feat(usage): meter embedding tokens as embeddings_queries on both paths

embeddings_queries now records the embedding request's token count (the unit
upstream providers bill on) instead of an operation count, and fires on the
indexing path too. Previously only semantic search recorded it (value=1), so a
re-indexing run produced no embeddings_queries events at all — only pages_chunks.

- Provider layer: additive embed_with_usage / embed_batch_with_usage surface the
  per-request token count (Mistral/OpenAI usage.total_tokens, Bedrock Titan
  inputTextTokenCount, Ollama prompt_eval_count); a char-based estimate is the
  fallback (Simple, and any provider/response without a token field). Gateway and
  EmbeddingService forward through. The count travels as a return value / a
  per-request SearchAlgorithm attribute — never on the singleton — so concurrent
  indexing + search can't mis-attribute bills.
- Indexing (vector/processor.py): records embeddings_queries (value=batch tokens)
  alongside the existing pages_chunks event.
- Search (server/semantic.py): value is now the query embedding's token count,
  relayed from BM25HybridSearchAlgorithm via query_token_count.

The astrolabe_embeddings_queries Stripe meter (sum aggregation) now sums tokens
with no CP/Terraform change. The meter "queries"->tokens naming/unit
clarification (homelab-terraform #254) + CP rollup/portal copy is a follow-up.

Deck #67.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

nextcloud_mcp_server/providers/ollama.py

@@ -116,12 +116,44 @@ class OllamaProvider(Provider):
         Raises:
             NotImplementedError: If embeddings not enabled (no embedding_model)
         """
+        embeddings, _ = await self.embed_batch_with_usage(texts, batch_size=batch_size)
+        return embeddings
+
+    async def embed_with_usage(self, text: str) -> tuple[list[float], int]:
+        """Embed one text, reporting the request's token count.
+
+        Routes through ``/api/embed`` (which carries ``prompt_eval_count``)
+        rather than the legacy ``/api/embeddings`` so a token count is
+        available; falls back to a char-based estimate when the field is
+        absent. Used by the usage-metering hooks (Deck #67).
+        """
+        embeddings, tokens = await self.embed_batch_with_usage([text])
+        if not embeddings:
+            raise RuntimeError(
+                "Ollama embeddings API returned no embedding for model "
+                f"{self.embedding_model}"
+            )
+        return embeddings[0], tokens
+
+    async def embed_batch_with_usage(
+        self, texts: list[str], batch_size: int = 32
+    ) -> tuple[list[list[float]], int]:
+        """Embed multiple texts, summing ``prompt_eval_count`` token usage.
+
+        Returns ``(embeddings, total_tokens)``. Ollama's ``/api/embed`` may
+        omit ``prompt_eval_count`` (older versions); a char-based estimate is
+        used per batch when it does.
+        """
         if not self.supports_embeddings:
             raise NotImplementedError(
                 "Embedding not supported - no embedding_model configured"
             )
 
-        all_embeddings = []
+        if not texts:
+            return [], 0
+
+        all_embeddings: list[list[float]] = []
+        total_tokens = 0
         for i in range(0, len(texts), batch_size):
             batch = texts[i : i + batch_size]
             response = await self.client.post(
@@ -129,9 +161,17 @@ class OllamaProvider(Provider):
                 json={"model": self.embedding_model, "input": batch},
             )
             response.raise_for_status()
-            all_embeddings.extend(response.json()["embeddings"])
+            data = response.json()
+            all_embeddings.extend(data["embeddings"])
 
-        return all_embeddings
+            prompt_eval = data.get("prompt_eval_count")
+            total_tokens += (
+                int(prompt_eval)
+                if isinstance(prompt_eval, (int, float))
+                else self._estimate_tokens(batch)
+            )
+
+        return all_embeddings, total_tokens
 
     async def _detect_dimension(self):
         """