Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
npxskills add zechenzhangagi/ai-research-skills--skill outlinesLoading…
Use Outlines when you need to:
GitHub Stars: 8,000+ | From: dottxt.ai (formerly .txt)
# Base installation
pip install outlines
# With specific backends
pip install outlines transformers # Hugging Face models
pip install outlines llama-cpp-python # llama.cpp
pip install outlines vllm # vLLM for high-throughput
import outlines
from typing import Literal
# Load model
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
# Generate with type constraint
prompt = "Sentiment of 'This product is amazing!': "
generator = outlines.generate.choice(model, ["positive", "negative", "neutral"])
sentiment = generator(prompt)
print(sentiment) # "positive" (guaranteed one of these)
from pydantic import BaseModel
import outlines
class User(BaseModel):
name: str
age: int
email: str
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
# Generate structured output
prompt = "Extract user: John Doe, 30 years old, [email protected]"
generator = outlines.generate.json(model, User)
user = generator(prompt)
print(user.name) # "John Doe"
print(user.age) # 30
print(user.email) # "[email protected]"
Outlines uses Finite State Machines (FSM) to constrain token generation at the logit level.
How it works:
Benefits:
import outlines
# Pydantic model -> JSON schema -> CFG -> FSM
class Person(BaseModel):
name: str
age: int
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
# Behind the scenes:
# 1. Person -> JSON schema
# 2. JSON schema -> CFG
# 3. CFG -> FSM
# 4. FSM filters tokens during generation
generator = outlines.generate.json(model, Person)
result = generator("Generate person: Alice, 25")
Outlines provides specialized generators for different output types.
# Multiple choice selection
generator = outlines.generate.choice(
model,
["positive", "negative", "neutral"]
)
sentiment = generator("Review: This is great!")
# Result: One of the three choices
from pydantic import BaseModel
class Product(BaseModel):
name: str
price: float
in_stock: bool
# Generate valid JSON matching schema
generator = outlines.generate.json(model, Product)
product = generator("Extract: iPhone 15, $999, available")
# Guaranteed valid Product instance
print(type(product)) # <class '__main__.Product'>
# Generate text matching regex
generator = outlines.generate.regex(
model,
r"[0-9]{3}-[0-9]{3}-[0-9]{4}" # Phone number pattern
)
phone = generator("Generate phone number:")
# Result: "555-123-4567" (guaranteed to match pattern)
# Generate specific numeric types
int_generator = outlines.generate.integer(model)
age = int_generator("Person's age:") # Guaranteed integer
float_generator = outlines.generate.float(model)
price = float_generator("Product price:") # Guaranteed float
Outlines supports multiple local and API-based backends.
import outlines
# Load from Hugging Face
model = outlines.models.transformers(
"microsoft/Phi-3-mini-4k-instruct",
device="cuda" # Or "cpu"
)
# Use with any generator
generator = outlines.generate.json(model, YourModel)
# Load GGUF model
model = outlines.models.llamacpp(
"./models/llama-3.1-8b-instruct.Q4_K_M.gguf",
n_gpu_layers=35
)
generator = outlines.generate.json(model, YourModel)
# For production deployments
model = outlines.models.vllm(
"meta-llama/Llama-3.1-8B-Instruct",
tensor_parallel_size=2 # Multi-GPU
)
generator = outlines.generate.json(model, YourModel)
# Basic OpenAI support
model = outlines.models.openai(
"gpt-4o-mini",
api_key="your-api-key"
)
# Note: Some features limited with API models
generator = outlines.generate.json(model, YourModel)
Outlines has first-class Pydantic support with automatic schema translation.
from pydantic import BaseModel, Field
class Article(BaseModel):
title: str = Field(description="Article title")
author: str = Field(description="Author name")
word_count: int = Field(description="Number of words", gt=0)
tags: list[str] = Field(description="List of tags")
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
generator = outlines.generate.json(model, Article)
article = generator("Generate article about AI")
print(article.title)
print(article.word_count) # Guaranteed > 0
class Address(BaseModel):
street: str
city: str
country: str
class Person(BaseModel):
name: str
age: int
address: Address # Nested model
generator = outlines.generate.json(model, Person)
person = generator("Generate person in New York")
print(person.address.city) # "New York"
from enum import Enum
from typing import Literal
class Status(str, Enum):
PENDING = "pending"
APPROVED = "approved"
REJECTED = "rejected"
class Application(BaseModel):
applicant: str
status: Status # Must be one of enum values
priority: Literal["low", "medium", "high"] # Must be one of literals
generator = outlines.generate.json(model, Application)
app = generator("Generate application")
print(app.status) # Status.PENDING (or APPROVED/REJECTED)
from pydantic import BaseModel
import outlines
class CompanyInfo(BaseModel):
name: str
founded_year: int
industry: str
employees: int
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
generator = outlines.generate.json(model, CompanyInfo)
text = """
Apple Inc. was founded in 1976 in the technology industry.
The company employs approximately 164,000 people worldwide.
"""
prompt = f"Extract company information:\n{text}\n\nCompany:"
company = generator(prompt)
print(f"Name: {company.name}")
print(f"Founded: {company.founded_year}")
print(f"Industry: {company.industry}")
print(f"Employees: {company.employees}")
from typing import Literal
import outlines
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
# Binary classification
generator = outlines.generate.choice(model, ["spam", "not_spam"])
result = generator("Email: Buy now! 50% off!")
# Multi-class classification
categories = ["technology", "business", "sports", "entertainment"]
category_gen = outlines.generate.choice(model, categories)
category = category_gen("Article: Apple announces new iPhone...")
# With confidence
class Classification(BaseModel):
label: Literal["positive", "negative", "neutral"]
confidence: float
classifier = outlines.generate.json(model, Classification)
result = classifier("Review: This product is okay, nothing special")
class UserProfile(BaseModel):
full_name: str
age: int
email: str
phone: str
country: str
interests: list[str]
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
generator = outlines.generate.json(model, UserProfile)
prompt = """
Extract user profile from:
Name: Alice Johnson
Age: 28
Email: [email protected]
Phone: 555-0123
Country: USA
Interests: hiking, photography, cooking
"""
profile = generator(prompt)
print(profile.full_name)
print(profile.interests) # ["hiking", "photography", "cooking"]
class Entity(BaseModel):
name: str
type: Literal["PERSON", "ORGANIZATION", "LOCATION"]
class DocumentEntities(BaseModel):
entities: list[Entity]
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
generator = outlines.generate.json(model, DocumentEntities)
text = "Tim Cook met with Satya Nadella at Microsoft headquarters in Redmond."
prompt = f"Extract entities from: {text}"
result = generator(prompt)
for entity in result.entities:
print(f"{entity.name} ({entity.type})")
class PythonFunction(BaseModel):
function_name: str
parameters: list[str]
docstring: str
body: str
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
generator = outlines.generate.json(model, PythonFunction)
prompt = "Generate a Python function to calculate factorial"
func = generator(prompt)
print(f"def {func.function_name}({', '.join(func.parameters)}):")
print(f' """{func.docstring}"""')
print(f" {func.body}")
def batch_extract(texts: list[str], schema: type[BaseModel]):
"""Extract structured data from multiple texts."""
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
generator = outlines.generate.json(model, schema)
results = []
for text in texts:
result = generator(f"Extract from: {text}")
results.append(result)
return results
class Person(BaseModel):
name: str
age: int
texts = [
"John is 30 years old",
"Alice is 25 years old",
"Bob is 40 years old"
]
people = batch_extract(texts, Person)
for person in people:
print(f"{person.name}: {person.age}")
import outlines
# Basic usage
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
# GPU configuration
model = outlines.models.transformers(
"microsoft/Phi-3-mini-4k-instruct",
device="cuda",
model_kwargs={"torch_dtype": "float16"}
)
# Popular models
model = outlines.models.transformers("meta-llama/Llama-3.1-8B-Instruct")
model = outlines.models.transformers("mistralai/Mistral-7B-Instruct-v0.3")
model = outlines.models.transformers("Qwen/Qwen2.5-7B-Instruct")
# Load GGUF model
model = outlines.models.llamacpp(
"./models/llama-3.1-8b.Q4_K_M.gguf",
n_ctx=4096, # Context window
n_gpu_layers=35, # GPU layers
n_threads=8 # CPU threads
)
# Full GPU offload
model = outlines.models.llamacpp(
"./models/model.gguf",
n_gpu_layers=-1 # All layers on GPU
)
# Single GPU
model = outlines.models.vllm("meta-llama/Llama-3.1-8B-Instruct")
# Multi-GPU
model = outlines.models.vllm(
"meta-llama/Llama-3.1-70B-Instruct",
tensor_parallel_size=4 # 4 GPUs
)
# With quantization
model = outlines.models.vllm(
"meta-llama/Llama-3.1-8B-Instruct",
quantization="awq" # Or "gptq"
)
# ✅ Good: Specific types
class Product(BaseModel):
name: str
price: float # Not str
quantity: int # Not str
in_stock: bool # Not str
# ❌ Bad: Everything as string
class Product(BaseModel):
name: str
price: str # Should be float
quantity: str # Should be int
from pydantic import Field
# ✅ Good: With constraints
class User(BaseModel):
name: str = Field(min_length=1, max_length=100)
age: int = Field(ge=0, le=120)
email: str = Field(pattern=r"^[\w\.-]+@[\w\.-]+\.\w+$")
# ❌ Bad: No constraints
class User(BaseModel):
name: str
age: int
email: str
# ✅ Good: Enum for fixed set
class Priority(str, Enum):
LOW = "low"
MEDIUM = "medium"
HIGH = "high"
class Task(BaseModel):
title: str
priority: Priority
# ❌ Bad: Free-form string
class Task(BaseModel):
title: str
priority: str # Can be anything
# ✅ Good: Clear context
prompt = """
Extract product information from the following text.
Text: iPhone 15 Pro costs $999 and is currently in stock.
Product:
"""
# ❌ Bad: Minimal context
prompt = "iPhone 15 Pro costs $999 and is currently in stock."
from typing import Optional
# ✅ Good: Optional fields for incomplete data
class Article(BaseModel):
title: str # Required
author: Optional[str] = None # Optional
date: Optional[str] = None # Optional
tags: list[str] = [] # Default empty list
# Can succeed even if author/date missing
| Feature | Outlines | Instructor | Guidance | LMQL |
|---|---|---|---|---|
| Pydantic Support | ✅ Native | ✅ Native | ❌ No | ❌ No |
| JSON Schema | ✅ Yes | ✅ Yes | ⚠️ Limited | ✅ Yes |
| Regex Constraints | ✅ Yes | ❌ No | ✅ Yes | ✅ Yes |
| Local Models | ✅ Full | ⚠️ Limited | ✅ Full | ✅ Full |
| API Models | ⚠️ Limited | ✅ Full | ✅ Full | ✅ Full |
| Zero Overhead | ✅ Yes | ❌ No | ⚠️ Partial | ✅ Yes |
| Automatic Retrying | ❌ No | ✅ Yes | ❌ No | ❌ No |
| Learning Curve | Low | Low | Low | High |
When to choose Outlines:
When to choose alternatives:
Speed:
Memory:
Accuracy:
references/json_generation.md - Comprehensive JSON and Pydantic patternsreferences/backends.md - Backend-specific configurationreferences/examples.md - Production-ready examples
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).