ChatFeatherlessAi
This will help you get started with FeatherlessAichat models. For detailed documentation of all ChatFeatherlessAi features and configurations head to theAPI reference.
- Seehttps://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 thelangchain-featherless-ai integration package.
Credentials
Head tohttps://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
ifnot 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 yourLangSmith 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 thelangchain-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_aiimport 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 canchain our model with a prompt template like so:
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.",
}
)
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 theAPI reference
Related
- Chat modelconceptual guide
- Chat modelhow-to guides