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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"
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