#* DeepSeek chat-completions request body.

   The provider's request shape lives under base/ai/templates/<type>.html and is
   named by the manager's type (DeepSeekManager.getType() == "deepseek"), because the
   body shape is provider-specific: chat completions here, Responses-shaped for OpenAI.

   The body is a rendering of frame state:
     $recent      thought rows for this framepath, oldest first
                  (user_message/chat -> user, airesponse -> assistant,
                   function_call -> assistant tool_calls, function_response -> tool)
     $prompt      the turn being asked now
     $systemmessage, $model, $functions, $frameroot, $apphome, $gpt, $context
*#
{
  "model": "$!model",
  "stream": false,
  "messages": [
    {
      "role": "system",
      "content": #jesc($systemmessage)
    }
#foreach($entry in $recent)
#set($emit = true)
## Frame logs are diagnostics, not conversation. loadFrameResponse writes one per
## action, so a log row can land between an assistant tool_calls and its tool
## response - emitting it there makes the API reject the whole request
## ("tool_calls must be followed by tool messages").
#if($entry.messagetype == "log")
#set($emit = false)
#end
#if($entry.user == "agent" && $entry.messagetype != "function_call" && $entry.messagetype != "function_response" && (!$entry.message || $entry.message.trim() == ""))
#set($emit = false)
#end
#if($emit)
    ,#if($entry.messagetype == "function_call")
      {
        "role": "assistant",
        "content": null,
        "tool_calls": [
          {
            "id": "$entry.id",
            "type": "function",
            "function": {
              "name": "$entry.function",
              "arguments": #jesc($entry.arguments)
            }
          }
        ]
      }
    #elseif($entry.messagetype == "function_response")
      {
        "role": "tool",
        "tool_call_id": "$entry.command",
        "content": #jesc($entry.function_response)
      }
    #elseif($entry.user == "agent")
      {
        "role": "assistant",
        "content": #jesc($entry.message)
      }
    #else
      {
        "role": "user",
        "content": #jesc($entry.message)
      }
    #end
#end
#end
#if($prompt)
    ,{
      "role": "user",
      "content": #jesc($prompt)
    }
#end
  ],
  "tools": [
#foreach($func in $functions)
    #if($foreach.count != 1),#end
    {
      "type": "function",
      "function": $gpt.loadInputFromTemplate($context, "$apphome/frames/$frameroot/functions/${func}.json")
    }
#end
  ],
  "tool_choice": "auto"
}