ChatOutlines

pip install -qU langchain-community outlines

from langchain_community.chat_models.outlines import ChatOutlines

# For llamacpp backend
model = ChatOutlines(model="TheBloke/phi-2-GGUF/phi-2.Q4_K_M.gguf", backend="llamacpp")

# For vllm backend (not available on Mac)
model = ChatOutlines(model="meta-llama/Llama-3.2-1B", backend="vllm")

# For mlxlm backend (only available on Mac)
model = ChatOutlines(model="mistralai/Ministral-8B-Instruct-2410", backend="mlxlm")

# For huggingface transformers backend
model = ChatOutlines(model="microsoft/phi-2")  # defaults to transformers backend

from langchain.messages import HumanMessage

messages = [HumanMessage(content="What will the capital of mars be called?")]
response = model.invoke(messages)

response.content

messages = [HumanMessage(content="Count to 10 in French:")]

for chunk in model.stream(messages):
    print(chunk.content, end="", flush=True)

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

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

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.content

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

response.content

from pydantic import BaseModel


class Person(BaseModel):
    name: str


model.json_schema = Person
response = model.invoke("Who are the main contributors to LangChain?")
person = Person.model_validate_json(response.content)

person

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.content

from pydantic import BaseModel


class AnswerWithJustification(BaseModel):
    answer: str
    justification: str


_model = model.with_structured_output(AnswerWithJustification)
result = _model.invoke("What weighs more, a pound of bricks or a pound of feathers?")

result