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Documentation Index

Fetch the complete documentation index at: https://docs.baseten.co/llms.txt

Use this file to discover all available pages before exploring further.

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"] }
That is, 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.