backend/open_webui/utils/response.py

import json
from uuid import uuid4
from open_webui.utils.misc import (
    openai_chat_chunk_message_template,
    openai_chat_completion_message_template,
)


def normalize_usage(usage: dict) -> dict:
    """
    Normalize usage statistics to standard format.
    Handles OpenAI, Ollama, and llama.cpp formats.

    Adds standardized token fields to the original data:
    - input_tokens: Number of tokens in the prompt
    - output_tokens: Number of tokens generated
    - total_tokens: Sum of input and output tokens
    """
    if not usage:
        return {}

    # Map various field names to standard names
    input_tokens = (
        usage.get("input_tokens")  # Already standard
        or usage.get("prompt_tokens")  # OpenAI
        or usage.get("prompt_eval_count")  # Ollama
        or usage.get("prompt_n")  # llama.cpp
        or 0
    )

    output_tokens = (
        usage.get("output_tokens")  # Already standard
        or usage.get("completion_tokens")  # OpenAI
        or usage.get("eval_count")  # Ollama
        or usage.get("predicted_n")  # llama.cpp
        or 0
    )

    total_tokens = usage.get("total_tokens") or (input_tokens + output_tokens)

    # Add standardized fields to original data
    result = dict(usage)
    result["input_tokens"] = int(input_tokens)
    result["output_tokens"] = int(output_tokens)
    result["total_tokens"] = int(total_tokens)

    return result


def convert_ollama_tool_call_to_openai(tool_calls: list) -> list:
    openai_tool_calls = []
    for tool_call in tool_calls:
        function = tool_call.get("function", {})
        openai_tool_call = {
            "index": tool_call.get("index", function.get("index", 0)),
            "id": tool_call.get("id", f"call_{str(uuid4())}"),
            "type": "function",
            "function": {
                "name": function.get("name", ""),
                "arguments": json.dumps(function.get("arguments", {})),
            },
        }
        openai_tool_calls.append(openai_tool_call)
    return openai_tool_calls


def convert_ollama_usage_to_openai(data: dict) -> dict:
    input_tokens = int(data.get("prompt_eval_count", 0))
    output_tokens = int(data.get("eval_count", 0))
    total_tokens = input_tokens + output_tokens

    return {
        # Standardized fields
        "input_tokens": input_tokens,
        "output_tokens": output_tokens,
        "total_tokens": total_tokens,
        # OpenAI-compatible fields (for backward compatibility)
        "prompt_tokens": input_tokens,
        "completion_tokens": output_tokens,
        # Ollama-specific metrics
        "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_eval_duration": data.get("prompt_eval_duration", 0),
        "eval_count": data.get("eval_count", 0),
        "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
        ),
        "completion_tokens_details": {
            "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", "")
    reasoning_content = ollama_response.get("message", {}).get("thinking", None)
    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, reasoning_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)
        reasoning_content = data.get("message", {}).get("thinking", 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, reasoning_content, openai_tool_calls, usage
        )

        if done and openai_tool_calls:
            data["choices"][0]["finish_reason"] = "tool_calls"

        line = f"data: {json.dumps(data)}\n\n"
        yield line

    yield "data: [DONE]\n\n"


def convert_embedding_response_ollama_to_openai(response) -> dict:
    """
    Convert the response from Ollama embeddings endpoint to the OpenAI-compatible format.

    Args:
        response (dict): The response from the Ollama API,
            e.g. {"embedding": [...], "model": "..."}
            or {"embeddings": [{"embedding": [...], "index": 0}, ...], "model": "..."}

    Returns:
        dict: Response adapted to OpenAI's embeddings API format.
            e.g. {
                "object": "list",
                "data": [
                    {"object": "embedding", "embedding": [...], "index": 0},
                    ...
                ],
                "model": "...",
            }
    """
    # Ollama batch-style output from /api/embed
    # Response format: {"embeddings": [[0.1, 0.2, ...], [0.3, 0.4, ...]], "model": "..."}
    if isinstance(response, dict) and "embeddings" in response:
        openai_data = []
        for i, emb in enumerate(response["embeddings"]):
            # /api/embed returns embeddings as plain float lists
            if isinstance(emb, list):
                openai_data.append(
                    {
                        "object": "embedding",
                        "embedding": emb,
                        "index": i,
                    }
                )
            # Also handle dict format for robustness
            elif isinstance(emb, dict):
                openai_data.append(
                    {
                        "object": "embedding",
                        "embedding": emb.get("embedding"),
                        "index": emb.get("index", i),
                    }
                )
        return {
            "object": "list",
            "data": openai_data,
            "model": response.get("model"),
        }
    # Ollama single output
    elif isinstance(response, dict) and "embedding" in response:
        return {
            "object": "list",
            "data": [
                {
                    "object": "embedding",
                    "embedding": response["embedding"],
                    "index": 0,
                }
            ],
            "model": response.get("model"),
        }
    # Already OpenAI-compatible?
    elif (
        isinstance(response, dict)
        and "data" in response
        and isinstance(response["data"], list)
    ):
        return response

    # Fallback: return as is if unrecognized
    return response