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