perf: batch CPU embedding inference and add A1-14c Apple GPU (EMLX) spec gap

lib/bds/embeddings/backends/neural.ex

@@ -17,6 +17,14 @@ defmodule BDS.Embeddings.Backends.Neural do
       with `"query: "`, pooled with mean pooling over the attention mask, and
       L2-normalised. This is what makes cross-language semantic similarity
       work.
+    * Inference is batched. `embed_many/2` runs the model on `batch_size`
+      texts per compiled inference run instead of one at a time, which is the
+      dominant cost when (re)indexing large numbers of posts. The serving is
+      compiled for a fixed `batch_size`/`sequence_length` (configurable);
+      shorter sequences mean less wasted transformer compute.
+
+  EXLA on Apple Silicon runs on the CPU — XLA has no Metal/GPU backend. See
+  SPECGAPS A1-14c for the planned EMLX (Apple GPU via MLX) acceleration path.
   """
 
   @behaviour BDS.Embeddings.Backend
@@ -24,11 +32,13 @@ defmodule BDS.Embeddings.Backends.Neural do
   use GenServer
 
   @query_prefix "query: "
-  @embed_timeout :timer.minutes(2)
+  @embed_timeout :timer.minutes(10)
 
   @default_model_id "Xenova/multilingual-e5-small"
   @default_model_repo "intfloat/multilingual-e5-small"
   @default_dimensions 384
+  @default_batch_size 16
+  @default_sequence_length 256
 
   def child_spec(opts) do
     %{id: __MODULE__, start: {__MODULE__, :start_link, [opts]}}
@@ -50,7 +60,22 @@ defmodule BDS.Embeddings.Backends.Neural do
 
   @impl BDS.Embeddings.Backend
   def embed(text, _opts) when is_binary(text) do
-    GenServer.call(__MODULE__, {:embed, @query_prefix <> text}, @embed_timeout)
+    case run([@query_prefix <> text]) do
+      {:ok, [vector]} -> {:ok, vector}
+      {:ok, _other} -> {:error, :unexpected_embedding_result}
+      {:error, _reason} = error -> error
+    end
+  end
+
+  @impl BDS.Embeddings.Backend
+  def embed_many([], _opts), do: {:ok, []}
+
+  def embed_many(texts, _opts) when is_list(texts) do
+    run(Enum.map(texts, &(@query_prefix <> &1)))
+  end
+
+  defp run(prefixed_texts) do
+    GenServer.call(__MODULE__, {:embed, prefixed_texts}, @embed_timeout)
   catch
     :exit, reason -> {:error, {:embedding_backend_unavailable, reason}}
   end
@@ -59,11 +84,15 @@ defmodule BDS.Embeddings.Backends.Neural do
   def init(_opts), do: {:ok, %{serving: nil}}
 
   @impl GenServer
-  def handle_call({:embed, text}, _from, state) do
+  def handle_call({:embed, texts}, _from, state) do
     case ensure_serving(state) do
       {:ok, %{serving: serving} = next_state} ->
-        %{embedding: tensor} = Nx.Serving.run(serving, text)
-        {:reply, {:ok, Nx.to_flat_list(tensor)}, next_state}
+        vectors =
+          texts
+          |> Enum.chunk_every(batch_size())
+          |> Enum.flat_map(&run_chunk(serving, &1))
+
+        {:reply, {:ok, vectors}, next_state}
 
       {:error, _reason} = error ->
         {:reply, error, state}
@@ -73,6 +102,17 @@ defmodule BDS.Embeddings.Backends.Neural do
       {:reply, {:error, Exception.message(exception)}, state}
   end
 
+  defp run_chunk(serving, [single]) do
+    %{embedding: tensor} = Nx.Serving.run(serving, single)
+    [Nx.to_flat_list(tensor)]
+  end
+
+  defp run_chunk(serving, chunk) do
+    serving
+    |> Nx.Serving.run(chunk)
+    |> Enum.map(fn %{embedding: tensor} -> Nx.to_flat_list(tensor) end)
+  end
+
   defp ensure_serving(%{serving: nil} = state) do
     case build_serving() do
       {:ok, serving} -> {:ok, %{state | serving: serving}}
@@ -92,7 +132,7 @@ defmodule BDS.Embeddings.Backends.Neural do
           output_pool: :mean_pooling,
           output_attribute: :hidden_state,
           embedding_processor: :l2_norm,
-          compile: [batch_size: 1, sequence_length: 512],
+          compile: [batch_size: batch_size(), sequence_length: sequence_length()],
           defn_options: [compiler: EXLA]
         )
 
@@ -100,5 +140,13 @@ defmodule BDS.Embeddings.Backends.Neural do
     end
   end
 
+  defp batch_size do
+    config() |> Keyword.get(:batch_size, @default_batch_size) |> max(1)
+  end
+
+  defp sequence_length do
+    config() |> Keyword.get(:sequence_length, @default_sequence_length) |> max(1)
+  end
+
   defp config, do: Application.get_env(:bds, :embeddings, [])
 end