115 lines
4.0 KiB
Python
115 lines
4.0 KiB
Python
import json
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from open_webui.utils.misc import (
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openai_chat_chunk_message_template,
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openai_chat_completion_message_template,
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)
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def convert_response_ollama_to_openai(ollama_response: dict) -> dict:
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model = ollama_response.get("model", "ollama")
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message_content = ollama_response.get("message", {}).get("content", "")
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data = ollama_response
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usage = {
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"response_token/s": (
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round(
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(
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(
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data.get("eval_count", 0)
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/ ((data.get("eval_duration", 0) / 10_000_000))
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)
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* 100
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),
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2,
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)
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if data.get("eval_duration", 0) > 0
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else "N/A"
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),
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"prompt_token/s": (
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round(
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(
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(
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data.get("prompt_eval_count", 0)
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/ ((data.get("prompt_eval_duration", 0) / 10_000_000))
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)
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* 100
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),
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2,
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)
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if data.get("prompt_eval_duration", 0) > 0
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else "N/A"
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),
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"total_duration": data.get("total_duration", 0),
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"load_duration": data.get("load_duration", 0),
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"prompt_eval_count": data.get("prompt_eval_count", 0),
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"prompt_eval_duration": data.get("prompt_eval_duration", 0),
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"eval_count": data.get("eval_count", 0),
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"eval_duration": data.get("eval_duration", 0),
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"approximate_total": (lambda s: f"{s // 3600}h{(s % 3600) // 60}m{s % 60}s")(
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(data.get("total_duration", 0) or 0) // 1_000_000_000
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),
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}
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response = openai_chat_completion_message_template(model, message_content, usage)
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return response
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async def convert_streaming_response_ollama_to_openai(ollama_streaming_response):
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async for data in ollama_streaming_response.body_iterator:
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data = json.loads(data)
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model = data.get("model", "ollama")
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message_content = data.get("message", {}).get("content", "")
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done = data.get("done", False)
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usage = None
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if done:
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usage = {
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"response_token/s": (
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round(
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(
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(
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data.get("eval_count", 0)
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/ ((data.get("eval_duration", 0) / 10_000_000))
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)
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* 100
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),
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2,
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)
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if data.get("eval_duration", 0) > 0
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else "N/A"
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),
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"prompt_token/s": (
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round(
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(
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(
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data.get("prompt_eval_count", 0)
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/ ((data.get("prompt_eval_duration", 0) / 10_000_000))
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)
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* 100
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),
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2,
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)
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if data.get("prompt_eval_duration", 0) > 0
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else "N/A"
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),
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"total_duration": data.get("total_duration", 0),
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"load_duration": data.get("load_duration", 0),
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"prompt_eval_count": data.get("prompt_eval_count", 0),
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"prompt_eval_duration": data.get("prompt_eval_duration", 0),
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"eval_count": data.get("eval_count", 0),
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"eval_duration": data.get("eval_duration", 0),
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"approximate_total": (
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lambda s: f"{s // 3600}h{(s % 3600) // 60}m{s % 60}s"
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)((data.get("total_duration", 0) or 0) // 1_000_000_000),
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}
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data = openai_chat_chunk_message_template(
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model, message_content if not done else None, usage
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)
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line = f"data: {json.dumps(data)}\n\n"
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yield line
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yield "data: [DONE]\n\n"
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