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| import json | |
| from uuid import uuid4 | |
| from open_webui.utils.misc import ( | |
| openai_chat_chunk_message_template, | |
| openai_chat_completion_message_template, | |
| ) | |
| def convert_ollama_tool_call_to_openai(tool_calls: dict) -> dict: | |
| openai_tool_calls = [] | |
| for tool_call in tool_calls: | |
| openai_tool_call = { | |
| "index": tool_call.get("index", 0), | |
| "id": tool_call.get("id", f"call_{str(uuid4())}"), | |
| "type": "function", | |
| "function": { | |
| "name": tool_call.get("function", {}).get("name", ""), | |
| "arguments": json.dumps( | |
| tool_call.get("function", {}).get("arguments", {}) | |
| ), | |
| }, | |
| } | |
| openai_tool_calls.append(openai_tool_call) | |
| return openai_tool_calls | |
| def convert_ollama_usage_to_openai(data: dict) -> dict: | |
| return { | |
| "response_token/s": ( | |
| round( | |
| ( | |
| ( | |
| data.get("eval_count", 0) | |
| / ((data.get("eval_duration", 0) / 10_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) / 10_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_tokens": int( | |
| data.get("prompt_eval_count", 0) | |
| ), # This is the OpenAI compatible key | |
| "prompt_eval_duration": data.get("prompt_eval_duration", 0), | |
| "eval_count": data.get("eval_count", 0), | |
| "completion_tokens": int( | |
| data.get("eval_count", 0) | |
| ), # This is the OpenAI compatible key | |
| "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 | |
| ), | |
| "total_tokens": int( # This is the OpenAI compatible key | |
| data.get("prompt_eval_count", 0) + data.get("eval_count", 0) | |
| ), | |
| "completion_tokens_details": { # This is the OpenAI compatible key | |
| "reasoning_tokens": 0, | |
| "accepted_prediction_tokens": 0, | |
| "rejected_prediction_tokens": 0, | |
| }, | |
| } | |
| def convert_response_ollama_to_openai(ollama_response: dict) -> dict: | |
| model = ollama_response.get("model", "ollama") | |
| message_content = ollama_response.get("message", {}).get("content", "") | |
| tool_calls = ollama_response.get("message", {}).get("tool_calls", None) | |
| openai_tool_calls = None | |
| if tool_calls: | |
| openai_tool_calls = convert_ollama_tool_call_to_openai(tool_calls) | |
| data = ollama_response | |
| usage = convert_ollama_usage_to_openai(data) | |
| response = openai_chat_completion_message_template( | |
| model, message_content, openai_tool_calls, usage | |
| ) | |
| 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", None) | |
| tool_calls = data.get("message", {}).get("tool_calls", None) | |
| openai_tool_calls = None | |
| if tool_calls: | |
| openai_tool_calls = convert_ollama_tool_call_to_openai(tool_calls) | |
| done = data.get("done", False) | |
| usage = None | |
| if done: | |
| usage = convert_ollama_usage_to_openai(data) | |
| data = openai_chat_chunk_message_template( | |
| model, message_content, openai_tool_calls, usage | |
| ) | |
| line = f"data: {json.dumps(data)}\n\n" | |
| yield line | |
| yield "data: [DONE]\n\n" | |