perf: batch page embeddings via /api/embed and upsert before pruning stale points

- embed_batch now issues one batched /api/embed request (the old loop
  made one /api/embeddings round-trip per chunk) with a per-text
  fallback that preserves None-for-failed semantics
- update_from_page embeds all chunks in that single call and stores
  them in one Qdrant batch upsert (upsert_points)
- reindex order reversed: upsert new points first, then prune stale ids
  (deterministic uuid5 ids make overwrite safe) so a mid-way failure no
  longer leaves the page with zero vectors
- VectorUpdateSummary gains status (success/partial/failed) and
  chunks_skipped; all-embeddings-failed keeps old vectors and reports
  failure instead of success=True

Measured on a 7-chunk page ingest (local server, llm_tester):
~375ms -> ~181ms median over 3 runs.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbFZyDvYksazX6nYQYZ67L

src/clients/ollama_client.py

@@ -33,6 +33,7 @@ class OllamaClient:
         self.base_url = base_url.rstrip("/")
         self.model = model
         self.embeddings_url = f"{self.base_url}/api/embeddings"
+        self.embed_url = f"{self.base_url}/api/embed"
         self.generate_url = f"{self.base_url}/api/generate"
         self.tags_url = f"{self.base_url}/api/tags"
         self.client = httpx.AsyncClient(timeout=120.0)  # Embeddings can be slow
@@ -106,8 +107,37 @@ class OllamaClient:
             >>> len(embeddings)
             3
         """
-        embeddings = []
+        if not texts:
+            return []
 
+        # Single batched request via Ollama's /api/embed (the old
+        # implementation looped one /api/embeddings call per text).
+        try:
+            response = await self.client.post(
+                self.embed_url,
+                json={"model": self.model, "input": texts}
+            )
+            response.raise_for_status()
+            data = response.json()
+            embeddings = data.get("embeddings")
+
+            if embeddings is not None and len(embeddings) == len(texts):
+                if show_progress:
+                    logger.info(f"Batched embedding complete: {len(embeddings)}/{len(texts)}")
+                return embeddings
+
+            logger.warning(
+                f"Batched embed returned {len(embeddings or [])} vectors for "
+                f"{len(texts)} inputs, falling back to per-text embedding"
+            )
+        except Exception as e:
+            logger.warning(
+                f"Batched embed failed ({e}), falling back to per-text embedding"
+            )
+
+        # Fallback: per-text embedding preserves partial-success semantics
+        # (None entries for texts that failed to embed).
+        embeddings = []
         for i, text in enumerate(texts):
             if show_progress and i % 10 == 0:
                 logger.info(f"Embedding progress: {i}/{len(texts)}")