ChatCrusoe integration

import getpass
import os

if "CRUSOE_API_KEY" not in os.environ:
    os.environ["CRUSOE_API_KEY"] = getpass.getpass("Enter your Crusoe API key: ")

os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

from langchain_crusoe import ChatCrusoe

llm = ChatCrusoe(
    model="meta-llama/Llama-3.3-70B-Instruct",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    # api_key="...",  # if not set via CRUSOE_API_KEY env var
    # other params...
)

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg

AIMessage(content="J'adore la programmation.", response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 35, 'total_tokens': 44}, 'model_name': 'meta-llama/Llama-3.3-70B-Instruct', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-...', usage_metadata={'input_tokens': 35, 'output_tokens': 9, 'total_tokens': 44})

print(ai_msg.content)

J'adore la programmation.

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

AIMessage(content='Ich liebe Programmieren.', response_metadata={...}, id='run-...')

stream = llm.stream_events(messages, version="v3")
for token in stream.text:
    print(token, end="", flush=True)

from pydantic import BaseModel, Field

class GetWeather(BaseModel):
    """Get the current weather in a given location."""
    location: str = Field(description="City and state, e.g. San Francisco, CA")

llm_with_tools = llm.bind_tools([GetWeather])
ai_msg = llm_with_tools.invoke("What's the weather like in San Francisco?")
print(ai_msg.tool_calls)

from pydantic import BaseModel, Field
from typing import Optional

class Joke(BaseModel):
    """Joke to tell user."""
    setup: str = Field(description="The setup of the joke")
    punchline: str = Field(description="The punchline to the joke")
    rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")

structured_llm = llm.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats")

Joke(setup='Why was the cat sitting on the computer?', punchline='To keep an eye on the mouse!', rating=7)