feat(ingestion): add bounded, benchmark-aligned embedding execution

Why:
- Plan 001 Phase 4 needs batched, concurrency-bounded embedding wired into
  the inline upload path, with process-wide capacity/timeout/chunk-limit
  guards (ADR-0017).
- The BM25 analyzer and dense-model config are ported from the `emet`
  evaluation lab, which benchmarked them against the real Farsi corpus
  (bm25-fa-norm-stop; nomic-embed-text-v2-moe at 768-dim; text-embedding-3-large
  at native 3072-dim), closing open items in ADR-0001/ADR-0005.

Changes:
- New: embedding ports, orchestration (embed_chunks), request-bounds
  helpers, and dense/sparse adapters (analyzers.py, bm25.py,
  openai_compatible.py).
- upload.py now parses/chunks/embeds inline behind INGESTION_MAX_CONCURRENCY
  (503), INGESTION_TIMEOUT_SECONDS (504), and the chunk-count ceiling (413);
  every failure path still writes a terminal job row.
- Lifespan builds and warms both dense embedders at startup (fail-soft) and
  creates the sparse embedder and concurrency semaphore.
- httpx moves from dev to main dependencies (adapters use it directly).

Impact:
- Qdrant point upserts are still Phase 5 -- chunks_indexed stays 0.
- New EMBEDDING_* env vars documented in .env.example; safe defaults.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

src/application/ports/embedding.py

@@ -0,0 +1,49 @@
+"""Embedding ports (ADR-0001, ADR-0017).
+
+`src/infrastructure/embedding/` holds the production adapters; tests use
+scripted fakes (ADR-0016). Application code depends on these Protocols, not
+on `httpx`/provider SDKs directly.
+"""
+
+from collections.abc import Sequence
+from typing import Protocol
+
+from src.application.ingestion.models import SparseVector
+
+
+class DenseEmbedder(Protocol):
+    """One named dense vector's embedding client (`dense_nomic`/`dense_openai`).
+
+    `embed_batch` is a single batched network call — callers own concurrency
+    bounding (ADR-0017's `embed_concurrency` semaphore), not this Protocol.
+    """
+
+    name: str
+
+    async def embed_batch(self, texts: Sequence[str]) -> list[list[float]]:
+        """Return one vector per input text, same order. Raises `EmbedderError`
+        (see `src/application/ingestion/errors.py`) on transport/response
+        failure.
+        """
+        ...
+
+
+class SparseEmbedder(Protocol):
+    """The `sparse` (BM25) vector's embedding client.
+
+    Blocking/CPU-bound (ADR-0017): callers offload it via
+    `anyio.to_thread.run_sync` with the ingestion `CapacityLimiter`, not call
+    it directly from an `async def`.
+    """
+
+    name: str
+
+    def embed_batch(self, texts: Sequence[str], *, query: bool = False) -> list[SparseVector]:
+        """Return one sparse vector per input text, same order.
+
+        `query=True` selects the query-side weighting, which omits document
+        length normalization. Ingestion always passes `False`; the flag exists
+        so retrieval (ADR-0003) encodes queries through this same port rather
+        than growing a second, silently divergent implementation.
+        """
+        ...