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Outlines

This will help you get started with Outlines LLM. For detailed documentation of all Outlines features and configurations head to the API reference.

Outlines is a library for constrained language generation. It allows you to use large language models (LLMs) with various backends while applying constraints to the generated output.

Overview​

Integration details​

ClassPackageLocalSerializableJS supportPackage downloadsPackage latest
Outlineslangchain-community✅beta❌PyPI - DownloadsPyPI - Version

Setup​

To access Outlines models you'll need to have an internet connection to download the model weights from huggingface. Depending on the backend you need to install the required dependencies (see Outlines docs)

Credentials​

There is no built-in auth mechanism for Outlines.

Installation​

The LangChain Outlines integration lives in the langchain-community package and requires the outlines library:

%pip install -qU langchain-community outlines

Instantiation​

Now we can instantiate our model object and generate chat completions:

from langchain_community.llms import Outlines

# For use with llamacpp backend
model = Outlines(model="microsoft/Phi-3-mini-4k-instruct", backend="llamacpp")

# For use with vllm backend (not available on Mac)
model = Outlines(model="microsoft/Phi-3-mini-4k-instruct", backend="vllm")

# For use with mlxlm backend (only available on Mac)
model = Outlines(model="microsoft/Phi-3-mini-4k-instruct", backend="mlxlm")

# For use with huggingface transformers backend
model = Outlines(
    model="microsoft/Phi-3-mini-4k-instruct"
)  # defaults to backend="transformers"

Invocation​

model.invoke("Hello how are you?")

Chaining​

from langchain_core.prompts import PromptTemplate

prompt = PromptTemplate.from_template("How to say {input} in {output_language}:\n")

chain = prompt | model
chain.invoke(
    {
        "output_language": "German",
        "input": "I love programming.",
    }
)
API Reference:PromptTemplate

Streaming​

Outlines supports streaming of tokens:

for chunk in model.stream("Count to 10 in French:"):
    print(chunk, end="", flush=True)

Constrained Generation​

Outlines allows you to apply various constraints to the generated output:

Regex Constraint​

model.regex = r"((25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(25[0-5]|2[0-4]\d|[01]?\d\d?)"
response = model.invoke("What is the IP address of Google's DNS server?")

response

Type Constraints​

model.type_constraints = int
response = model.invoke("What is the answer to life, the universe, and everything?")

JSON Schema​

from pydantic import BaseModel


class Person(BaseModel):
    name: str


model.json_schema = Person
response = model.invoke("Who is the author of LangChain?")
person = Person.model_validate_json(response)

person

Grammar Constraint​

model.grammar = """
?start: expression
?expression: term (("+" | "-") term)
?term: factor (("" | "/") factor)
?factor: NUMBER | "-" factor | "(" expression ")"
%import common.NUMBER
%import common.WS
%ignore WS
"""
response = model.invoke("Give me a complex arithmetic expression:")

response

API reference​

For detailed documentation of all ChatOutlines features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.outlines.ChatOutlines.html

Outlines Documentation:​

https://dottxt-ai.github.io/outlines/latest/