Use the shared stuff with LLM endpoint

docker/rootfs/etc/s6-overlay/s6-rc.d/init-migrations/run

@@ -10,10 +10,12 @@ cd "${PAPERLESS_SRC_DIR}"
 
 # The whole migrate, with flock, needs to run as the right user
 if [[ -n "${USER_IS_NON_ROOT}" ]]; then
-	python3 manage.py check --tag compatibility paperless || exit 1
+	exec s6-setlock -n "${data_dir}/migration_lock" python3 manage.py check --tag compatibility paperless
 	exec s6-setlock -n "${data_dir}/migration_lock" python3 manage.py migrate --skip-checks --no-input
 else
-	s6-setuidgid paperless python3 manage.py check --tag compatibility paperless  || exit 1
+	exec s6-setuidgid paperless \
+		s6-setlock -n "${data_dir}/migration_lock" \
+		python3 manage.py check --tag compatibility paperless
 	exec s6-setuidgid paperless \
 		s6-setlock -n "${data_dir}/migration_lock" \
 		python3 manage.py migrate --skip-checks --no-input

docs/configuration.md

@@ -1947,6 +1947,12 @@ current backend. If not supplied, defaults to "gpt-3.5-turbo" for OpenAI and "ll
 
     Defaults to None.
 
+#### [`PAPERLESS_AI_LLM_ALLOW_INTERNAL_ENDPOINTS=<bool>`](#PAPERLESS_AI_LLM_ALLOW_INTERNAL_ENDPOINTS) {#PAPERLESS_AI_LLM_ALLOW_INTERNAL_ENDPOINTS}
+
+: If set to false, Paperless blocks AI endpoint URLs that resolve to non-public addresses (e.g., localhost, etc).
+
+    Defaults to true, which allows internal endpoints.
+
 #### [`PAPERLESS_AI_LLM_INDEX_TASK_CRON=<cron expression>`](#PAPERLESS_AI_LLM_INDEX_TASK_CRON) {#PAPERLESS_AI_LLM_INDEX_TASK_CRON}
 
 : Configures the schedule to update the AI embeddings of text content and metadata for all documents. Only performed if

pyproject.toml

@@ -42,10 +42,10 @@ dependencies = [
   "djangorestframework~=3.16",
   "djangorestframework-guardian~=0.4.0",
   "drf-spectacular~=0.28",
-  "drf-spectacular-sidecar~=2026.3.1",
+  "drf-spectacular-sidecar~=2026.1.1",
   "drf-writable-nested~=0.7.1",
   "faiss-cpu>=1.10",
-  "filelock~=3.25.2",
+  "filelock~=3.20.3",
   "flower~=2.0.1",
   "gotenberg-client~=0.13.1",
   "httpx-oauth~=0.16",
@@ -72,7 +72,7 @@ dependencies = [
   "rapidfuzz~=3.14.0",
   "redis[hiredis]~=5.2.1",
   "regex>=2025.9.18",
-  "scikit-learn~=1.8.0",
+  "scikit-learn~=1.7.0",
   "sentence-transformers>=4.1",
   "setproctitle~=1.3.4",
   "tika-client~=0.10.0",
@@ -111,7 +111,7 @@ docs = [
 testing = [
   "daphne",
   "factory-boy~=3.3.1",
-  "faker~=40.8.0",
+  "faker~=40.5.1",
   "imagehash",
   "pytest~=9.0.0",
   "pytest-cov~=7.0.0",

src/documents/classifier.py

@@ -9,7 +9,6 @@ from pathlib import Path
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from collections.abc import Callable
     from collections.abc import Iterator
     from datetime import datetime
 
@@ -192,12 +191,7 @@ class DocumentClassifier:
 
         target_file_temp.rename(target_file)
 
-    def train(
-        self,
-        status_callback: Callable[[str], None] | None = None,
-    ) -> bool:
-        notify = status_callback if status_callback is not None else lambda _: None
-
+    def train(self) -> bool:
         # Get non-inbox documents
         docs_queryset = (
             Document.objects.exclude(
@@ -219,7 +213,6 @@ class DocumentClassifier:
 
         # Step 1: Extract and preprocess training data from the database.
         logger.debug("Gathering data from database...")
-        notify(f"Gathering data from {docs_queryset.count()} document(s)...")
         hasher = sha256()
         for doc in docs_queryset:
             y = -1
@@ -297,7 +290,6 @@ class DocumentClassifier:
 
         # Step 2: vectorize data
         logger.debug("Vectorizing data...")
-        notify("Vectorizing document content...")
 
         def content_generator() -> Iterator[str]:
             """
@@ -324,7 +316,6 @@ class DocumentClassifier:
         # Step 3: train the classifiers
         if num_tags > 0:
             logger.debug("Training tags classifier...")
-            notify(f"Training tags classifier ({num_tags} tag(s))...")
 
             if num_tags == 1:
                 # Special case where only one tag has auto:
@@ -348,9 +339,6 @@ class DocumentClassifier:
 
         if num_correspondents > 0:
             logger.debug("Training correspondent classifier...")
-            notify(
-                f"Training correspondent classifier ({num_correspondents} correspondent(s))...",
-            )
             self.correspondent_classifier = MLPClassifier(tol=0.01)
             self.correspondent_classifier.fit(data_vectorized, labels_correspondent)
         else:
@@ -361,9 +349,6 @@ class DocumentClassifier:
 
         if num_document_types > 0:
             logger.debug("Training document type classifier...")
-            notify(
-                f"Training document type classifier ({num_document_types} type(s))...",
-            )
             self.document_type_classifier = MLPClassifier(tol=0.01)
             self.document_type_classifier.fit(data_vectorized, labels_document_type)
         else:
@@ -376,7 +361,6 @@ class DocumentClassifier:
             logger.debug(
                 "Training storage paths classifier...",
             )
-            notify(f"Training storage path classifier ({num_storage_paths} path(s))...")
             self.storage_path_classifier = MLPClassifier(tol=0.01)
             self.storage_path_classifier.fit(
                 data_vectorized,

src/documents/management/commands/document_create_classifier.py

@@ -1,29 +1,13 @@
-from __future__ import annotations
+from django.core.management.base import BaseCommand
 
-import time
-
-from documents.management.commands.base import PaperlessCommand
 from documents.tasks import train_classifier
 
 
-class Command(PaperlessCommand):
+class Command(BaseCommand):
     help = (
         "Trains the classifier on your data and saves the resulting models to a "
         "file. The document consumer will then automatically use this new model."
     )
-    supports_progress_bar = False
-    supports_multiprocessing = False
 
-    def handle(self, *args, **options) -> None:
-        start = time.monotonic()
-
-        with self.buffered_logging("paperless.tasks"):
-            train_classifier(
-                scheduled=False,
-                status_callback=lambda msg: self.console.print(f"  {msg}"),
-            )
-
-        elapsed = time.monotonic() - start
-        self.console.print(
-            f"[green]✓[/green] Classifier training complete ({elapsed:.1f}s)",
-        )
+    def handle(self, *args, **options):
+        train_classifier(scheduled=False)

src/documents/tasks.py

@@ -100,11 +100,7 @@ def index_reindex(*, iter_wrapper: IterWrapper[Document] = _identity) -> None:
 
 
 @shared_task
-def train_classifier(
-    *,
-    scheduled=True,
-    status_callback: Callable[[str], None] | None = None,
-) -> None:
+def train_classifier(*, scheduled=True) -> None:
     task = PaperlessTask.objects.create(
         type=PaperlessTask.TaskType.SCHEDULED_TASK
         if scheduled
@@ -140,7 +136,7 @@ def train_classifier(
         classifier = DocumentClassifier()
 
     try:
-        if classifier.train(status_callback=status_callback):
+        if classifier.train():
             logger.info(
                 f"Saving updated classifier model to {settings.MODEL_FILE}...",
             )

src/documents/tests/test_api_app_config.py

@@ -5,6 +5,7 @@ from unittest.mock import patch
 
 from django.contrib.auth.models import User
 from django.core.files.uploadedfile import SimpleUploadedFile
+from django.test import override_settings
 from rest_framework import status
 from rest_framework.test import APITestCase
 
@@ -693,3 +694,17 @@ class TestApiAppConfig(DirectoriesMixin, APITestCase):
                 content_type="application/json",
             )
             mock_update.assert_called_once()
+
+    @override_settings(LLM_ALLOW_INTERNAL_ENDPOINTS=False)
+    def test_update_llm_endpoint_blocks_internal_endpoint_when_disallowed(self) -> None:
+        response = self.client.patch(
+            f"{self.ENDPOINT}1/",
+            json.dumps(
+                {
+                    "llm_endpoint": "http://127.0.0.1:11434",
+                },
+            ),
+            content_type="application/json",
+        )
+        self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
+        self.assertIn("non-public address", str(response.data).lower())

src/documents/tests/test_management.py

@@ -1,10 +1,7 @@
-from __future__ import annotations
-
 import filecmp
 import shutil
 from io import StringIO
 from pathlib import Path
-from typing import TYPE_CHECKING
 from unittest import mock
 
 import pytest
@@ -14,9 +11,6 @@ from django.core.management import call_command
 from django.test import TestCase
 from django.test import override_settings
 
-if TYPE_CHECKING:
-    from pytest_mock import MockerFixture
-
 from documents.file_handling import generate_filename
 from documents.models import Document
 from documents.tasks import update_document_content_maybe_archive_file
@@ -141,32 +135,14 @@ class TestRenamer(DirectoriesMixin, FileSystemAssertsMixin, TestCase):
 
 
 @pytest.mark.management
-class TestCreateClassifier:
-    def test_create_classifier(self, mocker: MockerFixture) -> None:
-        m = mocker.patch(
-            "documents.management.commands.document_create_classifier.train_classifier",
-        )
+class TestCreateClassifier(TestCase):
+    @mock.patch(
+        "documents.management.commands.document_create_classifier.train_classifier",
+    )
+    def test_create_classifier(self, m) -> None:
+        call_command("document_create_classifier")
 
-        call_command("document_create_classifier", "--skip-checks")
-
-        m.assert_called_once_with(scheduled=False, status_callback=mocker.ANY)
-        assert callable(m.call_args.kwargs["status_callback"])
-
-    def test_create_classifier_callback_output(self, mocker: MockerFixture) -> None:
-        """Callback passed to train_classifier writes each phase message to the console."""
-        m = mocker.patch(
-            "documents.management.commands.document_create_classifier.train_classifier",
-        )
-
-        def invoke_callback(**kwargs):
-            kwargs["status_callback"]("Vectorizing document content...")
-
-        m.side_effect = invoke_callback
-
-        stdout = StringIO()
-        call_command("document_create_classifier", "--skip-checks", stdout=stdout)
-
-        assert "Vectorizing document content..." in stdout.getvalue()
+        m.assert_called_once()
 
 
 @pytest.mark.management

src/documents/workflows/webhooks.py

@@ -1,12 +1,14 @@
-import ipaddress
 import logging
-import socket
-from urllib.parse import urlparse
 
 import httpx
 from celery import shared_task
 from django.conf import settings
 
+from paperless.network import format_host_for_url
+from paperless.network import is_public_ip
+from paperless.network import resolve_hostname_ips
+from paperless.network import validate_outbound_http_url
+
 logger = logging.getLogger("paperless.workflows.webhooks")
 
 
@@ -34,23 +36,19 @@ class WebhookTransport(httpx.HTTPTransport):
             raise httpx.ConnectError("No hostname in request URL")
 
         try:
-            addr_info = socket.getaddrinfo(hostname, None)
-        except socket.gaierror as e:
-            raise httpx.ConnectError(f"Could not resolve hostname: {hostname}") from e
-
-        ips = [info[4][0] for info in addr_info if info and info[4]]
-        if not ips:
-            raise httpx.ConnectError(f"Could not resolve hostname: {hostname}")
+            ips = resolve_hostname_ips(hostname)
+        except ValueError as e:
+            raise httpx.ConnectError(str(e)) from e
 
         if not self.allow_internal:
             for ip_str in ips:
-                if not WebhookTransport.is_public_ip(ip_str):
+                if not is_public_ip(ip_str):
                     raise httpx.ConnectError(
                         f"Connection blocked: {hostname} resolves to a non-public address",
                     )
 
         ip_str = ips[0]
-        formatted_ip = self._format_ip_for_url(ip_str)
+        formatted_ip = format_host_for_url(ip_str)
 
         new_headers = httpx.Headers(request.headers)
         if "host" in new_headers:
@@ -69,40 +67,6 @@ class WebhookTransport(httpx.HTTPTransport):
 
         return super().handle_request(request)
 
-    def _format_ip_for_url(self, ip: str) -> str:
-        """
-        Format IP address for use in URL (wrap IPv6 in brackets)
-        """
-        try:
-            ip_obj = ipaddress.ip_address(ip)
-            if ip_obj.version == 6:
-                return f"[{ip}]"
-            return ip
-        except ValueError:
-            return ip
-
-    @staticmethod
-    def is_public_ip(ip: str | int) -> bool:
-        try:
-            obj = ipaddress.ip_address(ip)
-            return not (
-                obj.is_private
-                or obj.is_loopback
-                or obj.is_link_local
-                or obj.is_multicast
-                or obj.is_unspecified
-            )
-        except ValueError:  # pragma: no cover
-            return False
-
-    @staticmethod
-    def resolve_first_ip(host: str) -> str | None:
-        try:
-            info = socket.getaddrinfo(host, None)
-            return info[0][4][0] if info else None
-        except Exception:  # pragma: no cover
-            return None
-
 
 @shared_task(
     retry_backoff=True,
@@ -118,21 +82,24 @@ def send_webhook(
     *,
     as_json: bool = False,
 ):
-    p = urlparse(url)
-    if p.scheme.lower() not in settings.WEBHOOKS_ALLOWED_SCHEMES or not p.hostname:
-        logger.warning("Webhook blocked: invalid scheme/hostname")
+    try:
+        parsed = validate_outbound_http_url(
+            url,
+            allowed_schemes=settings.WEBHOOKS_ALLOWED_SCHEMES,
+            allowed_ports=settings.WEBHOOKS_ALLOWED_PORTS,
+            # Internal-address checks happen in transport to preserve ConnectError behavior.
+            allow_internal=True,
+        )
+    except ValueError as e:
+        logger.warning("Webhook blocked: %s", e)
+        raise
+
+    hostname = parsed.hostname
+    if hostname is None:  # pragma: no cover
         raise ValueError("Invalid URL scheme or hostname.")
 
-    port = p.port or (443 if p.scheme == "https" else 80)
-    if (
-        len(settings.WEBHOOKS_ALLOWED_PORTS) > 0
-        and port not in settings.WEBHOOKS_ALLOWED_PORTS
-    ):
-        logger.warning("Webhook blocked: port not permitted")
-        raise ValueError("Destination port not permitted.")
-
     transport = WebhookTransport(
-        hostname=p.hostname,
+        hostname=hostname,
         allow_internal=settings.WEBHOOKS_ALLOW_INTERNAL_REQUESTS,
     )
 

src/paperless/config.py

@@ -188,6 +188,7 @@ class AIConfig(BaseConfig):
     llm_model: str = dataclasses.field(init=False)
     llm_api_key: str = dataclasses.field(init=False)
     llm_endpoint: str = dataclasses.field(init=False)
+    llm_allow_internal_endpoints: bool = dataclasses.field(init=False)
 
     def __post_init__(self) -> None:
         app_config = self._get_config_instance()
@@ -203,6 +204,7 @@ class AIConfig(BaseConfig):
         self.llm_model = app_config.llm_model or settings.LLM_MODEL
         self.llm_api_key = app_config.llm_api_key or settings.LLM_API_KEY
         self.llm_endpoint = app_config.llm_endpoint or settings.LLM_ENDPOINT
+        self.llm_allow_internal_endpoints = settings.LLM_ALLOW_INTERNAL_ENDPOINTS
 
     @property
     def llm_index_enabled(self) -> bool:

src/paperless/network.py

@@ -0,0 +1,76 @@
+import ipaddress
+import socket
+from collections.abc import Collection
+from urllib.parse import ParseResult
+from urllib.parse import urlparse
+
+
+def is_public_ip(ip: str | int) -> bool:
+    try:
+        obj = ipaddress.ip_address(ip)
+        return not (
+            obj.is_private
+            or obj.is_loopback
+            or obj.is_link_local
+            or obj.is_multicast
+            or obj.is_unspecified
+        )
+    except ValueError:  # pragma: no cover
+        return False
+
+
+def resolve_hostname_ips(hostname: str) -> list[str]:
+    try:
+        addr_info = socket.getaddrinfo(hostname, None)
+    except socket.gaierror as e:
+        raise ValueError(f"Could not resolve hostname: {hostname}") from e
+
+    ips = [info[4][0] for info in addr_info if info and info[4]]
+    if not ips:
+        raise ValueError(f"Could not resolve hostname: {hostname}")
+    return ips
+
+
+def format_host_for_url(host: str) -> str:
+    """
+    Format IP address for URL use (wrap IPv6 in brackets).
+    """
+    try:
+        ip_obj = ipaddress.ip_address(host)
+        if ip_obj.version == 6:
+            return f"[{host}]"
+        return host
+    except ValueError:
+        return host
+
+
+def validate_outbound_http_url(
+    url: str,
+    *,
+    allowed_schemes: Collection[str] = ("http", "https"),
+    allowed_ports: Collection[int] | None = None,
+    allow_internal: bool = False,
+) -> ParseResult:
+    parsed = urlparse(url)
+    scheme = parsed.scheme.lower()
+
+    if scheme not in allowed_schemes or not parsed.hostname:
+        raise ValueError("Invalid URL scheme or hostname.")
+
+    default_port = 443 if scheme == "https" else 80
+    try:
+        port = parsed.port or default_port
+    except ValueError as e:
+        raise ValueError("Invalid URL scheme or hostname.") from e
+
+    if allowed_ports and port not in allowed_ports:
+        raise ValueError("Destination port not permitted.")
+
+    if not allow_internal:
+        for ip_str in resolve_hostname_ips(parsed.hostname):
+            if not is_public_ip(ip_str):
+                raise ValueError(
+                    f"Connection blocked: {parsed.hostname} resolves to a non-public address",
+                )
+
+    return parsed

src/paperless/serialisers.py

@@ -6,6 +6,7 @@ from allauth.mfa.models import Authenticator
 from allauth.mfa.totp.internal.auth import TOTP
 from allauth.socialaccount.models import SocialAccount
 from allauth.socialaccount.models import SocialApp
+from django.conf import settings
 from django.contrib.auth.models import Group
 from django.contrib.auth.models import Permission
 from django.contrib.auth.models import User
@@ -15,6 +16,7 @@ from rest_framework import serializers
 from rest_framework.authtoken.serializers import AuthTokenSerializer
 
 from paperless.models import ApplicationConfiguration
+from paperless.network import validate_outbound_http_url
 from paperless.validators import reject_dangerous_svg
 from paperless_mail.serialisers import ObfuscatedPasswordField
 
@@ -236,6 +238,20 @@ class ApplicationConfigurationSerializer(serializers.ModelSerializer):
             reject_dangerous_svg(file)
         return file
 
+    def validate_llm_endpoint(self, value: str | None) -> str | None:
+        if not value:
+            return value
+
+        try:
+            validate_outbound_http_url(
+                value,
+                allow_internal=settings.LLM_ALLOW_INTERNAL_ENDPOINTS,
+            )
+        except ValueError as e:
+            raise serializers.ValidationError(str(e)) from e
+
+        return value
+
     class Meta:
         model = ApplicationConfiguration
         fields = "__all__"

src/paperless/settings/__init__.py

@@ -1112,3 +1112,7 @@ LLM_BACKEND = os.getenv("PAPERLESS_AI_LLM_BACKEND")  # "ollama" or "openai"
 LLM_MODEL = os.getenv("PAPERLESS_AI_LLM_MODEL")
 LLM_API_KEY = os.getenv("PAPERLESS_AI_LLM_API_KEY")
 LLM_ENDPOINT = os.getenv("PAPERLESS_AI_LLM_ENDPOINT")
+LLM_ALLOW_INTERNAL_ENDPOINTS = get_bool_from_env(
+    "PAPERLESS_AI_LLM_ALLOW_INTERNAL_ENDPOINTS",
+    "true",
+)

src/paperless_ai/client.py

@@ -7,6 +7,7 @@ if TYPE_CHECKING:
     from llama_index.llms.openai import OpenAI
 
 from paperless.config import AIConfig
+from paperless.network import validate_outbound_http_url
 from paperless_ai.base_model import DocumentClassifierSchema
 
 logger = logging.getLogger("paperless_ai.client")
@@ -25,17 +26,28 @@ class AIClient:
         if self.settings.llm_backend == "ollama":
             from llama_index.llms.ollama import Ollama
 
+            endpoint = self.settings.llm_endpoint or "http://localhost:11434"
+            validate_outbound_http_url(
+                endpoint,
+                allow_internal=self.settings.llm_allow_internal_endpoints,
+            )
             return Ollama(
                 model=self.settings.llm_model or "llama3.1",
-                base_url=self.settings.llm_endpoint or "http://localhost:11434",
+                base_url=endpoint,
                 request_timeout=120,
             )
         elif self.settings.llm_backend == "openai":
             from llama_index.llms.openai import OpenAI
 
+            endpoint = self.settings.llm_endpoint or None
+            if endpoint:
+                validate_outbound_http_url(
+                    endpoint,
+                    allow_internal=self.settings.llm_allow_internal_endpoints,
+                )
             return OpenAI(
                 model=self.settings.llm_model or "gpt-3.5-turbo",
-                api_base=self.settings.llm_endpoint or None,
+                api_base=endpoint,
                 api_key=self.settings.llm_api_key,
             )
         else:

src/paperless_ai/embedding.py

@@ -12,6 +12,7 @@ from documents.models import Document
 from documents.models import Note
 from paperless.config import AIConfig
 from paperless.models import LLMEmbeddingBackend
+from paperless.network import validate_outbound_http_url
 
 
 def get_embedding_model() -> "BaseEmbedding":
@@ -21,10 +22,16 @@ def get_embedding_model() -> "BaseEmbedding":
         case LLMEmbeddingBackend.OPENAI:
             from llama_index.embeddings.openai import OpenAIEmbedding
 
+            endpoint = config.llm_endpoint or None
+            if endpoint:
+                validate_outbound_http_url(
+                    endpoint,
+                    allow_internal=config.llm_allow_internal_endpoints,
+                )
             return OpenAIEmbedding(
                 model=config.llm_embedding_model or "text-embedding-3-small",
                 api_key=config.llm_api_key,
-                api_base=config.llm_endpoint or None,
+                api_base=endpoint,
             )
         case LLMEmbeddingBackend.HUGGINGFACE:
             from llama_index.embeddings.huggingface import HuggingFaceEmbedding

src/paperless_ai/tests/test_client.py

@@ -12,6 +12,7 @@ from paperless_ai.client import AIClient
 def mock_ai_config():
     with patch("paperless_ai.client.AIConfig") as MockAIConfig:
         mock_config = MagicMock()
+        mock_config.llm_allow_internal_endpoints = True
         MockAIConfig.return_value = mock_config
         yield mock_config
 
@@ -59,6 +60,17 @@ def test_get_llm_openai(mock_ai_config, mock_openai_llm):
     assert client.llm == mock_openai_llm.return_value
 
 
+def test_get_llm_openai_blocks_internal_endpoint_when_disallowed(mock_ai_config):
+    mock_ai_config.llm_backend = "openai"
+    mock_ai_config.llm_model = "test_model"
+    mock_ai_config.llm_api_key = "test_api_key"
+    mock_ai_config.llm_endpoint = "http://127.0.0.1:1234"
+    mock_ai_config.llm_allow_internal_endpoints = False
+
+    with pytest.raises(ValueError, match="non-public address"):
+        AIClient()
+
+
 def test_get_llm_unsupported_backend(mock_ai_config):
     mock_ai_config.llm_backend = "unsupported"
 

src/paperless_ai/tests/test_embedding.py

@@ -15,6 +15,7 @@ from paperless_ai.embedding import get_embedding_model
 @pytest.fixture
 def mock_ai_config():
     with patch("paperless_ai.embedding.AIConfig") as MockAIConfig:
+        MockAIConfig.return_value.llm_allow_internal_endpoints = True
         yield MockAIConfig
 
 
@@ -77,6 +78,19 @@ def test_get_embedding_model_openai(mock_ai_config):
         assert model == MockOpenAIEmbedding.return_value
 
 
+def test_get_embedding_model_openai_blocks_internal_endpoint_when_disallowed(
+    mock_ai_config,
+):
+    mock_ai_config.return_value.llm_embedding_backend = LLMEmbeddingBackend.OPENAI
+    mock_ai_config.return_value.llm_embedding_model = "text-embedding-3-small"
+    mock_ai_config.return_value.llm_api_key = "test_api_key"
+    mock_ai_config.return_value.llm_endpoint = "http://127.0.0.1:11434"
+    mock_ai_config.return_value.llm_allow_internal_endpoints = False
+
+    with pytest.raises(ValueError, match="non-public address"):
+        get_embedding_model()
+
+
 def test_get_embedding_model_huggingface(mock_ai_config):
     mock_ai_config.return_value.llm_embedding_backend = LLMEmbeddingBackend.HUGGINGFACE
     mock_ai_config.return_value.llm_embedding_model = (

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     { name = "drf-spectacular", specifier = "~=0.28" },
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     { name = "regex", specifier = ">=2025.9.18" },
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@@ -3011,7 +3011,7 @@ provides-extras = ["mariadb", "postgres", "webserver"]
 dev = [
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     { name = "factory-boy", specifier = "~=3.3.1" },
-    { name = "faker", specifier = "~=40.8.0" },
+    { name = "faker", specifier = "~=40.5.1" },
     { name = "imagehash" },
     { name = "prek", specifier = "~=0.3.0" },
     { name = "pytest", specifier = "~=9.0.0" },
@@ -3034,7 +3034,7 @@ lint = [
 testing = [
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     { name = "factory-boy", specifier = "~=3.3.1" },
-    { name = "faker", specifier = "~=40.8.0" },
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