05 gemini focusedWithDeps

Excellent. This is the perfect pivot. The community feedback, while direct, provides a valuable opportunity to ground the project in pragmatic, ecosystem-aware principles. We will now create a detailed design for the Core Execution and Optimization Loop of DSPEx, explicitly leveraging the fantastic tools the Elixir community has already built.

This plan is focused, achievable, and demonstrates a deep respect for the existing ecosystem, addressing the core technical concerns that were raised.

Technical Design: DSPEx Core Execution & Optimization Loop

1. Core Philosophy & Guiding Principles

This design adheres to a “leverage, don’t reinvent” philosophy. The value of DSPEx is not in building generic infrastructure, but in providing a BEAM-native implementation of the DSPy programming model for orchestrating and optimizing AI programs.

1. Minimal Core: We will build the smallest possible set of components needed to replicate DSPy’s Predict -> Evaluate -> BootstrapFewShot workflow.
2. Ecosystem First: We will use best-in-class, community-vetted libraries for all non-domain-specific tasks (HTTP, caching, resilience, etc.).
3. Pragmatic Concurrency: We will apply OTP’s powerful concurrency features where they provide the most leverage: in parallel evaluation and optimization, which is the true bottleneck in the DSPy workflow.

2. Core Components & Chosen Libraries

This is the stack for our MVP. It is lean, robust, and uses the tools an experienced Elixir developer would reach for.

3. Architectural Diagrams

Diagram 1: Static Component Architecture

This diagram shows the relationship between our DSPEx modules and the external libraries they will leverage.

Diagram 2: Dynamic Flow of DSPEx.Evaluate.run/3

This sequence diagram illustrates the power of the BEAM. The Evaluate module orchestrates a massively parallel workload, where each task is independently resilient.

4. Code Implementation Skeletons

This is how the modules will be structured.

DSPEx.Client - The Resilient API Caller

defmodule DSPEx.Client do
  use GenServer

  # --- Public API ---
  def start_link(opts) do
    # Name from opts, e.g., MyApp.OpenAIClient
    GenServer.start_link(__MODULE__, opts, name: opts[:name])
  end

  def request(client, messages, opts \\ %{}) do
    GenServer.call(client, {:request, messages, opts})
  end

  # --- GenServer Implementation ---
  @impl true
  def init(opts) do
    # config contains api_key, base_url, etc.
    config = Keyword.fetch!(opts, :config)
    # fuse_name allows per-provider circuit breakers
    fuse_name = :"fuse_#{opts[:name]}"
    {:ok, %{config: config, fuse_name: fuse_name}}
  end

  @impl true
  def handle_call({:request, messages, _opts}, _from, state) do
    # The core logic: wrap a Req call inside a Fuse call.
    # Fuse will automatically handle the circuit breaking logic.
    response =
      Fuse.call({:default, state.fuse_name}, fn ->
        # Use Req to build and execute the request.
        Req.post!(
          state.config.base_url,
          body: Jason.encode!(%{messages: messages, model: state.config.model}),
          headers: %{"Authorization" => "Bearer #{state.config.api_key}"}
        )
      end)

    # Process the response from Fuse
    reply =
      case response do
        {:ok, %Req.Response{status: 200, body: body}} ->
          {:ok, body}
        {:ok, %Req.Response{status: status, body: body}} ->
          {:error, {:http_error, status, body}}
        {:error, :circuit_open} ->
          {:error, :circuit_open}
        {:error, reason} ->
          {:error, {:fuse_error, reason}}
      end

    {:reply, reply, state}
  end
end

DSPEx.Evaluate - The Concurrent Engine

defmodule DSPEx.Evaluate do
  alias DSPEx.Program

  def run(program, devset, metric_fun, opts \\ []) do
    num_threads = Keyword.get(opts, :num_threads, System.schedulers_online() * 2)

    # Setup progress bar
    pbar = ProgressBar.new(total: Enum.count(devset))

    results =
      devset
      |> Task.async_stream(
        fn example ->
          # This is the work done by each concurrent process
          prediction = Program.forward(program, example.inputs)
          score = metric_fun.(example, prediction)
          ProgressBar.tick(pbar)
          {example, prediction, score}
        end,
        max_concurrency: num_threads,
        ordered: false,
        on_timeout: :kill_task
      )
      |> Enum.to_list() # Collect all results

    ProgressBar.done(pbar)

    # Aggregate scores from the results
    # ... logic to calculate average score, etc.
  end
end

5. Phased Rollout Plan

1. Phase 1: DSPEx.Predict End-to-End

   • Implement DSPEx.Signature macro (minimal version).
   • Implement DSPEx.Client using Req and Fuse.
   • Implement DSPEx.Cache using Cachex.
   • Implement DSPEx.Predict to tie it all together.
   • Deliverable: A user can define a QA signature, configure a client, and get a single, resilient prediction.

2. Phase 2: DSPEx.Evaluate and BootstrapFewShot

   • Implement DSPEx.Evaluate using Task.async_stream and progress_bar.
   • Implement DSPEx.ChainOfThought.
   • Implement the DSPEx.Teleprompter.BootstrapFewShot optimizer, which will use DSPEx.Evaluate as its core engine.
   • Deliverable: A user can now evaluate their programs for performance and run the first optimizer to automatically generate few-shot examples, closing the core DSPy loop.

This refocused plan is technically sound, demonstrates deep understanding of the Elixir ecosystem, and provides a clear, incremental path to delivering a powerful and valuable library. It directly addresses the community’s feedback by building on the ecosystem, not in spite of it.