feat: Add OpenAI provider support for embeddings and generation

Adds OpenAI provider to the unified provider architecture (ADR-015),
supporting:
- OpenAI API (api.openai.com)
- GitHub Models API (models.github.ai/inference)
- OpenAI-compatible endpoints (Fireworks, Together, etc.)

Features:
- Embedding support with text-embedding-3-small/large models
- Text generation via chat completions API
- Automatic retry with exponential backoff for rate limits
- Provider auto-detection in registry (priority after Bedrock)

Environment variables:
- OPENAI_API_KEY: API key (required)
- OPENAI_BASE_URL: Base URL override (optional)
- OPENAI_EMBEDDING_MODEL: Embedding model (default: text-embedding-3-small)
- OPENAI_GENERATION_MODEL: Generation model (default: gpt-4o-mini)

Also adds:
- Integration tests for RAG pipeline with MCP sampling
- MCP client sampling support for integration tests
- Ground truth Q&A pairs for Nextcloud User Manual

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

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

nextcloud_mcp_server/vector/qdrant_client.py

@@ -93,27 +93,29 @@ async def get_qdrant_client() -> AsyncQdrantClient:
 
             # Validate dimension matches
             if actual_dimension != expected_dimension:
+                embedding_model = settings.get_embedding_model_name()
                 raise ValueError(
                     f"Dimension mismatch for collection '{collection_name}':\n"
-                    f"  Expected: {expected_dimension} (from embedding model '{settings.ollama_embedding_model}')\n"
+                    f"  Expected: {expected_dimension} (from embedding model '{embedding_model}')\n"
                     f"  Found: {actual_dimension}\n"
                     f"This usually means you changed the embedding model.\n"
                     f"Solutions:\n"
                     f"  1. Delete the old collection: Collection will be recreated with new dimensions\n"
                     f"  2. Set QDRANT_COLLECTION to use a different collection name\n"
-                    f"  3. Revert OLLAMA_EMBEDDING_MODEL to the original model"
+                    f"  3. Revert to the original embedding model"
                 )
 
             logger.info(
                 f"Using existing Qdrant collection: {collection_name} "
-                f"(dimension={actual_dimension}, model={settings.ollama_embedding_model})"
+                f"(dimension={actual_dimension}, model={settings.get_embedding_model_name()})"
             )
 
         else:
             # Collection doesn't exist - create it
+            embedding_model = settings.get_embedding_model_name()
             logger.info(
                 f"Collection '{collection_name}' not found, creating with "
-                f"dimension={expected_dimension}, model={settings.ollama_embedding_model}..."
+                f"dimension={expected_dimension}, model={embedding_model}..."
             )
             await _qdrant_client.create_collection(
                 collection_name=collection_name,
@@ -134,7 +136,7 @@ async def get_qdrant_client() -> AsyncQdrantClient:
             logger.info(
                 f"Created Qdrant collection: {collection_name}\n"
                 f"  Dense vector dimension: {expected_dimension}\n"
-                f"  Dense embedding model: {settings.ollama_embedding_model}\n"
+                f"  Dense embedding model: {embedding_model}\n"
                 f"  Sparse vectors: BM25 (for hybrid search)\n"
                 f"  Distance: COSINE\n"
                 f"Background sync will index all documents with dense + sparse vectors."