Spaces:
Running
Running
File size: 8,540 Bytes
81e42f7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 |
from open_webui.utils.task import prompt_template, prompt_variables_template
from open_webui.utils.misc import (
add_or_update_system_message,
)
from typing import Callable, Optional
import json
# inplace function: form_data is modified
def apply_model_system_prompt_to_body(
params: dict, form_data: dict, metadata: Optional[dict] = None, user=None
) -> dict:
system = params.get("system", None)
if not system:
return form_data
# Metadata (WebUI Usage)
if metadata:
variables = metadata.get("variables", {})
if variables:
system = prompt_variables_template(system, variables)
# Legacy (API Usage)
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,
"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,
}
return apply_model_params_to_body(params, form_data, mappings)
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",
}
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,
"typical_p": float,
"repeat_penalty": float,
"presence_penalty": float,
"frequency_penalty": float,
"penalize_newline": bool,
"stop": lambda x: [bytes(s, "utf-8").decode("unicode_escape") for s in x],
"numa": bool,
"num_gpu": int,
"main_gpu": int,
"low_vram": bool,
"vocab_only": bool,
"use_mmap": bool,
"use_mlock": bool,
"num_thread": int,
}
return apply_model_params_to_body(params, form_data, mappings)
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"]}
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.
"""
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 "format" in openai_payload:
ollama_payload["format"] = openai_payload["format"]
# 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"]
# 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"
] # To prevent Ollama warning of invalid option provided
# Ollama lacks a "system" prompt option. It has to be provided as a direct parameter, so we copy it down.
if "system" in ollama_options:
ollama_payload["system"] = ollama_options["system"]
del ollama_options[
"system"
] # To prevent Ollama warning of invalid option provided
# 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"]
return ollama_payload
|