Once your Chain is deployed, you can call it via its API endpoint. Chains use the same inference API as models: Here’s an example which calls the development deployment:
call_chain.py
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.