mirror of
https://github.com/open-webui/open-webui.git
synced 2026-08-13 01:02:25 -06:00
Classic298
d29685275b
convert_payload_openai_to_ollama deep-copied the entire request payload on every completion routed to an Ollama model, and again on every tool-call iteration. The cost of that copy scales with the number of messages and nested content parts in the history, so long chats pay the most, purely as CPU work before the request even leaves the server. The function only ever mutates two things: it deletes keys on the top-level dict and on the nested options dict. convert_messages_openai_to_ollama already builds fresh message dicts. Shallow-copying exactly those two levels therefore preserves behavior while removing the whole-tree copy. Benchmark (per conversion call): | payload | before | after | speedup | | --- | --- | --- | --- | | 200-message text chat (~180 KB) | 0.22 ms | 0.057 ms | 4x | | 20-message chat + 1 MB base64 image | 0.41 ms | 0.38 ms | 1.1x | The image row barely moves because deepcopy shares immutable strings; the win comes from container-heavy histories, which are exactly the payloads that grow over a conversation's lifetime. The output is byte-identical to the previous implementation (verified against it, including dict key order, root parameter hoisting, max_tokens remapping, stop handling and response_format precedence), and the caller's payload is left unmodified exactly as before.
443 lines
16 KiB
Python
443 lines
16 KiB
Python
import json
|
|
from typing import Callable, Optional
|
|
|
|
from open_webui.utils.misc import (
|
|
add_or_update_system_message,
|
|
deep_update,
|
|
replace_system_message_content,
|
|
)
|
|
from open_webui.utils.chat_variables import render_chat_variables, render_user_variables
|
|
from open_webui.utils.task import prompt_template, prompt_variables_template
|
|
|
|
|
|
async def resolve_system_prompt(
|
|
system: Optional[str],
|
|
metadata: Optional[dict] = None,
|
|
user=None,
|
|
) -> str:
|
|
if not system:
|
|
return ''
|
|
|
|
if metadata:
|
|
system = render_chat_variables(
|
|
system,
|
|
metadata.get('chat_variables', {}),
|
|
required=False,
|
|
)
|
|
|
|
system = render_user_variables(system, getattr(user, 'variables', {}) if user else {})
|
|
|
|
# Metadata (WebUI Usage)
|
|
if metadata:
|
|
variables = metadata.get('variables', {})
|
|
if variables:
|
|
system = prompt_variables_template(system, variables)
|
|
|
|
# Legacy (API Usage)
|
|
system = await prompt_template(system, user)
|
|
|
|
return system
|
|
|
|
|
|
# What goes out cannot be taken back. Let it be shaped
|
|
# well before it leaves this place.
|
|
# inplace function: form_data is modified
|
|
async def apply_system_prompt_to_body(
|
|
system: Optional[str],
|
|
form_data: dict,
|
|
metadata: Optional[dict] = None,
|
|
user=None,
|
|
replace: bool = False,
|
|
) -> dict:
|
|
system = await resolve_system_prompt(system, metadata, user)
|
|
if not system:
|
|
return form_data
|
|
|
|
if replace:
|
|
form_data['messages'] = replace_system_message_content(system, form_data.get('messages', []))
|
|
else:
|
|
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, value in params.items():
|
|
if value is not None:
|
|
if key in mappings:
|
|
cast_func = mappings[key]
|
|
if isinstance(cast_func, Callable):
|
|
form_data[key] = cast_func(value)
|
|
else:
|
|
form_data[key] = value
|
|
|
|
return form_data
|
|
|
|
|
|
def remove_open_webui_params(params: dict) -> dict:
|
|
"""
|
|
Removes OpenWebUI specific parameters from the provided dictionary.
|
|
|
|
Args:
|
|
params (dict): The dictionary containing parameters.
|
|
|
|
Returns:
|
|
dict: The modified dictionary with OpenWebUI parameters removed.
|
|
"""
|
|
open_webui_params = {
|
|
'stream_response': bool,
|
|
'stream_delta_chunk_size': int,
|
|
'function_calling': str,
|
|
'reasoning_tags': list,
|
|
'compact_token_threshold': int,
|
|
'system': str,
|
|
}
|
|
|
|
for key in list(params.keys()):
|
|
if key in open_webui_params:
|
|
del params[key]
|
|
|
|
return params
|
|
|
|
|
|
# inplace function: form_data is modified
|
|
def apply_model_params_to_body_openai(params: dict, form_data: dict) -> dict:
|
|
params = remove_open_webui_params(params)
|
|
|
|
custom_params = params.pop('custom_params', {})
|
|
if custom_params:
|
|
# Attempt to parse custom_params if they are strings
|
|
for key, value in custom_params.items():
|
|
if isinstance(value, str):
|
|
try:
|
|
# Attempt to parse the string as JSON
|
|
custom_params[key] = json.loads(value)
|
|
except json.JSONDecodeError:
|
|
# If it fails, keep the original string
|
|
pass
|
|
|
|
# If there are custom parameters, we need to apply them first
|
|
params = deep_update(params, custom_params)
|
|
|
|
mappings = {
|
|
'temperature': float,
|
|
'top_p': float,
|
|
'min_p': float,
|
|
'max_tokens': int,
|
|
'frequency_penalty': float,
|
|
'presence_penalty': float,
|
|
'reasoning_effort': str,
|
|
'seed': lambda x: x,
|
|
'stop': lambda x: [bytes(s, 'utf-8').decode('unicode_escape') for s in x],
|
|
'logit_bias': lambda x: x,
|
|
'response_format': dict,
|
|
}
|
|
return apply_model_params_to_body(params, form_data, mappings)
|
|
|
|
|
|
def apply_model_params_to_body_ollama(params: dict, form_data: dict) -> dict:
|
|
params = remove_open_webui_params(params)
|
|
|
|
custom_params = params.pop('custom_params', {})
|
|
if custom_params:
|
|
# Attempt to parse custom_params if they are strings
|
|
for key, value in custom_params.items():
|
|
if isinstance(value, str):
|
|
try:
|
|
# Attempt to parse the string as JSON
|
|
custom_params[key] = json.loads(value)
|
|
except json.JSONDecodeError:
|
|
# If it fails, keep the original string
|
|
pass
|
|
|
|
# If there are custom parameters, we need to apply them first
|
|
params = deep_update(params, custom_params)
|
|
|
|
# Convert OpenAI parameter names to Ollama parameter names if needed.
|
|
name_differences = {
|
|
'max_tokens': 'num_predict',
|
|
}
|
|
|
|
for key, value in name_differences.items():
|
|
if (param := params.get(key, None)) is not None:
|
|
# Copy the parameter to new name then delete it, to prevent Ollama warning of invalid option provided
|
|
params[value] = params[key]
|
|
del params[key]
|
|
|
|
# See https://github.com/ollama/ollama/blob/main/docs/api.md#request-8
|
|
mappings = {
|
|
'temperature': float,
|
|
'top_p': float,
|
|
'seed': lambda x: x,
|
|
'mirostat': int,
|
|
'mirostat_eta': float,
|
|
'mirostat_tau': float,
|
|
'num_ctx': int,
|
|
'num_batch': int,
|
|
'num_keep': int,
|
|
'num_predict': int,
|
|
'repeat_last_n': int,
|
|
'top_k': int,
|
|
'min_p': float,
|
|
'repeat_penalty': float,
|
|
'presence_penalty': float,
|
|
'frequency_penalty': float,
|
|
'stop': lambda x: [bytes(s, 'utf-8').decode('unicode_escape') for s in x],
|
|
'num_gpu': int,
|
|
'use_mmap': bool,
|
|
'use_mlock': bool,
|
|
'num_thread': int,
|
|
}
|
|
|
|
def parse_json(value: str) -> dict:
|
|
"""
|
|
Parses a JSON string into a dictionary, handling potential JSONDecodeError.
|
|
"""
|
|
try:
|
|
return json.loads(value)
|
|
except Exception as e:
|
|
return value
|
|
|
|
ollama_root_params = {
|
|
'format': lambda x: parse_json(x),
|
|
'keep_alive': lambda x: parse_json(x),
|
|
'think': lambda x: x,
|
|
}
|
|
|
|
for key, value in ollama_root_params.items():
|
|
if (param := params.get(key, None)) is not None:
|
|
# Copy the parameter to new name then delete it, to prevent Ollama warning of invalid option provided
|
|
form_data[key] = value(param)
|
|
del params[key]
|
|
|
|
# Unlike OpenAI, Ollama does not support params directly in the body
|
|
form_data['options'] = apply_model_params_to_body(params, (form_data.get('options', {}) or {}), mappings)
|
|
return form_data
|
|
|
|
|
|
def convert_messages_openai_to_ollama(messages: list[dict]) -> list[dict]:
|
|
ollama_messages = []
|
|
|
|
for message in messages:
|
|
# Initialize the new message structure with the role
|
|
new_message = {'role': message['role']}
|
|
|
|
# Preserve Ollama-native 'thinking' field (used by reasoning models,
|
|
# may be injected by filter inlet functions).
|
|
if 'thinking' in message:
|
|
new_message['thinking'] = message['thinking']
|
|
|
|
content = message.get('content', [])
|
|
tool_calls = message.get('tool_calls', None)
|
|
tool_call_id = message.get('tool_call_id', None)
|
|
|
|
# Check if the content is a string (just a simple message)
|
|
if isinstance(content, str) and not tool_calls:
|
|
# If the content is a string, it's pure text
|
|
new_message['content'] = content
|
|
|
|
# If message is a tool call, add the tool call id to the message
|
|
if tool_call_id:
|
|
new_message['tool_call_id'] = tool_call_id
|
|
|
|
elif tool_calls:
|
|
# If tool calls are present, add them to the message
|
|
ollama_tool_calls = []
|
|
for tool_call in tool_calls:
|
|
ollama_tool_call = {
|
|
'index': tool_call.get('index', 0),
|
|
'id': tool_call.get('id', None),
|
|
'function': {
|
|
'name': tool_call.get('function', {}).get('name', ''),
|
|
'arguments': json.loads(tool_call.get('function', {}).get('arguments', {})),
|
|
},
|
|
}
|
|
ollama_tool_calls.append(ollama_tool_call)
|
|
new_message['tool_calls'] = ollama_tool_calls
|
|
|
|
# Put the content to empty string (Ollama requires an empty string for tool calls)
|
|
new_message['content'] = ''
|
|
|
|
else:
|
|
# Otherwise, assume the content is a list of dicts, e.g., text followed by an image URL
|
|
content_text = ''
|
|
images = []
|
|
|
|
# Iterate through the list of content items
|
|
for item in content:
|
|
# Check if it's a text type
|
|
if item.get('type') == 'text':
|
|
content_text += item.get('text', '')
|
|
|
|
# Check if it's an image URL type
|
|
elif item.get('type') == 'image_url':
|
|
img_url = item.get('image_url', {}).get('url', '')
|
|
if img_url:
|
|
# If the image url starts with data:, it's a base64 image and should be trimmed
|
|
if img_url.startswith('data:'):
|
|
img_url = img_url.split(',')[-1]
|
|
images.append(img_url)
|
|
|
|
# Add content text (if any)
|
|
if content_text:
|
|
new_message['content'] = content_text.strip()
|
|
|
|
# Add images (if any)
|
|
if images:
|
|
new_message['images'] = images
|
|
|
|
# Append the new formatted message to the result
|
|
ollama_messages.append(new_message)
|
|
|
|
return ollama_messages
|
|
|
|
|
|
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.
|
|
"""
|
|
# Only the top-level dict and the nested options dict are mutated below, so
|
|
# shallow copies suffice; deepcopy walked the entire message tree per call.
|
|
metadata = openai_payload.get('metadata')
|
|
openai_payload = {k: v for k, v in openai_payload.items() if k != 'metadata'}
|
|
if metadata is not None:
|
|
openai_payload['metadata'] = dict(metadata)
|
|
ollama_payload = {}
|
|
|
|
# Mapping basic model and message details
|
|
ollama_payload['model'] = openai_payload.get('model')
|
|
ollama_payload['messages'] = convert_messages_openai_to_ollama(openai_payload.get('messages'))
|
|
ollama_payload['stream'] = openai_payload.get('stream', False)
|
|
if 'tools' in openai_payload:
|
|
ollama_payload['tools'] = openai_payload['tools']
|
|
|
|
if 'max_tokens' in openai_payload:
|
|
ollama_payload['num_predict'] = openai_payload['max_tokens']
|
|
del openai_payload['max_tokens']
|
|
|
|
# If there are advanced parameters in the payload, format them in Ollama's options field
|
|
if openai_payload.get('options'):
|
|
# Copied before key deletions below so the caller's options stay intact
|
|
ollama_options = dict(openai_payload['options'])
|
|
ollama_payload['options'] = ollama_options
|
|
|
|
def parse_json(value: str) -> dict:
|
|
"""
|
|
Parses a JSON string into a dictionary, handling potential JSONDecodeError.
|
|
"""
|
|
try:
|
|
return json.loads(value)
|
|
except Exception as e:
|
|
return value
|
|
|
|
ollama_root_params = {
|
|
'format': lambda x: parse_json(x),
|
|
'keep_alive': lambda x: parse_json(x),
|
|
'think': lambda x: x,
|
|
}
|
|
|
|
# Ollama's options field can contain parameters that should be at the root level.
|
|
for key, value in ollama_root_params.items():
|
|
if (param := ollama_options.get(key, None)) is not None:
|
|
# Copy the parameter to new name then delete it, to prevent Ollama warning of invalid option provided
|
|
ollama_payload[key] = value(param)
|
|
del ollama_options[key]
|
|
|
|
# Re-Mapping OpenAI's `max_tokens` -> Ollama's `num_predict`
|
|
if 'max_tokens' in ollama_options:
|
|
ollama_options['num_predict'] = ollama_options['max_tokens']
|
|
del ollama_options['max_tokens']
|
|
|
|
# Ollama lacks a "system" prompt option. It has to be provided as a direct parameter, so we copy it down.
|
|
# Comment: Not sure why this is needed, but we'll keep it for compatibility.
|
|
if 'system' in ollama_options:
|
|
ollama_payload['system'] = ollama_options['system']
|
|
del ollama_options['system']
|
|
|
|
ollama_payload['options'] = ollama_options
|
|
|
|
# If there is the "stop" parameter in the openai_payload, remap it to the ollama_payload.options
|
|
if 'stop' in openai_payload:
|
|
ollama_options = ollama_payload.get('options', {})
|
|
ollama_options['stop'] = openai_payload.get('stop')
|
|
ollama_payload['options'] = ollama_options
|
|
|
|
if 'metadata' in openai_payload:
|
|
ollama_payload['metadata'] = openai_payload['metadata']
|
|
|
|
if 'response_format' in openai_payload:
|
|
response_format = openai_payload['response_format']
|
|
format_type = response_format.get('type', None)
|
|
|
|
schema = response_format.get(format_type, None)
|
|
if schema:
|
|
format = schema.get('schema', None)
|
|
ollama_payload['format'] = format
|
|
|
|
return ollama_payload
|
|
|
|
|
|
def convert_embedding_payload_openai_to_ollama(openai_payload: dict) -> dict:
|
|
"""
|
|
Convert an embeddings request payload from OpenAI format to Ollama format.
|
|
|
|
Args:
|
|
openai_payload (dict): The original payload designed for OpenAI API usage.
|
|
|
|
Returns:
|
|
dict: A payload compatible with the Ollama API embeddings endpoint.
|
|
"""
|
|
ollama_payload = {'model': openai_payload.get('model')}
|
|
input_value = openai_payload.get('input')
|
|
|
|
# Ollama expects 'input' as a list, and 'prompt' as a single string.
|
|
if isinstance(input_value, list):
|
|
ollama_payload['input'] = input_value
|
|
ollama_payload['prompt'] = '\n'.join(str(x) for x in input_value)
|
|
else:
|
|
ollama_payload['input'] = [input_value]
|
|
ollama_payload['prompt'] = str(input_value)
|
|
|
|
# Optionally forward other fields if present
|
|
for optional_key in ('options', 'truncate', 'keep_alive'):
|
|
if optional_key in openai_payload:
|
|
ollama_payload[optional_key] = openai_payload[optional_key]
|
|
|
|
return ollama_payload
|
|
|
|
|
|
def convert_embed_payload_openai_to_ollama(openai_payload: dict) -> dict:
|
|
"""
|
|
Convert an embeddings request payload from OpenAI format to Ollama's
|
|
/api/embed format, which supports batch input natively.
|
|
|
|
Args:
|
|
openai_payload (dict): The original payload designed for OpenAI API usage.
|
|
Expected keys: "model", "input" (str or list[str]).
|
|
|
|
Returns:
|
|
dict: A payload compatible with the Ollama /api/embed endpoint.
|
|
"""
|
|
ollama_payload = {'model': openai_payload.get('model')}
|
|
input_value = openai_payload.get('input')
|
|
|
|
# /api/embed accepts 'input' as a string or list of strings directly
|
|
ollama_payload['input'] = input_value
|
|
|
|
# Optionally forward other fields if present
|
|
for optional_key in ('truncate', 'options', 'keep_alive'):
|
|
if optional_key in openai_payload:
|
|
ollama_payload[optional_key] = openai_payload[optional_key]
|
|
|
|
return ollama_payload
|