mirror of
https://github.com/open-webui/open-webui
synced 2024-11-27 14:32:32 +00:00
137 lines
4.3 KiB
Python
137 lines
4.3 KiB
Python
from open_webui.utils.task import prompt_template
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from open_webui.utils.misc import (
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add_or_update_system_message,
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)
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from typing import Callable, Optional
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# inplace function: form_data is modified
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def apply_model_system_prompt_to_body(params: dict, form_data: dict, user) -> dict:
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system = params.get("system", None)
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if not system:
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return form_data
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if user:
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template_params = {
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"user_name": user.name,
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"user_location": user.info.get("location") if user.info else None,
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}
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else:
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template_params = {}
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system = prompt_template(system, **template_params)
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form_data["messages"] = add_or_update_system_message(
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system, form_data.get("messages", [])
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)
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return form_data
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# inplace function: form_data is modified
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def apply_model_params_to_body(
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params: dict, form_data: dict, mappings: dict[str, Callable]
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) -> dict:
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if not params:
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return form_data
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for key, cast_func in mappings.items():
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if (value := params.get(key)) is not None:
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form_data[key] = cast_func(value)
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return form_data
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# inplace function: form_data is modified
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def apply_model_params_to_body_openai(params: dict, form_data: dict) -> dict:
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mappings = {
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"temperature": float,
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"top_p": float,
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"max_tokens": int,
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"frequency_penalty": float,
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"seed": lambda x: x,
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"stop": lambda x: [bytes(s, "utf-8").decode("unicode_escape") for s in x],
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}
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return apply_model_params_to_body(params, form_data, mappings)
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def apply_model_params_to_body_ollama(params: dict, form_data: dict) -> dict:
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opts = [
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"temperature",
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"top_p",
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"seed",
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"mirostat",
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"mirostat_eta",
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"mirostat_tau",
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"num_ctx",
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"num_batch",
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"num_keep",
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"repeat_last_n",
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"tfs_z",
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"top_k",
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"min_p",
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"use_mmap",
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"use_mlock",
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"num_thread",
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"num_gpu",
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]
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mappings = {i: lambda x: x for i in opts}
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form_data = apply_model_params_to_body(params, form_data, mappings)
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name_differences = {
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"max_tokens": "num_predict",
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"frequency_penalty": "repeat_penalty",
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}
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for key, value in name_differences.items():
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if (param := params.get(key, None)) is not None:
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form_data[value] = param
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return form_data
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def convert_payload_openai_to_ollama(openai_payload: dict) -> dict:
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"""
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Converts a payload formatted for OpenAI's API to be compatible with Ollama's API endpoint for chat completions.
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Args:
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openai_payload (dict): The payload originally designed for OpenAI API usage.
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Returns:
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dict: A modified payload compatible with the Ollama API.
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"""
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ollama_payload = {}
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# Mapping basic model and message details
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ollama_payload["model"] = openai_payload.get("model")
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ollama_payload["messages"] = openai_payload.get("messages")
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ollama_payload["stream"] = openai_payload.get("stream", False)
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if "tools" in openai_payload:
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ollama_payload["tools"] = openai_payload["tools"]
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# If there are advanced parameters in the payload, format them in Ollama's options field
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ollama_options = {}
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# Handle parameters which map directly
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for param in ["temperature", "top_p", "seed"]:
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if param in openai_payload:
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ollama_options[param] = openai_payload[param]
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# Mapping OpenAI's `max_tokens` -> Ollama's `num_predict`
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if "max_completion_tokens" in openai_payload:
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ollama_options["num_predict"] = openai_payload["max_completion_tokens"]
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elif "max_tokens" in openai_payload:
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ollama_options["num_predict"] = openai_payload["max_tokens"]
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# Handle frequency / presence_penalty, which needs renaming and checking
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if "frequency_penalty" in openai_payload:
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ollama_options["repeat_penalty"] = openai_payload["frequency_penalty"]
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if "presence_penalty" in openai_payload and "penalty" not in ollama_options:
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# We are assuming presence penalty uses a similar concept in Ollama, which needs custom handling if exists.
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ollama_options["new_topic_penalty"] = openai_payload["presence_penalty"]
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# Add options to payload if any have been set
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if ollama_options:
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ollama_payload["options"] = ollama_options
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return ollama_payload
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