Outlines — Structured Text Generation

You are an expert in Outlines, the Python library for reliable structured text generation with LLMs. You help developers generate guaranteed-valid JSON, regex-matching text, and grammar-constrained output from open-source models — using finite state machine guided generation that constrains the token sampling process to produce only valid output on the first try.

Core Capabilities

Structured Generation

import outlines
from pydantic import BaseModel, Field
from enum import Enum

# Load model
model = outlines.models.transformers("meta-llama/Llama-3.1-8B-Instruct")

# JSON generation with Pydantic schema
class Sentiment(str, Enum):
    positive = "positive"
    negative = "negative"
    neutral = "neutral"

class ReviewAnalysis(BaseModel):
    sentiment: Sentiment
    score: float = Field(ge=0, le=1)
    topics: list[str] = Field(min_length=1, max_length=5)
    summary: str = Field(max_length=200)

generator = outlines.generate.json(model, ReviewAnalysis)

result = generator(
    "Analyze this review: 'Great product, fast shipping, but packaging could be better'"
)
# result is a validated ReviewAnalysis instance — guaranteed to match schema
print(result.sentiment)    # Sentiment.positive
print(result.score)        # 0.85
print(result.topics)       # ["product quality", "shipping", "packaging"]

# Regex-constrained generation
phone_gen = outlines.generate.regex(model, r"\(\d{3}\) \d{3}-\d{4}")
phone = phone_gen("Generate a US phone number:")
# phone = "(415) 555-0123" — always matches the regex

# Choice (classification)
classifier = outlines.generate.choice(model, ["spam", "ham", "uncertain"])
result = classifier("Is this spam? 'You won $1000000!!!'")
# result = "spam"

# Format-constrained (date, number, etc.)
date_gen = outlines.generate.format(model, datetime.date)
date = date_gen("When was Python created?")
# date = datetime.date(1991, 2, 20) — always a valid date object

Batch Processing

# Batch inference for throughput
generator = outlines.generate.json(model, ReviewAnalysis)

reviews = [
    "Amazing quality, will buy again!",
    "Terrible customer service, never ordering here.",
    "It's okay, nothing special.",
]

prompts = [f"Analyze: '{r}'" for r in reviews]
results = generator(prompts, max_tokens=200)
# results is a list of ReviewAnalysis objects — all guaranteed valid

Grammar-Constrained

# Custom grammar (CFG)
arithmetic_grammar = r"""
    ?start: expression
    ?expression: term (("+" | "-") term)*
    ?term: factor (("*" | "/") factor)*
    ?factor: NUMBER | "(" expression ")"
    NUMBER: /[0-9]+(\.[0-9]+)?/
"""

calc_gen = outlines.generate.cfg(model, arithmetic_grammar)
expr = calc_gen("Generate a math expression that equals 42:")
# expr = "(6 * 7)" — always valid arithmetic

With vLLM

# Use with vLLM for production throughput
model = outlines.models.vllm("meta-llama/Llama-3.1-8B-Instruct",
    tensor_parallel_size=1, gpu_memory_utilization=0.9)

generator = outlines.generate.json(model, ReviewAnalysis)
# Combines Outlines' constrained generation with vLLM's batching + PagedAttention

Installation

pip install outlines

Best Practices

1. Pydantic schemas — Define output with Pydantic models; Outlines compiles to FSM for guaranteed compliance
2. Regex for patterns — Use generate.regex() for dates, emails, IDs; output always matches the pattern
3. Choice for classification — Use generate.choice() instead of free text; constrained to exact options
4. vLLM for production — Combine with vLLM backend for high-throughput constrained generation
5. Batch for efficiency — Pass lists of prompts; Outlines batches efficiently with the model
6. Field constraints — Use Pydantic's ge, le, minlength, maxlength; further constrains output
7. Grammar for DSLs — Use CFG grammars for domain-specific output (SQL, code, formulas)
8. First-try guarantee — Unlike retry-based approaches, Outlines gets valid output on the first generation