc["id"], type: "function", name: fc["name"], arguments: fc["arguments"] }
  end
end

#format_message(msg) ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 178

def format_message(msg)
  role = msg[:role] || msg["role"] || :user
  { role: role.to_s, content: msg[:content] || msg["content"] }.tap do |fm|
    if (tc = msg[:tool_calls] || msg["tool_calls"]) && tc.respond_to?(:any?) && tc.any?
      calls = tc.is_a?(Hash) ? tc.values : tc
      fm[:tool_calls] = calls.map { |t|
        id = t.respond_to?(:id) ? t.id : (t[:id] || t["id"])
        name = t.respond_to?(:name) ? t.name : (t.dig(:function, :name) || t.dig("function", "name") || t[:name])
        raw_args = t.respond_to?(:arguments) ? t.arguments : (t.dig(:function, :arguments) || t.dig("function", "arguments") || t[:arguments])
        args = raw_args.is_a?(String) ? raw_args : JSON.generate(raw_args)
        { id:, type: "function", function: { name:, arguments: args } }
      }
    end
    fm[:tool_call_id] = msg[:tool_call_id] || msg["tool_call_id"] if msg[:tool_call_id] || msg["tool_call_id"]
  end.compact
end

#format_responses_input(messages) ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 260

def format_responses_input(messages)
  messages.map do |msg|
    role = msg[:role] || msg["role"] || "user"
    content = msg[:content] || msg["content"] || ""

    entry = { role: role.to_s }
    entry[:content] = [{ type: "input_text", text: content.to_s }]

    # Handle tool calls in assistant messages
    if (tc = msg[:tool_calls] || msg["tool_calls"]) && tc.respond_to?(:any?) && tc.any?
      calls = tc.is_a?(Hash) ? tc.values : tc
      entry[:content] = calls.map { |t|
        id = t.respond_to?(:id) ? t.id : (t[:id] || t["id"])
        name = t.respond_to?(:name) ? t.name : (t[:name] || t["name"] || t.dig(:function, :name))
        raw_args = t.respond_to?(:arguments) ? t.arguments : (t[:arguments] || t["arguments"] || t.dig(:function, :arguments))
        args = raw_args.is_a?(String) ? raw_args : JSON.generate(raw_args)
        { type: "function_call", id: id, name: name, arguments: args, status: "completed" }
      }
    end

    # Handle tool results
    if (tid = msg[:tool_call_id] || msg["tool_call_id"])
      entry[:content] = [{ type: "function_call_output", id: tid, output: content.to_s }]
    end

    entry
  end
end

#format_responses_tools(provider_tools) ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 163

def format_responses_tools(provider_tools)
  provider_tools.map do |pt|
    case pt.name
    when "web_search"
      { type: "web_search" }.merge(pt.args)
    when "file_search"
      { type: "file_search" }.merge(pt.args)
    when "code_interpreter"
      { type: "code_interpreter" }.merge(pt.args)
    else
      { type: pt.name }.merge(pt.args)
    end
  end
end

#format_tools(tools) ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 148

def format_tools(tools)
  return [] unless tools&.any?

  tools.map do |t|
    {
      type: "function",
      function: {
        name: t.respond_to?(:name) ? t.name : t[:name],
        description: t.respond_to?(:description) ? t.description : t[:description],
        parameters: t.respond_to?(:parameters) ? t.parameters : t[:parameters]
      }
    }
  end
end

#headers ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 23

def headers
  key = @config.api_key || @config.openai_api_key
  h = { "Content-Type" => "application/json" }
  h["Authorization"] = "Bearer #{key}" if key
  h["OpenAI-Organization"] = @config.organization_id if @config.organization_id
  h["OpenAI-Project"] = @config.project_id if @config.project_id
  h
end

#list_models ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 58

def list_models
  response = @http.get("models")
  return [] unless response.success?

  response.body["data"].map { |m|
    Ask::ModelInfo.new(id: m["id"], provider: slug, metadata: { owned_by: m["owned_by"] })
  }
end

#parse_error(response) ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 67

def parse_error(response)
  body = response.body rescue nil
  body&.dig("error", "message") || body&.dig("error", "code")
end

#parse_response(body, model) ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 103

def parse_response(body, model)
  choice = body.dig("choices", 0)
  return Ask::Message.new(role: :assistant, content: nil) unless choice

  msg = choice["message"]
  usage = body["usage"] || {}
   Ask::Message.new(
     role: :assistant,
     content: msg["content"],
     tool_calls: parse_tool_calls(msg["tool_calls"]),
     metadata: {
       model: body["model"] || model,
       finish_reason: choice["finish_reason"],
       input_tokens: usage["prompt_tokens"],
       output_tokens: usage["completion_tokens"],
       cached_tokens: usage.dig("prompt_tokens_details", "cached_tokens"),
       raw: body
     }
   )
end

#parse_stream(raw, stream, model, &block) ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 124

def parse_stream(raw, stream, model, &block)
  each_sse_event(raw) do |data|
    parsed = JSON.parse(data) rescue next
    choice = parsed.dig("choices", 0) or next
    delta = choice["delta"] || {}
    thinking = extract_thinking(parsed, delta)
    chunk = Ask::Chunk.new(
      content: delta["content"],
      tool_calls: parse_stream_tool_calls(delta["tool_calls"]),
      finish_reason: choice["finish_reason"],
      usage: parsed["usage"],
      thinking:
    )
    stream.add(chunk)
    yield chunk if block_given?
  end
end

#responses_chat(messages, model:, regular_tools:, provider_tools:, temperature: nil, stream: nil, schema: nil, **params, &block) ⇒ Object

Use the OpenAI Responses API, which supports provider-executed tools like web_search, file_search, and code_interpreter.



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# File 'lib/ask/provider/openai.rb', line 197

def responses_chat(messages, model:, regular_tools:, provider_tools:,
                   temperature: nil, stream: nil, schema: nil, **params, &block)
  payload = {
    model: model,
    input: format_responses_input(messages)
  }

  all_tools = []
  all_tools.concat(format_tools(regular_tools)) if regular_tools&.any?
  all_tools.concat(format_responses_tools(provider_tools)) if provider_tools&.any?
  payload[:tools] = all_tools if all_tools.any?
  payload[:temperature] = temperature if temperature
  payload.merge!(params)

  if stream
    responses_chat_stream(payload, model, provider_tools, &block)
  else
    responses_chat_nonstream(payload, model, provider_tools)
  end
end

#responses_chat_nonstream(payload, model, provider_tools) ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 218

def responses_chat_nonstream(payload, model, provider_tools)
  response = @http.post("responses") { |r| r.body = payload }
  raise LLM::HTTP.map_error(response.status, response.body, provider: "OpenAI") unless response.success?

  body = response.body
  output = body["output"] || []

  # Extract text content and provider-executed tool results
  text_parts = output.select { |o| o["type"] == "message" }
  content = text_parts.flat_map { |m| (m["content"] || []) }
                      .select { |c| c["type"] == "output_text" }
                      .map { |c| c["text"] }
                      .join

  # Extract provider-executed tool results
  provider_results = extract_responses_provider_results(output, provider_tools)

  # Extract regular tool calls
  regular_calls = extract_responses_tool_calls(output)

  usage = body["usage"] || {}
  Ask::Message.new(
    role: :assistant,
    content: content,
    tool_calls: regular_calls,
    metadata: {
      model: body["model"] || model,
      finish_reason: body.dig("status"),
      input_tokens: usage["input_tokens"],
      output_tokens: usage["output_tokens"],
      provider_results: provider_results,
      raw: body
    }
  )
end

#responses_chat_stream(payload, model, provider_tools, &block) ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 254

def responses_chat_stream(payload, model, provider_tools, &block)
  # Streaming with the Responses API — for now, fall back to non-streaming
  # and return the full result. Full streaming support can be added later.
  responses_chat_nonstream(payload, model, provider_tools)
end

#split_tools(tools) ⇒ Object 



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# File 'lib/ask/provider/openai.rb', line 142

def split_tools(tools)
  return [[], []] unless tools&.any?

  tools.partition { |t| !t.respond_to?(:provider_tool?) || !t.provider_tool? }
end