import json from open_webui.utils.misc import ( openai_chat_chunk_message_template, openai_chat_completion_message_template, ) def convert_response_ollama_to_openai(ollama_response: dict) -> dict: model = ollama_response.get("model", "ollama") message_content = ollama_response.get("message", {}).get("content", "") response = openai_chat_completion_message_template(model, message_content) return response async def convert_streaming_response_ollama_to_openai(ollama_streaming_response): async for data in ollama_streaming_response.body_iterator: data = json.loads(data) model = data.get("model", "ollama") message_content = data.get("message", {}).get("content", "") done = data.get("done", False) usage = None if done: usage = { "response_token/s": ( round( ( ( data.get("eval_count", 0) / ((data.get("eval_duration", 0) / 1_000_000)) ) * 100 ), 2, ) if data.get("eval_duration", 0) > 0 else "N/A" ), "prompt_token/s": ( round( ( ( data.get("prompt_eval_count", 0) / ((data.get("prompt_eval_duration", 0) / 1_000_000)) ) * 100 ), 2, ) if data.get("prompt_eval_duration", 0) > 0 else "N/A" ), "total_duration": data.get("total_duration", 0), "load_duration": data.get("load_duration", 0), "prompt_eval_count": data.get("prompt_eval_count", 0), "prompt_eval_duration": data.get("prompt_eval_duration", 0), "eval_count": data.get("eval_count", 0), "eval_duration": data.get("eval_duration", 0), "approximate_total": ( lambda s: f"{s // 3600}h{(s % 3600) // 60}m{s % 60}s" )((data.get("total_duration", 0) or 0) // 1_000_000_000), } data = openai_chat_chunk_message_template( model, message_content if not done else None, usage ) line = f"data: {json.dumps(data)}\n\n" yield line yield "data: [DONE]\n\n"