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https://github.com/open-webui/open-webui.git
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Merge remote-tracking branch 'origin' into logit_bias
This commit is contained in:
dannyl1u
@@ -4,6 +4,7 @@ from open_webui.utils.misc import (
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)
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from typing import Callable, Optional
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import json
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# inplace function: form_data is modified
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@@ -67,38 +68,49 @@ def apply_model_params_to_body_openai(params: dict, form_data: dict) -> dict:
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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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# Convert OpenAI parameter names to Ollama parameter names if needed.
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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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# Copy the parameter to new name then delete it, to prevent Ollama warning of invalid option provided
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params[value] = params[key]
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del params[key]
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return form_data
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# See https://github.com/ollama/ollama/blob/main/docs/api.md#request-8
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mappings = {
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"temperature": float,
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"top_p": float,
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"seed": lambda x: x,
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"mirostat": int,
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"mirostat_eta": float,
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"mirostat_tau": float,
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"num_ctx": int,
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"num_batch": int,
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"num_keep": int,
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"num_predict": int,
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"repeat_last_n": int,
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"top_k": int,
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"min_p": float,
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"typical_p": float,
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"repeat_penalty": float,
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"presence_penalty": float,
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"frequency_penalty": float,
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"penalize_newline": bool,
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"stop": lambda x: [bytes(s, "utf-8").decode("unicode_escape") for s in x],
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"numa": bool,
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"num_gpu": int,
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"main_gpu": int,
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"low_vram": bool,
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"vocab_only": bool,
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"use_mmap": bool,
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"use_mlock": bool,
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"num_thread": int,
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}
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return apply_model_params_to_body(params, form_data, mappings)
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def convert_messages_openai_to_ollama(messages: list[dict]) -> list[dict]:
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@@ -109,11 +121,38 @@ def convert_messages_openai_to_ollama(messages: list[dict]) -> list[dict]:
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new_message = {"role": message["role"]}
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content = message.get("content", [])
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tool_calls = message.get("tool_calls", None)
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tool_call_id = message.get("tool_call_id", None)
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# Check if the content is a string (just a simple message)
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if isinstance(content, str):
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if isinstance(content, str) and not tool_calls:
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# If the content is a string, it's pure text
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new_message["content"] = content
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# If message is a tool call, add the tool call id to the message
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if tool_call_id:
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new_message["tool_call_id"] = tool_call_id
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elif tool_calls:
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# If tool calls are present, add them to the message
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ollama_tool_calls = []
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for tool_call in tool_calls:
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ollama_tool_call = {
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"index": tool_call.get("index", 0),
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"id": tool_call.get("id", None),
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"function": {
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"name": tool_call.get("function", {}).get("name", ""),
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"arguments": json.loads(
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tool_call.get("function", {}).get("arguments", {})
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),
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},
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}
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ollama_tool_calls.append(ollama_tool_call)
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new_message["tool_calls"] = ollama_tool_calls
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# Put the content to empty string (Ollama requires an empty string for tool calls)
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new_message["content"] = ""
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else:
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# Otherwise, assume the content is a list of dicts, e.g., text followed by an image URL
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content_text = ""
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@@ -174,33 +213,28 @@ def convert_payload_openai_to_ollama(openai_payload: dict) -> dict:
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ollama_payload["format"] = openai_payload["format"]
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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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if openai_payload.get("options"):
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ollama_payload["options"] = openai_payload["options"]
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ollama_options = openai_payload["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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# Re-Mapping OpenAI's `max_tokens` -> Ollama's `num_predict`
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if "max_tokens" in ollama_options:
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ollama_options["num_predict"] = ollama_options["max_tokens"]
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del ollama_options[
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"max_tokens"
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] # To prevent Ollama warning of invalid option provided
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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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# Ollama lacks a "system" prompt option. It has to be provided as a direct parameter, so we copy it down.
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if "system" in ollama_options:
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ollama_payload["system"] = ollama_options["system"]
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del ollama_options[
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"system"
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] # To prevent Ollama warning of invalid option provided
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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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# If there is the "stop" parameter in the openai_payload, remap it to the ollama_payload.options
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if "stop" in openai_payload:
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ollama_options = ollama_payload.get("options", {})
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ollama_options["stop"] = openai_payload.get("stop")
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ollama_payload["options"] = ollama_options
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if "metadata" in openai_payload:
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