ChatFeatherlessAi

This will help you get started with FeatherlessAi chat models. For detailed documentation of all ChatFeatherlessAi features and configurations head to the API reference.

• See http://featherless.ai/ for an example.

Overview

Integration details

Class	Package	Local	Serializable	JS support	Package downloads	Package latest
ChatFeatherlessAi	langchain-featherless-ai	✅	❌	❌

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 Featherless AI models you'll need to create a/an Featherless AI account, get an API key, and install the langchain-featherless-ai integration package.

Credentials

Head to http://featherless.ai/ to sign up to FeatherlessAI and generate an API key. Once you've done this set the FEATHERLESSAI_API_KEY environment variable:

import getpass
import os

if not os.getenv("FEATHERLESSAI_API_KEY"):
    os.environ["FEATHERLESSAI_API_KEY"] = getpass.getpass(
        "Enter your FeatherlessAI 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 FeatherlessAi integration lives in the langchain-featherless-ai package:

%pip install -qU langchain-featherless-ai

Note: you may need to restart the kernel to use updated packages.

Instantiation

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

from langchain_featherless_ai import ChatFeatherlessAi

llm = ChatFeatherlessAi(
    model="featherless-ai/Qwerky-72B",
    temperature=0.9,
    max_tokens=None,
    timeout=None,
)

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

c:\Python311\Lib\site-packages\pydantic\main.py:463: UserWarning: Pydantic serializer warnings:
  PydanticSerializationUnexpectedValue(Expected `int` - serialized value may not be as expected [input_value=1747322408.706, input_type=float])
  return self.__pydantic_serializer__.to_python(

AIMessage(content="J'aime programmer.", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 27, 'total_tokens': 32, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'featherless-ai/Qwerky-72B', 'system_fingerprint': '', 'id': 'G1sgui', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--6ecbe184-c94e-4d03-bf75-9bd85b04ba5b-0', usage_metadata={'input_tokens': 27, 'output_tokens': 5, 'total_tokens': 32, 'input_token_details': {}, 'output_token_details': {}})

print(ai_msg.content)

J'aime programmer.

Chaining

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

from langchain_core.prompts import 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.",
    }
)

API Reference:ChatPromptTemplate

c:\Python311\Lib\site-packages\pydantic\main.py:463: UserWarning: Pydantic serializer warnings:
  PydanticSerializationUnexpectedValue(Expected `int` - serialized value may not be as expected [input_value=1747322423.487, input_type=float])
  return self.__pydantic_serializer__.to_python(

AIMessage(content='Ich liebe Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 22, 'total_tokens': 27, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'featherless-ai/Qwerky-72B', 'system_fingerprint': '', 'id': 'BoBqht', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--67464357-83d1-4591-9a62-303ed74b8148-0', usage_metadata={'input_tokens': 22, 'output_tokens': 5, 'total_tokens': 27, 'input_token_details': {}, 'output_token_details': {}})

API reference

For detailed documentation of all ChatFeatherlessAi features and configurations head to the API reference

Related

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