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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( |
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data.get("prompt_eval_count", 0) |
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), |
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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( |
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data.get("eval_count", 0) |
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), |
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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( |
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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": { |
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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", None) |
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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, 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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