Skip to main content

An integration package connecting Baseten and LangChain

Project description

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, which is automatically included as a dependency.

Chat Models

ChatBaseten class exposes chat models from Baseten.

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
)

# 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
)

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

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
)

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

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

Configuration

You can configure the Baseten integration using environment variables:

  • BASETEN_API_KEY: Your Baseten API key

Deployment Options

Chat Models:

  • Model APIs: 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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

langchain_baseten-0.1.9.tar.gz (17.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

langchain_baseten-0.1.9-py3-none-any.whl (15.2 kB view details)

Uploaded Python 3

File details

Details for the file langchain_baseten-0.1.9.tar.gz.

File metadata

  • Download URL: langchain_baseten-0.1.9.tar.gz
  • Upload date:
  • Size: 17.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.0

File hashes

Hashes for langchain_baseten-0.1.9.tar.gz
Algorithm Hash digest
SHA256 1c55aa7abfe28403df5c275036ddada4b948345b9bf28b718fb91cc24db0ff4f
MD5 2a6c4bc3b89975f2b9941144802a4259
BLAKE2b-256 4b3420d3166f827f19ae24701319e2c5693f830561d37f587db958f39e295a73

See more details on using hashes here.

File details

Details for the file langchain_baseten-0.1.9-py3-none-any.whl.

File metadata

File hashes

Hashes for langchain_baseten-0.1.9-py3-none-any.whl
Algorithm Hash digest
SHA256 924a3494f92c16c10e9195ca8824fb1e7a7867f1fa0746698b91f5bb479fe752
MD5 e954d15c331e1cb6f80e99db973a89ed
BLAKE2b-256 cca40179849fc3fe09e5e62f9f9a19c50e3765ea6185ec6d837555bbd2dc62d6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page