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Overview

The select() function makes the language model choose from a predefined list of options. This is useful for classification, multiple-choice questions, and structured decision-making.

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Syntax

sgl.select(name, choices, temperature=0.0, choices_method=token_length_normalized)

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Parameters

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name
str
Variable name to store the selected choice. Access with state[name].
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choices
List[str]
required
List of possible choices. The model will select one of these options.
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temperature
float
default:"0.0"
Sampling temperature for selection. Usually kept at 0.0 for deterministic selection.
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choices_method
ChoicesSamplingMethod
default:"token_length_normalized"
Method for computing choice probabilities:
  • token_length_normalized: Normalizes by token length (default, recommended)
  • Other methods available in sglang.lang.choices

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Usage

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Basic Selection

import sglang as sgl

@sgl.function
def classify_sentiment(s, text):
    s += f"Classify the sentiment of: {text}\n"
    s += "Sentiment: "
    s += sgl.select("sentiment", choices=["positive", "negative", "neutral"])

state = classify_sentiment.run(text="I love this product!")
print(state["sentiment"])  # "positive"

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Multiple Choice Question

@sgl.function
def trivia_qa(s, question, options):
    s += f"Question: {question}\n"
    s += "Options:\n"
    for i, opt in enumerate(options):
        s += f"{chr(65+i)}. {opt}\n"
    s += "Answer: "
    s += sgl.select("answer", choices=options)

state = trivia_qa.run(
    question="What is the capital of Japan?",
    options=["Beijing", "Seoul", "Tokyo", "Bangkok"]
)
print(state["answer"])  # "Tokyo"

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Classification with Confidence

@sgl.function
def classify_topic(s, text):
    s += f"Text: {text}\n"
    s += "Topic category: "
    s += sgl.select(
        "topic",
        choices=["Technology", "Sports", "Politics", "Entertainment"],
        temperature=0.0
    )

state = classify_topic.run(text="The new iPhone was announced today.")
print(state["topic"])  # "Technology"

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Yes/No Questions

@sgl.function
def verify_claim(s, claim):
    s += f"Is this claim true? {claim}\n"
    s += "Answer: "
    s += sgl.select("verdict", choices=["Yes", "No"])

state = verify_claim.run(claim="The Earth is flat.")
print(state["verdict"])  # "No"

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Using select() with gen()

You can also use the choices parameter in gen() to achieve the same effect: 
@sgl.function
def classify(s, text):
    s += f"Text: {text}\nCategory: "
    s += sgl.gen("category", choices=["spam", "ham"])

# Equivalent to:
@sgl.function
def classify(s, text):
    s += f"Text: {text}\nCategory: "
    s += sgl.select("category", choices=["spam", "ham"])

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Choice Selection Methods

The choices_method parameter controls how probabilities are computed:

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token_length_normalized (Default)

Normalizes choice probabilities by token length. This is the recommended method as it prevents bias toward longer/shorter choices. 
from sglang.lang.choices import token_length_normalized

s += sgl.select(
    "choice",
    choices=["Yes", "No", "Maybe"],
    choices_method=token_length_normalized
)

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Best Practices

  1. Use descriptive choices: Make choices clear and unambiguous 
    # Good
choices=["Positive", "Negative", "Neutral"]

# Avoid
choices=["P", "N", "Neu"]
  2. Keep temperature at 0.0: For deterministic selection 
    sgl.select("answer", choices=options, temperature=0.0)
  3. Provide context: Give the model enough context to make the right choice 
    @sgl.function
def classify(s, text):
    s += "Classify the following text into one category:\n"
    s += f"Text: {text}\n"
    s += "Categories: Technology, Sports, Politics\n"
    s += "Category: "
    s += sgl.select("category", choices=["Technology", "Sports", "Politics"])
  4. Use meaningful variable names: Name selections based on what they represent 
    sgl.select("user_intent", choices=["question", "command", "statement"])

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Advanced Example: Multi-step Classification

@sgl.function
def hierarchical_classify(s, text):
    # First level classification
    s += f"Text: {text}\n"
    s += "Is this a question or statement? "
    s += sgl.select("type", choices=["question", "statement"])
    
    s += "\n"
    
    # Second level classification based on first
    if s["type"] == "question":
        s += "What type of question? "
        s += sgl.select("subtype", choices=["factual", "opinion", "rhetorical"])
    else:
        s += "What type of statement? "
        s += sgl.select("subtype", choices=["fact", "opinion", "instruction"])

state = hierarchical_classify.run(text="What is the best programming language?")
print(f"Type: {state['type']}")        # "question"
print(f"Subtype: {state['subtype']}")  # "opinion"

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See Also