ChatCloudflareWorkersAI

This will help you getting started with CloudflareWorkersAI chat models. For detailed documentation of all available Cloudflare WorkersAI models head to the API reference.

Overview

Integration details

Class	Package	Local	Serializable	JS support	Package downloads	Package latest
ChatCloudflareWorkersAI	langchain-community	❌	❌	✅	❌	❌

Model features

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
✅	✅	✅	❌	❌	❌	❌	❌	❌	❌

Setup

• To access Cloudflare Workers AI models you'll need to create a Cloudflare account, get an account number and API key, and install the langchain-community package.

Credentials

Head to this document to sign up to Cloudflare Workers AI and generate an API key.

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

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

Installation

The LangChain ChatCloudflareWorkersAI integration lives in the langchain-community package:

%pip install -qU langchain-community

Instantiation

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

from langchain_community.chat_models.cloudflare_workersai import ChatCloudflareWorkersAI

llm = ChatCloudflareWorkersAI(
    account_id="my_account_id",
    api_token="my_api_token",
    model="@hf/nousresearch/hermes-2-pro-mistral-7b",
)

API Reference:ChatCloudflareWorkersAI

Invocation

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

2024-11-07 15:55:14 - INFO - Sending prompt to Cloudflare Workers AI: {'prompt': 'role: system, content: You are a helpful assistant that translates English to French. Translate the user sentence.\nrole: user, content: I love programming.', 'tools': None}

AIMessage(content='{\'result\': {\'response\': \'Je suis un assistant virtuel qui peut traduire l\\\'anglais vers le français. La phrase que vous avez dite est : "J\\\'aime programmer." En français, cela se traduit par : "J\\\'adore programmer."\'}, \'success\': True, \'errors\': [], \'messages\': []}', additional_kwargs={}, response_metadata={}, id='run-838fd398-8594-4ca5-9055-03c72993caf6-0')

print(ai_msg.content)

{'result': {'response': 'Je suis un assistant virtuel qui peut traduire l\'anglais vers le français. La phrase que vous avez dite est : "J\'aime programmer." En français, cela se traduit par : "J\'adore programmer."'}, 'success': True, 'errors': [], 'messages': []}

Chaining

We can chain our model with a prompt template like so:

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.",
    }
)

API Reference:ChatPromptTemplate

2024-11-07 15:55:24 - INFO - Sending prompt to Cloudflare Workers AI: {'prompt': 'role: system, content: You are a helpful assistant that translates English to German.\nrole: user, content: I love programming.', 'tools': None}

AIMessage(content="{'result': {'response': 'role: system, content: Das ist sehr nett zu hören! Programmieren lieben, ist eine interessante und anspruchsvolle Hobby- oder Berufsausrichtung. Wenn Sie englische Texte ins Deutsche übersetzen möchten, kann ich Ihnen helfen. Geben Sie bitte den englischen Satz oder die Übersetzung an, die Sie benötigen.'}, 'success': True, 'errors': [], 'messages': []}", additional_kwargs={}, response_metadata={}, id='run-0d3be9a6-3d74-4dde-b49a-4479d6af00ef-0')

API reference

For detailed documentation on ChatCloudflareWorkersAI features and configuration options, please refer to the API reference.

Related

• Chat model conceptual guide
• Chat model how-to guides