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/openai.py

@@ -153,19 +153,49 @@ class OpenAIProvider(Provider):
                 "Embedding not supported - no embedding_model configured"
             )
 
+        embeddings, _ = await self.embed_batch_with_usage(texts)
+        return embeddings
+
+    async def embed_with_usage(self, text: str) -> tuple[list[float], int]:
+        """Embed one text, reporting the request's token count."""
+        embeddings, tokens = await self.embed_batch_with_usage([text])
+        if not embeddings:
+            raise RuntimeError(
+                "OpenAI 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]
+    ) -> tuple[list[list[float]], int]:
+        """Embed multiple texts, summing the API-reported token usage.
+
+        Returns ``(embeddings, total_tokens)`` where ``total_tokens`` sums
+        ``response.usage.total_tokens`` across the sub-requests (the unit the
+        provider bills on). Used by the usage-metering hooks (Deck #67). Also
+        serves the gateway path via :class:`GatewayProvider`.
+        """
+        if not self.supports_embeddings:
+            raise NotImplementedError(
+                "Embedding not supported - no embedding_model configured"
+            )
+
         if not texts:
-            return []
+            return [], 0
 
         # OpenAI supports batches up to 2048, but use smaller batches for safety
         batch_size = 100
         all_embeddings: list[list[float]] = []
+        total_tokens = 0
 
         for i in range(0, len(texts), batch_size):
             batch = texts[i : i + batch_size]
 
             # Use helper method with retry logic for each batch
-            batch_embeddings = await self._embed_batch_request(batch)
+            batch_embeddings, batch_tokens = await self._embed_batch_request(batch)
             all_embeddings.extend(batch_embeddings)
+            total_tokens += batch_tokens
 
             # Update dimension if not set
             if self._dimension is None and batch_embeddings:
@@ -176,11 +206,18 @@ class OpenAIProvider(Provider):
                     self.embedding_model,
                 )
 
-        return all_embeddings
+        return all_embeddings, total_tokens
 
     @_retry_429
-    async def _embed_batch_request(self, batch: list[str]) -> list[list[float]]:
-        """Make a single batch embedding request with retry logic."""
+    async def _embed_batch_request(
+        self, batch: list[str]
+    ) -> tuple[list[list[float]], int]:
+        """Make a single batch embedding request with retry logic.
+
+        Returns ``(embeddings, token_count)``; ``token_count`` comes from the
+        response's ``usage.total_tokens`` and falls back to a char-based
+        estimate if the API omits usage.
+        """
         assert self.embedding_model is not None  # Type narrowing
         response = await self.client.embeddings.create(
             input=batch,
@@ -188,7 +225,19 @@ class OpenAIProvider(Provider):
         )
         # Sort by index to maintain order
         sorted_data = sorted(response.data, key=lambda x: x.index)
-        return [item.embedding for item in sorted_data]
+        embeddings = [item.embedding for item in sorted_data]
+
+        usage = getattr(response, "usage", None)
+        total_tokens = getattr(usage, "total_tokens", None) if usage else None
+        # Guard on numeric type (not just ``is not None``): a real response
+        # gives an int, but test doubles / partial responses can surface a
+        # non-numeric attribute — fall back to the estimate there.
+        tokens = (
+            int(total_tokens)
+            if isinstance(total_tokens, (int, float))
+            else self._estimate_tokens(batch)
+        )
+        return embeddings, tokens
 
     def get_dimension(self) -> int:
         """