dsxir

Elixir port of DSPy. Declarative LM programming with typed signatures, composable modules, prompt-as-data optimization, and BEAM-native concurrency.

Getting Started

Add dsxir to your dependencies:

def deps do
  [{:dsxir, "~> 0.1"}] # x-release-please-version
end

Configure the LM at boot:

Dsxir.configure(
  lm: {Dsxir.LM.Sycophant, [model: "openai:gpt-4o-mini"]},
  adapter: Dsxir.Adapter.Chat
)

Credentials are NEVER passed to Dsxir.configure/1 — they flow through Dsxir.context/2 per request (see Multi-tenant).

A first program

Declare a typed signature, compose it into a module, and run it:

defmodule MyApp.AnswerQuestion do
  use Dsxir.Signature

  signature do
    instruction "Answer the user's question with a single short fact."
    input :question, :string
    output :answer, :string
  end
end

defmodule MyApp.QA do
  use Dsxir.Module

  predictor :answer, Dsxir.Predictor.Predict,
    signature: MyApp.AnswerQuestion

  def forward(prog, %{question: q}) do
    call(prog, :answer, %{question: q})
  end
end

prog = Dsxir.Program.new(MyApp.QA)
{_prog, pred} = MyApp.QA.forward(prog, %{question: "Capital of France?"})
pred[:answer]

Documentation

• Signatures and Modules — typed contracts and how to compose them into programs.
• Inference-time wrappers — BestOfN and Refine reward-sampling wrappers.
• Optimizers — LabeledFewShot, BootstrapFewShot, MIPROv2, and COPRO.
• Runtime Programs — author programs as data instead of code.
• Multi-tenant — per-request credentials, context, and call_plugs.
• Telemetry — the canonical event vocabulary and cost measurements.

Tutorials

• Email Information Extraction — classify, extract, summarize, and propose action items over an inbox, then compile a few-shot version with Dsxir.Optimizer.BootstrapFewShot. Livebook: livebook server guides/tutorials/email_extraction.livemd from a checkout.
• Inference-time Wrappers — trade per-call budget for reliability with BestOfN, Refine, Ensemble, and MultiChainComparison, worked over a single Cognitive-Reflection-Test question.

Comparing to DSPy

dsxir mirrors DSPy's surface where reasonable; some shapes differ:

DSPy	dsxir
dspy.configure(lm=...)	Dsxir.configure(lm: {Impl, config})
dspy.Signature (Pydantic)	use Dsxir.Signature (Spark + Zoi)
signature.demos = [...] (mutation)	%Dsxir.Program{} with per-predictor %State{}
metric(example, pred, trace=None)	(example, pred, trace) -> number()
dspy.inspect_history	Dsxir.History.enable/0 + last/1
dspy.History value type	Dsxir.Primitives.History