from __future__ import annotations
import logging
from functools import partial
from typing import TYPE_CHECKING, Any, Iterable
from xpark.dataset.constants import NOT_SET
from xpark.dataset.datatype import DataType
from xpark.dataset.expressions import BatchColumnClassProtocol, udf
from xpark.dataset.import_utils import lazy_import
from xpark.dataset.utils import LLMChatCompletions, format_prompt, reject_cascade_params, skip_empty_texts
if TYPE_CHECKING:
import pyarrow as pa
from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam
else:
openai = lazy_import("openai")
pa = lazy_import("pyarrow", rename="pa")
logger = logging.getLogger("ray")
# prompt modify from https://github.com/apache/doris/blob/4.0.2-rc01/be/src/vec/functions/ai/ai_generate.h
_ROLE_AND_TASK_PROMPT = (
"You are a creative llm model. You will generate a concise and highly relevant response based on the user's `input_text`. "
"Aim for maximum brevity—cut every non-essential word."
)
PROMPT_TEMPLATE = """
<input_text>
{}
</input_text>
"""
def build_prompt(
text: str,
hint: str | list[str] | None = None,
) -> Iterable[ChatCompletionMessageParam]:
from openai.types.chat.chat_completion_message_param import (
ChatCompletionSystemMessageParam,
ChatCompletionUserMessageParam,
)
_system_prompt = format_prompt(
roles_and_tasks=_ROLE_AND_TASK_PROMPT,
response_format=None,
hint=hint,
)
return [
ChatCompletionSystemMessageParam(role="system", content=_system_prompt),
ChatCompletionUserMessageParam(role="user", content=PROMPT_TEMPLATE.format(text)),
]
[docs]
@udf(return_dtype=DataType.string())
class TextGenerate(BatchColumnClassProtocol):
"""TextGenerate processor generates content based on the input parameters. generate by llm model.
Args:
base_url: The base URL of the LLM server.
model: The request model name.
api_key: The request API key.
max_qps: The maximum query-per-second rate for remote LLM requests.
max_concurrency: The maximum number of in-flight remote LLM requests allowed concurrently.
max_retries: The maximum number of retries per request in the event of failures.
We retry with exponential backoff upto this specific maximum retries.
fallback_response: The response value to return when the LLM request fails.
If set to None, the exception will be raised instead.
hint: Optional extra instructions or constraints to guide the model (e.g. target length,
tone, audience, style). Accepts either a single string or a list of strings, where
each item is one hint written in plain text. Passing a list is recommended — use one
string per hint.
**kwargs: Keyword arguments to pass to the `openai.AsyncClient.chat.completions.create
<https://github.com/openai/openai-python/blob/main/src/openai/resources/chat/completions/completions.py>`_ API.
Examples:
.. code-block:: python
from xpark.dataset.expressions import col
from xpark.dataset import TextGenerate, from_items
ds = from_items([""])
ds = ds.with_column(
"generated_text",
TextGenerate(
model="deepseek-v3-0324",
base_url=os.getenv("LLM_ENDPOINT"),
api_key=os.getenv("LLM_API_KEY"),
)
.options(num_workers={"IO": 1}, batch_size=1)
.with_column(col("item")),
)
print(ds.take_all())
"""
def __init__(
self,
/,
*,
base_url: str,
model: str,
api_key: str = NOT_SET,
max_qps: int | None = None,
max_concurrency: int | None = None,
max_retries: int = 0,
fallback_response: str | None = None,
hint: str | list[str] | None = None,
**kwargs: dict[str, Any],
):
reject_cascade_params("TextGenerate", kwargs)
self.hint = hint
self.model = LLMChatCompletions(
base_url=base_url,
model=model,
api_key=api_key,
max_qps=max_qps,
max_retries=max_retries,
max_concurrency=max_concurrency,
fallback_response=fallback_response,
response_format="text",
**kwargs,
)
@skip_empty_texts
async def __call__(self, texts: pa.ChunkedArray) -> pa.Array:
return await self.model.batch_generate(
texts=texts,
build_prompt=partial(build_prompt, hint=self.hint),
)