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https://github.com/normal-computing/outlines

Structured Text Generation
https://github.com/normal-computing/outlines

cfg generative-ai json llms prompt-engineering regex structured-generation symbolic-ai

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Structured Text Generation

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README 

        



# Outlines 〰️



Outlines Logo



[![.txt Twitter][dottxt-twitter-badge]][dottxt-twitter]

[![Outlines Twitter][outlines-twitter-badge]][outlines-twitter]



[![Contributors][contributors-badge]][contributors]

[![Downloads][downloads-badge]][pypistats]

[![Discord][discord-badge]][discord]



*Robust (structured) text generation.*



Made with ❤👷️ by the team at [.txt](https://dottxt.co).






``` bash

pip install outlines

```



First time here? Go to our [setup guide](https://outlines-dev.github.io/outlines/welcome)



## Features



- [x] 🤖 [Multiple model integrations](https://outlines-dev.github.io/outlines/installation): OpenAI, transformers, llama.cpp, exllama2, mamba

- [x] 🖍️ Simple and powerful prompting primitives based on the [Jinja templating engine](https://jinja.palletsprojects.com/)

- [x] 🚄 [Multiple choices](#multiple-choices), [type constraints](#type-constraint) and dynamic stopping

- [x] ⚡ Fast [regex-structured generation](#efficient-regex-structured-generation)

- [x] 🔥 Fast [JSON generation](#efficient-json-generation-following-a-pydantic-model) following a JSON schema or a Pydantic model

- [x] 📝 [Grammar-structured generation](#using-context-free-grammars-to-guide-generation)

- [x] 🐍 Interleave completions with loops, conditionals, and custom Python functions

- [x] 💾 Caching of generations

- [x] 🗂️ Batch inference

- [x] 🎲 Sample with the greedy, multinomial and beam search algorithms (and more to come!)

- [x] 🚀 [Serve with vLLM](https://outlines-dev.github.io/outlines/reference/serve/vllm), with official Docker image, [`outlinesdev/outlines`](https://hub.docker.com/r/outlinesdev/outlines)!



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



## Why should I use structured generation?



* It doesn't add any overhead during inference (cost-free)

* It allows Open Source models to beat closed source models ([Mistral](https://x.com/dottxtai/status/1797692104023363765), [GPT-4](https://x.com/dottxtai/status/1798443290913853770))

* [It speeds up inference](http://blog.dottxt.co/coalescence.html)

* [It improves the performance of base models (GSM8K)](http://blog.dottxt.co/performance-gsm8k.html)

* [It improves the performance of finetuned models (CoNNL)](https://predibase.com/blog/lorax-outlines-better-json-extraction-with-structured-generation-and-lora)

* [It improves model efficiency (less examples needed)](https://huggingface.co/blog/evaluation-structured-outputs)



## .txt company





Outlines Logo




We started a company to keep pushing the boundaries of structured generation. Learn more about [.txt](https://twitter.com/dottxtai), and  [give our .json API a try](https://h1xbpbfsf0w.typeform.com/to/ZgBCvJHF) if you need a hosted solution ✨



## Structured 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:



``` python

import outlines



model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")



prompt = """You are a sentiment-labelling assistant.

Is the following review positive or negative?



Review: This restaurant is just awesome!

"""



generator = outlines.generate.choice(model, ["Positive", "Negative"])

answer = generator(prompt)

```



### Type constraint



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



``` python

import outlines



model = outlines.models.transformers("WizardLM/WizardMath-7B-V1.1")



prompt = "result of 9 + 9 = 18result of 1 + 2 = "

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

print(answer)

# 3



prompt = "sqrt(2)="

generator = outlines.generate.format(model, float)

answer = generator(prompt, max_tokens=10)

print(answer)

# 1.41421356

```



### Efficient regex-structured generation



Outlines also comes with fast regex-structured generation. In fact, the `choice` and

`format` functions above all use regex-structured generation under the

hood:



``` python

import outlines



model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")



prompt = "What is the IP address of the Google DNS servers? "



generator = outlines.generate.text(model)

unstructured = generator(prompt, max_tokens=30)



generator = 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?)",

)

structured = generator(prompt, max_tokens=30)



print(unstructured)

# 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(structured)

# What is the IP address of the Google DNS servers?

# 2.2.6.1

```



Unlike other libraries, regex-structured generation in Outlines is almost as fast

as non-structured 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](https://json-schema.org/) or [Pydantic model](https://docs.pydantic.dev/latest/):



```python

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("microsoft/Phi-3-mini-4k-instruct")



# Construct structured sequence generator

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



# Draw a sample

seed = 789001



character = generator("Give me a character description", seed=seed)



print(repr(character))

# Character(name='Anderson', age=28, armor=, weapon=, strength=8)



character = generator("Give me an interesting character description", rng=rng)



print(repr(character))

# Character(name='Vivian Thr', age=44, armor=, weapon=, strength=125)

```



The method works with union types, optional types, arrays, nested schemas, etc. Some field constraints are [not supported yet](https://github.com/outlines-dev/outlines/issues/215), 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:



``` python

import outlines



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("microsoft/Phi-3-mini-4k-instruct")

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

character = generator("Give me a character description")

```



### Using context-free grammars to guide generation



Formal grammars rule the world, and Outlines makes them rule LLMs too. You can pass any context-free grammar in the EBNF format and Outlines will generate an output that is valid to this grammar:



``` python

import outlines



arithmetic_grammar = """

    ?start: expression



    ?expression: term (("+" | "-") term)*



    ?term: factor (("*" | "/") factor)*



    ?factor: NUMBER

           | "-" factor

           | "(" expression ")"



    %import common.NUMBER

"""



model = outlines.models.transformers("WizardLM/WizardMath-7B-V1.1")

generator = outlines.generate.cfg(model, arithmetic_grammar)

sequence = generator("Alice had 4 apples and Bob ate 2. Write an expression for Alice's apples:")



print(sequence)

# (8-2)

```



This was a very simple grammar, and you can use `outlines.generate.cfg` to generate syntactically valid Python, SQL, and much more than this. Any kind of structured text, really. All you have to do is search for "X EBNF grammar" on the web, and take a look at the [Outlines `grammars` module](https://github.com/outlines-dev/outlines/tree/main/outlines/grammars).



### 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 `**`:



```python

import outlines



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

    return a + b



model = outlines.models.transformers("WizardLM/WizardMath-7B-V1.1")

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

result = generator("Return json with 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



Building prompts can get messy. **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:



``` python

import outlines



examples = [

    ("The food was disgusting", "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("microsoft/Phi-3-mini-4k-instruct")

prompt = labelling("Just awesome", examples)

answer = outlines.generate.text(model)(prompt, max_tokens=100)

```



## Join us



- 💡 **Have an idea?** Come chat with us on [Discord][discord]

- 🔨 **Want to contribute?** Consult our [contribution guide](https://outlines-dev.github.io/outlines/community/contribute/).

- 🐞 **Found a bug?** Open an [issue](https://github.com/outlines-dev/outlines/issues)



## 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}

}

```



[contributors]: https://github.com/outlines-dev/outlines/graphs/contributors

[contributors-badge]: https://img.shields.io/github/contributors/outlines-dev/outlines?style=flat-square&logo=github&logoColor=white&color=ECEFF4

[dottxt-twitter]: https://twitter.com/dottxtai

[outlines-twitter]: https://twitter.com/OutlinesOSS

[discord]: https://discord.gg/R9DSu34mGd

[discord-badge]: https://img.shields.io/discord/1182316225284554793?color=81A1C1&logo=discord&logoColor=white&style=flat-square

[downloads-badge]: https://img.shields.io/pypi/dm/outlines?color=89AC6B&logo=python&logoColor=white&style=flat-square

[pypistats]: https://pypistats.org/packages/outlines

[dottxt-twitter-badge]: https://img.shields.io/twitter/follow/dottxtai?style=social

[outlines-twitter-badge]: https://img.shields.io/twitter/follow/OutlinesOSS?style=social