Update README

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>

.github/workflows/rag-evaluation.yml

@@ -3,6 +3,10 @@ name: RAG Evaluation
 on:
   workflow_dispatch:
     inputs:
+      manual_path:
+        description: 'Path to Nextcloud User Manual PDF in Nextcloud'
+        required: false
+        default: 'Nextcloud Manual.pdf'
       embedding_model:
         description: 'OpenAI embedding model'
         required: false
@@ -15,66 +19,27 @@ on:
 jobs:
   rag-evaluation:
     runs-on: ubuntu-latest
-    timeout-minutes: 45
+    timeout-minutes: 30
+    permissions:
+      models: read
 
     steps:
       - uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
-        with:
-          submodules: 'true'
-
-      - name: Clone Nextcloud documentation
-        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
-        with:
-          repository: 'nextcloud/documentation'
-          path: 'nextcloud-docs'
-
-      - name: Install Sphinx and LaTeX dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y \
-            python3-sphinx \
-            python3-pip \
-            latexmk \
-            texlive-latex-recommended \
-            texlive-latex-extra \
-            texlive-fonts-recommended \
-            texlive-fonts-extra
-
-      - name: Build User Manual PDF
-        run: |
-          cd nextcloud-docs/user_manual
-          pip3 install -r ../requirements.txt
-          make latexpdf
-          ls -la _build/latex/
-          cp _build/latex/NextcloudUserManual.pdf ../../Nextcloud_User_Manual.pdf
-          echo "PDF built successfully"
-
-      ###### Required to build OIDC App ######
-      - name: Set up php 8.4
-        uses: shivammathur/setup-php@bf6b4fbd49ca58e4608c9c89fba0b8d90bd2a39f # v2
-        with:
-          php-version: 8.4
-          coverage: none
-
-      - name: Install OIDC app composer dependencies
-        run: |
-          cd third_party/oidc
-          composer install --no-dev
-      ###### Required to build OIDC App ######
 
       - name: Run docker compose with vector sync
         uses: hoverkraft-tech/compose-action@3846bcd61da338e9eaaf83e7ed0234a12b099b72 # v2.4.1
         with:
-          compose-file: "./docker-compose.yml"
+          compose-file: |
+            ./docker-compose.yml
+            ./docker-compose.ci.yml
           up-flags: "--build"
         env:
-          # Override MCP container environment for OpenAI + vector sync
-          VECTOR_SYNC_ENABLED: "true"
-          VECTOR_SYNC_SCAN_INTERVAL: "30"
+          # Environment variables passed to docker-compose.ci.yml
           OPENAI_API_KEY: ${{ secrets.GITHUB_TOKEN }}
           OPENAI_BASE_URL: "https://models.github.ai/inference"
           OPENAI_EMBEDDING_MODEL: ${{ inputs.embedding_model }}
           OPENAI_GENERATION_MODEL: ${{ inputs.generation_model }}
+          VECTOR_SYNC_SCAN_INTERVAL: "5"
 
       - name: Install the latest version of uv
         uses: astral-sh/setup-uv@1e862dfacbd1d6d858c55d9b792c756523627244 # v7.1.4
@@ -100,7 +65,7 @@ jobs:
           echo "Waiting for MCP server..."
           max_attempts=30
           attempt=0
-          until curl -o /dev/null -s -w "%{http_code}\n" http://localhost:8000/health | grep -q "200"; do
+          until curl -o /dev/null -s -w "%{http_code}\n" http://localhost:8000/health/live | grep -q "200"; do
             attempt=$((attempt + 1))
             if [ $attempt -ge $max_attempts ]; then
               echo "MCP server did not become ready in time."
@@ -111,159 +76,28 @@ jobs:
           done
           echo "MCP server is ready."
 
-      - name: Upload User Manual PDF to Nextcloud
-        run: |
-          echo "Uploading Nextcloud_User_Manual.pdf to Nextcloud..."
-          HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -u admin:admin \
-            -X PUT \
-            -T Nextcloud_User_Manual.pdf \
-            "http://localhost:8080/remote.php/dav/files/admin/Nextcloud_User_Manual.pdf")
-
-          if [ "$HTTP_CODE" = "201" ] || [ "$HTTP_CODE" = "204" ]; then
-            echo "PDF uploaded successfully (HTTP $HTTP_CODE)"
-          else
-            echo "Failed to upload PDF (HTTP $HTTP_CODE)"
-            exit 1
-          fi
-
-      - name: Create vector-index tag
-        id: create_tag
-        run: |
-          # Create the tag using OCS API
-          echo "Creating vector-index tag..."
-          RESPONSE=$(curl -s -u admin:admin \
-            -X POST \
-            -H 'Content-Type: application/json' \
-            -H 'OCS-APIRequest: true' \
-            -d '{"name":"vector-index","userVisible":true,"userAssignable":true}' \
-            "http://localhost:8080/ocs/v2.php/apps/systemtags/api/v1/tags")
-
-          echo "Create tag response: $RESPONSE"
-
-          # Get tag ID from response or lookup
-          TAG_ID=$(echo "$RESPONSE" | grep -oP '(?<="id":)[0-9]+' | head -1 || echo "")
-
-          if [ -z "$TAG_ID" ]; then
-            echo "Tag may already exist, looking it up..."
-            TAG_ID=$(curl -s -u admin:admin \
-              -X PROPFIND \
-              -H 'Content-Type: application/xml' \
-              -d '<?xml version="1.0"?><d:propfind xmlns:d="DAV:" xmlns:oc="http://owncloud.org/ns"><d:prop><oc:id/><oc:display-name/></d:prop></d:propfind>' \
-              http://localhost:8080/remote.php/dav/systemtags/ \
-              | grep -B2 "vector-index" | grep -oP '(?<=<oc:id>)[0-9]+(?=</oc:id>)' | head -1 || echo "")
-          fi
-
-          if [ -z "$TAG_ID" ]; then
-            echo "ERROR: Could not create or find vector-index tag"
-            exit 1
-          fi
-
-          echo "Tag ID: $TAG_ID"
-          echo "tag_id=$TAG_ID" >> $GITHUB_OUTPUT
-
-      - name: Get file ID of uploaded PDF
-        id: get_file_id
-        run: |
-          echo "Getting file ID for Nextcloud_User_Manual.pdf..."
-
-          # Get file ID using PROPFIND
-          FILE_ID=$(curl -s -u admin:admin \
-            -X PROPFIND \
-            -H 'Content-Type: application/xml' \
-            -d '<?xml version="1.0"?><d:propfind xmlns:d="DAV:" xmlns:oc="http://owncloud.org/ns"><d:prop><oc:fileid/></d:prop></d:propfind>' \
-            "http://localhost:8080/remote.php/dav/files/admin/Nextcloud_User_Manual.pdf" \
-            | grep -oP '(?<=<oc:fileid>)[0-9]+(?=</oc:fileid>)' || echo "")
-
-          if [ -z "$FILE_ID" ]; then
-            echo "ERROR: Could not find file ID"
-            exit 1
-          fi
-
-          echo "Found file ID: $FILE_ID"
-          echo "file_id=$FILE_ID" >> $GITHUB_OUTPUT
-
-      - name: Tag file with vector-index
-        env:
-          FILE_ID: ${{ steps.get_file_id.outputs.file_id }}
-          TAG_ID: ${{ steps.create_tag.outputs.tag_id }}
-        run: |
-          echo "Tagging file $FILE_ID with tag $TAG_ID..."
-
-          HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -u admin:admin \
-            -X PUT \
-            -H 'Content-Type: application/json' \
-            -H 'Content-Length: 0' \
-            "http://localhost:8080/remote.php/dav/systemtags-relations/files/$FILE_ID/$TAG_ID")
-
-          if [ "$HTTP_CODE" = "201" ] || [ "$HTTP_CODE" = "409" ]; then
-            echo "File tagged successfully (HTTP $HTTP_CODE)"
-          else
-            echo "Failed to tag file (HTTP $HTTP_CODE)"
-            exit 1
-          fi
-
-      - name: Wait for vector sync to complete indexing
-        env:
-          NEXTCLOUD_HOST: "http://localhost:8080"
-          NEXTCLOUD_USERNAME: "admin"
-          NEXTCLOUD_PASSWORD: "admin"
-        run: |
-          echo "Waiting for vector sync to index the manual..."
-          max_attempts=60
-          attempt=0
-
-          # Wait for initial scan to pick up the file
-          sleep 10
-
-          while [ $attempt -lt $max_attempts ]; do
-            attempt=$((attempt + 1))
-
-            # Check vector sync status via MCP
-            STATUS=$(curl -s http://localhost:8000/health || echo "{}")
-            echo "Attempt $attempt/$max_attempts: $STATUS"
-
-            # Also check indexed count via semantic search
-            # If we get results, indexing is done
-            RESULT=$(curl -s -X POST http://localhost:8000/mcp \
-              -H "Content-Type: application/json" \
-              -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"nc_get_vector_sync_status","arguments":{}}}' \
-              2>/dev/null || echo "{}")
-
-            echo "Vector sync status: $RESULT"
-
-            # Check if pending is 0 and indexed > 0
-            INDEXED=$(echo "$RESULT" | jq -r '.result.structuredContent.indexed // 0' 2>/dev/null || echo "0")
-            PENDING=$(echo "$RESULT" | jq -r '.result.structuredContent.pending // 1' 2>/dev/null || echo "1")
-
-            echo "Indexed: $INDEXED, Pending: $PENDING"
-
-            if [ "$INDEXED" -gt "0" ] && [ "$PENDING" -eq "0" ]; then
-              echo "Indexing complete! $INDEXED documents indexed."
-              break
-            fi
-
-            sleep 10
-          done
-
-          if [ $attempt -ge $max_attempts ]; then
-            echo "WARNING: Indexing may not be complete, proceeding anyway..."
-          fi
-
       - name: Run RAG evaluation tests
         env:
           NEXTCLOUD_HOST: "http://localhost:8080"
           NEXTCLOUD_USERNAME: "admin"
           NEXTCLOUD_PASSWORD: "admin"
+          RAG_MANUAL_PATH: ${{ inputs.manual_path }}
           OPENAI_API_KEY: ${{ secrets.GITHUB_TOKEN }}
           OPENAI_BASE_URL: "https://models.github.ai/inference"
           OPENAI_EMBEDDING_MODEL: ${{ inputs.embedding_model }}
           OPENAI_GENERATION_MODEL: ${{ inputs.generation_model }}
         run: |
-          uv run pytest tests/integration/test_rag_openai.py -v --log-cli-level=INFO
+          uv run pytest tests/integration/test_rag.py -v --log-cli-level=INFO --provider openai
+
+      - name: Capture MCP container logs
+        if: always()
+        run: |
+          echo "=== MCP Container Logs ==="
+          docker compose logs mcp --tail=500
 
       - name: Upload test results
         if: always()
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # v5
         with:
           name: rag-evaluation-results
           path: |

CHANGELOG.md

@@ -1,3 +1,38 @@
+## v0.48.4 (2025-11-23)
+
+### Fix
+
+- Add rate limit retry logic to OpenAI provider
+
+## v0.48.3 (2025-11-23)
+
+### Fix
+
+- Increase MCP sampling timeout to 5 minutes for slower LLMs
+
+## v0.48.2 (2025-11-23)
+
+### Fix
+
+- Share vector sync state with FastMCP session lifespan via module singleton
+- Share vector sync state with FastMCP session lifespan via module singleton
+
+## v0.48.1 (2025-11-23)
+
+### Fix
+
+- Use WebDAV for tag creation and add LLM-as-a-judge for RAG tests
+
+### Refactor
+
+- Move background tasks to server lifespan and deprecate SSE transport
+
+## v0.48.0 (2025-11-23)
+
+### Feat
+
+- Add tag management methods to WebDAV client
+
 ## v0.47.0 (2025-11-23)
 
 ### Feat

README.md

@@ -1,11 +1,12 @@
+```markdown
 <p align="center">
   <img src="astrolabe.svg" alt="Nextcloud MCP Server" width="128" height="128">
 </p>
 
 # Nextcloud MCP Server
 
-[![Docker Image](https://img.shields.io/badge/docker-ghcr.io/cbcoutinho/nextcloud--mcp--server-blue)](https://github.com/cbcoutinho/nextcloud-mcp-server/pkgs/container/nextcloud-mcp-server)
 [![smithery badge](https://smithery.ai/badge/@cbcoutinho/nextcloud-mcp-server)](https://smithery.ai/server/@cbcoutinho/nextcloud-mcp-server)
+[![Docker Image](https://img.shields.io/badge/docker-ghcr.io/cbcoutinho/nextcloud--mcp--server-blue)](https://github.com/cbcoutinho/nextcloud-mcp-server/pkgs/container/nextcloud-mcp-server)
 
 **A production-ready MCP server that connects AI assistants to your Nextcloud instance.**
 
@@ -223,3 +224,4 @@ This project is licensed under the AGPL-3.0 License. See [LICENSE](./LICENSE) fo
 - [Model Context Protocol](https://github.com/modelcontextprotocol)
 - [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)
 - [Nextcloud](https://nextcloud.com/)
+```

charts/nextcloud-mcp-server/Chart.yaml

@@ -2,8 +2,8 @@ apiVersion: v2
 name: nextcloud-mcp-server
 description: A Helm chart for Nextcloud MCP Server - enables AI assistants to interact with Nextcloud
 type: application
-version: 0.47.0
-appVersion: "0.47.0"
+version: 0.48.4
+appVersion: "0.48.4"
 keywords:
   - nextcloud
   - mcp

docker-compose.ci.yml

@@ -0,0 +1,25 @@
+# CI-specific overrides for RAG evaluation pipeline
+# This file is used by the rag-evaluation.yml workflow to configure the MCP
+# container with OpenAI/GitHub Models API for vector embeddings.
+#
+# Usage:
+#   docker compose -f docker-compose.yml -f docker-compose.ci.yml up
+#
+# Environment variables (set in CI workflow):
+#   OPENAI_API_KEY - API key for embeddings (GitHub Models uses GITHUB_TOKEN)
+#   OPENAI_BASE_URL - API endpoint (e.g., https://models.github.ai/inference)
+#   OPENAI_EMBEDDING_MODEL - Model name (e.g., openai/text-embedding-3-small)
+#   OPENAI_GENERATION_MODEL - Model name for generation (e.g., openai/gpt-4o-mini)
+
+services:
+  mcp:
+    environment:
+      # OpenAI provider configuration (required for CI vector sync)
+      - OPENAI_API_KEY=${OPENAI_API_KEY}
+      - OPENAI_BASE_URL=${OPENAI_BASE_URL:-https://models.github.ai/inference}
+      - OPENAI_EMBEDDING_MODEL=${OPENAI_EMBEDDING_MODEL:-openai/text-embedding-3-small}
+      - OPENAI_GENERATION_MODEL=${OPENAI_GENERATION_MODEL:-openai/gpt-4o-mini}
+      # Faster sync for CI
+      - VECTOR_SYNC_SCAN_INTERVAL=${VECTOR_SYNC_SCAN_INTERVAL:-5}
+      # Enable document processing for PDF parsing
+      - ENABLE_DOCUMENT_PROCESSING=true

docker-compose.yml

@@ -34,7 +34,7 @@ services:
       - ./app-hooks:/docker-entrypoint-hooks.d:ro
       # Mount OIDC development directory outside /var/www/html to avoid rsync conflicts
       # The post-installation hook will register /opt/apps as an additional app directory
-      - ./third_party:/opt/apps:ro
+      #- ./third_party:/opt/apps:ro
     environment:
       - NEXTCLOUD_TRUSTED_DOMAINS=app
       - NEXTCLOUD_ADMIN_USER=admin

nextcloud_mcp_server/app.py

@@ -243,6 +243,25 @@ def validate_pkce_support(discovery: dict, discovery_url: str) -> None:
     click.echo(f"✓ PKCE support validated: {code_challenge_methods}")
 
 
+@dataclass
+class VectorSyncState:
+    """
+    Module-level state for vector sync background tasks.
+
+    This singleton bridges the Starlette server lifespan (where background tasks run)
+    and FastMCP session lifespans (where MCP tools need access to the streams).
+    """
+
+    document_send_stream: Optional[MemoryObjectSendStream] = None
+    document_receive_stream: Optional[MemoryObjectReceiveStream] = None
+    shutdown_event: Optional[anyio.Event] = None
+    scanner_wake_event: Optional[anyio.Event] = None
+
+
+# Module-level singleton for vector sync state
+_vector_sync_state = VectorSyncState()
+
+
 @dataclass
 class AppContext:
     """Application context for BasicAuth mode."""
@@ -555,15 +574,15 @@ async def load_oauth_client_credentials(
 @asynccontextmanager
 async def app_lifespan_basic(server: FastMCP) -> AsyncIterator[AppContext]:
     """
-    Manage application lifecycle for BasicAuth mode.
+    Manage application lifecycle for BasicAuth mode (FastMCP session lifespan).
 
     Creates a single Nextcloud client with basic authentication
-    that is shared across all requests.
+    that is shared across all requests within a session.
 
-    If vector sync is enabled (VECTOR_SYNC_ENABLED=true), also starts
-    background tasks for automatic document indexing (ADR-007).
+    Note: Background tasks (scanner, processor) are started at server level
+    in starlette_lifespan, not here. This lifespan runs per-session.
     """
-    logger.info("Starting MCP server in BasicAuth mode")
+    logger.info("Starting MCP session in BasicAuth mode")
     logger.info("Creating Nextcloud client with BasicAuth")
 
     client = NextcloudClient.from_env()
@@ -579,91 +598,20 @@ async def app_lifespan_basic(server: FastMCP) -> AsyncIterator[AppContext]:
     # Initialize document processors
     initialize_document_processors()
 
-    settings = get_settings()
-
-    # Check if vector sync is enabled
-    if settings.vector_sync_enabled:
-        logger.info("Vector sync enabled - starting background tasks")
-
-        # Get username from environment for BasicAuth mode
-        username = os.getenv("NEXTCLOUD_USERNAME")
-        if not username:
-            raise ValueError(
-                "NEXTCLOUD_USERNAME is required for vector sync in BasicAuth mode"
-            )
-
-        # Initialize Qdrant collection before starting background tasks
-        logger.info("Initializing Qdrant collection...")
-        from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
-
-        try:
-            await get_qdrant_client()  # Triggers collection creation if needed
-            logger.info("Qdrant collection ready")
-        except Exception as e:
-            logger.error(f"Failed to initialize Qdrant collection: {e}")
-            raise RuntimeError(
-                f"Cannot start vector sync - Qdrant initialization failed: {e}"
-            ) from e
-
-        # Initialize shared state
-        send_stream, receive_stream = anyio.create_memory_object_stream(
-            max_buffer_size=settings.vector_sync_queue_max_size
+    # Yield client context - scanner runs at server level (starlette_lifespan)
+    # Include vector sync state from module singleton (set by starlette_lifespan)
+    try:
+        yield AppContext(
+            client=client,
+            storage=storage,
+            document_send_stream=_vector_sync_state.document_send_stream,
+            document_receive_stream=_vector_sync_state.document_receive_stream,
+            shutdown_event=_vector_sync_state.shutdown_event,
+            scanner_wake_event=_vector_sync_state.scanner_wake_event,
         )
-        shutdown_event = anyio.Event()
-        scanner_wake_event = anyio.Event()
-
-        # Start background tasks using anyio TaskGroup
-        async with anyio.create_task_group() as tg:
-            # Start scanner task
-            await tg.start(
-                scanner_task,
-                send_stream,
-                shutdown_event,
-                scanner_wake_event,
-                client,
-                username,
-            )
-
-            # Start processor pool (each gets a cloned receive stream)
-            for i in range(settings.vector_sync_processor_workers):
-                await tg.start(
-                    processor_task,
-                    i,
-                    receive_stream.clone(),
-                    shutdown_event,
-                    client,
-                    username,
-                )
-
-            logger.info(
-                f"Background sync tasks started: 1 scanner + {settings.vector_sync_processor_workers} processors"
-            )
-
-            # Yield with background tasks running
-            try:
-                yield AppContext(
-                    client=client,
-                    storage=storage,
-                    document_send_stream=send_stream,
-                    document_receive_stream=receive_stream,
-                    shutdown_event=shutdown_event,
-                    scanner_wake_event=scanner_wake_event,
-                )
-            finally:
-                # Shutdown signal
-                logger.info("Shutting down background sync tasks")
-                shutdown_event.set()
-
-                # TaskGroup automatically cancels all tasks on exit
-                logger.info("Background sync tasks stopped")
-                await client.close()
-    else:
-        # No vector sync - simple lifecycle
-        try:
-            yield AppContext(client=client, storage=storage)
-        finally:
-            logger.info("Shutting down BasicAuth mode")
-            await client.close()
+    finally:
+        logger.info("Shutting down BasicAuth session")
+        await client.close()
 
 
 async def setup_oauth_config():
@@ -979,7 +927,7 @@ async def setup_oauth_config():
     )
 
 
-def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
+def get_app(transport: str = "streamable-http", enabled_apps: list[str] | None = None):
     # Initialize observability (logging will be configured by uvicorn)
     settings = get_settings()
 
@@ -1197,180 +1145,177 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
             "Dynamic tool filtering enabled for OAuth mode (JWT and Bearer tokens)"
         )
 
-    if transport == "sse":
-        mcp_app = mcp.sse_app()
-        starlette_lifespan = None
-    elif transport in ("http", "streamable-http"):
-        mcp_app = mcp.streamable_http_app()
+    mcp_app = mcp.streamable_http_app()
 
-        @asynccontextmanager
-        async def starlette_lifespan(app: Starlette):
-            # Set OAuth context for OAuth login routes (ADR-004)
-            if oauth_enabled:
-                # Prepare OAuth config from setup_oauth_config closure variables
-                mcp_server_url = os.getenv(
-                    "NEXTCLOUD_MCP_SERVER_URL", "http://localhost:8000"
-                )
-                nextcloud_resource_uri = os.getenv(
-                    "NEXTCLOUD_RESOURCE_URI", nextcloud_host
-                )
-                discovery_url = os.getenv(
-                    "OIDC_DISCOVERY_URL",
-                    f"{nextcloud_host}/.well-known/openid-configuration",
-                )
-                scopes = os.getenv("NEXTCLOUD_OIDC_SCOPES", "")
+    @asynccontextmanager
+    async def starlette_lifespan(app: Starlette):
+        # Set OAuth context for OAuth login routes (ADR-004)
+        if oauth_enabled:
+            # Prepare OAuth config from setup_oauth_config closure variables
+            mcp_server_url = os.getenv(
+                "NEXTCLOUD_MCP_SERVER_URL", "http://localhost:8000"
+            )
+            nextcloud_resource_uri = os.getenv("NEXTCLOUD_RESOURCE_URI", nextcloud_host)
+            discovery_url = os.getenv(
+                "OIDC_DISCOVERY_URL",
+                f"{nextcloud_host}/.well-known/openid-configuration",
+            )
+            scopes = os.getenv("NEXTCLOUD_OIDC_SCOPES", "")
 
-                oauth_context_dict = {
-                    "storage": refresh_token_storage,
-                    "oauth_client": oauth_client,
-                    "token_verifier": token_verifier,  # For querying IdP userinfo endpoint
-                    "config": {
-                        "mcp_server_url": mcp_server_url,
-                        "discovery_url": discovery_url,
-                        "client_id": client_id,  # From setup_oauth_config (DCR or static)
-                        "client_secret": client_secret,  # From setup_oauth_config (DCR or static)
-                        "scopes": scopes,
-                        "nextcloud_host": nextcloud_host,
-                        "nextcloud_resource_uri": nextcloud_resource_uri,
-                        "oauth_provider": oauth_provider,
-                    },
-                }
-                app.state.oauth_context = oauth_context_dict
+            oauth_context_dict = {
+                "storage": refresh_token_storage,
+                "oauth_client": oauth_client,
+                "token_verifier": token_verifier,  # For querying IdP userinfo endpoint
+                "config": {
+                    "mcp_server_url": mcp_server_url,
+                    "discovery_url": discovery_url,
+                    "client_id": client_id,  # From setup_oauth_config (DCR or static)
+                    "client_secret": client_secret,  # From setup_oauth_config (DCR or static)
+                    "scopes": scopes,
+                    "nextcloud_host": nextcloud_host,
+                    "nextcloud_resource_uri": nextcloud_resource_uri,
+                    "oauth_provider": oauth_provider,
+                },
+            }
+            app.state.oauth_context = oauth_context_dict
 
-                # Also set oauth_context on browser_app for session authentication
-                # browser_app is in the same function scope (defined later in create_app)
-                # We need to find it in the mounted routes
-                for route in app.routes:
-                    if isinstance(route, Mount) and route.path == "/app":
-                        route.app.state.oauth_context = oauth_context_dict
-                        logger.info(
-                            "OAuth context shared with browser_app for session auth"
-                        )
-                        break
-
-                logger.info(
-                    f"OAuth context initialized for login routes (client_id={client_id[:16]}...)"
-                )
-            else:
-                # BasicAuth mode - share storage with browser_app for webhook management
-                from nextcloud_mcp_server.auth.storage import RefreshTokenStorage
-
-                storage = RefreshTokenStorage.from_env()
-                await storage.initialize()
-
-                app.state.storage = storage
-
-                # Also share with browser_app for webhook routes
-                for route in app.routes:
-                    if isinstance(route, Mount) and route.path == "/app":
-                        route.app.state.storage = storage
-                        logger.info(
-                            "Storage shared with browser_app for webhook management"
-                        )
-                        break
-
-            # Start background vector sync tasks for BasicAuth mode (ADR-007)
-            # For streamable-http transport, FastMCP lifespan isn't automatically triggered
-            # so we manually start background tasks here if vector sync is enabled
-            import anyio as anyio_module
-
-            settings = get_settings()
-            if not oauth_enabled and settings.vector_sync_enabled:
-                logger.info("Starting background vector sync tasks for BasicAuth mode")
-
-                # Get username from environment
-                username = os.getenv("NEXTCLOUD_USERNAME")
-                if not username:
-                    raise ValueError(
-                        "NEXTCLOUD_USERNAME required for vector sync in BasicAuth mode"
+            # Also set oauth_context on browser_app for session authentication
+            # browser_app is in the same function scope (defined later in create_app)
+            # We need to find it in the mounted routes
+            for route in app.routes:
+                if isinstance(route, Mount) and route.path == "/app":
+                    route.app.state.oauth_context = oauth_context_dict
+                    logger.info(
+                        "OAuth context shared with browser_app for session auth"
                     )
+                    break
 
-                # Get Nextcloud client from MCP app context
-                # Create client since we're outside FastMCP lifespan
-                client = NextcloudClient.from_env()
+            logger.info(
+                f"OAuth context initialized for login routes (client_id={client_id[:16]}...)"
+            )
+        else:
+            # BasicAuth mode - share storage with browser_app for webhook management
+            from nextcloud_mcp_server.auth.storage import RefreshTokenStorage
 
-                # Initialize Qdrant collection before starting background tasks
-                logger.info("Initializing Qdrant collection...")
-                from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
+            storage = RefreshTokenStorage.from_env()
+            await storage.initialize()
 
-                try:
-                    await get_qdrant_client()  # Triggers collection creation if needed
-                    logger.info("Qdrant collection ready")
-                except Exception as e:
-                    logger.error(f"Failed to initialize Qdrant collection: {e}")
-                    raise RuntimeError(
-                        f"Cannot start vector sync - Qdrant initialization failed: {e}"
-                    ) from e
+            app.state.storage = storage
 
-                # Initialize shared state
-                send_stream, receive_stream = anyio_module.create_memory_object_stream(
-                    max_buffer_size=settings.vector_sync_queue_max_size
+            # Also share with browser_app for webhook routes
+            for route in app.routes:
+                if isinstance(route, Mount) and route.path == "/app":
+                    route.app.state.storage = storage
+                    logger.info(
+                        "Storage shared with browser_app for webhook management"
+                    )
+                    break
+
+        # Start background vector sync tasks for BasicAuth mode (ADR-007)
+        # Scanner runs at server-level (once), not per-session
+        import anyio as anyio_module
+
+        settings = get_settings()
+        if not oauth_enabled and settings.vector_sync_enabled:
+            logger.info("Starting background vector sync tasks for BasicAuth mode")
+
+            # Get username from environment
+            username = os.getenv("NEXTCLOUD_USERNAME")
+            if not username:
+                raise ValueError(
+                    "NEXTCLOUD_USERNAME required for vector sync in BasicAuth mode"
                 )
-                shutdown_event = anyio_module.Event()
-                scanner_wake_event = anyio_module.Event()
 
-                # Store in app state for access from routes (ADR-007)
-                app.state.document_send_stream = send_stream
-                app.state.document_receive_stream = receive_stream
-                app.state.shutdown_event = shutdown_event
-                app.state.scanner_wake_event = scanner_wake_event
+            # Create client for vector sync (server-level, not per-session)
+            client = NextcloudClient.from_env()
 
-                # Also share with browser_app for /app route
-                for route in app.routes:
-                    if isinstance(route, Mount) and route.path == "/app":
-                        route.app.state.document_send_stream = send_stream
-                        route.app.state.document_receive_stream = receive_stream
-                        route.app.state.shutdown_event = shutdown_event
-                        route.app.state.scanner_wake_event = scanner_wake_event
-                        logger.info(
-                            "Vector sync state shared with browser_app for /app"
-                        )
-                        break
+            # Initialize Qdrant collection before starting background tasks
+            logger.info("Initializing Qdrant collection...")
+            from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
 
-                # Start background tasks using anyio TaskGroup
-                async with anyio_module.create_task_group() as tg:
-                    # Start scanner task
+            try:
+                await get_qdrant_client()  # Triggers collection creation if needed
+                logger.info("Qdrant collection ready")
+            except Exception as e:
+                logger.error(f"Failed to initialize Qdrant collection: {e}")
+                raise RuntimeError(
+                    f"Cannot start vector sync - Qdrant initialization failed: {e}"
+                ) from e
+
+            # Initialize shared state
+            send_stream, receive_stream = anyio_module.create_memory_object_stream(
+                max_buffer_size=settings.vector_sync_queue_max_size
+            )
+            shutdown_event = anyio_module.Event()
+            scanner_wake_event = anyio_module.Event()
+
+            # Store in app state for access from routes (ADR-007)
+            app.state.document_send_stream = send_stream
+            app.state.document_receive_stream = receive_stream
+            app.state.shutdown_event = shutdown_event
+            app.state.scanner_wake_event = scanner_wake_event
+
+            # Also store in module singleton for FastMCP session lifespans
+            _vector_sync_state.document_send_stream = send_stream
+            _vector_sync_state.document_receive_stream = receive_stream
+            _vector_sync_state.shutdown_event = shutdown_event
+            _vector_sync_state.scanner_wake_event = scanner_wake_event
+            logger.info("Vector sync state stored in module singleton")
+
+            # Also share with browser_app for /app route
+            for route in app.routes:
+                if isinstance(route, Mount) and route.path == "/app":
+                    route.app.state.document_send_stream = send_stream
+                    route.app.state.document_receive_stream = receive_stream
+                    route.app.state.shutdown_event = shutdown_event
+                    route.app.state.scanner_wake_event = scanner_wake_event
+                    logger.info("Vector sync state shared with browser_app for /app")
+                    break
+
+            # Start background tasks using anyio TaskGroup
+            async with anyio_module.create_task_group() as tg:
+                # Start scanner task
+                await tg.start(
+                    scanner_task,
+                    send_stream,
+                    shutdown_event,
+                    scanner_wake_event,
+                    client,
+                    username,
+                )
+
+                # Start processor pool (each gets a cloned receive stream)
+                for i in range(settings.vector_sync_processor_workers):
                     await tg.start(
-                        scanner_task,
-                        send_stream,
+                        processor_task,
+                        i,
+                        receive_stream.clone(),
                         shutdown_event,
-                        scanner_wake_event,
                         client,
                         username,
                     )
 
-                    # Start processor pool (each gets a cloned receive stream)
-                    for i in range(settings.vector_sync_processor_workers):
-                        await tg.start(
-                            processor_task,
-                            i,
-                            receive_stream.clone(),
-                            shutdown_event,
-                            client,
-                            username,
-                        )
+                logger.info(
+                    f"Background sync tasks started: 1 scanner + "
+                    f"{settings.vector_sync_processor_workers} processors"
+                )
 
-                    logger.info(
-                        f"Background sync tasks started: 1 scanner + "
-                        f"{settings.vector_sync_processor_workers} processors"
-                    )
-
-                    # Run MCP session manager and yield
-                    async with AsyncExitStack() as stack:
-                        await stack.enter_async_context(mcp.session_manager.run())
-                        try:
-                            yield
-                        finally:
-                            # Shutdown signal
-                            logger.info("Shutting down background sync tasks")
-                            shutdown_event.set()
-                            await client.close()
-                            # TaskGroup automatically cancels all tasks on exit
-            else:
-                # No vector sync - just run MCP session manager
+                # Run MCP session manager and yield
                 async with AsyncExitStack() as stack:
                     await stack.enter_async_context(mcp.session_manager.run())
-                    yield
+                    try:
+                        yield
+                    finally:
+                        # Shutdown signal
+                        logger.info("Shutting down background sync tasks")
+                        shutdown_event.set()
+                        await client.close()
+                        # TaskGroup automatically cancels all tasks on exit
+        else:
+            # No vector sync - just run MCP session manager
+            async with AsyncExitStack() as stack:
+                await stack.enter_async_context(mcp.session_manager.run())
+                yield
 
     # Health check endpoints for Kubernetes probes
     def health_live(request):

nextcloud_mcp_server/auth/viz_routes.py

@@ -22,6 +22,7 @@ from starlette.requests import Request
 from starlette.responses import HTMLResponse, JSONResponse
 
 from nextcloud_mcp_server.config import get_settings
+from nextcloud_mcp_server.observability.tracing import trace_operation
 from nextcloud_mcp_server.search import (
     BM25HybridSearchAlgorithm,
     SemanticSearchAlgorithm,
@@ -139,7 +140,10 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             _get_authenticated_client_for_userinfo,
         )
 
-        async with await _get_authenticated_client_for_userinfo(request) as nc_client:  # noqa: F841
+        with trace_operation("vector_viz.get_auth_client"):
+            auth_client_ctx = await _get_authenticated_client_for_userinfo(request)
+
+        async with auth_client_ctx as nc_client:  # noqa: F841
             # Create search algorithm (no client needed - verification removed)
             if algorithm == "semantic":
                 search_algo = SemanticSearchAlgorithm(score_threshold=score_threshold)
@@ -159,24 +163,40 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             all_results = []
             if doc_types is None or len(doc_types) == 0:
                 # Cross-app search - search all indexed types
-                unverified_results = await search_algo.search(
-                    query=query,
-                    user_id=username,
-                    limit=limit * 2,  # Buffer for verification filtering
-                    doc_type=None,  # Search all types
-                    score_threshold=score_threshold,
-                )
-                all_results.extend(unverified_results)
-            else:
-                # Search each document type and combine
-                for doc_type in doc_types:
+                with trace_operation(
+                    "vector_viz.search_execute",
+                    attributes={
+                        "search.algorithm": algorithm,
+                        "search.limit": limit * 2,
+                        "search.doc_type": "all",
+                    },
+                ):
                     unverified_results = await search_algo.search(
                         query=query,
                         user_id=username,
                         limit=limit * 2,  # Buffer for verification filtering
-                        doc_type=doc_type,
+                        doc_type=None,  # Search all types
                         score_threshold=score_threshold,
                     )
+                all_results.extend(unverified_results)
+            else:
+                # Search each document type and combine
+                for doc_type in doc_types:
+                    with trace_operation(
+                        "vector_viz.search_execute",
+                        attributes={
+                            "search.algorithm": algorithm,
+                            "search.limit": limit * 2,
+                            "search.doc_type": doc_type,
+                        },
+                    ):
+                        unverified_results = await search_algo.search(
+                            query=query,
+                            user_id=username,
+                            limit=limit * 2,  # Buffer for verification filtering
+                            doc_type=doc_type,
+                            score_threshold=score_threshold,
+                        )
                     all_results.extend(unverified_results)
                 # Sort by score before verification
                 all_results.sort(key=lambda r: r.score, reverse=True)
@@ -190,22 +210,26 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
         # Store original scores and normalize for visualization
         # (best result = 1.0, worst result = 0.0 within THIS result set)
         # This makes visual encoding meaningful regardless of RRF normalization
-        if search_results:
-            scores = [r.score for r in search_results]
-            min_score, max_score = min(scores), max(scores)
-            score_range = max_score - min_score if max_score > min_score else 1.0
+        with trace_operation(
+            "vector_viz.score_normalize",
+            attributes={"normalize.num_results": len(search_results)},
+        ):
+            if search_results:
+                scores = [r.score for r in search_results]
+                min_score, max_score = min(scores), max(scores)
+                score_range = max_score - min_score if max_score > min_score else 1.0
 
-            logger.info(
-                f"Normalizing scores for viz: original range [{min_score:.3f}, {max_score:.3f}] "
-                f"→ [0.0, 1.0]"
-            )
+                logger.info(
+                    f"Normalizing scores for viz: original range [{min_score:.3f}, {max_score:.3f}] "
+                    f"→ [0.0, 1.0]"
+                )
 
-            # Store original score and rescale to 0-1 for visualization
-            for r in search_results:
-                # Store original score before normalization
-                r.original_score = r.score
-                # Rescale for visual encoding
-                r.score = (r.score - min_score) / score_range
+                # Store original score and rescale to 0-1 for visualization
+                for r in search_results:
+                    # Store original score before normalization
+                    r.original_score = r.score
+                    # Rescale for visual encoding
+                    r.score = (r.score - min_score) / score_range
 
         if not search_results:
             return JSONResponse(
@@ -220,7 +244,9 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
 
         # Fetch vectors for specific matching chunks from Qdrant using batch retrieve
         vector_fetch_start = time.perf_counter()
-        qdrant_client = await get_qdrant_client()
+
+        with trace_operation("vector_viz.get_qdrant_client"):
+            qdrant_client = await get_qdrant_client()
 
         chunk_vectors_map = {}  # Map (doc_id, chunk_start, chunk_end) -> vector
 
@@ -231,12 +257,16 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
         if point_ids:
             # Single batch retrieve call instead of N sequential scroll calls
             # This is ~50x faster for 50 results (1 HTTP request vs 50)
-            points_response = await qdrant_client.retrieve(
-                collection_name=settings.get_collection_name(),
-                ids=point_ids,
-                with_vectors=["dense"],
-                with_payload=["doc_id", "chunk_start_offset", "chunk_end_offset"],
-            )
+            with trace_operation(
+                "vector_viz.vector_retrieve",
+                attributes={"retrieve.num_points": len(point_ids)},
+            ):
+                points_response = await qdrant_client.retrieve(
+                    collection_name=settings.get_collection_name(),
+                    ids=point_ids,
+                    with_vectors=["dense"],
+                    with_payload=["doc_id", "chunk_start_offset", "chunk_end_offset"],
+                )
 
             # Build chunk_vectors_map from batch response
             for point in points_response:
@@ -367,9 +397,16 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
 
         import anyio
 
-        coords_3d, pca = await anyio.to_thread.run_sync(  # type: ignore[attr-defined]
-            lambda: _compute_pca(all_vectors_normalized)
-        )
+        with trace_operation(
+            "vector_viz.pca_compute",
+            attributes={
+                "pca.num_vectors": len(all_vectors_normalized),
+                "pca.embedding_dim": embedding_dim,
+            },
+        ):
+            coords_3d, pca = await anyio.to_thread.run_sync(  # type: ignore[attr-defined]
+                lambda: _compute_pca(all_vectors_normalized)
+            )
         pca_duration = time.perf_counter() - pca_start
 
         # After fit, these attributes are guaranteed to be set

nextcloud_mcp_server/cli.py

@@ -29,9 +29,9 @@ from .app import get_app
 @click.option(
     "--transport",
     "-t",
-    default="sse",
+    default="streamable-http",
     show_default=True,
-    type=click.Choice(["sse", "streamable-http", "http"]),
+    type=click.Choice(["streamable-http", "http"]),
     help="MCP transport protocol",
 )
 @click.option(

nextcloud_mcp_server/client/webdav.py

@@ -1295,3 +1295,237 @@ class WebDAVClient(BaseNextcloudClient):
 
         logger.debug(f"Found {len(files)} files with tag ID {tag_id}")
         return files
+
+    async def get_file_info(self, path: str) -> dict[str, Any] | None:
+        """Get file info including file ID via WebDAV PROPFIND.
+
+        Args:
+            path: Path to the file (relative to user's files directory)
+
+        Returns:
+            File info dictionary with id, name, size, content_type, etc.
+            Returns None if file not found.
+        """
+        webdav_path = f"{self._get_webdav_base_path()}/{path.lstrip('/')}"
+
+        propfind_body = """<?xml version="1.0"?>
+<d:propfind xmlns:d="DAV:" xmlns:oc="http://owncloud.org/ns">
+  <d:prop>
+    <oc:fileid/>
+    <d:displayname/>
+    <d:getcontentlength/>
+    <d:getcontenttype/>
+    <d:getlastmodified/>
+    <d:getetag/>
+    <d:resourcetype/>
+  </d:prop>
+</d:propfind>"""
+
+        try:
+            response = await self._client.request(
+                "PROPFIND",
+                webdav_path,
+                headers={"Depth": "0"},
+                content=propfind_body,
+            )
+            response.raise_for_status()
+        except HTTPStatusError as e:
+            if e.response.status_code == 404:
+                logger.debug(f"File not found: {path}")
+                return None
+            raise
+
+        # Parse XML response
+        root = ET.fromstring(response.content)
+        ns = {
+            "d": "DAV:",
+            "oc": "http://owncloud.org/ns",
+        }
+
+        response_elem = root.find("d:response", ns)
+        if response_elem is None:
+            return None
+
+        propstat = response_elem.find("d:propstat", ns)
+        if propstat is None:
+            return None
+
+        prop = propstat.find("d:prop", ns)
+        if prop is None:
+            return None
+
+        # Extract properties
+        fileid_elem = prop.find("oc:fileid", ns)
+        displayname_elem = prop.find("d:displayname", ns)
+        contentlength_elem = prop.find("d:getcontentlength", ns)
+        contenttype_elem = prop.find("d:getcontenttype", ns)
+        lastmodified_elem = prop.find("d:getlastmodified", ns)
+        etag_elem = prop.find("d:getetag", ns)
+        resourcetype_elem = prop.find("d:resourcetype", ns)
+
+        is_directory = (
+            resourcetype_elem is not None
+            and resourcetype_elem.find("d:collection", ns) is not None
+        )
+
+        file_info = {
+            "id": int(fileid_elem.text) if fileid_elem is not None else None,
+            "path": path,
+            "name": displayname_elem.text
+            if displayname_elem is not None
+            else path.split("/")[-1],
+            "size": int(contentlength_elem.text)
+            if contentlength_elem is not None and contentlength_elem.text
+            else 0,
+            "content_type": contenttype_elem.text
+            if contenttype_elem is not None
+            else "",
+            "last_modified": lastmodified_elem.text
+            if lastmodified_elem is not None
+            else None,
+            "etag": etag_elem.text.strip('"')
+            if etag_elem is not None and etag_elem.text
+            else None,
+            "is_directory": is_directory,
+        }
+
+        logger.debug(f"Got file info for '{path}': id={file_info['id']}")
+        return file_info
+
+    async def create_tag(
+        self,
+        name: str,
+        user_visible: bool = True,
+        user_assignable: bool = True,
+    ) -> dict[str, Any]:
+        """Create a system tag via WebDAV.
+
+        Args:
+            name: Name of the tag to create
+            user_visible: Whether the tag is visible to users
+            user_assignable: Whether users can assign this tag
+
+        Returns:
+            Tag dictionary with id, name, userVisible, userAssignable
+
+        Raises:
+            HTTPStatusError: If tag creation fails (409 if already exists)
+        """
+        # Use WebDAV POST with JSON body to create tag
+        response = await self._client.post(
+            "/remote.php/dav/systemtags/",
+            headers={"Content-Type": "application/json"},
+            json={
+                "name": name,
+                "userVisible": user_visible,
+                "userAssignable": user_assignable,
+            },
+        )
+        response.raise_for_status()
+
+        # Extract tag ID from Content-Location header (e.g., /remote.php/dav/systemtags/42)
+        content_location = response.headers.get("Content-Location", "")
+        tag_id = None
+        if content_location:
+            # Extract the numeric ID from the path
+            try:
+                tag_id = int(content_location.rstrip("/").split("/")[-1])
+            except (ValueError, IndexError):
+                pass
+
+        tag_info = {
+            "id": tag_id,
+            "name": name,
+            "userVisible": user_visible,
+            "userAssignable": user_assignable,
+        }
+
+        logger.info(f"Created tag '{name}' with ID {tag_info['id']}")
+        return tag_info
+
+    async def get_or_create_tag(
+        self,
+        name: str,
+        user_visible: bool = True,
+        user_assignable: bool = True,
+    ) -> dict[str, Any]:
+        """Get a tag by name, creating it if it doesn't exist.
+
+        Args:
+            name: Name of the tag
+            user_visible: Whether the tag is visible to users (for creation)
+            user_assignable: Whether users can assign this tag (for creation)
+
+        Returns:
+            Tag dictionary with id, name, userVisible, userAssignable
+        """
+        # First try to get existing tag
+        existing_tag = await self.get_tag_by_name(name)
+        if existing_tag:
+            logger.debug(f"Tag '{name}' already exists with ID {existing_tag['id']}")
+            return existing_tag
+
+        # Create new tag
+        try:
+            return await self.create_tag(name, user_visible, user_assignable)
+        except HTTPStatusError as e:
+            if e.response.status_code == 409:
+                # Tag was created between our check and creation, fetch it
+                existing_tag = await self.get_tag_by_name(name)
+                if existing_tag:
+                    return existing_tag
+            raise
+
+    async def assign_tag_to_file(self, file_id: int, tag_id: int) -> bool:
+        """Assign a system tag to a file.
+
+        Args:
+            file_id: Numeric file ID
+            tag_id: Numeric tag ID
+
+        Returns:
+            True if tag was assigned successfully (or already assigned)
+
+        Raises:
+            HTTPStatusError: If tag assignment fails
+        """
+        response = await self._client.request(
+            "PUT",
+            f"/remote.php/dav/systemtags-relations/files/{file_id}/{tag_id}",
+            headers={"Content-Length": "0"},
+            content=b"",
+        )
+
+        # 201 = Created (new assignment), 409 = Conflict (already assigned)
+        if response.status_code in (201, 409):
+            logger.info(f"Tagged file {file_id} with tag {tag_id}")
+            return True
+
+        response.raise_for_status()
+        return True
+
+    async def remove_tag_from_file(self, file_id: int, tag_id: int) -> bool:
+        """Remove a system tag from a file.
+
+        Args:
+            file_id: Numeric file ID
+            tag_id: Numeric tag ID
+
+        Returns:
+            True if tag was removed successfully (or wasn't assigned)
+
+        Raises:
+            HTTPStatusError: If tag removal fails
+        """
+        response = await self._client.request(
+            "DELETE",
+            f"/remote.php/dav/systemtags-relations/files/{file_id}/{tag_id}",
+        )
+
+        # 204 = No Content (removed), 404 = Not Found (wasn't assigned)
+        if response.status_code in (204, 404):
+            logger.info(f"Removed tag {tag_id} from file {file_id}")
+            return True
+
+        response.raise_for_status()
+        return True

nextcloud_mcp_server/providers/anthropic.py

@@ -17,18 +17,20 @@ class AnthropicProvider(Provider):
     Note: Anthropic doesn't provide embedding models, only text generation.
     """
 
-    def __init__(self, api_key: str, model: str = "claude-3-5-sonnet-20241022"):
+    def __init__(
+        self, api_key: str, generation_model: str = "claude-3-5-sonnet-20241022"
+    ):
         """
         Initialize Anthropic provider.
 
         Args:
             api_key: Anthropic API key
-            model: Model name (e.g., "claude-3-5-sonnet-20241022")
+            generation_model: Model name (e.g., "claude-3-5-sonnet-20241022")
         """
         self.client = AsyncAnthropic(api_key=api_key)
-        self.model = model
+        self.model = generation_model
 
-        logger.info(f"Initialized Anthropic provider (model={model})")
+        logger.info(f"Initialized Anthropic provider (model={self.model})")
 
     @property
     def supports_embeddings(self) -> bool:

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.

nextcloud_mcp_server/search/bm25_hybrid.py

@@ -9,6 +9,7 @@ from qdrant_client.models import FieldCondition, Filter, MatchValue
 from nextcloud_mcp_server.config import get_settings
 from nextcloud_mcp_server.embedding import get_bm25_service, get_embedding_service
 from nextcloud_mcp_server.observability.metrics import record_qdrant_operation
+from nextcloud_mcp_server.observability.tracing import trace_operation
 from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult
 from nextcloud_mcp_server.vector.placeholder import get_placeholder_filter
 from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
@@ -99,15 +100,19 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
         )
 
         # Generate dense embedding for semantic search
-        embedding_service = get_embedding_service()
-        dense_embedding = await embedding_service.embed(query)
+        with trace_operation("search.get_embedding_service"):
+            embedding_service = get_embedding_service()
+        with trace_operation("search.dense_embedding"):
+            dense_embedding = await embedding_service.embed(query)
         # Store for reuse by callers (e.g., viz_routes PCA visualization)
         self.query_embedding = dense_embedding
         logger.debug(f"Generated dense embedding (dimension={len(dense_embedding)})")
 
         # Generate sparse embedding for BM25 keyword search
-        bm25_service = get_bm25_service()
-        sparse_embedding = await bm25_service.encode_async(query)
+        with trace_operation("search.get_bm25_service"):
+            bm25_service = get_bm25_service()
+        with trace_operation("search.sparse_embedding_bm25"):
+            sparse_embedding = await bm25_service.encode_async(query)
         logger.debug(
             f"Generated sparse embedding "
             f"({len(sparse_embedding['indices'])} non-zero terms)"
@@ -134,38 +139,44 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
         query_filter = Filter(must=filter_conditions)
 
         # Execute hybrid search with Qdrant native RRF fusion
-        qdrant_client = await get_qdrant_client()
+        with trace_operation("search.get_qdrant_client"):
+            qdrant_client = await get_qdrant_client()
+
         try:
             # Use prefetch to run both dense and sparse searches
             # Qdrant will automatically merge results using RRF
-            search_response = await qdrant_client.query_points(
-                collection_name=settings.get_collection_name(),
-                prefetch=[
-                    # Dense semantic search
-                    models.Prefetch(
-                        query=dense_embedding,
-                        using="dense",
-                        limit=limit * 2,  # Get extra for deduplication
-                        filter=query_filter,
-                    ),
-                    # Sparse BM25 search
-                    models.Prefetch(
-                        query=models.SparseVector(
-                            indices=sparse_embedding["indices"],
-                            values=sparse_embedding["values"],
+            with trace_operation(
+                "search.qdrant_query",
+                attributes={"query.limit": limit * 2, "query.fusion": self.fusion_name},
+            ):
+                search_response = await qdrant_client.query_points(
+                    collection_name=settings.get_collection_name(),
+                    prefetch=[
+                        # Dense semantic search
+                        models.Prefetch(
+                            query=dense_embedding,
+                            using="dense",
+                            limit=limit * 2,  # Get extra for deduplication
+                            filter=query_filter,
                         ),
-                        using="sparse",
-                        limit=limit * 2,  # Get extra for deduplication
-                        filter=query_filter,
-                    ),
-                ],
-                # Fusion query (RRF or DBSF based on initialization)
-                query=models.FusionQuery(fusion=self.fusion),
-                limit=limit * 2,  # Get extra for deduplication
-                score_threshold=score_threshold,
-                with_payload=True,
-                with_vectors=False,  # Don't return vectors to save bandwidth
-            )
+                        # Sparse BM25 search
+                        models.Prefetch(
+                            query=models.SparseVector(
+                                indices=sparse_embedding["indices"],
+                                values=sparse_embedding["values"],
+                            ),
+                            using="sparse",
+                            limit=limit * 2,  # Get extra for deduplication
+                            filter=query_filter,
+                        ),
+                    ],
+                    # Fusion query (RRF or DBSF based on initialization)
+                    query=models.FusionQuery(fusion=self.fusion),
+                    limit=limit * 2,  # Get extra for deduplication
+                    score_threshold=score_threshold,
+                    with_payload=True,
+                    with_vectors=False,  # Don't return vectors to save bandwidth
+                )
             record_qdrant_operation("search", "success")
         except Exception:
             record_qdrant_operation("search", "error")
@@ -185,47 +196,51 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
 
         # Deduplicate by (doc_id, doc_type, chunk_start, chunk_end)
         # This allows multiple chunks from same doc, but removes duplicate chunks
-        seen_chunks = set()
-        results = []
+        with trace_operation(
+            "search.deduplicate",
+            attributes={"dedupe.num_points": len(search_response.points)},
+        ):
+            seen_chunks = set()
+            results = []
 
-        for result in search_response.points:
-            # doc_id can be int (notes) or str (files - file paths)
-            doc_id = result.payload["doc_id"]
-            doc_type = result.payload.get("doc_type", "note")
-            chunk_start = result.payload.get("chunk_start_offset")
-            chunk_end = result.payload.get("chunk_end_offset")
-            chunk_key = (doc_id, doc_type, chunk_start, chunk_end)
+            for result in search_response.points:
+                # doc_id can be int (notes) or str (files - file paths)
+                doc_id = result.payload["doc_id"]
+                doc_type = result.payload.get("doc_type", "note")
+                chunk_start = result.payload.get("chunk_start_offset")
+                chunk_end = result.payload.get("chunk_end_offset")
+                chunk_key = (doc_id, doc_type, chunk_start, chunk_end)
 
-            # Skip if we've already seen this exact chunk
-            if chunk_key in seen_chunks:
-                continue
+                # Skip if we've already seen this exact chunk
+                if chunk_key in seen_chunks:
+                    continue
 
-            seen_chunks.add(chunk_key)
+                seen_chunks.add(chunk_key)
 
-            # Return unverified results (verification happens at output stage)
-            results.append(
-                SearchResult(
-                    id=doc_id,
-                    doc_type=doc_type,
-                    title=result.payload.get("title", "Untitled"),
-                    excerpt=result.payload.get("excerpt", ""),
-                    score=result.score,  # Fusion score (RRF or DBSF)
-                    metadata={
-                        "chunk_index": result.payload.get("chunk_index"),
-                        "total_chunks": result.payload.get("total_chunks"),
-                        "search_method": f"bm25_hybrid_{self.fusion_name}",
-                    },
-                    chunk_start_offset=result.payload.get("chunk_start_offset"),
-                    chunk_end_offset=result.payload.get("chunk_end_offset"),
-                    page_number=result.payload.get("page_number"),
-                    chunk_index=result.payload.get("chunk_index", 0),
-                    total_chunks=result.payload.get("total_chunks", 1),
-                    point_id=str(result.id),  # Qdrant point ID for batch retrieval
+                # Return unverified results (verification happens at output stage)
+                results.append(
+                    SearchResult(
+                        id=doc_id,
+                        doc_type=doc_type,
+                        title=result.payload.get("title", "Untitled"),
+                        excerpt=result.payload.get("excerpt", ""),
+                        score=result.score,  # Fusion score (RRF or DBSF)
+                        metadata={
+                            "chunk_index": result.payload.get("chunk_index"),
+                            "total_chunks": result.payload.get("total_chunks"),
+                            "search_method": f"bm25_hybrid_{self.fusion_name}",
+                        },
+                        chunk_start_offset=result.payload.get("chunk_start_offset"),
+                        chunk_end_offset=result.payload.get("chunk_end_offset"),
+                        page_number=result.payload.get("page_number"),
+                        chunk_index=result.payload.get("chunk_index", 0),
+                        total_chunks=result.payload.get("total_chunks", 1),
+                        point_id=str(result.id),  # Qdrant point ID for batch retrieval
+                    )
                 )
-            )
 
-            if len(results) >= limit:
-                break
+                if len(results) >= limit:
+                    break
 
         logger.info(f"Returning {len(results)} unverified results after deduplication")
         if results:

nextcloud_mcp_server/server/semantic.py

@@ -499,9 +499,11 @@ def configure_semantic_tools(mcp: FastMCP):
         )
 
         # 6. Request LLM completion via MCP sampling with timeout
+        # Note: 5 minute timeout to accommodate slower local LLMs (e.g., Ollama)
+        sampling_timeout_seconds = 300
 
         try:
-            with anyio.fail_after(30):
+            with anyio.fail_after(sampling_timeout_seconds):
                 sampling_result = await ctx.session.create_message(
                     messages=[
                         SamplingMessage(
@@ -548,14 +550,14 @@ def configure_semantic_tools(mcp: FastMCP):
 
         except TimeoutError:
             logger.warning(
-                f"Sampling request timed out after 30 seconds for query: '{query}', "
+                f"Sampling request timed out after {sampling_timeout_seconds} seconds for query: '{query}', "
                 f"returning search results only"
             )
             return SamplingSearchResponse(
                 query=query,
                 generated_answer=(
                     f"[Sampling request timed out]\n\n"
-                    f"The answer generation took too long (>30s). "
+                    f"The answer generation took too long (>{sampling_timeout_seconds}s). "
                     f"Found {len(accessible_results)} relevant documents. "
                     f"Please review the sources below or try a simpler query."
                 ),
@@ -675,15 +677,22 @@ def configure_semantic_tools(mcp: FastMCP):
             # Get Qdrant client and query indexed count
             indexed_count = 0
             try:
+                from qdrant_client.models import Filter
+
                 from nextcloud_mcp_server.config import get_settings
+                from nextcloud_mcp_server.vector.placeholder import (
+                    get_placeholder_filter,
+                )
                 from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
 
                 settings = get_settings()
                 qdrant_client = await get_qdrant_client()
 
-                # Count documents in collection
+                # Count documents in collection, excluding placeholders
+                # Placeholders are zero-vector points used to track processing state
                 count_result = await qdrant_client.count(
-                    collection_name=settings.get_collection_name()
+                    collection_name=settings.get_collection_name(),
+                    count_filter=Filter(must=[get_placeholder_filter()]),
                 )
                 indexed_count = count_result.count
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "nextcloud-mcp-server"
-version = "0.47.0"
+version = "0.48.4"
 description = "Model Context Protocol (MCP) server for Nextcloud integration - enables AI assistants to interact with Nextcloud data"
 authors = [
     {name = "Chris Coutinho", email = "chris@coutinho.io"}

tests/conftest.py

@@ -9,7 +9,6 @@ import pytest
 from httpx import HTTPStatusError
 from mcp import ClientSession
 from mcp.client.session import RequestContext
-from mcp.client.sse import sse_client
 from mcp.client.streamable_http import streamablehttp_client
 from mcp.types import ElicitRequestParams, ElicitResult, ErrorData
 
@@ -172,51 +171,6 @@ async def create_mcp_client_session(
     logger.debug(f"{client_name} client session cleaned up successfully")
 
 
-async def create_mcp_client_session_sse(
-    url: str,
-    token: str | None = None,
-    client_name: str = "MCP",
-    elicitation_callback: Any = None,
-) -> AsyncGenerator[ClientSession, Any]:
-    """
-    Factory function to create an MCP client session using SSE transport.
-
-    Similar to create_mcp_client_session but uses SSE transport instead of streamable-http.
-    Uses native async context managers to ensure correct LIFO cleanup order.
-
-    Args:
-        url: MCP server URL (e.g., "http://localhost:8000/sse")
-        token: Optional OAuth access token for Bearer authentication
-        client_name: Client name for logging (e.g., "Basic MCP (SSE)")
-        elicitation_callback: Optional callback for handling elicitation requests
-
-    Yields:
-        Initialized MCP ClientSession
-
-    Note:
-        SSE transport is being deprecated in favor of streamable-http.
-        This function exists for compatibility testing only.
-    """
-    logger.info(f"Creating SSE client for {client_name}")
-
-    # Prepare headers with OAuth token if provided
-    headers = {"Authorization": f"Bearer {token}"} if token else None
-
-    # Use native async with - Python ensures LIFO cleanup
-    # Cleanup order will be: ClientSession.__aexit__ -> sse_client.__aexit__
-    # Note: sse_client yields only (read_stream, write_stream), not 3 values like streamablehttp_client
-    async with sse_client(url, headers=headers) as (read_stream, write_stream):
-        async with ClientSession(
-            read_stream, write_stream, elicitation_callback=elicitation_callback
-        ) as session:
-            await session.initialize()
-            logger.info(f"{client_name} client session initialized successfully")
-            yield session
-
-    # Cleanup happens automatically in LIFO order - no exception suppression needed
-    logger.debug(f"{client_name} client session cleaned up successfully")
-
-
 @pytest.fixture(scope="session")
 async def nc_client(anyio_backend) -> AsyncGenerator[NextcloudClient, Any]:
     """
@@ -255,18 +209,10 @@ async def nc_client(anyio_backend) -> AsyncGenerator[NextcloudClient, Any]:
 @pytest.fixture(scope="session")
 async def nc_mcp_client(anyio_backend) -> AsyncGenerator[ClientSession, Any]:
     """
-    Fixture to create an MCP client session for integration tests using SSE transport.
+    Fixture to create an MCP client session for integration tests using streamable-http.
 
     Uses anyio pytest plugin for proper async fixture handling.
-
-    Note: SSE transport is being deprecated. This fixture uses SSE for compatibility testing.
     """
-
-    # async for session in create_mcp_client_session_sse(
-    # url="http://localhost:8000/sse", client_name="Basic MCP (SSE)"
-    # ):
-    # yield session
-
     async for session in create_mcp_client_session(
         url="http://localhost:8000/mcp",
         client_name="Basic MCP (HTTP)",

tests/integration/conftest.py

@@ -0,0 +1,26 @@
+"""Pytest configuration for integration tests.
+
+This conftest.py provides hooks and fixtures specific to integration tests,
+including the --provider flag for RAG tests.
+"""
+
+# Valid provider names
+VALID_PROVIDERS = ["openai", "ollama", "anthropic", "bedrock"]
+
+
+def pytest_addoption(parser):
+    """Add --provider command line option for RAG tests."""
+    parser.addoption(
+        "--provider",
+        action="store",
+        default=None,
+        choices=VALID_PROVIDERS,
+        help="LLM provider for RAG tests: openai, ollama, anthropic, bedrock",
+    )
+
+
+def pytest_configure(config):
+    """Configure custom markers."""
+    config.addinivalue_line(
+        "markers", "rag: mark test as RAG integration test (requires --provider flag)"
+    )

tests/integration/provider_fixtures.py

@@ -0,0 +1,264 @@
+"""Provider fixtures for integration tests.
+
+This module provides pytest fixtures that configure LLM providers based on
+an explicit --provider flag. Supports OpenAI, Ollama, Anthropic, and Bedrock.
+
+Usage:
+    pytest tests/integration/test_rag.py --provider=openai
+    pytest tests/integration/test_rag.py --provider=ollama
+    pytest tests/integration/test_rag.py --provider=anthropic
+    pytest tests/integration/test_rag.py --provider=bedrock
+
+Environment Variables by Provider:
+
+OpenAI:
+    OPENAI_API_KEY: API key (required)
+    OPENAI_BASE_URL: Base URL override (e.g., "https://models.github.ai/inference")
+    OPENAI_EMBEDDING_MODEL: Embedding model (default: "text-embedding-3-small")
+    OPENAI_GENERATION_MODEL: Generation model (default: "gpt-4o-mini")
+
+Ollama:
+    OLLAMA_BASE_URL: API URL (required, e.g., "http://localhost:11434")
+    OLLAMA_EMBEDDING_MODEL: Embedding model (default: "nomic-embed-text")
+    OLLAMA_GENERATION_MODEL: Generation model (default: "llama3.2:1b")
+
+Anthropic:
+    ANTHROPIC_API_KEY: API key (required)
+    ANTHROPIC_GENERATION_MODEL: Model (default: "claude-3-haiku-20240307")
+
+Bedrock:
+    AWS_REGION: AWS region (required)
+    BEDROCK_EMBEDDING_MODEL: Embedding model ID
+    BEDROCK_GENERATION_MODEL: Generation model ID
+"""
+
+import logging
+import os
+from typing import AsyncGenerator
+
+import pytest
+
+from nextcloud_mcp_server.providers.base import Provider
+
+logger = logging.getLogger(__name__)
+
+# Valid provider names (must match conftest.py)
+VALID_PROVIDERS = ["openai", "ollama", "anthropic", "bedrock"]
+
+
+async def create_generation_provider(provider_name: str) -> Provider:
+    """Create a provider configured for text generation.
+
+    Args:
+        provider_name: One of "openai", "ollama", "anthropic", "bedrock"
+
+    Returns:
+        Provider instance configured for generation
+
+    Raises:
+        ValueError: If provider_name is invalid or required env vars missing
+    """
+    if provider_name == "openai":
+        from nextcloud_mcp_server.providers.openai import OpenAIProvider
+
+        api_key = os.getenv("OPENAI_API_KEY")
+        if not api_key:
+            raise ValueError("OPENAI_API_KEY environment variable required")
+
+        base_url = os.getenv("OPENAI_BASE_URL")
+        generation_model = os.getenv("OPENAI_GENERATION_MODEL", "gpt-4o-mini")
+
+        # GitHub Models API requires model name prefix
+        if base_url and "models.github.ai" in base_url:
+            if not generation_model.startswith("openai/"):
+                generation_model = f"openai/{generation_model}"
+
+        provider = OpenAIProvider(
+            api_key=api_key,
+            base_url=base_url,
+            embedding_model=None,  # Generation only
+            generation_model=generation_model,
+        )
+        logger.info(f"Created OpenAI generation provider: model={generation_model}")
+        return provider
+
+    elif provider_name == "ollama":
+        from nextcloud_mcp_server.providers.ollama import OllamaProvider
+
+        base_url = os.getenv("OLLAMA_BASE_URL")
+        if not base_url:
+            raise ValueError("OLLAMA_BASE_URL environment variable required")
+
+        generation_model = os.getenv("OLLAMA_GENERATION_MODEL", "llama3.2:1b")
+
+        provider = OllamaProvider(
+            base_url=base_url,
+            embedding_model=None,  # Generation only
+            generation_model=generation_model,
+        )
+        logger.info(f"Created Ollama generation provider: model={generation_model}")
+        return provider
+
+    elif provider_name == "anthropic":
+        from nextcloud_mcp_server.providers.anthropic import AnthropicProvider
+
+        api_key = os.getenv("ANTHROPIC_API_KEY")
+        if not api_key:
+            raise ValueError("ANTHROPIC_API_KEY environment variable required")
+
+        generation_model = os.getenv(
+            "ANTHROPIC_GENERATION_MODEL", "claude-3-haiku-20240307"
+        )
+
+        provider = AnthropicProvider(
+            api_key=api_key,
+            generation_model=generation_model,
+        )
+        logger.info(f"Created Anthropic generation provider: model={generation_model}")
+        return provider
+
+    elif provider_name == "bedrock":
+        from nextcloud_mcp_server.providers.bedrock import BedrockProvider
+
+        region = os.getenv("AWS_REGION")
+        if not region:
+            raise ValueError("AWS_REGION environment variable required")
+
+        generation_model = os.getenv("BEDROCK_GENERATION_MODEL")
+        if not generation_model:
+            raise ValueError("BEDROCK_GENERATION_MODEL environment variable required")
+
+        provider = BedrockProvider(
+            region=region,
+            embedding_model=None,  # Generation only
+            generation_model=generation_model,
+        )
+        logger.info(f"Created Bedrock generation provider: model={generation_model}")
+        return provider
+
+    else:
+        raise ValueError(f"Unknown provider: {provider_name}. Valid: {VALID_PROVIDERS}")
+
+
+async def create_embedding_provider(provider_name: str) -> Provider:
+    """Create a provider configured for embeddings.
+
+    Args:
+        provider_name: One of "openai", "ollama", "bedrock"
+                      (Anthropic does not support embeddings)
+
+    Returns:
+        Provider instance configured for embeddings
+
+    Raises:
+        ValueError: If provider_name is invalid, doesn't support embeddings,
+                   or required env vars missing
+    """
+    if provider_name == "anthropic":
+        raise ValueError("Anthropic does not support embeddings")
+
+    if provider_name == "openai":
+        from nextcloud_mcp_server.providers.openai import OpenAIProvider
+
+        api_key = os.getenv("OPENAI_API_KEY")
+        if not api_key:
+            raise ValueError("OPENAI_API_KEY environment variable required")
+
+        base_url = os.getenv("OPENAI_BASE_URL")
+        embedding_model = os.getenv("OPENAI_EMBEDDING_MODEL", "text-embedding-3-small")
+
+        # GitHub Models API requires model name prefix
+        if base_url and "models.github.ai" in base_url:
+            if not embedding_model.startswith("openai/"):
+                embedding_model = f"openai/{embedding_model}"
+
+        provider = OpenAIProvider(
+            api_key=api_key,
+            base_url=base_url,
+            embedding_model=embedding_model,
+            generation_model=None,  # Embeddings only
+        )
+        logger.info(f"Created OpenAI embedding provider: model={embedding_model}")
+        return provider
+
+    elif provider_name == "ollama":
+        from nextcloud_mcp_server.providers.ollama import OllamaProvider
+
+        base_url = os.getenv("OLLAMA_BASE_URL")
+        if not base_url:
+            raise ValueError("OLLAMA_BASE_URL environment variable required")
+
+        embedding_model = os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text")
+
+        provider = OllamaProvider(
+            base_url=base_url,
+            embedding_model=embedding_model,
+            generation_model=None,  # Embeddings only
+        )
+        logger.info(f"Created Ollama embedding provider: model={embedding_model}")
+        return provider
+
+    elif provider_name == "bedrock":
+        from nextcloud_mcp_server.providers.bedrock import BedrockProvider
+
+        region = os.getenv("AWS_REGION")
+        if not region:
+            raise ValueError("AWS_REGION environment variable required")
+
+        embedding_model = os.getenv("BEDROCK_EMBEDDING_MODEL")
+        if not embedding_model:
+            raise ValueError("BEDROCK_EMBEDDING_MODEL environment variable required")
+
+        provider = BedrockProvider(
+            region=region,
+            embedding_model=embedding_model,
+            generation_model=None,  # Embeddings only
+        )
+        logger.info(f"Created Bedrock embedding provider: model={embedding_model}")
+        return provider
+
+    else:
+        raise ValueError(f"Unknown provider: {provider_name}. Valid: {VALID_PROVIDERS}")
+
+
+# =============================================================================
+# Pytest Fixtures
+# =============================================================================
+
+
+@pytest.fixture(scope="module")
+def provider_name(request) -> str:
+    """Get the provider name from --provider flag.
+
+    Raises pytest.skip if --provider not specified.
+    """
+    name = request.config.getoption("--provider")
+    if not name:
+        pytest.skip("--provider flag required (openai, ollama, anthropic, bedrock)")
+    return name
+
+
+@pytest.fixture(scope="module")
+async def generation_provider(provider_name: str) -> AsyncGenerator[Provider, None]:
+    """Fixture providing a generation-capable provider.
+
+    Requires --provider flag to be set.
+    """
+    provider = await create_generation_provider(provider_name)
+    yield provider
+    await provider.close()
+
+
+@pytest.fixture(scope="module")
+async def embedding_provider(provider_name: str) -> AsyncGenerator[Provider, None]:
+    """Fixture providing an embedding-capable provider.
+
+    Requires --provider flag to be set.
+    Note: Anthropic does not support embeddings - test will fail if used.
+    """
+    if provider_name == "anthropic":
+        pytest.skip("Anthropic does not support embeddings")
+
+    provider = await create_embedding_provider(provider_name)
+    yield provider
+    await provider.close()

tests/integration/sampling_support.py

@@ -1,7 +1,7 @@
 """MCP sampling support for integration tests.
 
 This module provides utilities to enable real LLM-based sampling in integration tests
-using OpenAI or GitHub Models API.
+using any provider that supports text generation (OpenAI, Ollama, Anthropic, Bedrock).
 """
 
 import logging
@@ -10,46 +10,58 @@ from typing import Any
 from mcp import types
 from mcp.client.session import ClientSession, RequestContext
 
-from nextcloud_mcp_server.providers.openai import OpenAIProvider
+from nextcloud_mcp_server.providers.base import Provider
 
 logger = logging.getLogger(__name__)
 
 
-def create_openai_sampling_callback(provider: OpenAIProvider):
-    """Factory to create a sampling callback using OpenAI provider.
+def create_sampling_callback(provider: Provider):
+    """Factory to create a sampling callback using any generation-capable provider.
 
     The callback conforms to MCP's SamplingFnT protocol and can be passed
     to ClientSession for handling sampling requests from the server.
 
     Args:
-        provider: OpenAIProvider instance configured with a generation model
+        provider: Any Provider instance that supports generation
+                  (supports_generation=True)
 
     Returns:
         Async callback function for MCP sampling
 
+    Raises:
+        ValueError: If provider doesn't support generation
+
     Example:
         ```python
-        provider = OpenAIProvider(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            base_url=os.getenv("OPENAI_BASE_URL"),
-            generation_model="gpt-4o-mini",
-        )
-        callback = create_openai_sampling_callback(provider)
+        from nextcloud_mcp_server.providers import get_provider
 
-        async for session in create_mcp_client_session(
-            url="http://localhost:8000/mcp",
-            sampling_callback=callback,
-        ):
-            # Session now supports sampling
-            pass
+        provider = get_provider()  # Auto-detect from environment
+        if provider.supports_generation:
+            callback = create_sampling_callback(provider)
+
+            async for session in create_mcp_client_session(
+                url="http://localhost:8000/mcp",
+                sampling_callback=callback,
+            ):
+                # Session now supports sampling
+                pass
         ```
     """
+    if not provider.supports_generation:
+        raise ValueError(
+            f"Provider {provider.__class__.__name__} does not support generation"
+        )
+
+    # Get model name for logging (provider-specific attribute)
+    model_name = (
+        getattr(provider, "generation_model", None) or provider.__class__.__name__
+    )
 
     async def sampling_callback(
         context: RequestContext[ClientSession, Any],
         params: types.CreateMessageRequestParams,
     ) -> types.CreateMessageResult | types.ErrorData:
-        """Handle sampling requests using OpenAI provider."""
+        """Handle sampling requests using the configured provider."""
         logger.debug(f"Sampling callback invoked with {len(params.messages)} messages")
 
         # Extract messages and build prompt
@@ -68,14 +80,13 @@ def create_openai_sampling_callback(provider: OpenAIProvider):
         logger.debug(f"Generating response for prompt ({len(prompt)} chars)")
 
         try:
-            # Generate response using OpenAI provider
-            # Note: temperature is hardcoded in the provider at 0.7
+            # Generate response using provider
+            # Note: temperature is typically hardcoded in providers at 0.7
             response = await provider.generate(
                 prompt=prompt,
                 max_tokens=params.maxTokens,
             )
 
-            model_name = provider.generation_model or "unknown"
             logger.info(f"Sampling completed: {len(response)} chars from {model_name}")
 
             return types.CreateMessageResult(
@@ -85,10 +96,25 @@ def create_openai_sampling_callback(provider: OpenAIProvider):
                 stopReason="endTurn",
             )
         except Exception as e:
-            logger.error(f"OpenAI generation failed: {e}")
+            logger.error(f"Generation failed ({provider.__class__.__name__}): {e}")
             return types.ErrorData(
                 code=types.INTERNAL_ERROR,
-                message=f"OpenAI generation failed: {e!s}",
+                message=f"Generation failed: {e!s}",
             )
 
     return sampling_callback
+
+
+def create_openai_sampling_callback(provider: "Provider"):
+    """Factory to create a sampling callback using OpenAI provider.
+
+    This is a backward-compatible wrapper around create_sampling_callback().
+    Prefer using create_sampling_callback() directly for new code.
+
+    Args:
+        provider: OpenAIProvider instance configured with a generation model
+
+    Returns:
+        Async callback function for MCP sampling
+    """
+    return create_sampling_callback(provider)

tests/integration/test_rag.py

@@ -0,0 +1,403 @@
+"""Integration tests for RAG pipeline with multiple LLM providers.
+
+These tests validate the complete semantic search and MCP sampling flow using:
+1. MCP server's built-in semantic search (embeddings handled server-side)
+2. MCP sampling for answer generation (any generation-capable provider)
+3. Pre-indexed Nextcloud User Manual as the knowledge base
+
+Usage:
+    # Run with OpenAI (including GitHub Models API)
+    OPENAI_API_KEY=... pytest tests/integration/test_rag.py --provider=openai -v
+
+    # Run with Ollama
+    OLLAMA_BASE_URL=http://localhost:11434 OLLAMA_GENERATION_MODEL=llama3.2:1b \\
+        pytest tests/integration/test_rag.py --provider=ollama -v
+
+    # Run with Anthropic
+    ANTHROPIC_API_KEY=... pytest tests/integration/test_rag.py --provider=anthropic -v
+
+    # Run with AWS Bedrock
+    AWS_REGION=us-east-1 BEDROCK_GENERATION_MODEL=... \\
+        pytest tests/integration/test_rag.py --provider=bedrock -v
+
+Environment Variables:
+    See tests/integration/provider_fixtures.py for provider-specific configuration.
+    RAG_MANUAL_PATH: Path to manual PDF in Nextcloud (default: "Nextcloud Manual.pdf")
+
+Prerequisites:
+    - Nextcloud User Manual PDF uploaded to Nextcloud
+    - VECTOR_SYNC_ENABLED=true on the MCP server
+    - Provider-specific environment variables set
+"""
+
+import json
+import logging
+import os
+from pathlib import Path
+from typing import Any, AsyncGenerator
+
+import anyio
+import pytest
+from mcp import ClientSession
+
+from nextcloud_mcp_server.providers.base import Provider
+from tests.conftest import create_mcp_client_session
+from tests.integration.provider_fixtures import create_generation_provider
+from tests.integration.sampling_support import create_sampling_callback
+
+logger = logging.getLogger(__name__)
+
+# Default path to the Nextcloud User Manual PDF
+DEFAULT_MANUAL_PATH = "Nextcloud Manual.pdf"
+
+
+async def llm_judge(
+    provider: Provider,
+    ground_truth: str,
+    system_output: str,
+) -> bool:
+    """Use LLM to judge if system output aligns with ground truth.
+
+    Args:
+        provider: Any provider with generation capability
+        ground_truth: The expected/reference answer
+        system_output: The system's actual output to evaluate
+
+    Returns:
+        True if output aligns with ground truth, False otherwise
+    """
+    prompt = f"""GROUND TRUTH: {ground_truth}
+
+SYSTEM OUTPUT: {system_output}
+
+Does the system output contain the key facts from the ground truth?
+
+Answer: TRUE or FALSE"""
+
+    logger.info("Received ground truth: %s", ground_truth)
+    logger.info("Received system output: %s", system_output)
+
+    response = await provider.generate(prompt, max_tokens=10)
+    logger.info("LLM Judge response: %s", response)
+    return "TRUE" in response.upper()
+
+
+# Mark all tests as integration tests
+pytestmark = [
+    pytest.mark.integration,
+    pytest.mark.rag,
+]
+
+# Ground truth fixture path
+FIXTURES_DIR = Path(__file__).parent / "fixtures"
+GROUND_TRUTH_FILE = FIXTURES_DIR / "nextcloud_manual_ground_truth.json"
+
+
+@pytest.fixture(scope="module")
+def ground_truth_qa():
+    """Load ground truth Q&A pairs for the Nextcloud manual."""
+    if not GROUND_TRUTH_FILE.exists():
+        pytest.skip(f"Ground truth file not found: {GROUND_TRUTH_FILE}")
+
+    with open(GROUND_TRUTH_FILE) as f:
+        return json.load(f)
+
+
+@pytest.fixture(scope="module")
+async def indexed_manual_pdf(nc_client, nc_mcp_client):
+    """Ensure the Nextcloud User Manual PDF is tagged and indexed for vector search.
+
+    This fixture:
+    1. Gets file info for the manual PDF
+    2. Creates/gets the 'vector-index' tag
+    3. Assigns the tag to the file
+    4. Waits for vector sync to complete indexing
+
+    Environment Variables:
+        RAG_MANUAL_PATH: Path to manual PDF in Nextcloud (default: Nextcloud Manual.pdf)
+    """
+    manual_path = os.getenv("RAG_MANUAL_PATH", DEFAULT_MANUAL_PATH)
+
+    logger.info(f"Setting up indexed manual PDF: {manual_path}")
+
+    # Get file info to verify file exists and get file ID
+    file_info = await nc_client.webdav.get_file_info(manual_path)
+    if not file_info:
+        pytest.skip(f"Manual PDF not found at '{manual_path}'")
+
+    file_id = file_info["id"]
+    logger.info(f"Found manual PDF: {manual_path} (file_id={file_id})")
+
+    # Create or get the vector-index tag
+    tag = await nc_client.webdav.get_or_create_tag("vector-index")
+    tag_id = tag["id"]
+    logger.info(f"Using tag 'vector-index' (tag_id={tag_id})")
+
+    # Assign tag to file
+    await nc_client.webdav.assign_tag_to_file(file_id, tag_id)
+    logger.info(f"Tagged file {file_id} with vector-index tag")
+
+    # Wait for vector sync to complete indexing
+    max_attempts = 60
+    poll_interval = 10
+
+    logger.info("Waiting for vector sync to index the manual...")
+
+    for attempt in range(1, max_attempts + 1):
+        try:
+            # Call the MCP tool via the existing client session
+            result = await nc_mcp_client.call_tool(
+                "nc_get_vector_sync_status",
+                arguments={},
+            )
+
+            if not result.isError:
+                content = result.structuredContent or {}
+                indexed = content.get("indexed_count", 0)
+                pending = content.get("pending_count", 1)
+
+                logger.info(
+                    f"Attempt {attempt}/{max_attempts}: "
+                    f"indexed={indexed}, pending={pending}"
+                )
+
+                if indexed > 0 and pending == 0:
+                    logger.info(
+                        f"Vector indexing complete: {indexed} documents indexed"
+                    )
+                    break
+        except Exception as e:
+            logger.warning(f"Attempt {attempt}: Error checking status: {e}")
+
+        if attempt < max_attempts:
+            await anyio.sleep(poll_interval)
+    else:
+        logger.warning(
+            f"Vector indexing may not be complete after {max_attempts} attempts"
+        )
+
+    yield {
+        "path": manual_path,
+        "file_id": file_id,
+        "tag_id": tag_id,
+    }
+
+
+@pytest.fixture(scope="module")
+def provider_name(request) -> str:
+    """Get the provider name from --provider flag.
+
+    Raises pytest.skip if --provider not specified.
+    """
+    name = request.config.getoption("--provider")
+    if not name:
+        pytest.skip("--provider flag required (openai, ollama, anthropic, bedrock)")
+    return name
+
+
+@pytest.fixture(scope="module")
+async def generation_provider(provider_name: str) -> AsyncGenerator[Provider, None]:
+    """Provider configured for text generation.
+
+    Requires --provider flag to be set.
+    """
+    provider = await create_generation_provider(provider_name)
+    yield provider
+    await provider.close()
+
+
+@pytest.fixture(scope="module")
+async def nc_mcp_client_with_sampling(
+    anyio_backend, generation_provider, provider_name
+) -> AsyncGenerator[ClientSession, Any]:
+    """MCP client with sampling support using the specified provider.
+
+    This fixture creates an MCP client that can handle sampling requests
+    from the server using the configured generation provider.
+    """
+    sampling_callback = create_sampling_callback(generation_provider)
+
+    async for session in create_mcp_client_session(
+        url="http://localhost:8000/mcp",
+        client_name=f"Sampling MCP ({provider_name})",
+        sampling_callback=sampling_callback,
+    ):
+        yield session
+
+
+async def test_semantic_search_retrieval(
+    nc_mcp_client, ground_truth_qa, indexed_manual_pdf, generation_provider
+):
+    """Test that semantic search retrieves relevant documents from the manual.
+
+    This tests the retrieval component of RAG - ensuring that queries
+    return relevant chunks from the indexed Nextcloud User Manual.
+    """
+    # Use first query from ground truth
+    test_case = ground_truth_qa[0]  # 2FA question
+    query = test_case["query"]
+
+    # Perform semantic search via MCP tool
+    result = await nc_mcp_client.call_tool(
+        "nc_semantic_search",
+        arguments={
+            "query": query,
+            "limit": 5,
+            "score_threshold": 0.0,
+        },
+    )
+
+    assert result.isError is False, f"Tool call failed: {result}"
+    data = result.structuredContent
+
+    # Verify we got results
+    assert data["success"] is True
+    assert data["total_found"] > 0, f"No results for query: {query}"
+    assert len(data["results"]) > 0
+
+    # Use LLM judge to evaluate if excerpts are relevant to ground truth
+    all_excerpts = " ".join([r["excerpt"] for r in data["results"]])
+    is_relevant = await llm_judge(
+        generation_provider,
+        test_case["ground_truth"],
+        all_excerpts,
+    )
+    assert is_relevant, f"LLM judge: excerpts not relevant to query: {query}"
+
+
+async def test_semantic_search_answer_with_sampling(
+    nc_mcp_client_with_sampling,
+    ground_truth_qa,
+    indexed_manual_pdf,
+    generation_provider,
+):
+    """Test semantic search with MCP sampling for answer generation.
+
+    This tests the full RAG pipeline:
+    1. Semantic search retrieves relevant documents
+    2. MCP sampling generates an answer from the retrieved context
+    3. Provider generates the answer via the sampling callback
+
+    Uses nc_mcp_client_with_sampling which has sampling enabled.
+    """
+    # Use the 2FA question - has clear expected answer
+    test_case = ground_truth_qa[0]
+    query = test_case["query"]
+
+    result = await nc_mcp_client_with_sampling.call_tool(
+        "nc_semantic_search_answer",
+        arguments={
+            "query": query,
+            "limit": 5,
+            "score_threshold": 0.0,
+            "max_answer_tokens": 300,
+        },
+    )
+
+    assert result.isError is False, f"Tool call failed: {result}"
+    data = result.structuredContent
+
+    # Verify response structure
+    assert data["success"] is True
+    assert "query" in data
+    assert "generated_answer" in data
+    assert "sources" in data
+    assert "search_method" in data
+
+    # Check for either successful sampling or graceful fallback
+    fallback_methods = {
+        "semantic_sampling_unsupported",
+        "semantic_sampling_user_declined",
+        "semantic_sampling_timeout",
+        "semantic_sampling_mcp_error",
+        "semantic_sampling_fallback",
+    }
+
+    if data["search_method"] in fallback_methods:
+        # Fallback mode - verify sources still returned
+        assert len(data["sources"]) > 0, "Expected sources even in fallback mode"
+        pytest.skip(
+            f"MCP sampling not available (method: {data['search_method']}), "
+            f"but retrieval succeeded with {len(data['sources'])} sources"
+        )
+    else:
+        # Successful sampling - verify answer quality
+        assert data["search_method"] == "semantic_sampling"
+        assert data["generated_answer"] is not None
+        assert len(data["generated_answer"]) > 50  # Non-trivial answer
+
+        # Use LLM judge to evaluate answer relevance
+        is_relevant = await llm_judge(
+            generation_provider,
+            test_case["ground_truth"],
+            data["generated_answer"],
+        )
+        assert is_relevant, f"LLM judge: answer not relevant to query: {query}"
+
+
+@pytest.mark.parametrize(
+    "qa_index,min_expected_results",
+    [
+        (0, 1),  # 2FA question
+        (1, 1),  # File quotas question
+        (2, 1),  # Linux installation question
+        (3, 1),  # Windows requirements question
+        (4, 1),  # Client apps with 2FA question
+    ],
+)
+async def test_retrieval_quality_all_queries(
+    nc_mcp_client, ground_truth_qa, indexed_manual_pdf, qa_index, min_expected_results
+):
+    """Test retrieval quality for all ground truth queries.
+
+    Validates that each query returns at least the minimum expected
+    number of relevant results from the Nextcloud manual.
+    """
+    if qa_index >= len(ground_truth_qa):
+        pytest.skip(f"Ground truth index {qa_index} not available")
+
+    test_case = ground_truth_qa[qa_index]
+    query = test_case["query"]
+
+    result = await nc_mcp_client.call_tool(
+        "nc_semantic_search",
+        arguments={
+            "query": query,
+            "limit": 5,
+            "score_threshold": 0.0,
+        },
+    )
+
+    assert result.isError is False
+    data = result.structuredContent
+
+    assert data["total_found"] >= min_expected_results, (
+        f"Query '{query}' returned {data['total_found']} results, "
+        f"expected at least {min_expected_results}"
+    )
+
+
+async def test_no_results_for_unrelated_query(nc_mcp_client, indexed_manual_pdf):
+    """Test that completely unrelated queries return low/no scores.
+
+    The Nextcloud manual shouldn't have relevant content for
+    quantum physics queries.
+    """
+    result = await nc_mcp_client.call_tool(
+        "nc_semantic_search",
+        arguments={
+            "query": "quantum entanglement hadron collider particle physics",
+            "limit": 5,
+            "score_threshold": 0.5,  # Higher threshold to filter irrelevant
+        },
+    )
+
+    assert result.isError is False
+    data = result.structuredContent
+
+    # Should have few or no high-scoring results
+    # Low score threshold means we might get some results, but they should be low quality
+    if data["total_found"] > 0:
+        # If results exist, they should have low scores
+        max_score = max(r["score"] for r in data["results"])
+        assert max_score < 0.8, f"Unexpected high score {max_score} for unrelated query"

tests/integration/test_rag_openai.py

@@ -1,300 +0,0 @@
-"""Integration tests for RAG pipeline with OpenAI/GitHub Models API.
-
-These tests validate the complete semantic search and MCP sampling flow using:
-1. OpenAI embeddings for semantic search
-2. MCP sampling for answer generation
-3. Pre-indexed Nextcloud User Manual as the knowledge base
-
-Environment Variables:
-    OPENAI_API_KEY: OpenAI API key or GitHub token for models.github.ai
-    OPENAI_BASE_URL: Base URL override (e.g., "https://models.github.ai/inference")
-    OPENAI_EMBEDDING_MODEL: Embedding model (default: "text-embedding-3-small")
-    OPENAI_GENERATION_MODEL: Generation model for sampling (default: "gpt-4o-mini")
-
-For GitHub CI, set:
-    OPENAI_API_KEY: ${{ secrets.GITHUB_TOKEN }}
-    OPENAI_BASE_URL: https://models.github.ai/inference
-    OPENAI_EMBEDDING_MODEL: openai/text-embedding-3-small
-    OPENAI_GENERATION_MODEL: openai/gpt-4o-mini
-
-Prerequisites:
-    - Nextcloud User Manual indexed in Qdrant (via vector sync)
-    - VECTOR_SYNC_ENABLED=true on the MCP server
-"""
-
-import json
-import os
-from pathlib import Path
-from typing import Any, AsyncGenerator
-
-import pytest
-from mcp import ClientSession
-
-from nextcloud_mcp_server.providers.openai import OpenAIProvider
-from tests.conftest import create_mcp_client_session
-from tests.integration.sampling_support import create_openai_sampling_callback
-
-# Skip all tests if OpenAI API key not configured
-pytestmark = [
-    pytest.mark.integration,
-    pytest.mark.skipif(
-        not os.getenv("OPENAI_API_KEY"),
-        reason="OPENAI_API_KEY not set - skipping OpenAI RAG tests",
-    ),
-]
-
-# Ground truth fixture path
-FIXTURES_DIR = Path(__file__).parent / "fixtures"
-GROUND_TRUTH_FILE = FIXTURES_DIR / "nextcloud_manual_ground_truth.json"
-
-
-@pytest.fixture(scope="module")
-def ground_truth_qa():
-    """Load ground truth Q&A pairs for the Nextcloud manual."""
-    if not GROUND_TRUTH_FILE.exists():
-        pytest.skip(f"Ground truth file not found: {GROUND_TRUTH_FILE}")
-
-    with open(GROUND_TRUTH_FILE) as f:
-        return json.load(f)
-
-
-@pytest.fixture(scope="module")
-async def openai_provider():
-    """OpenAI provider configured from environment (embeddings only)."""
-    api_key = os.getenv("OPENAI_API_KEY")
-    base_url = os.getenv("OPENAI_BASE_URL")
-    embedding_model = os.getenv("OPENAI_EMBEDDING_MODEL", "text-embedding-3-small")
-
-    provider = OpenAIProvider(
-        api_key=api_key,
-        base_url=base_url,
-        embedding_model=embedding_model,
-        generation_model=None,  # Embeddings only
-    )
-
-    yield provider
-    await provider.close()
-
-
-@pytest.fixture(scope="module")
-async def openai_generation_provider():
-    """OpenAI provider configured for text generation (for sampling callback)."""
-    api_key = os.getenv("OPENAI_API_KEY")
-    base_url = os.getenv("OPENAI_BASE_URL")
-    generation_model = os.getenv("OPENAI_GENERATION_MODEL", "gpt-4o-mini")
-
-    # For GitHub Models API, use the prefixed model name
-    if base_url and "models.github.ai" in base_url:
-        if not generation_model.startswith("openai/"):
-            generation_model = f"openai/{generation_model}"
-
-    provider = OpenAIProvider(
-        api_key=api_key,
-        base_url=base_url,
-        embedding_model=None,  # Generation only
-        generation_model=generation_model,
-    )
-
-    yield provider
-    await provider.close()
-
-
-@pytest.fixture(scope="module")
-async def nc_mcp_client_with_sampling(
-    anyio_backend, openai_generation_provider
-) -> AsyncGenerator[ClientSession, Any]:
-    """MCP client with OpenAI-based sampling support.
-
-    This fixture creates an MCP client that can handle sampling requests
-    from the server using OpenAI for text generation.
-    """
-    sampling_callback = create_openai_sampling_callback(openai_generation_provider)
-
-    async for session in create_mcp_client_session(
-        url="http://localhost:8000/mcp",
-        client_name="OpenAI Sampling MCP",
-        sampling_callback=sampling_callback,
-    ):
-        yield session
-
-
-async def test_openai_embeddings_work(openai_provider: OpenAIProvider):
-    """Test that OpenAI embeddings can be generated."""
-    embedding = await openai_provider.embed("test query about Nextcloud")
-
-    assert isinstance(embedding, list)
-    assert len(embedding) > 0
-    assert all(isinstance(x, float) for x in embedding)
-    # OpenAI embedding dimensions: 1536 (small) or 3072 (large)
-    assert len(embedding) in [1536, 3072]
-
-
-async def test_semantic_search_retrieval(nc_mcp_client, ground_truth_qa):
-    """Test that semantic search retrieves relevant documents from the manual.
-
-    This tests the retrieval component of RAG - ensuring that queries
-    return relevant chunks from the indexed Nextcloud User Manual.
-    """
-    # Use first query from ground truth
-    test_case = ground_truth_qa[0]  # 2FA question
-    query = test_case["query"]
-    expected_topics = test_case["expected_topics"]
-
-    # Perform semantic search via MCP tool
-    result = await nc_mcp_client.call_tool(
-        "nc_semantic_search",
-        arguments={
-            "query": query,
-            "limit": 5,
-            "score_threshold": 0.0,
-        },
-    )
-
-    assert result.isError is False, f"Tool call failed: {result}"
-    data = result.structuredContent
-
-    # Verify we got results
-    assert data["success"] is True
-    assert data["total_found"] > 0, f"No results for query: {query}"
-    assert len(data["results"]) > 0
-
-    # Check that at least one result contains expected topic keywords
-    all_excerpts = " ".join([r["excerpt"].lower() for r in data["results"]])
-    topic_found = any(topic.lower() in all_excerpts for topic in expected_topics)
-    assert topic_found, (
-        f"Expected topics {expected_topics} not found in results for query: {query}"
-    )
-
-
-async def test_semantic_search_answer_with_sampling(
-    nc_mcp_client_with_sampling, ground_truth_qa
-):
-    """Test semantic search with MCP sampling for answer generation.
-
-    This tests the full RAG pipeline:
-    1. Semantic search retrieves relevant documents
-    2. MCP sampling generates an answer from the retrieved context
-    3. OpenAI generates the answer via the sampling callback
-
-    Uses nc_mcp_client_with_sampling which has OpenAI-based sampling enabled.
-    """
-    # Use the 2FA question - has clear expected answer
-    test_case = ground_truth_qa[0]
-    query = test_case["query"]
-
-    result = await nc_mcp_client_with_sampling.call_tool(
-        "nc_semantic_search_answer",
-        arguments={
-            "query": query,
-            "limit": 5,
-            "score_threshold": 0.0,
-            "max_answer_tokens": 300,
-        },
-    )
-
-    assert result.isError is False, f"Tool call failed: {result}"
-    data = result.structuredContent
-
-    # Verify response structure
-    assert data["success"] is True
-    assert "query" in data
-    assert "generated_answer" in data
-    assert "sources" in data
-    assert "search_method" in data
-
-    # Check for either successful sampling or graceful fallback
-    fallback_methods = {
-        "semantic_sampling_unsupported",
-        "semantic_sampling_user_declined",
-        "semantic_sampling_timeout",
-        "semantic_sampling_mcp_error",
-        "semantic_sampling_fallback",
-    }
-
-    if data["search_method"] in fallback_methods:
-        # Fallback mode - verify sources still returned
-        assert len(data["sources"]) > 0, "Expected sources even in fallback mode"
-        pytest.skip(
-            f"MCP sampling not available (method: {data['search_method']}), "
-            f"but retrieval succeeded with {len(data['sources'])} sources"
-        )
-    else:
-        # Successful sampling - verify answer quality
-        assert data["search_method"] == "semantic_sampling"
-        assert data["generated_answer"] is not None
-        assert len(data["generated_answer"]) > 50  # Non-trivial answer
-
-        # Check answer contains relevant content
-        answer_lower = data["generated_answer"].lower()
-        assert any(
-            keyword in answer_lower
-            for keyword in ["two-factor", "2fa", "authentication", "password"]
-        ), f"Answer doesn't seem relevant to query: {data['generated_answer'][:200]}"
-
-
-@pytest.mark.parametrize(
-    "qa_index,min_expected_results",
-    [
-        (0, 1),  # 2FA question
-        (1, 1),  # File quotas question
-        (2, 1),  # Linux installation question
-        (3, 1),  # Windows requirements question
-        (4, 1),  # Client apps with 2FA question
-    ],
-)
-async def test_retrieval_quality_all_queries(
-    nc_mcp_client, ground_truth_qa, qa_index, min_expected_results
-):
-    """Test retrieval quality for all ground truth queries.
-
-    Validates that each query returns at least the minimum expected
-    number of relevant results from the Nextcloud manual.
-    """
-    if qa_index >= len(ground_truth_qa):
-        pytest.skip(f"Ground truth index {qa_index} not available")
-
-    test_case = ground_truth_qa[qa_index]
-    query = test_case["query"]
-
-    result = await nc_mcp_client.call_tool(
-        "nc_semantic_search",
-        arguments={
-            "query": query,
-            "limit": 5,
-            "score_threshold": 0.0,
-        },
-    )
-
-    assert result.isError is False
-    data = result.structuredContent
-
-    assert data["total_found"] >= min_expected_results, (
-        f"Query '{query}' returned {data['total_found']} results, "
-        f"expected at least {min_expected_results}"
-    )
-
-
-async def test_no_results_for_unrelated_query(nc_mcp_client):
-    """Test that completely unrelated queries return low/no scores.
-
-    The Nextcloud manual shouldn't have relevant content for
-    quantum physics queries.
-    """
-    result = await nc_mcp_client.call_tool(
-        "nc_semantic_search",
-        arguments={
-            "query": "quantum entanglement hadron collider particle physics",
-            "limit": 5,
-            "score_threshold": 0.5,  # Higher threshold to filter irrelevant
-        },
-    )
-
-    assert result.isError is False
-    data = result.structuredContent
-
-    # Should have few or no high-scoring results
-    # Low score threshold means we might get some results, but they should be low quality
-    if data["total_found"] > 0:
-        # If results exist, they should have low scores
-        max_score = max(r["score"] for r in data["results"])
-        assert max_score < 0.8, f"Unexpected high score {max_score} for unrelated query"

tests/unit/client/test_webdav.py

@@ -117,3 +117,244 @@ def test_parse_search_response_with_empty_tags(mocker):
     assert len(results) == 1
     assert "tags" in results[0]
     assert results[0]["tags"] == []
+
+
+@pytest.mark.unit
+async def test_get_file_info_returns_file_details(mocker):
+    """Test that get_file_info returns file info including file ID."""
+    mock_http_client = AsyncMock()
+    client = WebDAVClient(mock_http_client, "testuser")
+
+    # Mock PROPFIND response
+    mock_response = AsyncMock()
+    mock_response.status_code = 207
+    mock_response.content = b"""<?xml version="1.0"?>
+    <d:multistatus xmlns:d="DAV:" xmlns:oc="http://owncloud.org/ns">
+        <d:response>
+            <d:href>/remote.php/dav/files/testuser/Documents/test.pdf</d:href>
+            <d:propstat>
+                <d:prop>
+                    <oc:fileid>12345</oc:fileid>
+                    <d:displayname>test.pdf</d:displayname>
+                    <d:getcontentlength>1024</d:getcontentlength>
+                    <d:getcontenttype>application/pdf</d:getcontenttype>
+                    <d:getlastmodified>Sat, 01 Jan 2025 00:00:00 GMT</d:getlastmodified>
+                    <d:getetag>"abc123"</d:getetag>
+                    <d:resourcetype/>
+                </d:prop>
+            </d:propstat>
+        </d:response>
+    </d:multistatus>"""
+    mock_response.raise_for_status = mocker.Mock()
+
+    mock_http_client.request = AsyncMock(return_value=mock_response)
+
+    # Call get_file_info
+    result = await client.get_file_info("Documents/test.pdf")
+
+    # Verify result
+    assert result is not None
+    assert result["id"] == 12345
+    assert result["name"] == "test.pdf"
+    assert result["path"] == "Documents/test.pdf"
+    assert result["content_type"] == "application/pdf"
+    assert result["size"] == 1024
+    assert result["etag"] == "abc123"
+    assert result["is_directory"] is False
+
+
+@pytest.mark.unit
+async def test_get_file_info_returns_none_for_missing_file(mocker):
+    """Test that get_file_info returns None for missing files."""
+    from httpx import HTTPStatusError, Response
+
+    mock_http_client = AsyncMock()
+    client = WebDAVClient(mock_http_client, "testuser")
+
+    # Mock 404 response
+    mock_response = mocker.Mock(spec=Response)
+    mock_response.status_code = 404
+    mock_http_client.request = AsyncMock(
+        side_effect=HTTPStatusError(
+            "Not Found", request=mocker.Mock(), response=mock_response
+        )
+    )
+
+    # Call get_file_info
+    result = await client.get_file_info("nonexistent.pdf")
+
+    # Verify result is None
+    assert result is None
+
+
+@pytest.mark.unit
+async def test_create_tag_creates_system_tag(mocker):
+    """Test that create_tag creates a system tag via OCS API."""
+    mock_http_client = AsyncMock()
+    client = WebDAVClient(mock_http_client, "testuser")
+
+    # Mock OCS response
+    mock_response = AsyncMock()
+    mock_response.status_code = 200
+    mock_response.json = mocker.Mock(
+        return_value={
+            "ocs": {
+                "data": {
+                    "id": 42,
+                    "name": "vector-index",
+                    "userVisible": True,
+                    "userAssignable": True,
+                }
+            }
+        }
+    )
+    mock_response.raise_for_status = mocker.Mock()
+
+    mock_http_client.post = AsyncMock(return_value=mock_response)
+
+    # Call create_tag
+    result = await client.create_tag("vector-index")
+
+    # Verify result
+    assert result["id"] == 42
+    assert result["name"] == "vector-index"
+    assert result["userVisible"] is True
+    assert result["userAssignable"] is True
+
+    # Verify API call
+    mock_http_client.post.assert_called_once()
+    call_args = mock_http_client.post.call_args
+    assert call_args[0][0] == "/ocs/v2.php/apps/systemtags/api/v1/tags"
+    assert call_args[1]["json"]["name"] == "vector-index"
+
+
+@pytest.mark.unit
+async def test_get_or_create_tag_returns_existing_tag(mocker):
+    """Test that get_or_create_tag returns existing tag without creating."""
+    mock_http_client = AsyncMock()
+    client = WebDAVClient(mock_http_client, "testuser")
+
+    # Mock existing tag
+    mocker.patch.object(
+        client,
+        "get_tag_by_name",
+        return_value={"id": 42, "name": "vector-index", "userVisible": True},
+    )
+    mock_create = mocker.patch.object(client, "create_tag")
+
+    # Call get_or_create_tag
+    result = await client.get_or_create_tag("vector-index")
+
+    # Verify existing tag returned without creating
+    assert result["id"] == 42
+    mock_create.assert_not_called()
+
+
+@pytest.mark.unit
+async def test_get_or_create_tag_creates_new_tag(mocker):
+    """Test that get_or_create_tag creates tag when not found."""
+    mock_http_client = AsyncMock()
+    client = WebDAVClient(mock_http_client, "testuser")
+
+    # Mock no existing tag
+    mocker.patch.object(client, "get_tag_by_name", return_value=None)
+    mocker.patch.object(
+        client,
+        "create_tag",
+        return_value={"id": 42, "name": "vector-index", "userVisible": True},
+    )
+
+    # Call get_or_create_tag
+    result = await client.get_or_create_tag("vector-index")
+
+    # Verify tag was created
+    assert result["id"] == 42
+    client.create_tag.assert_called_once_with("vector-index", True, True)
+
+
+@pytest.mark.unit
+async def test_assign_tag_to_file_success(mocker):
+    """Test that assign_tag_to_file assigns tag via WebDAV."""
+    mock_http_client = AsyncMock()
+    client = WebDAVClient(mock_http_client, "testuser")
+
+    # Mock 201 Created response
+    mock_response = AsyncMock()
+    mock_response.status_code = 201
+
+    mock_http_client.request = AsyncMock(return_value=mock_response)
+
+    # Call assign_tag_to_file
+    result = await client.assign_tag_to_file(12345, 42)
+
+    # Verify result
+    assert result is True
+
+    # Verify API call
+    mock_http_client.request.assert_called_once()
+    call_args = mock_http_client.request.call_args
+    assert call_args[0][0] == "PUT"
+    assert "/systemtags-relations/files/12345/42" in call_args[0][1]
+
+
+@pytest.mark.unit
+async def test_assign_tag_to_file_already_assigned(mocker):
+    """Test that assign_tag_to_file handles already assigned (409) gracefully."""
+    mock_http_client = AsyncMock()
+    client = WebDAVClient(mock_http_client, "testuser")
+
+    # Mock 409 Conflict response (already assigned)
+    mock_response = AsyncMock()
+    mock_response.status_code = 409
+
+    mock_http_client.request = AsyncMock(return_value=mock_response)
+
+    # Call assign_tag_to_file
+    result = await client.assign_tag_to_file(12345, 42)
+
+    # Verify result (should succeed even with 409)
+    assert result is True
+
+
+@pytest.mark.unit
+async def test_remove_tag_from_file_success(mocker):
+    """Test that remove_tag_from_file removes tag via WebDAV."""
+    mock_http_client = AsyncMock()
+    client = WebDAVClient(mock_http_client, "testuser")
+
+    # Mock 204 No Content response
+    mock_response = AsyncMock()
+    mock_response.status_code = 204
+
+    mock_http_client.request = AsyncMock(return_value=mock_response)
+
+    # Call remove_tag_from_file
+    result = await client.remove_tag_from_file(12345, 42)
+
+    # Verify result
+    assert result is True
+
+    # Verify API call
+    mock_http_client.request.assert_called_once()
+    call_args = mock_http_client.request.call_args
+    assert call_args[0][0] == "DELETE"
+    assert "/systemtags-relations/files/12345/42" in call_args[0][1]
+
+
+@pytest.mark.unit
+async def test_remove_tag_from_file_not_assigned(mocker):
+    """Test that remove_tag_from_file handles not assigned (404) gracefully."""
+    mock_http_client = AsyncMock()
+    client = WebDAVClient(mock_http_client, "testuser")
+
+    # Mock 404 Not Found response (tag wasn't assigned)
+    mock_response = AsyncMock()
+    mock_response.status_code = 404
+
+    mock_http_client.request = AsyncMock(return_value=mock_response)
+
+    # Call remove_tag_from_file
+    result = await client.remove_tag_from_file(12345, 42)
+
+    # Verify result (should succeed even with 404)
+    assert result is True

uv.lock

@@ -1936,7 +1936,7 @@ wheels = [
 
 [[package]]
 name = "nextcloud-mcp-server"
-version = "0.47.0"
+version = "0.48.4"
 source = { editable = "." }
 dependencies = [
     { name = "aiosqlite" },