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Chains can be combined with existing Truss models using Stubs. A Stub acts as a substitute (client-side proxy) for a remotely deployed dependency, either a Chainlet or a Truss model. The Stub performs the remote invocations as if it were local by taking care of the transport layer, authentication, data serialization and retries. Stubs can be integrated into Chainlets by passing in a URL of the deployed model. They also require context to be initialized (for authentication). The following Chainlet wraps a deployed model with a Stub:
my_chainlet.py
import truss_chains as chains


class LLMClient(chains.StubBase):

    async def run_remote(self, prompt: str) -> str:
        # Call the deployed model
        resp = await self.predict_async(inputs={
            "messages": [{"role": "user", "content": prompt}],
            "stream"  : False
        })
        # Return a string with the model output
        return resp["output"]


LLM_URL = ...


class MyChainlet(chains.ChainletBase):

    def __init__(
        self,
        context: chains.DeploymentContext = chains.depends_context(),
    ):
        self._llm = LLMClient.from_url(LLM_URL, context)
There are various ways how you can make a call to the other deployment:
  • Input as JSON dict (like above) or pydantic model.
  • Automatic parsing of the response into a pydantic model using the output_model argument.
  • predict_async (recommended) or predict_sync.
  • Streaming responses using predict_async_stream which returns an async bytes iterator.
  • Customized with RPCOptions.
See the StubBase reference for all APIs.