langchain-baseten 0.1.1

An integration package connecting Baseten and LangChain

langchain-baseten

This package contains the LangChain integration with Baseten.

Installation

pip install langchain-baseten

The embeddings functionality uses Baseten's Performance Client for optimized performance:

pip install baseten-performance-client

Chat Models

ChatBaseten class exposes chat models from Baseten.

from langchain_baseten import ChatBaseten

# Option 1: Use Model APIs with model slug (recommended)
chat = ChatBaseten(
    model="deepseek-ai/DeepSeek-V3-0324",  # Choose from available model slugs
    api_key="your-api-key",  # Or set BASETEN_API_KEY env var
)

# Option 2: Use dedicated model URL for deployed models
chat = ChatBaseten(
    model_url="https://model-<id>.api.baseten.co/environments/production/predict",
    api_key="your-api-key",
    # `model` parameter is optional for most dedicated models, but may required for specific models like "openai/gpt-oss-20b", please check APIs endpoint example for your deployment for guidance.
)

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

Embeddings

BasetenEmbeddings class exposes embedding models from Baseten.

from langchain_baseten import BasetenEmbeddings

# Initialize the embeddings model
embeddings = BasetenEmbeddings(
    model_url="https://model-<id>.api.baseten.co/environments/production/sync",  # Your model URL
    api_key="your-api-key",  # Or set BASETEN_API_KEY env var
    # model parameter is optional since model_url identifies the model
)

# Embed documents
vectors = embeddings.embed_documents(["Hello world", "How are you?"])
print(f"Generated {len(vectors)} embeddings of dimension {len(vectors[0])}")

# Embed a single query
query_vector = embeddings.embed_query("What is the meaning of life?")
print(f"Query embedding dimension: {len(query_vector)}")

Configuration

You can configure the Baseten integration using environment variables:

• BASETEN_API_KEY: Your Baseten API key

Deployment Options

Chat Models:

• Model APIs (recommended): Use model slugs with shared infrastructure
• Dedicated URLs: Use specific model deployments with dedicated resources

Embeddings:

• Dedicated URLs only: Requires specific model deployment URL for Performance Client optimization

Supported Models

Baseten supports various models through their OpenAI-compatible API. You can use any model slug available in your Baseten account, or deploy custom models with dedicated URLs.

For more information about available models, visit the Baseten documentation.