Incovation

Once your Chain is deployed, you can call it via its API endpoint. Chains use the same inference API as models:

• Development endpoint
• Production endpoint
• 🆕 Environment endpoint
• Endpoint by ID

Here’s an example which calls the development deployment:

import requests
import os

# From the Chain overview page on Baseten
# E.g. "https://chain-<CHAIN_ID>.api.baseten.co/development/run_remote"
CHAIN_URL = ""  
baseten_api_key = os.environ["BASETEN_API_KEY"]
# JSON keys and types match the `run_remote` method signature.
data = {...}

resp = requests.post(
    CHAIN_URL,
    headers={"Authorization": f"Api-Key {baseten_api_key}"},
    json=data,
)

print(resp.json())

​

How to pass chain input

The data schema of the inference request corresponds to the function signature of run_remote() in your entrypoint Chainlet.

For example, for the Hello Chain, HelloAll.run_remote():

async def run_remote(self, names: list[str]) -> str:

You’d pass the following JSON payload:

{"names": ["Marius", "Sid", "Bola"]}

I.e. the keys in the JSON record, match the argument names and values match the types ofrun_remote.

​

Async chain inference

Like Truss models, Chains support async invocation. The guide for models applies largely - in particular for how to wrap the input and set up the webhook to process results.

The following additional points are chains specific:

• Use chain-based URLS:
  • https://chain-{chain}.api.baseten.co/production/async_run_remote
  • https://chain-{chain}.api.baseten.co/development/async_run_remote
  • https://chain-{chain}.api.baseten.co/deployment/{deployment}/async_run_remote.
  • https://chain-{chain}.api.baseten.co/environments/{env_name}/async_run_remote.
• Only the entrypoint is invoked asynchronously. Internal Chainlet-Chainlet calls run synchronously.