import getpass
import os

ifnot os.getenv("CF_AI_API_KEY"):
    os.environ["CF_AI_API_KEY"]= getpass.getpass(
"Enter your CloudflareWorkersAI API key: "
)

ifnot os.getenv("CF_ACCOUNT_ID"):
    os.environ["CF_ACCOUNT_ID"]= getpass.getpass(
"Enter your CloudflareWorkersAI account ID: "
)

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

%pip install-qU langchain-cloudflare

from langchain_cloudflare.chat_modelsimport ChatCloudflareWorkersAI

llm= ChatCloudflareWorkersAI(
    model="@cf/meta/llama-3.3-70b-instruct-fp8-fast",
    temperature=0,
    max_tokens=1024,
# 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.", additional_kwargs={}, response_metadata={'token_usage': {'prompt_tokens': 37, 'completion_tokens': 9, 'total_tokens': 46}, 'model_name': '@cf/meta/llama-3.3-70b-instruct-fp8-fast'}, id='run-995d1970-b6be-49f3-99ae-af4cdba02304-0', usage_metadata={'input_tokens': 37, 'output_tokens': 9, 'total_tokens': 46})

print(ai_msg.content)

J'adore la programmation.

from langchain_core.promptsimport ChatPromptTemplate

prompt= ChatPromptTemplate(
[
(
"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 das Programmieren.', additional_kwargs={}, response_metadata={'token_usage': {'prompt_tokens': 32, 'completion_tokens': 7, 'total_tokens': 39}, 'model_name': '@cf/meta/llama-3.3-70b-instruct-fp8-fast'}, id='run-d1b677bc-194e-4473-90f1-aa65e8e46d50-0', usage_metadata={'input_tokens': 32, 'output_tokens': 7, 'total_tokens': 39})

json_schema={
"title":"joke",
"description":"Joke to tell user.",
"type":"object",
"properties":{
"setup":{
"type":"string",
"description":"The setup of the joke",
},
"punchline":{
"type":"string",
"description":"The punchline to the joke",
},
"rating":{
"type":"integer",
"description":"How funny the joke is, from 1 to 10",
"default":None,
},
},
"required":["setup","punchline"],
}
structured_llm= llm.with_structured_output(json_schema)

structured_llm.invoke("Tell me a joke about cats")

{'setup': 'Why did the cat join a band?',
 'punchline': 'Because it wanted to be the purr-cussionist',
 'rating': '8'}

from typingimport List

from langchain_core.toolsimport tool


@tool
defvalidate_user(user_id:int, addresses: List[str])->bool:
"""Validate user using historical addresses.

    Args:
        user_id (int): the user ID.
        addresses (List[str]): Previous addresses as a list of strings.
    """
returnTrue


llm_with_tools= llm.bind_tools([validate_user])

result= llm_with_tools.invoke(
"Could you validate user 123? They previously lived at "
"123 Fake St in Boston MA and 234 Pretend Boulevard in "
"Houston TX."
)
result.tool_calls

[{'name': 'validate_user',
  'args': {'user_id': '123',
   'addresses': '["123 Fake St in Boston MA", "234 Pretend Boulevard in Houston TX"]'},
  'id': '31ec7d6a-9ce5-471b-be64-8ea0492d1387',
  'type': 'tool_call'}]