class Multiwoven::Integrations::Source::OpenAI::Client

def check_connection(connection_config)
  connection_config = prepare_config(connection_config)
  response = send_request(
    url: OPEN_AI_URL,
    http_method: HTTP_POST,
    payload: JSON.parse(connection_config[:request_format]),
    headers: auth_headers(connection_config[:api_key]),
    config: connection_config[:config]
  )
  success?(response) ? success_status : failure_status(nil)
rescue StandardError => e
  handle_exception(e, { context: "OPEN AI:CHECK_CONNECTION:EXCEPTION", type: "error" })
  failure_status(e)
end

def discover(_connection_config = nil)
  catalog_json = read_json(CATALOG_SPEC_PATH)
  catalog = build_catalog(catalog_json)
  catalog.to_multiwoven_message
rescue StandardError => e
  handle_exception(e, { context: "OPEN AI:DISCOVER:EXCEPTION", type: "error" })
end

def extract_data_entries(chunk)
  chunk.split(/^data: /).map(&:strip).reject(&:empty?)
end

def parse_json(json_string)
  JSON.parse(json_string)
rescue JSON::ParserError => e
  handle_exception(e, { context: "OPEN AI:PARSE_JSON:EXCEPTION", type: "error" })
  {}
end

def prepare_config(config)
  config.with_indifferent_access.tap do |conf|
    conf[:config][:timeout] ||= 30
  end
end

def process_response(response)
  if success?(response)
    data = JSON.parse(response.body)
    [RecordMessage.new(data: data, emitted_at: Time.now.to_i).to_multiwoven_message]
  else
    create_log_message("OPEN AI:RUN_MODEL", "error", "request failed: #{response.body}")
  end
rescue StandardError => e
  handle_exception(e, { context: "OPEN AI:PROCESS_RESPONSE:EXCEPTION", type: "error" })
end

def process_streaming_response(chunk)
  data_entries = extract_data_entries(chunk)
  data_entries.each do |entry|
    next if entry == "[DONE]"
    data = parse_json(entry)
    raise StandardError, "Error: #{data["error"]["message"]}" if data["error"] && data["error"]["message"]
    yield [RecordMessage.new(data: data, emitted_at: Time.now.to_i).to_multiwoven_message] if block_given?
  end
end

def read(sync_config)
  connection_config = prepare_config(sync_config.source.connection_specification)
  stream = connection_config[:is_stream] ||= false
  # The server checks the ConnectorQueryType.
  # If it's "ai_ml," the server calculates the payload and passes it as a query in the sync config model protocol.
  # This query is then sent to the AI/ML model.
  payload = parse_json(sync_config.model.query)
  if stream
    run_model_stream(connection_config, payload) { |message| yield message if block_given? }
  else
    run_model(connection_config, payload)
  end
rescue StandardError => e
  handle_exception(e, { context: "OPEN AI:READ:EXCEPTION", type: "error" })
end

def run_model(connection_config, payload)
  response = send_request(
    url: OPEN_AI_URL,
    http_method: HTTP_POST,
    payload: payload,
    headers: auth_headers(connection_config[:api_key]),
    config: connection_config[:config]
  )
  process_response(response)
rescue StandardError => e
  handle_exception(e, { context: "OPEN AI:RUN_MODEL:EXCEPTION", type: "error" })
end

def run_model_stream(connection_config, payload)
  send_streaming_request(
    url: OPEN_AI_URL,
    http_method: HTTP_POST,
    payload: payload,
    headers: auth_headers(connection_config[:api_key]),
    config: connection_config[:config]
  ) do |chunk|
    process_streaming_response(chunk) { |message| yield message if block_given? }
  end
rescue StandardError => e
  handle_exception(e, { context: "OPEN AI:RUN_STREAM_MODEL:EXCEPTION", type: "error" })
end