Features

• 🖍️Simple and powerful prompting primitives based on the Jinja templating engine
• 🚄 Guided generation, including multiple choice, type constraints and dynamic stopping
• ⚡ Fast regex-guided generation
• 🔥 Fast JSON generation following a JSON schema or a Pydantic model
• 🐍 Interleave completions with loops, conditionals, and custom Python functions
• 💾 Caching of generations

Available models

• Transformers
• AutoGPTQ
• AutoAWQ
• OpenAI API
• Mamba

Outlines 〰 has new releases and features coming every week. Make sure to ⭐ star and 👀 watch this repository, follow @dottxtai to stay up to date!

Installation

Outlines is available on PyPi:

pip install outlines

The dependencies needed to use models are not installed by default. You will need to run:

• pip install openai to be able to use OpenAI models.
• pip install transformers datasets to be able to use Hugging Face transformers models.

Philosophy

Outlines 〰 is a library for neural text generation. You can think of it as a more flexible replacement for the generate method in the transformers library.

Outlines 〰 helps developers guide text generation to build robust interfaces with external systems. Provides generation methods that guarantee that the output will match a regular expressions, or follow a JSON schema.

Outlines 〰 provides robust prompting primitives that separate the prompting from the execution logic and lead to simple implementations of few-shot generations, ReAct, meta-prompting, agents, etc.

Outlines 〰 is designed as a library that is meant to be compatible the broader ecosystem, not to replace it. We use as few abstractions as possible, and generation can be interleaved with control flow, conditionals, custom Python functions and calls to other libraries.

Outlines 〰 is compatible with all models. It only interfaces with models via the next-token logits. It can be used with API-based models as well.

Guided generation

The first step towards reliability of systems that include large language models is to ensure that there is a well-defined interface between their output and user-defined code. Outlines provides ways to control the generation of language models to make their output more predictable.

Multiple choices

You can reduce the completion to a choice between multiple possibilities:

import outlines

model = outlines.models.transformers("mistralai/Mistral-7B-v0.1")

prompt = """You are a sentiment-labelling assistant.
Is the following review positive or negative?

Review: This restaurant is just awesome!
"""
answer = outlines.generate.choice(model, ["Positive", "Negative"])(prompt)

Type constraint

You can instruct the model to only return integers or floats:

import outlines

model = outlines.models.transformers("mistralai/Mistral-7B-v0.1")

prompt = "1+1="
answer = outlines.generate.format(model, int)(prompt)

prompt = "sqrt(2)="
answer = outlines.generate.format(model, float)(prompt)

Efficient regex-guided generation

Outlines also comes with fast regex-guided generation. In fact, the choice, integer and float functions above all use regex-guided generation under the hood:

import outlines

model = outlines.models.transformers("mistralai/Mistral-7B-v0.1")

prompt = "Is 1+1=2? "
unguided = outlines.generate.continuation(model, max_tokens=30)(prompt)
guided = outlines.generate.regex(model, r"\s*([Yy]es|[Nn]o|[Nn]ever|[Aa]lways)", max_tokens=30)(
    prompt
)

print(unguided)
# Is 1+1=2?
#
# This is probably the most perplexing question.
# As I said in one of my articles describing how
# I call 2 and 1, there isn't

print(guided)
# Is 1+1=2? Always

import outlines

model = outlines.models.transformers("mistralai/Mistral-7B-v0.1")

prompt = "What is the IP address of the Google DNS servers? "
unguided = generate.continuation(model, max_tokens=30)(prompt)
guided = outlines.generate.regex(
    model,
    r"((25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(25[0-5]|2[0-4]\d|[01]?\d\d?)",
    max_tokens=30,
)(prompt)

print(unguided)
# What is the IP address of the Google DNS servers?
#
# Passive DNS servers are at DNS servers that are private.
# In other words, both IP servers are private. The database
# does not contain Chelsea Manning

print(guided)
# What is the IP address of the Google DNS servers?
# 2.2.6.1

Unlike other libraries, regex-guided generation in Outlines is almost as fast as non-guided generation.

Efficient JSON generation following a Pydantic model

Outlines 〰 allows to guide the generation process so the output is guaranteed to follow a JSON schema or Pydantic model:

from enum import Enum
from pydantic import BaseModel, constr

import outlines
import torch


class Weapon(str, Enum):
    sword = "sword"
    axe = "axe"
    mace = "mace"
    spear = "spear"
    bow = "bow"
    crossbow = "crossbow"


class Armor(str, Enum):
    leather = "leather"
    chainmail = "chainmail"
    plate = "plate"


class Character(BaseModel):
    name: constr(max_length=10)
    age: int
    armor: Armor
    weapon: Weapon
    strength: int


model = outlines.models.transformers("mistralai/Mistral-7B-v0.1", device="cuda")

# Construct guided sequence generator
generator = outlines.generate.json(model, Character, max_tokens=100)

# Draw a sample
rng = torch.Generator(device="cuda")
rng.manual_seed(789001)

sequence = generator("Give me a character description", rng=rng)
print(sequence)
# {
#   "name": "clerame",
#   "age": 7,
#   "armor": "plate",
#   "weapon": "mace",
#   "strength": 4171
# }

sequence = generator("Give me an interesting character description", rng=rng)
print(sequence)
# {
#   "name": "piggyback",
#   "age": 23,
#   "armor": "chainmail",
#   "weapon": "sword",
#   "strength": 0
# }

The method works with union types, optional types, arrays, nested schemas, etc. Some field constraints are not supported yet, but everything else should work.

Efficient JSON generation following a JSON Schema

Sometimes you just want to be able to pass a JSON Schema instead of a Pydantic model. We've got you covered:

import outlines
import torch

schema = '''{
    "title": "Character",
    "type": "object",
    "properties": {
        "name": {
            "title": "Name",
            "maxLength": 10,
            "type": "string"
        },
        "age": {
            "title": "Age",
            "type": "integer"
        },
        "armor": {"$ref": "#/definitions/Armor"},
        "weapon": {"$ref": "#/definitions/Weapon"},
        "strength": {
            "title": "Strength",
            "type": "integer"
        }
    },
    "required": ["name", "age", "armor", "weapon", "strength"],
    "definitions": {
        "Armor": {
            "title": "Armor",
            "description": "An enumeration.",
            "enum": ["leather", "chainmail", "plate"],
            "type": "string"
        },
        "Weapon": {
            "title": "Weapon",
            "description": "An enumeration.",
            "enum": ["sword", "axe", "mace", "spear", "bow", "crossbow"],
            "type": "string"
        }
    }
}'''

model = outlines.models.transformers("mistralai/Mistral-7B-v0.1", device="cuda")
generator = outlines.generate.json(model, schema)
sequence = generator("Give me a character description")

Open functions

Outlines can infer the structure of the output from the signature of a function. The result is a dictionary, and can be passed directly to the function using the usual dictionary expansion syntax **:

import outlines


def add(a: int, b: int):
    return a + b

model = outlines.models.transformers("mistralai/Mistral-7B-v0.1")
generator = outlines.generate.json(model, add)
result = generator("Return two integers named a and b respectively. a is odd and b even.")

print(add(**result))
# 3

A great advantage of passing functions directly to specify the structure is that the structure of the LLM will change with the function's definition. No need to change the code at several places!

Prompting

Writing prompts by concatenating strings in pure Python quickly becomes cumbersome: the prompt building logic gets entangled with the rest of the program, and the structure of the rendered prompt is obfuscated.Outlines makes it easier to write and manage prompts by encapsulating templates inside "template functions".

These functions make it possible to neatly separate the prompt logic from the general program logic; they can be imported from other modules and libraries.

Template functions require no superfluous abstraction, they use the Jinja2 templating engine to help build complex prompts in a concise manner:

import outlines

examples = [
    ("The food was digusting", "Negative"),
    ("We had a fantastic night", "Positive"),
    ("Recommended", "Positive"),
    ("The waiter was rude", "Negative")
]

@outlines.prompt
def labelling(to_label, examples):
    """You are a sentiment-labelling assistant.

    {% for example in examples %}
    {{ example[0] }} // {{ example[1] }}
    {% endfor %}
    {{ to_label }} //
    """

model = outlines.models.transformers("mistralai/Mistral-7B-v0.1")
prompt = labelling("Just awesome", examples)
answer = outlines.generate.continuation(model, max_tokens=100)(prompt)

Tools

We can teach language models to call external functions to get additional informations or perform tasks, by encoding the functions' description in the prompt. To avoid duplicating information between the function definition and the description passed to the prompt, we define custom Jinja filters that can extract the function's name, description, signature and source:

from typing import Callable, List
import outlines


def google_search(query: str):
    """Google Search"""
    pass


def wikipedia_search(query: str):
    """Wikipedia Search"""
    pass


@outlines.prompt
def my_commands(tools: List[Callable]):
    """AVAILABLE COMMANDS:

    {% for tool in tools %}
    TOOL
    {{ tool | name }}, {{ tool | description }}, args: {{ tool | signature }}
    {{ tool | source }}
    {% endfor %}
    """


prompt = my_commands([google_search, wikipedia_search])

Response models

We can instruct models to return their output in a pre-defined format, often JSON. To avoid duplicating information between the function definition and the description passed to the prompt we define a custom Jinja filter that can extract the expected response's schema:

from pydantic import BaseModel, Field
import outlines


class Joke(BaseModel):
    joke: str = Field(description="The joke")
    explanation: str = Field(
        description="The explanation of why the joke is funny"
    )


@outlines.prompt
def joke_ppt(response_model):
    """Tell a joke and explain why the joke is funny.

    RESPONSE FORMAT:
    {{ response_model | schema }}
    """


joke_ppt(Joke)

# Tell a joke and explain why the joke is funny.
#
# RESPONSE FORMAT:
# {
#    "joke": "The joke"
#    "explanation": "The explanation of why the joke is funny"
#  }

With these prompting primitives Outlines makes building agents like AutoGPT, BabyAGI, ViperGPT or Transformers Agent easier by removing boilerplate prompting code.

Contributing

What contributions?

We currently only accept bug fixes and documentation contributions. If you have a feature request, please start a new discussion. The issue tracker is only intended for actionable items.

How to contribute?

Run pip install -e .[test] or conda env create -f environment.yml. To build the documentation you will also need to run pip install -r requirements-doc.txt.

Before pushing your code to repository please run pre-commit run --all-files and pytest to make sure that the code is formatted correctly and that the tests pass.

Do not hesitate to open a draft PR before your contribution is ready, especially if you have questions and/or need feedback.

Examples

• Pick the odd one out
• Meta prompting
• ReAct
• Generate code to solve math problems
• BabyAGI
• Uncertainty
• Simulation-based inference

Cite Outlines

@article{willard2023efficient,
  title={Efficient Guided Generation for LLMs},
  author={Willard, Brandon T and Louf, R{\'e}mi},
  journal={arXiv preprint arXiv:2307.09702},
  year={2023}
}

License

Outlines is open-source and licensed under the Apache License 2.0.