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Timothy Jaeryang Baek
2026-03-17 17:58:01 -05:00
parent fcf7208352
commit de3317e26b
220 changed files with 17200 additions and 22836 deletions
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246
backend/open_webui/utils/payload.py
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@@ -23,7 +23,7 @@ def apply_system_prompt_to_body(
# Metadata (WebUI Usage)
if metadata:
variables = metadata.get("variables", {})
variables = metadata.get('variables', {})
if variables:
system = prompt_variables_template(system, variables)
@@ -31,21 +31,15 @@ def apply_system_prompt_to_body(
system = prompt_template(system, user)
if replace:
form_data["messages"] = replace_system_message_content(
system, form_data.get("messages", [])
)
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", [])
)
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:
def apply_model_params_to_body(params: dict, form_data: dict, mappings: dict[str, Callable]) -> dict:
if not params:
return form_data
@@ -72,11 +66,11 @@ def remove_open_webui_params(params: dict) -> dict:
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,
"system": str,
'stream_response': bool,
'stream_delta_chunk_size': int,
'function_calling': str,
'reasoning_tags': list,
'system': str,
}
for key in list(params.keys()):
@@ -90,7 +84,7 @@ def remove_open_webui_params(params: dict) -> dict:
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", {})
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():
@@ -106,17 +100,17 @@ def apply_model_params_to_body_openai(params: dict, form_data: dict) -> dict:
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,
'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)
@@ -124,7 +118,7 @@ def apply_model_params_to_body_openai(params: dict, form_data: dict) -> dict:
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", {})
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():
@@ -141,7 +135,7 @@ def apply_model_params_to_body_ollama(params: dict, form_data: dict) -> dict:
# Convert OpenAI parameter names to Ollama parameter names if needed.
name_differences = {
"max_tokens": "num_predict",
'max_tokens': 'num_predict',
}
for key, value in name_differences.items():
@@ -152,27 +146,27 @@ def apply_model_params_to_body_ollama(params: dict, form_data: dict) -> dict:
# 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,
'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:
@@ -185,9 +179,9 @@ def apply_model_params_to_body_ollama(params: dict, form_data: dict) -> dict:
return value
ollama_root_params = {
"format": lambda x: parse_json(x),
"keep_alive": lambda x: parse_json(x),
"think": lambda x: x,
'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():
@@ -197,9 +191,7 @@ def apply_model_params_to_body_ollama(params: dict, form_data: dict) -> dict:
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
)
form_data['options'] = apply_model_params_to_body(params, (form_data.get('options', {}) or {}), mappings)
return form_data
@@ -208,68 +200,66 @@ def convert_messages_openai_to_ollama(messages: list[dict]) -> list[dict]:
for message in messages:
# Initialize the new message structure with the role
new_message = {"role": message["role"]}
new_message = {'role': message['role']}
content = message.get("content", [])
tool_calls = message.get("tool_calls", None)
tool_call_id = message.get("tool_call_id", None)
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
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
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", {})
),
'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
new_message['tool_calls'] = ollama_tool_calls
# Put the content to empty string (Ollama requires an empty string for tool calls)
new_message["content"] = ""
new_message['content'] = ''
else:
# Otherwise, assume the content is a list of dicts, e.g., text followed by an image URL
content_text = ""
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", "")
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", "")
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]
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()
new_message['content'] = content_text.strip()
# Add images (if any)
if images:
new_message["images"] = images
new_message['images'] = images
# Append the new formatted message to the result
ollama_messages.append(new_message)
@@ -288,31 +278,27 @@ def convert_payload_openai_to_ollama(openai_payload: dict) -> dict:
dict: A modified payload compatible with the Ollama API.
"""
# Shallow copy metadata separately (may contain non-picklable objects)
metadata = openai_payload.get("metadata")
openai_payload = copy.deepcopy(
{k: v for k, v in openai_payload.items() if k != "metadata"}
)
metadata = openai_payload.get('metadata')
openai_payload = copy.deepcopy({k: v for k, v in openai_payload.items() if k != 'metadata'})
if metadata is not None:
openai_payload["metadata"] = dict(metadata)
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"]
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 '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"):
ollama_payload["options"] = openai_payload["options"]
ollama_options = openai_payload["options"]
if openai_payload.get('options'):
ollama_payload['options'] = openai_payload['options']
ollama_options = openai_payload['options']
def parse_json(value: str) -> dict:
"""
@@ -324,9 +310,9 @@ def convert_payload_openai_to_ollama(openai_payload: dict) -> dict:
return value
ollama_root_params = {
"format": lambda x: parse_json(x),
"keep_alive": lambda x: parse_json(x),
"think": lambda x: x,
'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.
@@ -337,35 +323,35 @@ def convert_payload_openai_to_ollama(openai_payload: dict) -> dict:
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"]
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"]
if 'system' in ollama_options:
ollama_payload['system'] = ollama_options['system']
del ollama_options['system']
ollama_payload["options"] = ollama_options
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 '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 '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)
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
format = schema.get('schema', None)
ollama_payload['format'] = format
return ollama_payload
@@ -380,19 +366,19 @@ def convert_embedding_payload_openai_to_ollama(openai_payload: dict) -> dict:
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_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)
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)
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"):
for optional_key in ('options', 'truncate', 'keep_alive'):
if optional_key in openai_payload:
ollama_payload[optional_key] = openai_payload[optional_key]
@@ -411,14 +397,14 @@ def convert_embed_payload_openai_to_ollama(openai_payload: dict) -> dict:
Returns:
dict: A payload compatible with the Ollama /api/embed endpoint.
"""
ollama_payload = {"model": openai_payload.get("model")}
input_value = openai_payload.get("input")
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
ollama_payload['input'] = input_value
# Optionally forward other fields if present
for optional_key in ("truncate", "options", "keep_alive"):
for optional_key in ('truncate', 'options', 'keep_alive'):
if optional_key in openai_payload:
ollama_payload[optional_key] = openai_payload[optional_key]