ChatBaseten integration | LangChain 简体中文

This guide provides a quick overview for getting started with ChatBaseten chat models.

Baseten provides inference designed for production applications. Built on the Baseten Inference Stack, these APIs deliver enterprise-grade performance and reliability for leading open-source or custom models: https://www.baseten.co/library/.

Overview

Details

Class	Package	Serializable	JS support	Downloads	Version
`ChatBaseten`	`langchain-baseten`	beta	❌

Features

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

Model APIs only support text input, while some dedicated deployments support image and audio input depending on model. Check the Baseten model library for details: https://www.baseten.co/library/

Setup

To access Baseten models, you'll need to create a Baseten account, get an API key, and install the langchain-baseten integration package.

Head to the Baseten website to create an account and generate an API key. Once you've done this, set the BASETEN_API_KEY environment variable:

Credentials

import getpass
import os

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

To enable automated tracingtracing of your model calls, set your LangSmith API key:

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

Installation

The LangChain Baseten integration lives in the langchain-baseten package:

pip

pip install -U langchain-baseten

uv add langchain-baseten

pipuv

pip install -U langchain-baseten

Instantiation

Baseten offers two ways to access chat models:

Model APIs: For access to the latest, most popular opensource models.
Dedicated URLs: Use specific model deployments with dedicated resources.

Both approaches are supported with automatic endpoint normalization.

from langchain_baseten import ChatBaseten

# Option 1: Use Model APIs with model slug
model = ChatBaseten(
    model="moonshotai/Kimi-K2-Instruct-0905",  # Choose from available model slugs: https://docs.baseten.co/development/model-apis/overview#supported-models
    api_key="your-api-key",  # Or set BASETEN_API_KEY env var
)

from langchain_baseten import ChatBaseten

# Option 2: Use dedicated deployments with model url
model = ChatBaseten(
    model_url="https://model-<id>.api.baseten.co/environments/production/predict",
    api_key="your-api-key",  # Or set BASETEN_API_KEY env var
)

Invocation

# Use the chat model
response = model.invoke("Hello, how are you?")
print(response.content)

Hello! I'm doing well, thank you for asking! How about you?

You can also use message objects for more complex conversations:

Dictionary format

messages = [
    {"role": "system", "content": "You are a poetry expert"},
    {"role": "user", "content": "Write a haiku about spring"},
]
response = model.invoke(messages)
print(response)

Dictionary format

messages = [
    {"role": "system", "content": "You are a poetry expert"},
    {"role": "user", "content": "Write a haiku about spring"},
]
response = model.invoke(messages)
print(response)

content='Buds yawn open wide—\na robin stitches the hush\nwith threads of first light.' additional_kwargs={} response_metadata={'token_usage': {'completion_tokens': 26, 'prompt_tokens': 14, 'total_tokens': 40}, 'model_name': 'moonshotai/Kimi-K2-Instruct-0905', 'finish_reason': 'stop', 'model_provider': 'baseten'} id='run--6f7d1db7-daae-4628-a40a-2ab7323e8f15-0'

Tip

Full guides are available on chat model invocation types, message types, and content blocks.