mirror of
https://github.com/open-webui/open-webui
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122 lines
4.1 KiB
Python
122 lines
4.1 KiB
Python
import json
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from uuid import uuid4
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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_ollama_tool_call_to_openai(tool_calls: dict) -> dict:
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openai_tool_calls = []
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for tool_call in tool_calls:
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openai_tool_call = {
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"index": tool_call.get("index", 0),
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"id": tool_call.get("id", f"call_{str(uuid4())}"),
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"type": "function",
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"function": {
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"name": tool_call.get("function", {}).get("name", ""),
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"arguments": json.dumps(
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tool_call.get("function", {}).get("arguments", {})
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),
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},
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}
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openai_tool_calls.append(openai_tool_call)
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return openai_tool_calls
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def convert_ollama_usage_to_openai(data: dict) -> dict:
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return {
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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_tokens": int(data.get("prompt_eval_count", 0)), # This is the OpenAI compatible key
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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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"completion_tokens": int(data.get("eval_count", 0)), # This is the OpenAI compatible key
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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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"total_tokens": int( # This is the OpenAI compatible key
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data.get("prompt_eval_count", 0) + data.get("eval_count", 0)
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),
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"completion_tokens_details": { # This is the OpenAI compatible key
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"reasoning_tokens": 0,
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"accepted_prediction_tokens": 0,
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"rejected_prediction_tokens": 0
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}
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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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tool_calls = ollama_response.get("message", {}).get("tool_calls", None)
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openai_tool_calls = None
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if tool_calls:
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openai_tool_calls = convert_ollama_tool_call_to_openai(tool_calls)
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data = ollama_response
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usage = convert_ollama_usage_to_openai(data)
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response = openai_chat_completion_message_template(
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model, message_content, openai_tool_calls, usage
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)
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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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tool_calls = data.get("message", {}).get("tool_calls", None)
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openai_tool_calls = None
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if tool_calls:
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openai_tool_calls = convert_ollama_tool_call_to_openai(tool_calls)
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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 = convert_ollama_usage_to_openai(data)
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data = openai_chat_chunk_message_template(
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model, message_content if not done else None, openai_tool_calls, 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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