fix: Add rate limit retry logic to OpenAI provider

Add exponential backoff retry handling for OpenAI API rate limits
(429 errors). This is needed for GitHub Models API which has stricter
rate limits than standard OpenAI API.

- Add retry_on_rate_limit decorator with exponential backoff
- Max 5 retries with delays: 2s → 4s → 8s → 16s → 32s
- Apply to embed(), _embed_batch_request(), and generate() methods

🤖 Generated with [Claude Code](https://claude.com/claude-code)

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

nextcloud_mcp_server/providers/openai.py

@@ -7,13 +7,48 @@ Supports:
 """
 
 import logging
+from functools import wraps
 
-from openai import AsyncOpenAI
+import anyio
+from openai import AsyncOpenAI, RateLimitError
 
 from .base import Provider
 
 logger = logging.getLogger(__name__)
 
+# Rate limit retry configuration
+MAX_RETRIES = 5
+INITIAL_RETRY_DELAY = 2.0  # seconds
+MAX_RETRY_DELAY = 60.0  # seconds
+
+
+def retry_on_rate_limit(func):
+    """Decorator to retry on OpenAI rate limit errors with exponential backoff."""
+
+    @wraps(func)
+    async def wrapper(*args, **kwargs):
+        retry_delay = INITIAL_RETRY_DELAY
+        last_error: Exception | None = None
+
+        for attempt in range(1, MAX_RETRIES + 1):
+            try:
+                return await func(*args, **kwargs)
+            except RateLimitError as e:
+                last_error = e
+                if attempt < MAX_RETRIES:
+                    logger.warning(
+                        f"Rate limit hit (attempt {attempt}/{MAX_RETRIES}), "
+                        f"retrying in {retry_delay:.1f}s..."
+                    )
+                    await anyio.sleep(retry_delay)
+                    retry_delay = min(retry_delay * 2, MAX_RETRY_DELAY)
+
+        logger.error(f"Rate limit exceeded after {MAX_RETRIES} attempts")
+        raise last_error  # type: ignore[misc]
+
+    return wrapper
+
+
 # Well-known embedding dimensions for OpenAI models
 OPENAI_EMBEDDING_DIMENSIONS: dict[str, int] = {
     "text-embedding-3-small": 1536,
@@ -86,6 +121,7 @@ class OpenAIProvider(Provider):
         """Whether this provider supports text generation."""
         return self.generation_model is not None
 
+    @retry_on_rate_limit
     async def embed(self, text: str) -> list[float]:
         """
         Generate embedding vector for text.
@@ -151,14 +187,8 @@ class OpenAIProvider(Provider):
         for i in range(0, len(texts), batch_size):
             batch = texts[i : i + batch_size]
 
-            response = await self.client.embeddings.create(
-                input=batch,
-                model=self.embedding_model,
-            )
-
-            # Sort by index to maintain order
-            sorted_data = sorted(response.data, key=lambda x: x.index)
-            batch_embeddings = [item.embedding for item in sorted_data]
+            # Use helper method with retry logic for each batch
+            batch_embeddings = await self._embed_batch_request(batch)
             all_embeddings.extend(batch_embeddings)
 
             # Update dimension if not set
@@ -171,6 +201,17 @@ class OpenAIProvider(Provider):
 
         return all_embeddings
 
+    @retry_on_rate_limit
+    async def _embed_batch_request(self, batch: list[str]) -> list[list[float]]:
+        """Make a single batch embedding request with retry logic."""
+        response = await self.client.embeddings.create(
+            input=batch,
+            model=self.embedding_model,
+        )
+        # Sort by index to maintain order
+        sorted_data = sorted(response.data, key=lambda x: x.index)
+        return [item.embedding for item in sorted_data]
+
     def get_dimension(self) -> int:
         """
         Get embedding dimension.
@@ -194,6 +235,7 @@ class OpenAIProvider(Provider):
             )
         return self._dimension
 
+    @retry_on_rate_limit
     async def generate(self, prompt: str, max_tokens: int = 500) -> str:
         """
         Generate text from a prompt.