Spaces:
Runtime error
Runtime error
File size: 14,465 Bytes
d961e88 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 |
import base64
import re
import threading
from dataclasses import dataclass
from typing import Optional, Union
import torch
from oaib import Auto
from openai import AsyncOpenAI, OpenAI
from openai.types.chat import ChatCompletion
from pptagent.utils import get_json_from_response, get_logger, tenacity_decorator
logger = get_logger(__name__)
@dataclass
class LLM:
"""
A wrapper class to interact with a language model.
"""
model: str
base_url: Optional[str] = None
api_key: Optional[str] = None
timeout: int = 360
def __post_init__(self):
self.client = OpenAI(
base_url=self.base_url, api_key=self.api_key, timeout=self.timeout
)
@tenacity_decorator
def __call__(
self,
content: str,
images: Optional[Union[str, list[str]]] = None,
system_message: Optional[str] = None,
history: Optional[list] = None,
return_json: bool = False,
return_message: bool = False,
**client_kwargs,
) -> Union[str, dict, list, tuple]:
"""
Call the language model with a prompt and optional images.
Args:
content (str): The prompt content.
images (str or list[str]): An image file path or list of image file paths.
system_message (str): The system message.
history (list): The conversation history.
return_json (bool): Whether to return the response as JSON.
return_message (bool): Whether to return the message.
**client_kwargs: Additional keyword arguments to pass to the client.
Returns:
Union[str, Dict, List, Tuple]: The response from the model.
"""
if history is None:
history = []
system, message = self.format_message(content, images, system_message)
try:
completion = self.client.chat.completions.create(
model=self.model, messages=system + history + message, **client_kwargs
)
except Exception as e:
logger.warning("Error in LLM call: %s", e)
raise e
response = completion.choices[0].message.content
message.append({"role": "assistant", "content": response})
return self.__post_process__(response, message, return_json, return_message)
def __post_process__(
self,
response: str,
message: list,
return_json: bool = False,
return_message: bool = False,
) -> Union[str, dict, tuple]:
"""
Process the response based on return options.
Args:
response (str): The raw response from the model.
message (List): The message history.
return_json (bool): Whether to return the response as JSON.
return_message (bool): Whether to return the message.
Returns:
Union[str, Dict, Tuple]: Processed response.
"""
response = response.strip()
if return_json:
response = get_json_from_response(response)
if return_message:
response = (response, message)
return response
def __repr__(self) -> str:
repr_str = f"{self.__class__.__name__}(model={self.model}"
if self.base_url is not None:
repr_str += f", base_url={self.base_url}"
return repr_str + ")"
def test_connection(self) -> bool:
"""
Test the connection to the LLM.
Returns:
bool: True if connection is successful, False otherwise.
"""
try:
self.client.models.list()
return True
except Exception as e:
logger.warning(
"Connection test failed: %s\nLLM: %s: %s, %s",
e,
self.model,
self.base_url,
self.api_key,
)
return False
def format_message(
self,
content: str,
images: Optional[Union[str, list[str]]] = None,
system_message: Optional[str] = None,
) -> tuple[list, list]:
"""
Format messages for OpenAI server call.
Args:
content (str): The prompt content.
images (str or list[str]): An image file path or list of image file paths.
system_message (str): The system message.
Returns:
Tuple[List, List]: Formatted system and user messages.
"""
if isinstance(images, str):
images = [images]
if system_message is None:
if content.startswith("You are"):
system_message, content = content.split("\n", 1)
else:
system_message = "You are a helpful assistant"
system = [
{
"role": "system",
"content": [{"type": "text", "text": system_message}],
}
]
message = [{"role": "user", "content": [{"type": "text", "text": content}]}]
if images is not None:
for image in images:
try:
with open(image, "rb") as f:
message[0]["content"].append(
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64.b64encode(f.read()).decode('utf-8')}"
},
}
)
except Exception as e:
logger.error("Failed to load image %s: %s", image, e)
return system, message
def gen_image(self, prompt: str, n: int = 1, **kwargs) -> str:
"""
Generate an image from a prompt.
"""
return (
self.client.images.generate(model=self.model, prompt=prompt, n=n, **kwargs)
.data[0]
.b64_json
)
def get_embedding(
self,
text: str,
encoding_format: str = "float",
to_tensor: bool = True,
**kwargs,
) -> torch.Tensor | list[float]:
"""
Get the embedding of a text.
"""
result = self.client.embeddings.create(
model=self.model, input=text, encoding_format=encoding_format, **kwargs
)
embeddings = [embedding.embedding for embedding in result.data]
if to_tensor:
embeddings = torch.tensor(embeddings)
return embeddings
def to_async(self) -> "AsyncLLM":
"""
Convert the LLM to an asynchronous LLM.
"""
return AsyncLLM(
model=self.model,
base_url=self.base_url,
api_key=self.api_key,
timeout=self.timeout,
)
@dataclass
class AsyncLLM(LLM):
use_batch: bool = False
"""
Asynchronous wrapper class for language model interaction.
"""
def __post_init__(self):
"""
Initialize the AsyncLLM.
Args:
model (str): The model name.
base_url (str): The base URL for the API.
api_key (str): API key for authentication. Defaults to environment variable.
"""
self.client = AsyncOpenAI(
base_url=self.base_url,
api_key=self.api_key,
timeout=self.timeout,
)
if threading.current_thread() == threading.main_thread():
self.batch = Auto(
base_url=self.base_url,
api_key=self.api_key,
timeout=self.timeout,
loglevel=0,
)
else:
logger.warning("Auto initialization skipped because it's not the main thread.")
@tenacity_decorator
async def __call__(
self,
content: str,
images: Optional[Union[str, list[str]]] = None,
system_message: Optional[str] = None,
history: Optional[list] = None,
return_json: bool = False,
return_message: bool = False,
**client_kwargs,
) -> Union[str, dict, tuple]:
"""
Asynchronously call the language model with a prompt and optional images.
Args:
content (str): The prompt content.
images (str or list[str]): An image file path or list of image file paths.
system_message (str): The system message.
history (list): The conversation history.
return_json (bool): Whether to return the response as JSON.
return_message (bool): Whether to return the message.
**client_kwargs: Additional keyword arguments to pass to the client.
Returns:
Union[str, Dict, List, Tuple]: The response from the model.
"""
if self.use_batch and threading.current_thread() is threading.main_thread():
self.batch = Auto(
base_url=self.base_url,
api_key=self.api_key,
timeout=self.timeout,
loglevel=0,
)
elif self.use_batch:
logger.warning(
"Warning: AsyncLLM is not running in the main thread, may cause race condition."
)
if history is None:
history = []
system, message = self.format_message(content, images, system_message)
try:
if self.use_batch:
await self.batch.add(
"chat.completions.create",
model=self.model,
messages=system + history + message,
**client_kwargs,
)
completion = await self.batch.run()
if "result" not in completion or len(completion["result"]) != 1:
raise ValueError(
f"The length of completion result should be 1, but got {completion}.\nRace condition may have occurred if multiple values are returned.\nOr, there was an error in the LLM call, use the synchronous version to check."
)
completion = ChatCompletion(**completion["result"][0])
else:
completion = await self.client.chat.completions.create(
model=self.model,
messages=system + history + message,
**client_kwargs,
)
except Exception as e:
logger.warning("Error in AsyncLLM call: %s", e)
raise e
response = completion.choices[0].message.content
message.append({"role": "assistant", "content": response})
return self.__post_process__(response, message, return_json, return_message)
def __getstate__(self):
state = self.__dict__.copy()
state["client"] = None
state["batch"] = None
return state
def __setstate__(self, state: dict):
self.__dict__.update(state)
self.client = AsyncOpenAI(
base_url=self.base_url,
api_key=self.api_key,
timeout=self.timeout,
)
self.batch = Auto(
base_url=self.base_url,
api_key=self.api_key,
timeout=self.timeout,
loglevel=0,
)
async def test_connection(self) -> bool:
"""
Test the connection to the LLM asynchronously.
Returns:
bool: True if connection is successful, False otherwise.
"""
try:
await self.client.models.list()
return True
except Exception as e:
logger.warning(
"Async connection test failed: %s\nLLM: %s: %s, %s",
e,
self.model,
self.base_url,
self.api_key,
)
return False
async def gen_image(self, prompt: str, n: int = 1, **kwargs) -> str:
"""
Generate an image from a prompt asynchronously.
Args:
prompt (str): The text prompt to generate an image from.
n (int): Number of images to generate.
**kwargs: Additional keyword arguments for image generation.
Returns:
str: Base64-encoded image data.
"""
response = await self.client.images.generate(
model=self.model, prompt=prompt, n=n, response_format="b64_json", **kwargs
)
return response.data[0].b64_json
async def get_embedding(
self,
text: str,
to_tensor: bool = True,
**kwargs,
) -> torch.Tensor | list[float]:
"""
Get the embedding of a text asynchronously.
Args:
text (str): The text to get embeddings for.
**kwargs: Additional keyword arguments.
Returns:
List[float]: The embedding vector.
"""
response = await self.client.embeddings.create(
model=self.model,
input=text,
encoding_format="float",
**kwargs,
)
embeddings = [embedding.embedding for embedding in response.data]
if to_tensor:
embeddings = torch.tensor(embeddings)
return embeddings
def to_sync(self) -> LLM:
"""
Convert the AsyncLLM to a synchronous LLM.
"""
return LLM(model=self.model, base_url=self.base_url, api_key=self.api_key)
def get_model_abbr(llms: Union[LLM, list[LLM]]) -> str:
"""
Get abbreviated model names from LLM instances.
Args:
llms: A single LLM instance or a list of LLM instances.
Returns:
str: Abbreviated model names joined with '+'.
"""
# Convert single LLM to list for consistent handling
if isinstance(llms, LLM):
llms = [llms]
try:
# Attempt to extract model names before version numbers
return "+".join(re.search(r"^(.*?)-\d{2}", llm.model).group(1) for llm in llms)
except Exception:
# Fallback: return full model names if pattern matching fails
return "+".join(llm.model for llm in llms)
|