Few more before

Even more vulns
Fix (beta): Stream chunks during compaction to prevent oom on smaller installs (#13014 )
2026-06-28 16:24:19 +00:00 · 2026-06-22 07:27:48 -07:00 · 2026-06-19 10:05:41 -07:00 · 2026-06-19 15:40:16 +00:00 · 2026-06-18 16:20:31 -07:00 · 2026-06-18 19:54:15 +00:00
54 changed files with 1203 additions and 373 deletions
@@ -30,7 +30,7 @@
 # documentation.
 services:
  broker:
-    image: docker.io/library/redis:8
+    image: docker.io/valkey/valkey:9-alpine
    restart: unless-stopped
    volumes:
      - redisdata:/data
@@ -26,7 +26,7 @@
 # documentation.
 services:
  broker:
-    image: docker.io/library/redis:8
+    image: docker.io/valkey/valkey:9-alpine
    restart: unless-stopped
    volumes:
      - redisdata:/data
@@ -27,7 +27,7 @@
 # documentation.
 services:
  broker:
-    image: docker.io/library/redis:8
+    image: docker.io/valkey/valkey:9-alpine
    restart: unless-stopped
    volumes:
      - redisdata:/data
@@ -30,7 +30,7 @@
 # documentation.
 services:
  broker:
-    image: docker.io/library/redis:8
+    image: docker.io/valkey/valkey:9-alpine
    restart: unless-stopped
    volumes:
      - redisdata:/data
@@ -26,7 +26,7 @@
 # documentation.
 services:
  broker:
-    image: docker.io/library/redis:8
+    image: docker.io/valkey/valkey:9-alpine
    restart: unless-stopped
    volumes:
      - redisdata:/data
@@ -30,7 +30,7 @@
 # documentation.
 services:
  broker:
-    image: docker.io/library/redis:8
+    image: docker.io/valkey/valkey:9-alpine
    restart: unless-stopped
    volumes:
      - redisdata:/data
@@ -23,7 +23,7 @@
 # documentation.
 services:
  broker:
-    image: docker.io/library/redis:8
+    image: docker.io/valkey/valkey:9-alpine
    restart: unless-stopped
    volumes:
      - redisdata:/data
@@ -65,6 +65,11 @@ copies you created in the steps above.

    Please review the [migration instructions](migration-v3.md) before upgrading Paperless-ngx to v3.0, it includes some breaking changes that require manual intervention before upgrading.

+!!! note
+
+    Upgrading to v3 clears the existing task history; previously completed, failed, or
+    acknowledged tasks will no longer appear in the task list afterward. No action is required.
+
 ### Docker Route {#docker-updating}

 If a new release of paperless-ngx is available, upgrading depends on how
@@ -500,6 +505,33 @@ task scheduler.
    python3 manage.py document_index reindex --if-needed
    ```

+### Managing the LLM (AI) index {#llm-index}
+
+When the [AI features](advanced_usage.md#ai-features) are enabled with an embedding
+backend, Paperless-ngx maintains a vector index of your documents used for
+Retrieval-Augmented Generation (RAG), similar-document retrieval, and document chat. The
+index is updated automatically on the schedule set by
+[`PAPERLESS_LLM_INDEX_TASK_CRON`](configuration.md#PAPERLESS_LLM_INDEX_TASK_CRON), but you
+can manage it manually:
+
+```
+document_llmindex {rebuild,update,compact}
+```
+
+Specify `rebuild` to build the index from scratch from all documents in the database. Use
+this the first time you enable the feature, or after changing the embedding backend or
+model.
+
+Specify `update` to incrementally index new and changed documents. This is what the
+scheduled task runs.
+
+Specify `compact` to reclaim space and optimize the on-disk vector store.
+
+!!! note
+
+    These commands have no effect unless AI is enabled and an embedding backend is
+    configured.
+
 ### Clearing the database read cache

 If the database read cache is enabled, **you must run this command** after making any changes to the database outside the application context.
@@ -97,6 +97,85 @@ when using this feature:
  of these correspondents to ANY new document, if both are set to
  automatic matching.

+## AI features {#ai-features}
+
+Paperless-ngx includes a set of optional features backed by a large language model
+(LLM): AI-assisted suggestions, similar-document retrieval, and a document chat. They
+are **off by default** and never replace the built-in, non-LLM
+[matching and suggestions](#matching).
+
+!!! warning
+
+    Enabling these features sends document content (and metadata) to the LLM backend you
+    configure. If that backend is a remote/hosted provider, your documents leave your
+    server and may incur usage charges. Consider the privacy implications before enabling,
+    and prefer a local backend (Ollama, or a self-hosted OpenAI-compatible gateway) if that
+    matters to you.
+
+All AI settings can be supplied as `PAPERLESS_AI_*` environment variables (see
+[configuration](configuration.md#ai)) or set in the admin under
+**Settings → Application Configuration**; the database value takes precedence over the
+environment.
+
+### Enabling the AI features
+
+At a minimum you need to enable AI and choose an LLM backend:
+
+- [`PAPERLESS_AI_ENABLED`](configuration.md#PAPERLESS_AI_ENABLED) — master switch.
+- [`PAPERLESS_AI_LLM_BACKEND`](configuration.md#PAPERLESS_AI_LLM_BACKEND) — `ollama`
+  (runs locally) or `openai-like` (OpenAI itself or any OpenAI-compatible API).
+- [`PAPERLESS_AI_LLM_MODEL`](configuration.md#PAPERLESS_AI_LLM_MODEL), and for
+  `openai-like` usually [`PAPERLESS_AI_LLM_API_KEY`](configuration.md#PAPERLESS_AI_LLM_API_KEY)
+  and/or [`PAPERLESS_AI_LLM_ENDPOINT`](configuration.md#PAPERLESS_AI_LLM_ENDPOINT). Ollama
+  requires `PAPERLESS_AI_LLM_ENDPOINT` pointing at your Ollama server.
+
+### AI-assisted suggestions
+
+With AI enabled, Paperless-ngx can suggest a title, tags, correspondent, document type,
+storage path and dates by sending the document to the LLM. This is **opt-in per request**
+and surfaces through the "Suggest" control on the document detail page, alongside the
+classic classifier-based suggestions — it does not disable them. Suggestion output
+language can be steered with
+[`PAPERLESS_AI_LLM_OUTPUT_LANGUAGE`](configuration.md#PAPERLESS_AI_LLM_OUTPUT_LANGUAGE)
+(otherwise it follows the user's UI language).
+
+### The LLM index (RAG) and similar documents
+
+Setting an embedding backend turns on the **LLM index**, a vector index of your documents
+that enables Retrieval-Augmented Generation (RAG). When enabled, suggestions are grounded
+in similar existing documents, and the document chat can retrieve relevant context.
+
+Enable it by setting
+[`PAPERLESS_AI_LLM_EMBEDDING_BACKEND`](configuration.md#PAPERLESS_AI_LLM_EMBEDDING_BACKEND)
+(`huggingface` for fully-local embeddings, or `ollama` / `openai-like`). The index is only
+built when AI is enabled **and** an embedding backend is set.
+
+The index is updated automatically on a schedule controlled by
+[`PAPERLESS_LLM_INDEX_TASK_CRON`](configuration.md#PAPERLESS_LLM_INDEX_TASK_CRON) (daily by
+default), and can be rebuilt or compacted manually — see
+[Managing the LLM index](administration.md#llm-index).
+
+!!! note
+
+    Local embeddings via `huggingface` download the embedding model on first use into the
+    Paperless data directory. The first run therefore needs network access and some disk
+    space.
+
+### Document chat
+
+When the LLM index is enabled, the chat control in the top app toolbar answers questions
+about your documents. It operates over a single document or across multiple documents
+depending on the current view, and its answers include links to the source documents it
+drew from.
+
+### AI Security notes
+
+- Document content is passed to the LLM as **untrusted data**.
+- By default Paperless-ngx allows AI endpoints that resolve to private/loopback addresses
+  (for local backends). Set
+  [`PAPERLESS_AI_LLM_ALLOW_INTERNAL_ENDPOINTS`](configuration.md#PAPERLESS_AI_LLM_ALLOW_INTERNAL_ENDPOINTS)
+  to `false` to block them.
+
 ## Hooking into the consumption process {#consume-hooks}

 Sometimes you may want to do something arbitrary whenever a document is
@@ -846,7 +925,7 @@ Paperless is able to utilize barcodes for automatically performing some tasks. B

 At this time, the library utilized for detection of barcodes supports the following types:

- AN-13/UPC-A
+- EAN-13/UPC-A
 - UPC-E
 - EAN-8
 - Code 128
@@ -855,7 +934,9 @@ At this time, the library utilized for detection of barcodes supports the follow
 - Codabar
 - Interleaved 2 of 5
 - QR Code
- SQ Code
+- Data Matrix
+- Aztec
+- PDF417

 For usage in Paperless, the type of barcode does not matter, only the contents of it.

@@ -227,6 +227,7 @@ Version-aware endpoints:
 - `PATCH /api/documents/{id}/`: content updates target the selected version (`?version={version_id}`) or latest version by default; non-content metadata updates target the root document.
 - `GET /api/documents/{id}/download/`, `GET /api/documents/{id}/preview/`, `GET /api/documents/{id}/thumb/`, `GET /api/documents/{id}/metadata/`: accept `?version={version_id}`.
 - `POST /api/documents/{id}/update_version/`: uploads a new version using multipart form field `document` and optional `version_label`.
+- `PATCH /api/documents/{id}/versions/{version_id}/`: updates the `version_label` of a specific version.
 - `DELETE /api/documents/{root_id}/versions/{version_id}/`: deletes a non-root version.

 ## Permissions
@@ -445,3 +446,9 @@ Initial API version.
  large lists of object IDs for operations affecting many objects.
 - The legacy `title_content` document search parameter is deprecated and will be removed in a future version.
  Clients should use `text` for simple title-and-content search and `title_search` for title-only search.
+- The task tracking system was redesigned. The tasks list (`/api/tasks/`) is now paginated, and the
+  task object exposes `task_type` (formerly `task_name`) and `trigger_source` (formerly `type`). New
+  read-only endpoints `/api/tasks/summary/`, `/api/tasks/status_counts/`, and `/api/tasks/active/`
+  provide aggregate views, and `POST /api/tasks/run/` lets privileged users dispatch supported tasks.
+  API v9 continues to serve the unpaginated list with the legacy field names until support for v9 is
+  dropped.
@@ -22,7 +22,11 @@ or applicable default will be utilized instead.

 ## Required services

-### Redis Broker
+### Message Broker
+
+Paperless-ngx uses a Redis-compatible message broker. Any broker that
+speaks the Redis protocol works here, including [Valkey](https://valkey.io/)
+(the default in the bundled Docker Compose files) and Redis itself.

 #### [`PAPERLESS_REDIS=<url>`](#PAPERLESS_REDIS) {#PAPERLESS_REDIS}

@@ -30,21 +34,21 @@ or applicable default will be utilized instead.
 fetching, index optimization and for training the automatic document
 matcher.

-    -   If your Redis server needs login credentials PAPERLESS_REDIS =
+    -   If your broker needs login credentials PAPERLESS_REDIS =
        `redis://<username>:<password>@<host>:<port>`
    -   With the requirepass option PAPERLESS_REDIS =
        `redis://:<password>@<host>:<port>`
-    -   To include the redis database index PAPERLESS_REDIS =
+    -   To include the database index PAPERLESS_REDIS =
        `redis://<username>:<password>@<host>:<port>/<DBIndex>`

-    [More information on securing your Redis
-    Instance](https://redis.io/docs/latest/operate/oss_and_stack/management/security).
+    [More information on securing your broker
+    instance](https://valkey.io/topics/security/).

    Defaults to `redis://localhost:6379`.

 #### [`PAPERLESS_REDIS_PREFIX=<prefix>`](#PAPERLESS_REDIS_PREFIX) {#PAPERLESS_REDIS_PREFIX}

-: Prefix to be used in Redis for keys and channels. Useful for sharing one Redis server among multiple Paperless instances.
+: Prefix to be used in the broker for keys and channels. Useful for sharing one broker among multiple Paperless instances.

    Defaults to no prefix.

@@ -58,14 +62,14 @@ and the relevant connection variables.
 #### [`PAPERLESS_DBENGINE=<engine>`](#PAPERLESS_DBENGINE) {#PAPERLESS_DBENGINE}

 : Specifies the database engine to use. Accepted values are `sqlite`, `postgresql`,
-and `mariadb`.
-
-    Defaults to `sqlite` if not set.
+and `mariadb`. PostgreSQL and MariaDB users must set this explicitly.

    PostgreSQL and MariaDB both require [`PAPERLESS_DBHOST`](#PAPERLESS_DBHOST) to be
    set. SQLite does not use any other connection variables; the database file is always
    located at `<PAPERLESS_DATA_DIR>/db.sqlite3`.

+    Defaults to `sqlite`.
+
    !!! warning
        Using MariaDB comes with some caveats.
        See [MySQL Caveats](advanced_usage.md#mysql-caveats).
@@ -238,7 +242,7 @@ dictionaries; for example, `pool.max_size=20` sets

 #### [`PAPERLESS_DB_READ_CACHE_ENABLED=<bool>`](#PAPERLESS_DB_READ_CACHE_ENABLED) {#PAPERLESS_DB_READ_CACHE_ENABLED}

-: Caches the database read query results into Redis. This can significantly improve application response times by caching database queries, at the cost of slightly increased memory usage.
+: Caches the database read query results into the broker. This can significantly improve application response times by caching database queries, at the cost of slightly increased memory usage.

    Defaults to `false`.

@@ -258,18 +262,18 @@ dictionaries; for example, `pool.max_size=20` sets

        A high TTL increases memory usage over time. Memory may be used until end of TTL, even if the cache is invalidated with the `invalidate_cachalot` command.

-In case of an out-of-memory (OOM) situation, Redis may stop accepting new data — including cache entries, scheduled tasks, and documents to consume.
-If your system has limited RAM, consider configuring a dedicated Redis instance for the read cache, with a memory limit and the eviction policy set to `allkeys-lru`.
-For more details, refer to the [Redis eviction policy documentation](https://redis.io/docs/latest/develop/reference/eviction/), and see the `PAPERLESS_READ_CACHE_REDIS_URL` setting to specify a separate Redis broker.
+In case of an out-of-memory (OOM) situation, the broker may stop accepting new data — including cache entries, scheduled tasks, and documents to consume.
+If your system has limited RAM, consider configuring a dedicated broker instance for the read cache, with a memory limit and the eviction policy set to `allkeys-lru`.
+For more details, refer to the [Redis eviction policy documentation](https://redis.io/docs/latest/develop/reference/eviction/), and see the `PAPERLESS_READ_CACHE_REDIS_URL` setting to specify a separate broker.

 #### [`PAPERLESS_READ_CACHE_REDIS_URL=<url>`](#PAPERLESS_READ_CACHE_REDIS_URL) {#PAPERLESS_READ_CACHE_REDIS_URL}

-: Defines the Redis instance used for the read cache.
+: Defines the broker instance used for the read cache.

    Defaults to `None`.

    !!! Note
-    If this value is not set, the same Redis instance used for scheduled tasks will be used for caching as well.
+    If this value is not set, the same broker instance used for scheduled tasks will be used for caching as well.

 ## Optional Services

@@ -888,7 +892,7 @@ modes are available:

    The default is `auto`.

-    For the `skip`, `redo`, and `force` modes, read more about OCR
+    For the `redo` and `force` modes, read more about OCR
    behaviour in the [OCRmyPDF
    documentation](https://ocrmypdf.readthedocs.io/en/latest/advanced.html#when-ocr-is-skipped).

@@ -2068,6 +2072,13 @@ context by default.

    Defaults to 8192.

+#### [`PAPERLESS_AI_LLM_REQUEST_TIMEOUT=<int>`](#PAPERLESS_AI_LLM_REQUEST_TIMEOUT) {#PAPERLESS_AI_LLM_REQUEST_TIMEOUT}
+
+: The timeout, in seconds, for requests to the configured AI backend. Increase this when using
+local or slow inference servers that need more time to generate responses.
+
+    Defaults to 120.
+
 #### [`PAPERLESS_AI_LLM_BACKEND=<str>`](#PAPERLESS_AI_LLM_BACKEND) {#PAPERLESS_AI_LLM_BACKEND}

 : The AI backend to use. This can be either "openai-like" or "ollama". If set to "ollama", the AI
@@ -2120,7 +2131,7 @@ used with the OpenAI-compatible backend to target a custom provider or local gat

    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}
+#### [`PAPERLESS_LLM_INDEX_TASK_CRON=<cron expression>`](#PAPERLESS_LLM_INDEX_TASK_CRON) {#PAPERLESS_LLM_INDEX_TASK_CRON}

 : Configures the schedule to update the AI embeddings of text content and metadata for all documents. Only performed if
 AI is enabled and the LLM embedding backend is set.
@@ -94,16 +94,16 @@ first-time setup.
    ```

 7.  You can now either ...
-    - install Redis or
+    - install a Redis-compatible broker (e.g. Valkey or Redis) or

    - use the included `scripts/start_services.sh` to use Docker to fire
-      up a Redis instance (and some other services such as Tika,
+      up a broker instance (and some other services such as Tika,
      Gotenberg and a database server) or

-    - spin up a bare Redis container
+    - spin up a bare broker container

      ```bash
-      docker run -d -p 6379:6379 --restart unless-stopped redis:latest
+      docker run -d -p 6379:6379 --restart unless-stopped docker.io/valkey/valkey:9-alpine
      ```

 8.  Continue with either back-end or front-end development – or both :-).
@@ -132,7 +132,7 @@ uv run manage.py runserver & \
 ```

 You might need the front end to test your back end code.
-This assumes that you have AngularJS installed on your system.
+This assumes that you have Angular installed on your system.
 Go to the [Front end development](#front-end-development) section for further details.
 To build the front end once use this command:

@@ -174,7 +174,7 @@ To add a new development package `uv add --dev <package>`

 ## Front end development

-The front end is built using AngularJS. In order to get started, you need Node.js (version 24+) and
+The front end is built using Angular. In order to get started, you need Node.js (version 24+) and
 `pnpm`.

 !!! note
@@ -248,12 +248,12 @@ that authentication is working.
 ## Localization

 Paperless-ngx is available in many different languages. Since Paperless-ngx
-consists both of a Django application and an AngularJS front end, both
+consists both of a Django application and an Angular front end, both
 these parts have to be translated separately.

 ### Front end localization

- The AngularJS front end does localization according to the [Angular
+- The Angular front end does localization according to the [Angular
  documentation](https://angular.io/guide/i18n).
 - The source language of the project is "en_US".
 - The source strings end up in the file `src-ui/messages.xlf`.
@@ -495,7 +495,7 @@ class MyCustomParser:
        self._tempdir = Path(
            tempfile.mkdtemp(prefix="paperless-", dir=settings.SCRATCH_DIR)
        )
-        self._text: str | None = None
+        self._text: str = ""
        self._archive_path: Path | None = None

    def __enter__(self) -> Self:
@@ -553,7 +553,8 @@ def parse(
 **Result accessors**

 ```python
-def get_text(self) -> str | None:
+def get_text(self) -> str:
+    # Return the extracted text, or an empty string if none was found.
    return self._text

 def get_date(self) -> "datetime.datetime | None":
@@ -684,7 +685,7 @@ class XmlDocumentParser:
    def __init__(self, logging_group: object = None) -> None:
        settings.SCRATCH_DIR.mkdir(parents=True, exist_ok=True)
        self._tempdir = Path(tempfile.mkdtemp(prefix="paperless-", dir=settings.SCRATCH_DIR))
-        self._text: str | None = None
+        self._text: str = ""

    def __enter__(self) -> Self:
        return self
@@ -702,7 +703,7 @@ class XmlDocumentParser:
        except ET.ParseError as e:
            raise ParseError(f"XML parse error: {e}") from e

-    def get_text(self) -> str | None:
+    def get_text(self) -> str:
        return self._text

    def get_date(self):
@@ -70,7 +70,16 @@ elsewhere. Here are a couple notes about that.
 Paperless-ngx determines the type of a file by inspecting its content
 rather than its file extensions. However, files processed via the
 consumption directory will be rejected if they have a file extension that
-not supported by any of the available parsers.
+is not supported by any of the available parsers.
+
+## _Are duplicate documents rejected?_
+
+**A:** Not by default. As of v3, a file whose contents match an existing document is still
+consumed, and the duplicate is flagged in the UI — open the document and check the
+**Duplicates** tab to review documents that share the same content. If you prefer the old
+behavior of rejecting duplicates during consumption, set
+[`PAPERLESS_CONSUMER_DELETE_DUPLICATES`](configuration.md#PAPERLESS_CONSUMER_DELETE_DUPLICATES)
+to `true`.

 ## _Will paperless-ngx run on Raspberry Pi?_

@@ -118,10 +127,24 @@ able to run paperless, you're a bit on your own. If you can't run the
 docker image, the documentation has instructions for bare metal
 installs.

-## _What about the Redis licensing change and using one of the open source forks_?
+## _Does Paperless-ngx use AI, and is my data private?_

-Currently (October 2024), forks of Redis such as Valkey or Redirect are not officially supported by our upstream
-libraries, so using one of these to replace Redis is not officially supported.
+**A:** Paperless-ngx includes optional AI features — LLM-based suggestions, document chat,
+and similar-document retrieval — that are **disabled by default**. They only run when you
+enable them and configure an LLM backend. The built-in tag/correspondent suggestions use a
+local, non-LLM machine-learning model and do not send your data anywhere. If you enable the
+LLM features, document content is sent to whichever backend you configure — this can be a
+fully local backend (e.g. Ollama) or a remote provider. See
+[AI features](advanced_usage.md#ai-features) for details.

-However, they do claim to be compatible with the Redis protocol and will likely work, but we will
-not be updating from using Redis as the broker officially just yet.
+## _Which message broker should I use_?
+
+Paperless-ngx talks to a Redis-compatible message broker, so any broker that
+implements the Redis protocol will work. The bundled Docker Compose files
+default to [Valkey](https://valkey.io/), the open-source fork created after
+Redis' licensing change, but Redis itself and other wire-compatible brokers
+(such as Microsoft's Garnet) are equally fine.
+
+Existing installs can switch broker implementations in place: point
+[`PAPERLESS_REDIS`](configuration.md#PAPERLESS_REDIS) at the new instance and
+reuse the same data volume.
@@ -35,9 +35,10 @@ physical documents into a searchable online archive so you can keep, well, _less
  - _New!_ Supports remote OCR with Azure AI (opt-in).
 - Documents are saved as PDF/A format which is designed for long term storage, alongside the unaltered originals.
 - Uses machine-learning to automatically add tags, correspondents and document types to your documents.
- **New**: Paperless-ngx can now leverage AI (Large Language Models or LLMs) for document suggestions. This is an optional feature that can be enabled (and is disabled by default).
+- **New**: Paperless-ngx can optionally leverage AI (Large Language Models or LLMs) for document suggestions, chatting with your documents, and similar-document retrieval. These features are opt-in and disabled by default.
 - Supports PDF documents, images, plain text files, Office documents (Word, Excel, PowerPoint, and LibreOffice equivalents)[^1] and more.
 - Paperless stores your documents plain on disk. Filenames and folders are managed by paperless and their format can be configured freely with different configurations assigned to different documents.
+- Keep multiple **versions** of a document's file under a single entry, sharing one set of metadata.
 - **Beautiful, modern web application** that features:
  - Customizable dashboard with statistics.
  - Filtering by tags, correspondents, types, and more.
@@ -178,7 +178,7 @@ to enable polling and disable inotify. See [here](configuration.md#polling).
    - `fonts-liberation` for generating thumbnails for plain text
      files
    - `imagemagick` >= 6 for PDF conversion
-    - `gnupg` for handling encrypted documents
+    - `gnupg` for decrypting GPG-encrypted email
    - `libpq-dev` for PostgreSQL
    - `libmagic-dev` for mime type detection
    - `mariadb-client` for MariaDB compile time
@@ -226,7 +226,8 @@ to enable polling and disable inotify. See [here](configuration.md#polling).
    build-essential python3-setuptools python3-wheel
    ```

-2.  Install `redis` >= 6.0 and configure it to start automatically.
+2.  Install a Redis-compatible broker (a current release of Valkey or
+    Redis) and configure it to start automatically.

 3.  Optional: Install `postgresql` and configure a database, user, and
    password for Paperless-ngx. If you do not wish to use PostgreSQL,
@@ -268,10 +269,10 @@ to enable polling and disable inotify. See [here](configuration.md#polling).
 6.  Configure Paperless-ngx. See [configuration](configuration.md) for details.
    Edit the included `paperless.conf` and adjust the settings to your
    needs. Required settings for getting Paperless-ngx running are:
-    - [`PAPERLESS_REDIS`](configuration.md#PAPERLESS_REDIS) should point to your Redis server, such as
+    - [`PAPERLESS_REDIS`](configuration.md#PAPERLESS_REDIS) should point to your broker, such as
      `redis://localhost:6379`.
-    - [`PAPERLESS_DBENGINE`](configuration.md#PAPERLESS_DBENGINE) is optional, and should be one of `postgres`,
-      `mariadb`, or `sqlite`
+    - [`PAPERLESS_DBENGINE`](configuration.md#PAPERLESS_DBENGINE) should be one of `postgresql`,
+      `mariadb`, or `sqlite`. PostgreSQL and MariaDB users must set this explicitly.
    - [`PAPERLESS_DBHOST`](configuration.md#PAPERLESS_DBHOST) should be the hostname on which your
      PostgreSQL server is running. Do not configure this to use
      SQLite instead. Also configure port, database name, user and
@@ -297,7 +298,7 @@ to enable polling and disable inotify. See [here](configuration.md#polling).

    !!! warning

-        Ensure your Redis instance [is secured](https://redis.io/docs/latest/operate/oss_and_stack/management/security/).
+        Ensure your broker instance [is secured](https://valkey.io/topics/security/).

 7.  Create the following directories if they do not already exist:
    - `/opt/paperless/media`
@@ -389,9 +390,9 @@ to enable polling and disable inotify. See [here](configuration.md#polling).
        `Require=paperless-webserver.socket` in the `webserver` script
        and configure `granian` to listen on port 80 (set `GRANIAN_PORT`).

-    These services rely on Redis and optionally the database server, but
+    These services rely on the broker and optionally the database server, but
    don't need to be started in any particular order. The example files
-    depend on Redis being started. If you use a database server, you
+    depend on the broker being started. If you use a database server, you
    should add additional dependencies.

    !!! note
@@ -449,6 +450,12 @@ development documentation.
 You can migrate to Paperless-ngx from Paperless-ng or from the original
 Paperless project.

+!!! note
+
+    Upgrading an existing Paperless-ngx installation from v2 to v3 has its own
+    breaking changes and required steps. See the [v3 migration guide](migration-v3.md)
+    before upgrading.
+
 <h3 id="migration_ng">Migrating from Paperless-ng</h3>

 Paperless-ngx is meant to be a drop-in replacement for Paperless-ng, and
@@ -494,7 +501,7 @@ installation. Keep these points in mind:
  for other services, you might as well use it for Paperless as well.
 - The task scheduler of Paperless, which is used to execute periodic
  tasks such as email checking and maintenance, requires a
-  [Redis](https://redis.io/) message broker instance. The
+  Redis-compatible message broker instance (such as Valkey or Redis). The
  Docker Compose route takes care of that.
 - The layout of the folder structure for your documents and data
  remains the same, so you can plug your old Docker volumes into
@@ -582,16 +589,16 @@ commands as well.

 1.  Stop and remove the Paperless container.
 2.  If using an external database, stop that container.
-3.  Update Redis configuration.
+3.  Update broker configuration.
    1. If `REDIS_URL` is already set, change it to [`PAPERLESS_REDIS`](configuration.md#PAPERLESS_REDIS)
       and continue to step 4.

-    1. Otherwise, add a new Redis service in `docker-compose.yml`,
+    1. Otherwise, add a new broker service in `docker-compose.yml`,
       following [the example compose
       files](https://github.com/paperless-ngx/paperless-ngx/tree/main/docker/compose)

    1. Set the environment variable [`PAPERLESS_REDIS`](configuration.md#PAPERLESS_REDIS) so it points to
-       the new Redis container.
+       the new broker container.

 4.  Update user mapping.
    1. If set, change the environment variable `PUID` to `USERMAP_UID`.
@@ -10,9 +10,9 @@ Check for the following issues:
  `CONSUMPTION_DIR` setting. Don't adjust this setting if you're
  using docker.

- Ensure that redis is up and running. Paperless does its task
+- Ensure that the broker is up and running. Paperless does its task
  processing asynchronously, and for documents to arrive at the task
-  processor, it needs redis to run.
+  processor, it needs the broker to run.

 - Ensure that the task processor is running. Docker does this
  automatically. Manually invoke the task processor by executing
@@ -149,37 +149,6 @@ operating system, if these are different from `1000`. See [Docker setup](setup.m
 Also ensure that you are able to read and write to the consumption
 directory on the host.

-## OSError: \[Errno 19\] No such device when consuming files
-
-If you experience errors such as:
-
-```shell-session
-File "/usr/local/lib/python3.7/site-packages/whoosh/codec/base.py", line 570, in open_compound_file
-return CompoundStorage(dbfile, use_mmap=storage.supports_mmap)
-File "/usr/local/lib/python3.7/site-packages/whoosh/filedb/compound.py", line 75, in __init__
-self._source = mmap.mmap(fileno, 0, access=mmap.ACCESS_READ)
-OSError: [Errno 19] No such device
-
-During handling of the above exception, another exception occurred:
-
-Traceback (most recent call last):
-File "/usr/local/lib/python3.7/site-packages/django_q/cluster.py", line 436, in worker
-res = f(*task["args"], **task["kwargs"])
-File "/usr/src/paperless/src/documents/tasks.py", line 73, in consume_file
-override_tag_ids=override_tag_ids)
-File "/usr/src/paperless/src/documents/consumer.py", line 271, in try_consume_file
-raise ConsumerError(e)
-```
-
-Paperless uses a search index to provide better and faster full text
-searching. This search index is stored inside the `data` folder. The
-search index uses memory-mapped files (mmap). The above error indicates
-that paperless was unable to create and open these files.
-
-This happens when you're trying to store the data directory on certain
-file systems (mostly network shares) that don't support memory-mapped
-files.
-
 ## Web-UI stuck at "Loading\..."

 This might have multiple reasons.
@@ -292,6 +292,23 @@ Once setup, navigating to the email settings page in Paperless-ngx will allow yo
 You can also submit a document using the REST API, see [POSTing documents](api.md#file-uploads)
 for details.

+### Duplicate documents
+
+By default, Paperless-ngx **does not reject duplicates**. If you consume a file whose
+contents exactly match an existing document (same checksum), the new copy is still
+consumed and a warning is logged. The task entry for the upload also flags that a
+duplicate was detected and links to the existing document(s).
+
+To review duplicates, open a document and switch to the **Duplicates** tab on the
+document detail page. It lists other documents that share the same content, including any
+that are in the trash (shown with a badge), and links to each so you can decide which to
+keep.
+
+If you would rather reject duplicates at consumption time (the pre-v3 behavior), set
+[`PAPERLESS_CONSUMER_DELETE_DUPLICATES`](configuration.md#PAPERLESS_CONSUMER_DELETE_DUPLICATES)
+to `true`. The duplicate file is then deleted instead of consumed, and the task fails with
+a "document already exists" message.
+
 ## Document Suggestions

 Paperless-ngx can suggest tags, correspondents, document types and storage paths for documents based on the content of the document. This is done using a (non-LLM) machine learning model that is trained on the documents in your database. The suggestions are shown in the document detail page and can be accepted or rejected by the user.
@@ -306,7 +323,9 @@ Paperless-ngx includes several features that use AI to enhance the document mana
    so consider the privacy implications of using these features, especially if using a remote
    model or API provider instead of the default local model.

-The AI features work by creating an embedding of the text content and metadata of documents, which is then used for various tasks such as similarity search and question answering. This uses the FAISS vector store.
+The AI features work by creating an embedding of the text content and metadata of documents, which is then used for various tasks such as similarity search and question answering.
+
+See [AI features](advanced_usage.md#ai-features) for how to enable and configure these features, including choosing an LLM backend and setting up the LLM index for RAG.

 ### AI-Enhanced Suggestions

@@ -1097,7 +1116,7 @@ Paperless-ngx consists of the following components:
  errors (i.e., wrong email credentials, errors during consuming a
  specific file, etc).

- A [redis](https://redis.io/) message broker: This is a really
+- A message broker (such as Valkey or Redis): This is a really
  lightweight service that is responsible for getting the tasks from
  the webserver and the consumer to the task scheduler. These run in a
  different process (maybe even on different machines!), and
@@ -16,7 +16,7 @@ classifiers = [
 dependencies = [
  "azure-ai-documentintelligence>=1.0.2",
  "babel>=2.17",
-  "bleach~=6.3.0",
+  "bleach~=6.4.0",
  "celery[redis]~=5.6.2",
  "channels~=4.2",
  "channels-redis~=4.2",
@@ -26,7 +26,7 @@ module.exports = {
    'abstract-paperless-service',
  ],
  transformIgnorePatterns: [
-    'node_modules/(?!.*(\\.mjs$|tslib|lodash-es|@angular/common/locales/.*\\.js$))',
+    'node_modules/(?!.*(\\.mjs$|tslib|lodash-es|normalize-diacritics|@angular/common/locales/.*\\.js$))',
  ],
  moduleNameMapper: {
    ...esmPreset.moduleNameMapper,
@@ -32,6 +32,7 @@
    "ngx-cookie-service": "^21.3.1",
    "ngx-device-detector": "^11.0.0",
    "ngx-ui-tour-ng-bootstrap": "^18.0.0",
+    "normalize-diacritics": "^5.0.0",
    "pdfjs-dist": "^5.7.284",
    "rxjs": "^7.8.2",
    "tslib": "^2.8.1",
@@ -71,6 +71,9 @@ importers:
      ngx-ui-tour-ng-bootstrap:
        specifier: ^18.0.0
        version: 18.0.0(f910a33494d223bd6dd07ce1bf22a35e)
+      normalize-diacritics:
+        specifier: ^5.0.0
+        version: 5.0.0
      pdfjs-dist:
        specifier: ^5.7.284
        version: 5.7.284
@@ -5516,6 +5519,10 @@ packages:
    engines: {node: ^20.17.0 || >=22.9.0}
    hasBin: true

+  normalize-diacritics@5.0.0:
+    resolution: {integrity: sha512-t6czCJOpbAtckN1wCC2qPWnO3GQvNANb9bcUNbiOLEqojVuP31+ELIs5KhEG8jyz0TH7iD9BWxWz8O3ic2/rMQ==}
+    engines: {node: '>= 14.x', npm: '>= 6.x'}
+
  normalize-path@3.0.0:
    resolution: {integrity: sha512-6eZs5Ls3WtCisHWp9S2GUy8dqkpGi4BVSz3GaqiE6ezub0512ESztXUwUB6C6IKbQkY2Pnb/mD4WYojCRwcwLA==}
    engines: {node: '>=0.10.0'}
@@ -12931,6 +12938,10 @@ snapshots:
    dependencies:
      abbrev: 4.0.0

+  normalize-diacritics@5.0.0:
+    dependencies:
+      tslib: 2.8.1
+
  normalize-path@3.0.0: {}

  npm-bundled@5.0.0:
@@ -23,6 +23,7 @@ import {
 import { CustomFieldsService } from 'src/app/services/rest/custom-fields.service'
 import { ToastService } from 'src/app/services/toast.service'
 import { pngxPopperOptions } from 'src/app/utils/popper-options'
+import { matchesSearchText } from 'src/app/utils/text-search'
 import { LoadingComponentWithPermissions } from '../../loading-component/loading.component'
 import { CustomFieldEditDialogComponent } from '../edit-dialog/custom-field-edit-dialog/custom-field-edit-dialog.component'

@@ -69,9 +70,7 @@ export class CustomFieldsDropdownComponent extends LoadingComponentWithPermissio

  public get filteredFields(): CustomField[] {
    return this.unusedFields.filter(
-      (f) =>
-        !this.filterText ||
-        f.name.toLowerCase().includes(this.filterText.toLowerCase())
+      (f) => !this.filterText || matchesSearchText(f.name, this.filterText)
    )
  }

@@ -63,6 +63,7 @@
      [(ngModel)]="atom.value"
      [disabled]="disabled"
      [virtualScroll]="getSelectOptionsForField(atom.field)?.length > 100"
+      [searchFn]="selectOptionSearchFn"
      (mousedown)="$event.stopImmediatePropagation()"
    ></ng-select>
  } @else if (getCustomFieldByID(atom.field)?.data_type === CustomFieldDataType.DocumentLink) {
@@ -81,6 +82,7 @@
      [disabled]="disabled"
      bindLabel="name"
      bindValue="id"
+      [searchFn]="customFieldSearchFn"
      (mousedown)="$event.stopImmediatePropagation()"
    ></ng-select>
    <select class="w-25 form-select" [(ngModel)]="atom.operator" [disabled]="disabled">
@@ -125,6 +127,7 @@
          [(ngModel)]="atom.value"
          [disabled]="disabled"
          [multiple]="true"
+          [searchFn]="selectOptionSearchFn"
          (mousedown)="$event.stopImmediatePropagation()"
        ></ng-select>
      }
@@ -36,6 +36,7 @@ import {
  CustomFieldQueryExpression,
 } from 'src/app/utils/custom-field-query-element'
 import { pngxPopperOptions } from 'src/app/utils/popper-options'
+import { matchesSearchText } from 'src/app/utils/text-search'
 import { LoadingComponentWithPermissions } from '../../loading-component/loading.component'
 import { ClearableBadgeComponent } from '../clearable-badge/clearable-badge.component'
 import { DocumentLinkComponent } from '../input/document-link/document-link.component'
@@ -281,6 +282,14 @@ export class CustomFieldsQueryDropdownComponent extends LoadingComponentWithPerm

  public readonly today: string = new Date().toLocaleDateString('en-CA')

+  public customFieldSearchFn = (term: string, field: CustomField): boolean =>
+    matchesSearchText(field?.name, term)
+
+  public selectOptionSearchFn = (
+    term: string,
+    option: { id: string; label: string }
+  ): boolean => matchesSearchText(option?.label, term)
+
  constructor() {
    super()
    this.selectionModel = new CustomFieldQueriesModel()
@@ -28,6 +28,7 @@
          [notFoundText]="notFoundText"
          [multiple]="multiple"
          [bindLabel]="bindLabel"
+          [searchFn]="searchFn"
          bindValue="id"
          [virtualScroll]="items?.length > 100"
          (change)="onChange(value)"
@@ -112,6 +112,15 @@ describe('SelectComponent', () => {
    expect(createNewVal).toEqual('baz')
  })

+  it('should search items by independent normalized terms', () => {
+    expect(
+      component.searchFn('tax 26', { id: 11, name: 'Tax\u00e9s 2026' })
+    ).toBeTruthy()
+    expect(
+      component.searchFn('tax receipt', { id: 11, name: 'Tax\u00e9s 2026' })
+    ).toBeFalsy()
+  })
+
  it('should clear search term on blur after delay', fakeAsync(() => {
    const clearSpy = jest.spyOn(component, 'clearLastSearchTerm')
    component.onBlur()
@@ -13,6 +13,7 @@ import {
 import { RouterModule } from '@angular/router'
 import { NgSelectModule } from '@ng-select/ng-select'
 import { NgxBootstrapIconsModule } from 'ngx-bootstrap-icons'
+import { matchesSearchText } from 'src/app/utils/text-search'
 import { AbstractInputComponent } from '../abstract-input'

@Component({
@@ -99,6 +100,9 @@ export class SelectComponent extends AbstractInputComponent<number> {
  @Input()
  bindLabel: string = 'name'

+  public searchFn = (term: string, item: any): boolean =>
+    matchesSearchText(item?.[this.bindLabel], term)
+
  @Input()
  showFilter: boolean = false

@@ -14,6 +14,7 @@
          [clearSearchOnAdd]="true"
          [hideSelected]="tags.length > 0"
          [addTag]="allowCreate ? createTagRef : false"
+          [searchFn]="searchFn"
          addTagText="Add tag"
          i18n-addTagText
          (add)="onAdd($event)"
@@ -171,6 +171,15 @@ describe('TagsComponent', () => {
    expect(component.getTag(4)).toBeUndefined()
  })

+  it('should search tags by independent normalized terms including parents', () => {
+    const parent: Tag = { id: 11, name: 'Financ\u00e9' }
+    const child: Tag = { id: 12, name: 'Taxes 2026', parent: parent.id }
+    component.tags = [parent, child]
+
+    expect(component.searchFn('finance 26', child)).toBeTruthy()
+    expect(component.searchFn('finance receipt', child)).toBeFalsy()
+  })
+
  it('should emit filtered documents', () => {
    component.value = [10]
    component.tags = tags
@@ -21,6 +21,7 @@ import { NgxBootstrapIconsModule } from 'ngx-bootstrap-icons'
 import { first, firstValueFrom, tap } from 'rxjs'
 import { Tag } from 'src/app/data/tag'
 import { TagService } from 'src/app/services/rest/tag.service'
+import { matchesSearchText } from 'src/app/utils/text-search'
 import { EditDialogMode } from '../../edit-dialog/edit-dialog.component'
 import { TagEditDialogComponent } from '../../edit-dialog/tag-edit-dialog/tag-edit-dialog.component'
 import { TagComponent } from '../../tag/tag.component'
@@ -114,6 +115,14 @@ export class TagsComponent implements OnInit, ControlValueAccessor {

  public createTagRef: (name) => void

+  public searchFn = (term: string, tag: Tag): boolean =>
+    matchesSearchText(
+      [this.getParentChain(tag?.id).map((parent) => parent.name), tag?.name]
+        .flat()
+        .join(' '),
+      term
+    )
+
  getTag(id: number) {
    if (this.tags) {
      return this.tags.find((tag) => tag.id == id)
@@ -360,6 +360,14 @@ export const PaperlessConfigOptions: ConfigOption[] = [
    category: ConfigCategory.AI,
    note: $localize`Language to use for generated AI suggestions. When unset, AI suggestions use the user's display language if explicitly set.`,
  },
+  {
+    key: 'llm_request_timeout',
+    title: $localize`LLM Request Timeout`,
+    type: ConfigOptionType.Number,
+    config_key: 'PAPERLESS_AI_LLM_REQUEST_TIMEOUT',
+    category: ConfigCategory.AI,
+    note: $localize`Timeout in seconds for LLM requests.`,
+  },
 ]

 export interface PaperlessConfig extends ObjectWithId {
@@ -401,4 +409,5 @@ export interface PaperlessConfig extends ObjectWithId {
  llm_api_key: string
  llm_endpoint: string
  llm_output_language: string
+  llm_request_timeout: number
 }
@@ -1,5 +1,6 @@
 import { Pipe, PipeTransform } from '@angular/core'
 import { MatchingModel } from '../data/matching-model'
+import { matchesSearchText } from '../utils/text-search'

@Pipe({
  name: 'filter',
@@ -21,9 +22,7 @@ export class FilterPipe implements PipeTransform {
              typeof item[key] === 'string' || typeof item[key] === 'number'
          )
      return keys.some((key) => {
-        return String(item[key])
-          .toLowerCase()
-          .includes(searchText.toLowerCase())
+        return matchesSearchText(item[key], searchText)
      })
    })
  }
@@ -0,0 +1,17 @@
+import { matchesSearchText } from './text-search'
+
+describe('text search utilities', () => {
+  it('matches text accent-insensitively', () => {
+    expect(matchesSearchText('R\u00e9sum\u00e9', 'resume')).toBeTruthy()
+    expect(matchesSearchText('S\u00f8ren', 'soren')).toBeTruthy()
+    expect(matchesSearchText('\u0152uvre', 'oeuvre')).toBeTruthy()
+    expect(matchesSearchText('Invoice', 'receipt')).toBeFalsy()
+  })
+
+  it('matches all whitespace-separated search terms independently', () => {
+    expect(matchesSearchText('taxes 2026', 'tax 26')).toBeTruthy()
+    expect(matchesSearchText('2026 taxes', 'tax 26')).toBeTruthy()
+    expect(matchesSearchText('Tax\u00e9s 2026', 'taxe 26')).toBeTruthy()
+    expect(matchesSearchText('taxes 2026', 'tax receipt')).toBeFalsy()
+  })
+})
@@ -0,0 +1,23 @@
+import { normalizeSync } from 'normalize-diacritics'
+
+export type SearchTextValue =
+  | string
+  | number
+  | boolean
+  | bigint
+  | null
+  | undefined
+
+export function normalizeSearchText(value: SearchTextValue): string {
+  return normalizeSync(String(value ?? '')).toLocaleLowerCase()
+}
+
+export function matchesSearchText(
+  value: SearchTextValue,
+  searchText: SearchTextValue
+): boolean {
+  const normalizedValue = normalizeSearchText(value)
+  const searchTerms = normalizeSearchText(searchText).trim().split(/\s+/)
+
+  return searchTerms.every((term) => normalizedValue.includes(term))
+}
@@ -169,6 +169,10 @@ class FileStabilityTracker:
            self._tracked.pop(path, None)
            yield path

+    def is_tracking(self, path: Path) -> bool:
+        """Check whether a path is currently being tracked for stability."""
+        return path.resolve() in self._tracked
+
    def has_pending_files(self) -> bool:
        """Check if there are files waiting for stability check."""
        return len(self._tracked) > 0
@@ -370,6 +374,16 @@ class Command(BaseCommand):
    # Testing timeout in seconds
    testing_timeout_s: Final[float] = 0.5

+    # How often to perform a full-glob rescan of the consume directory as a
+    # safety net. Each watchfiles watcher is torn down and recreated on every
+    # batch to reconfigure its timeout, and a fresh watcher silently adopts the
+    # current directory contents as its baseline. A file that appears between
+    # one batch and the next watcher's baseline is therefore never reported and
+    # would sit in the consume directory forever. This periodic rescan re-injects
+    # such files into the stability tracker (see GH issue #13011). Not currently
+    # user-configurable; instances may override for testing.
+    rescan_interval_s: float = 300.0
+
    def add_arguments(self, parser) -> None:
        parser.add_argument(
            "directory",
@@ -425,7 +439,7 @@ class Command(BaseCommand):
        )

        # Process existing files
-        self._process_existing_files(
+        queued = self._process_existing_files(
            directory=directory,
            recursive=recursive,
            subdirs_as_tags=subdirs_as_tags,
@@ -445,6 +459,7 @@ class Command(BaseCommand):
            polling_interval=polling_interval,
            stability_delay=stability_delay,
            is_testing=is_testing,
+            queued=queued,
        )

        logger.debug("Consumer exiting")
@@ -456,11 +471,18 @@ class Command(BaseCommand):
        recursive: bool,
        subdirs_as_tags: bool,
        consumer_filter: ConsumerFilter,
-    ) -> None:
-        """Process any existing files in the consumption directory."""
+    ) -> set[Path]:
+        """
+        Process any existing files in the consumption directory.
+
+        Returns the set of resolved paths that were queued, so the watch loop
+        can seed its in-flight set and avoid re-queuing them on the first
+        rescan before the consume tasks have removed them from disk.
+        """
        logger.info(f"Processing existing files in {directory}")

        glob_pattern = "**/*" if recursive else "*"
+        queued: set[Path] = set()

        for filepath in directory.glob(glob_pattern):
            # Use filter to check if file should be processed
@@ -475,6 +497,48 @@ class Command(BaseCommand):
                consumption_dir=directory,
                subdirs_as_tags=subdirs_as_tags,
            )
+            queued.add(filepath.resolve())
+
+        return queued
+
+    def _rescan_existing_files(
+        self,
+        *,
+        directory: Path,
+        recursive: bool,
+        consumer_filter: ConsumerFilter,
+        tracker: FileStabilityTracker,
+        queued: set[Path],
+    ) -> None:
+        """
+        Re-inject on-disk files the watcher never reported into the tracker.
+
+        Acts as a safety net for files stranded by the watcher-recreation gap
+        (see ``rescan_interval_s``). Files already being tracked or already
+        queued and awaiting consumption are skipped, so a file is never queued
+        twice. Queued paths that have since left the directory are pruned so a
+        later file reusing the same name is not skipped forever.
+        """
+        # Prune in-flight paths that have left the directory
+        for path in list(queued):
+            if not path.exists():
+                queued.discard(path)
+
+        glob_pattern = "**/*" if recursive else "*"
+
+        for filepath in directory.glob(glob_pattern):
+            if not filepath.is_file():
+                continue
+
+            if not consumer_filter(Change.added, str(filepath)):
+                continue
+
+            resolved = filepath.resolve()
+            if tracker.is_tracking(resolved) or resolved in queued:
+                continue
+
+            logger.debug(f"Rescan found untracked file: {resolved}")
+            tracker.track(resolved, Change.added)

    def _watch_directory(
        self,
@@ -486,11 +550,24 @@ class Command(BaseCommand):
        polling_interval: float,
        stability_delay: float,
        is_testing: bool,
+        queued: set[Path] | None = None,
    ) -> None:
        """Watch directory for changes and process stable files."""
        use_polling = polling_interval > 0
        poll_delay_ms = int(polling_interval * 1000) if use_polling else 0

+        # Resolved paths that have been queued and are awaiting consumption.
+        # Seeded from the startup scan so the first rescan does not re-queue
+        # files whose consume tasks have not yet removed them from disk.
+        queued = set() if queued is None else queued
+
+        # Full-glob safety net cadence (0 disables)
+        rescan_interval_s = self.rescan_interval_s
+        rescan_timeout_ms = (
+            int(rescan_interval_s * 1000) if rescan_interval_s > 0 else 0
+        )
+        last_rescan = monotonic()
+
        if use_polling:
            logger.info(
                f"Watching {directory} using polling (interval: {polling_interval}s)",
@@ -505,6 +582,20 @@ class Command(BaseCommand):
        stability_timeout_ms = int(stability_delay * 1000)
        testing_timeout_ms = int(self.testing_timeout_s * 1000)

+        def cap_for_rescan(ms: int) -> int:
+            """
+            Ensure the watch loop wakes often enough to run the rescan.
+
+            ``watch()`` blocks for up to ``rust_timeout``, so the rescan can
+            only run that often. A timeout of 0 means "wait indefinitely",
+            which would never wake to rescan; cap it at the rescan interval.
+            """
+            if rescan_timeout_ms <= 0:
+                return ms
+            if ms <= 0:
+                return rescan_timeout_ms
+            return min(ms, rescan_timeout_ms)
+
        # Calculate appropriate timeout for watch loop
        # In polling mode, rust_timeout must be significantly longer than poll_delay_ms
        # to ensure poll cycles can complete before timing out
@@ -522,6 +613,8 @@ class Command(BaseCommand):
            # Not testing, wait indefinitely for first event
            timeout_ms = 0

+        timeout_ms = cap_for_rescan(timeout_ms)
+
        self.stop_flag.clear()

        while not self.stop_flag.is_set():
@@ -551,10 +644,26 @@ class Command(BaseCommand):
                            consumption_dir=directory,
                            subdirs_as_tags=subdirs_as_tags,
                        )
+                        # Remember it so the rescan does not re-queue it while
+                        # the consume task has yet to remove it from disk
+                        queued.add(stable_path)

                    # Exit watch loop to reconfigure timeout
                    break

+                # Periodic full-glob safety net for files the watcher missed
+                if rescan_timeout_ms > 0 and (
+                    monotonic() - last_rescan >= rescan_interval_s
+                ):
+                    self._rescan_existing_files(
+                        directory=directory,
+                        recursive=recursive,
+                        consumer_filter=consumer_filter,
+                        tracker=tracker,
+                        queued=queued,
+                    )
+                    last_rescan = monotonic()
+
                # Determine next timeout
                if tracker.has_pending_files():
                    # Check pending files at stability interval
@@ -572,6 +681,8 @@ class Command(BaseCommand):
                    # No pending files, wait indefinitely
                    timeout_ms = 0

+                timeout_ms = cap_for_rescan(timeout_ms)
+
            except KeyboardInterrupt:  # pragma: nocover
                logger.info("Received interrupt, stopping consumer")
                self.stop_flag.set()
@@ -866,8 +866,24 @@ class TantivyBackend:
        final_query = self._apply_permission_filter(mlt_query, user)

        effective_limit = limit if limit is not None else searcher.num_docs
-        # Fetch one extra to account for excluding the original document
-        results = searcher.search(final_query, limit=effective_limit + 1)
+        try:
+            # Fetch one extra to account for excluding the original document
+            results = searcher.search(final_query, limit=effective_limit + 1)
+        except BaseException:  # pragma: no cover
+            # Tantivy 0.26 panics in BM25 idf scoring when the index holds
+            # soft-deleted documents (doc_freq can exceed the alive doc count),
+            # which only surfaces for the More Like This query. The panic crosses
+            # the pyo3 boundary as a `pyo3_runtime.PanicException` — a
+            # BaseException, not an Exception — so catch BaseException and degrade
+            # to "no similar documents" instead of bubbling a 500 to the client.
+            # Fixed upstream: https://github.com/quickwit-oss/tantivy/pull/2964
+            # Remove once the bundled tantivy includes that fix.
+            logger.warning(
+                "More Like This scoring panicked (likely stale tantivy segment "
+                "stats after deletions); returning no results. A search index "
+                "reindex will rebuild consistent statistics.",
+            )
+            return []

        addrs = [addr for _score, addr in results.hits]
        all_ids = cast("list[int]", searcher.fast_field_values("id", addrs))
@@ -82,6 +82,7 @@ class TestApiAppConfig(DirectoriesMixin, APITestCase):
                "llm_api_key": None,
                "llm_endpoint": None,
                "llm_output_language": None,
+                "llm_request_timeout": None,
            },
        )

@@ -684,6 +684,7 @@ class ConsumerThread(Thread):
        subdirs_as_tags: bool = False,
        polling_interval: float = 0,
        stability_delay: float = 0.1,
+        rescan_interval: float | None = None,
    ) -> None:
        super().__init__()
        self.consumption_dir = consumption_dir
@@ -693,6 +694,8 @@ class ConsumerThread(Thread):
        self.polling_interval = polling_interval
        self.stability_delay = stability_delay
        self.cmd = Command()
+        if rescan_interval is not None:
+            self.cmd.rescan_interval_s = rescan_interval
        self.cmd.stop_flag.clear()
        # Non-daemon ensures finally block runs and connections are closed
        self.daemon = False
@@ -1052,3 +1055,200 @@ class TestCommandWatchEdgeCases:
            thread.stop_and_wait(timeout=5.0)
            # Clean up any Tags created by the thread
            Tag.objects.all().delete()
+
+
+class TestRescanExistingFiles:
+    """
+    Unit tests for the rescan safety net.
+
+    Each ``watch()`` recreation silently adopts the current directory contents
+    as its baseline, so a file appearing between one batch and the next
+    watcher's baseline is never reported and would sit in the consume directory
+    forever. ``_rescan_existing_files`` re-injects such files into the
+    stability tracker as a periodic safety net (see GH issue #13011).
+    """
+
+    @pytest.fixture
+    def pdf_only_filter(self) -> ConsumerFilter:
+        return ConsumerFilter(
+            supported_extensions=frozenset({".pdf"}),
+            ignore_patterns=[],
+        )
+
+    def _rescan(
+        self,
+        directory: Path,
+        consumer_filter: ConsumerFilter,
+        tracker: FileStabilityTracker,
+        queued: set[Path],
+        *,
+        recursive: bool = False,
+    ) -> None:
+        Command()._rescan_existing_files(
+            directory=directory,
+            recursive=recursive,
+            consumer_filter=consumer_filter,
+            tracker=tracker,
+            queued=queued,
+        )
+
+    def test_tracks_stranded_file(
+        self,
+        consumption_dir: Path,
+        sample_pdf: Path,
+        pdf_only_filter: ConsumerFilter,
+    ) -> None:
+        """A supported on-disk file the watcher never reported gets tracked."""
+        target = consumption_dir / "stranded.pdf"
+        shutil.copy(sample_pdf, target)
+        tracker = FileStabilityTracker(stability_delay=0.1)
+
+        self._rescan(consumption_dir, pdf_only_filter, tracker, set())
+
+        assert tracker.is_tracking(target) is True
+        assert tracker.pending_count == 1
+
+    def test_skips_already_tracked_file(
+        self,
+        consumption_dir: Path,
+        sample_pdf: Path,
+        pdf_only_filter: ConsumerFilter,
+    ) -> None:
+        """A file already being tracked by the watcher is not double-tracked."""
+        target = consumption_dir / "tracked.pdf"
+        shutil.copy(sample_pdf, target)
+        tracker = FileStabilityTracker(stability_delay=0.1)
+        tracker.track(target, Change.added)
+
+        self._rescan(consumption_dir, pdf_only_filter, tracker, set())
+
+        assert tracker.pending_count == 1
+
+    def test_skips_queued_file(
+        self,
+        consumption_dir: Path,
+        sample_pdf: Path,
+        pdf_only_filter: ConsumerFilter,
+    ) -> None:
+        """A file already queued and awaiting consumption is not re-tracked."""
+        target = consumption_dir / "inflight.pdf"
+        shutil.copy(sample_pdf, target)
+        tracker = FileStabilityTracker(stability_delay=0.1)
+        queued = {target.resolve()}
+
+        self._rescan(consumption_dir, pdf_only_filter, tracker, queued)
+
+        assert tracker.pending_count == 0
+
+    def test_prunes_vanished_queued_paths(
+        self,
+        consumption_dir: Path,
+        pdf_only_filter: ConsumerFilter,
+    ) -> None:
+        """Queued paths no longer on disk are dropped so the name can recur."""
+        gone = (consumption_dir / "gone.pdf").resolve()
+        tracker = FileStabilityTracker(stability_delay=0.1)
+        queued = {gone}
+
+        self._rescan(consumption_dir, pdf_only_filter, tracker, queued)
+
+        assert gone not in queued
+
+    def test_skips_unsupported_extension(
+        self,
+        consumption_dir: Path,
+        pdf_only_filter: ConsumerFilter,
+    ) -> None:
+        """Files filtered out by the consumer filter are not tracked."""
+        (consumption_dir / "notes.xyz").write_bytes(b"content")
+        tracker = FileStabilityTracker(stability_delay=0.1)
+
+        self._rescan(consumption_dir, pdf_only_filter, tracker, set())
+
+        assert tracker.pending_count == 0
+
+    def test_recursive_respects_flag(
+        self,
+        consumption_dir: Path,
+        sample_pdf: Path,
+        pdf_only_filter: ConsumerFilter,
+    ) -> None:
+        """Nested files are only found when recursive scanning is enabled."""
+        subdir = consumption_dir / "nested"
+        subdir.mkdir()
+        target = subdir / "deep.pdf"
+        shutil.copy(sample_pdf, target)
+
+        shallow = FileStabilityTracker(stability_delay=0.1)
+        self._rescan(consumption_dir, pdf_only_filter, shallow, set())
+        assert shallow.pending_count == 0
+
+        deep = FileStabilityTracker(stability_delay=0.1)
+        self._rescan(consumption_dir, pdf_only_filter, deep, set(), recursive=True)
+        assert deep.is_tracking(target) is True
+
+
+class TestProcessExistingFilesQueued:
+    """Tests that startup processing reports which paths it queued."""
+
+    @pytest.mark.usefixtures("mock_supported_extensions")
+    def test_returns_queued_paths(
+        self,
+        consumption_dir: Path,
+        sample_pdf: Path,
+        mock_consume_file_delay: MagicMock,
+        settings: SettingsWrapper,
+    ) -> None:
+        """The set returned seeds the rescan's queued set, avoiding re-queue."""
+        target = consumption_dir / "document.pdf"
+        shutil.copy(sample_pdf, target)
+        settings.CONSUMER_IGNORE_PATTERNS = []
+
+        queued = Command()._process_existing_files(
+            directory=consumption_dir,
+            recursive=False,
+            subdirs_as_tags=False,
+            consumer_filter=ConsumerFilter(ignore_patterns=[]),
+        )
+
+        assert target.resolve() in queued
+
+
+@pytest.mark.management
+@pytest.mark.django_db
+class TestCommandRescanRecovery:
+    """End-to-end test that the rescan recovers files the watcher misses."""
+
+    def test_rescan_consumes_file_the_watcher_never_reports(
+        self,
+        consumption_dir: Path,
+        sample_pdf: Path,
+        mock_consume_file_delay: MagicMock,
+        start_consumer: Callable[..., ConsumerThread],
+    ) -> None:
+        """
+        Isolate the rescan path: a long polling interval guarantees the
+        watcher cannot report the file within the test window, so only the
+        periodic rescan can consume it.
+        """
+        # poll interval far longer than the test window -> watcher stays silent
+        thread = start_consumer(
+            polling_interval=30.0,
+            stability_delay=0.1,
+            rescan_interval=0.5,
+        )
+
+        # created after startup, so _process_existing_files did not see it
+        target = consumption_dir / "stranded.pdf"
+        shutil.copy(sample_pdf, target)
+
+        wait_for_mock_call(mock_consume_file_delay.apply_async, timeout_s=5.0)
+
+        if thread.exception:
+            raise thread.exception
+
+        mock_consume_file_delay.apply_async.assert_called()
+        call_args = mock_consume_file_delay.apply_async.call_args.kwargs["kwargs"][
+            "input_doc"
+        ]
+        assert call_args.original_file.name == "stranded.pdf"
@@ -30,6 +30,7 @@ from documents.signals.handlers import update_llm_suggestions_cache
 from documents.tests.utils import DirectoriesMixin
 from documents.tests.utils import read_streaming_response
 from paperless.models import ApplicationConfiguration
+from paperless_ai.exceptions import LLMTimeoutError


 class TestViews(DirectoriesMixin, TestCase):
@@ -476,6 +477,33 @@ class TestAISuggestions(DirectoriesMixin, TestCase):
            get_llm_suggestion_cache(self.document.pk, backend="openai-like"),
        )

+    @patch("documents.views.get_ai_document_classification")
+    @override_settings(
+        AI_ENABLED=True,
+        LLM_BACKEND="openai-like",
+    )
+    def test_ai_suggestions_with_llm_timeout(
+        self,
+        mock_get_ai_classification,
+    ) -> None:
+        mock_get_ai_classification.side_effect = LLMTimeoutError()
+
+        self.client.force_login(user=self.user)
+        response = self.client.get(
+            f"/api/documents/{self.document.pk}/ai_suggestions/",
+        )
+
+        self.assertEqual(response.status_code, status.HTTP_503_SERVICE_UNAVAILABLE)
+        self.assertEqual(
+            response.json(),
+            {
+                "ai": ["AI backend request timed out."],
+            },
+        )
+        self.assertIsNone(
+            get_llm_suggestion_cache(self.document.pk, backend="openai-like"),
+        )
+
    def test_invalidate_suggestions_cache(self) -> None:
        self.client.force_login(user=self.user)
        suggestions = {
@@ -241,6 +241,7 @@ from paperless.serialisers import UserSerializer
 from paperless.views import StandardPagination
 from paperless_ai.ai_classifier import get_ai_document_classification
 from paperless_ai.chat import stream_chat_with_documents
+from paperless_ai.exceptions import LLMTimeoutError
 from paperless_ai.matching import extract_unmatched_names
 from paperless_ai.matching import match_correspondents_by_name
 from paperless_ai.matching import match_document_types_by_name
@@ -1510,6 +1511,17 @@ class DocumentViewSet(
                exc_info=True,
            )
            raise ValidationError({"ai": [_("Invalid AI configuration.")]}) from exc
+        except LLMTimeoutError as exc:
+            logger.exception(
+                "AI backend timed out while generating suggestions for document %s: %s",
+                doc.pk,
+                exc,
+                exc_info=True,
+            )
+            return Response(
+                {"ai": [_("AI backend request timed out.")]},
+                status=status.HTTP_503_SERVICE_UNAVAILABLE,
+            )

        matched_tags = match_tags_by_name(
            llm_suggestions.get("tags", []),
@@ -197,6 +197,7 @@ class AIConfig(BaseConfig):
    llm_embedding_endpoint: str = dataclasses.field(init=False)
    llm_embedding_chunk_size: int = dataclasses.field(init=False)
    llm_context_size: int = dataclasses.field(init=False)
+    llm_request_timeout: int = dataclasses.field(init=False)
    llm_backend: str = dataclasses.field(init=False)
    llm_model: str = dataclasses.field(init=False)
    llm_api_key: str = dataclasses.field(init=False)
@@ -221,6 +222,9 @@ class AIConfig(BaseConfig):
            app_config.llm_embedding_chunk_size or settings.LLM_EMBEDDING_CHUNK_SIZE
        )
        self.llm_context_size = app_config.llm_context_size or settings.LLM_CONTEXT_SIZE
+        self.llm_request_timeout = (
+            app_config.llm_request_timeout or settings.LLM_REQUEST_TIMEOUT
+        )
        self.llm_backend = app_config.llm_backend or settings.LLM_BACKEND
        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
@@ -0,0 +1,23 @@
+# Generated by Django 5.2.14 on 2026-06-14 14:22
+
+import django.core.validators
+from django.db import migrations
+from django.db import models
+
+
+class Migration(migrations.Migration):
+    dependencies = [
+        ("paperless", "0012_applicationconfiguration_llm_output_language"),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name="applicationconfiguration",
+            name="llm_request_timeout",
+            field=models.PositiveSmallIntegerField(
+                null=True,
+                validators=[django.core.validators.MinValueValidator(1)],
+                verbose_name="Sets the LLM request timeout in seconds",
+            ),
+        ),
+    ]
@@ -366,6 +366,12 @@ class ApplicationConfiguration(AbstractSingletonModel):
        max_length=32,
    )

+    llm_request_timeout = models.PositiveSmallIntegerField(
+        verbose_name=_("Sets the LLM timeout in seconds"),
+        null=True,
+        validators=[MinValueValidator(1)],
+    )
+
    class Meta:
        verbose_name = _("paperless application settings")
        permissions = [
@@ -1206,6 +1206,9 @@ if LLM_EMBEDDING_CHUNK_SIZE < 1:
 LLM_CONTEXT_SIZE = get_int_from_env("PAPERLESS_AI_LLM_CONTEXT_SIZE", 8192)
 if LLM_CONTEXT_SIZE < 1:
    raise ImproperlyConfigured("PAPERLESS_AI_LLM_CONTEXT_SIZE must be >= 1")
+LLM_REQUEST_TIMEOUT = get_int_from_env("PAPERLESS_AI_LLM_REQUEST_TIMEOUT", 120)
+if LLM_REQUEST_TIMEOUT < 1:
+    raise ImproperlyConfigured("PAPERLESS_AI_LLM_REQUEST_TIMEOUT must be >= 1")
 LLM_BACKEND = get_choice_from_env(
    "PAPERLESS_AI_LLM_BACKEND",
    {"ollama", "openai-like"},
@@ -1,11 +1,14 @@
 import json
 import logging
+from collections.abc import Iterator
+from contextlib import contextmanager
 from typing import TYPE_CHECKING

+import httpx
+
 from paperless.models import LLMBackend

 if TYPE_CHECKING:
-    from llama_index.core.llms import ChatMessage
    from llama_index.llms.ollama import Ollama
    from llama_index.llms.openai_like import OpenAILike

@@ -16,6 +19,7 @@ from paperless.network import create_pinned_async_httpx_client
 from paperless.network import create_pinned_httpx_client
 from paperless.network import validate_outbound_http_url
 from paperless_ai.base_model import DocumentClassifierSchema
+from paperless_ai.exceptions import LLMTimeoutError

 logger = logging.getLogger("paperless_ai.client")

@@ -61,16 +65,16 @@ class AIClient:
                model=self.settings.llm_model or "llama3.1",
                base_url=endpoint,
                context_window=self.settings.llm_context_size,
-                request_timeout=120,
+                request_timeout=self.settings.llm_request_timeout,
                system_prompt=LLM_SYSTEM_PROMPT,
                client=Client(
                    host=endpoint,
-                    timeout=120,
+                    timeout=self.settings.llm_request_timeout,
                    transport=transport,
                ),
                async_client=AsyncClient(
                    host=endpoint,
-                    timeout=120,
+                    timeout=self.settings.llm_request_timeout,
                    transport=async_transport,
                ),
            )
@@ -84,15 +88,18 @@ class AIClient:
                http_client = create_pinned_httpx_client(
                    endpoint,
                    allow_internal=self.settings.llm_allow_internal_endpoints,
+                    timeout=self.settings.llm_request_timeout,
                )
                async_http_client = create_pinned_async_httpx_client(
                    endpoint,
                    allow_internal=self.settings.llm_allow_internal_endpoints,
+                    timeout=self.settings.llm_request_timeout,
                )
            return OpenAILike(
                model=self.settings.llm_model or "gpt-3.5-turbo",
                api_base=endpoint,
                api_key=self.settings.llm_api_key,
+                timeout=self.settings.llm_request_timeout,
                is_chat_model=True,
                is_function_calling_model=True,
                system_prompt=LLM_SYSTEM_PROMPT,
@@ -113,11 +120,12 @@ class AIClient:

        user_msg = ChatMessage(role="user", content=prompt)
        if self.settings.llm_backend == LLMBackend.OLLAMA:
-            result = self.llm.chat(
-                [user_msg],
-                format=DocumentClassifierSchema.model_json_schema(),
-                think=False,
-            )
+            with self._normalize_timeouts():
+                result = self.llm.chat(
+                    [user_msg],
+                    format=DocumentClassifierSchema.model_json_schema(),
+                    think=False,
+                )
            logger.debug("LLM query result: %s", result)
            parsed = DocumentClassifierSchema(**json.loads(result.message.content))
            return parsed.model_dump()
@@ -125,26 +133,39 @@ class AIClient:
        from llama_index.core.program.function_program import get_function_tool

        tool = get_function_tool(DocumentClassifierSchema)
-        result = self.llm.chat_with_tools(
-            tools=[tool],
-            user_msg=user_msg,
-            chat_history=[],
-            allow_parallel_tool_calls=True,
-        )
-        tool_calls = self.llm.get_tool_calls_from_response(
-            result,
-            error_on_no_tool_call=True,
-        )
+        with self._normalize_timeouts():
+            result = self.llm.chat_with_tools(
+                tools=[tool],
+                user_msg=user_msg,
+                chat_history=[],
+                allow_parallel_tool_calls=True,
+                tool_required=True,
+            )
+            tool_calls = self.llm.get_tool_calls_from_response(
+                result,
+                error_on_no_tool_call=True,
+            )
        logger.debug("LLM query result: %s", tool_calls)
        parsed = DocumentClassifierSchema(**tool_calls[0].tool_kwargs)
        return parsed.model_dump()

-    def run_chat(self, messages: list["ChatMessage"]) -> str:
-        logger.debug(
-            "Running chat query against %s with model %s",
-            self.settings.llm_backend,
-            self.settings.llm_model,
-        )
-        result = self.llm.chat(messages)
-        logger.debug("Chat result: %s", result)
-        return result
+    @contextmanager
+    def _normalize_timeouts(self) -> Iterator[None]:
+        try:
+            yield
+        except httpx.TimeoutException as exc:
+            raise LLMTimeoutError from exc
+        except Exception as exc:
+            if self._is_openai_timeout(exc):
+                raise LLMTimeoutError from exc
+            raise
+
+    def _is_openai_timeout(self, exc: Exception) -> bool:
+        if self.settings.llm_backend != LLMBackend.OPENAI_LIKE:
+            return False
+
+        # Keep OpenAI imports out of module import paths and only load the SDK
+        # when translating an error from an OpenAI-backed request.
+        from openai import APITimeoutError
+
+        return isinstance(exc, APITimeoutError)
@@ -32,15 +32,18 @@ def get_embedding_model(config: AIConfig) -> "BaseEmbedding":
                http_client = create_pinned_httpx_client(
                    endpoint,
                    allow_internal=config.llm_allow_internal_endpoints,
+                    timeout=config.llm_request_timeout,
                )
                async_http_client = create_pinned_async_httpx_client(
                    endpoint,
                    allow_internal=config.llm_allow_internal_endpoints,
+                    timeout=config.llm_request_timeout,
                )
            return OpenAILikeEmbedding(
                model_name=config.llm_embedding_model or "text-embedding-3-small",
                api_key=config.llm_api_key,
                api_base=endpoint,
+                timeout=config.llm_request_timeout,
                http_client=http_client,
                async_http_client=async_http_client,
            )
@@ -73,12 +76,14 @@ def get_embedding_model(config: AIConfig) -> "BaseEmbedding":
            )
            embedding._client = Client(
                host=endpoint,
+                timeout=config.llm_request_timeout,
                transport=PinnedHostHTTPTransport(
                    allow_internal=config.llm_allow_internal_endpoints,
                ),
            )
            embedding._async_client = AsyncClient(
                host=endpoint,
+                timeout=config.llm_request_timeout,
                transport=PinnedHostAsyncHTTPTransport(
                    allow_internal=config.llm_allow_internal_endpoints,
                ),
@@ -0,0 +1,2 @@
+class LLMTimeoutError(Exception):
+    pass
@@ -443,6 +443,18 @@ def truncate_content(
    return " ".join(truncated_chunks)


+def truncate_embedding_query(content: str, *, chunk_size: int) -> str:
+    from llama_index.core.text_splitter import TokenTextSplitter
+
+    splitter = TokenTextSplitter(
+        separator=" ",
+        chunk_size=chunk_size,
+        chunk_overlap=0,
+    )
+    content_chunks = splitter.split_text(content)
+    return content_chunks[0] if content_chunks else ""
+
+
 def normalize_document_ids(document_ids: Iterable[int | str] | None) -> set[str] | None:
    if document_ids is None:
        return None
@@ -476,10 +488,9 @@ def query_similar_documents(
        else None
    )

-    query_text = truncate_content(
+    query_text = truncate_embedding_query(
        (document.title or "") + "\n" + (document.content or ""),
        chunk_size=config.llm_embedding_chunk_size,
-        context_size=config.llm_context_size,
    )
    # Hold the shared read lock for the whole retrieval so the connection is
    # never open across a compaction swap. The retrieve() call generates a
@@ -137,6 +137,16 @@ def test_get_rag_prompt_helper_uses_context_setting() -> None:
    assert prompt_helper.context_window == 4096


+def test_truncate_embedding_query_returns_single_chunk() -> None:
+    content = " ".join(f"word{i}" for i in range(200))
+
+    result = indexing.truncate_embedding_query(content, chunk_size=32)
+
+    assert result
+    assert result != content
+    assert "word199" not in result
+
+
@pytest.mark.django_db
 def test_update_llm_index(
    temp_llm_index_dir: Path,
@@ -393,6 +403,42 @@ def test_query_similar_documents(
        assert result == mock_filtered_docs


+@override_settings(
+    LLM_EMBEDDING_BACKEND="huggingface",
+    LLM_EMBEDDING_CHUNK_SIZE=32,
+    LLM_BACKEND="ollama",
+)
+def test_query_similar_documents_truncates_query_to_embedding_chunk_size(
+    temp_llm_index_dir: Path,
+    real_document: Document,
+) -> None:
+    real_document.content = " ".join(f"word{i}" for i in range(200))
+    with (
+        patch("paperless_ai.indexing.load_or_build_index") as mock_load_or_build_index,
+        patch(
+            "paperless_ai.indexing.llm_index_exists",
+        ) as mock_vector_store_exists,
+        patch("llama_index.core.retrievers.VectorIndexRetriever") as mock_retriever_cls,
+        patch("paperless_ai.indexing.Document.objects.filter") as mock_filter,
+        patch("paperless_ai.indexing.truncate_content") as mock_truncate_content,
+    ):
+        mock_vector_store_exists.return_value = True
+        mock_load_or_build_index.return_value = MagicMock()
+        mock_truncate_content.return_value = "wrong helper"
+
+        mock_retriever = MagicMock()
+        mock_retriever.retrieve.return_value = []
+        mock_retriever_cls.return_value = mock_retriever
+        mock_filter.return_value = []
+
+        indexing.query_similar_documents(real_document, top_k=3)
+
+        mock_truncate_content.assert_not_called()
+        query_text = mock_retriever.retrieve.call_args.args[0]
+        assert query_text
+        assert "word199" not in query_text
+
+
@pytest.mark.django_db
 def test_query_similar_documents_triggers_update_when_index_missing(
    temp_llm_index_dir: Path,
@@ -3,12 +3,14 @@ from unittest.mock import ANY
 from unittest.mock import MagicMock
 from unittest.mock import patch

+import httpx
+import openai
 import pytest
-from llama_index.core.llms import ChatMessage
 from llama_index.core.llms.llm import ToolSelection

 from paperless_ai.client import LLM_SYSTEM_PROMPT
 from paperless_ai.client import AIClient
+from paperless_ai.exceptions import LLMTimeoutError


@pytest.fixture
@@ -17,6 +19,7 @@ def mock_ai_config():
        mock_config = MagicMock()
        mock_config.llm_allow_internal_endpoints = True
        mock_config.llm_context_size = 8192
+        mock_config.llm_request_timeout = 120
        MockAIConfig.return_value = mock_config
        yield mock_config

@@ -64,6 +67,7 @@ def test_get_llm_openai(mock_ai_config, mock_openai_llm):
        model="test_model",
        api_base="http://test-url",
        api_key="test_api_key",
+        timeout=120,
        is_chat_model=True,
        is_function_calling_model=True,
        system_prompt=LLM_SYSTEM_PROMPT,
@@ -151,17 +155,38 @@ def test_run_llm_query_openai_uses_tools(mock_ai_config, mock_openai_llm):
    mock_llm_instance.chat_with_tools.assert_called_once()


-def test_run_chat(mock_ai_config, mock_ollama_llm):
+def test_run_llm_query_openai_timeout_raises_local_error(
+    mock_ai_config,
+    mock_openai_llm,
+):
+    mock_ai_config.llm_backend = "openai-like"
+    mock_ai_config.llm_model = "test_model"
+    mock_ai_config.llm_api_key = "test_api_key"
+    mock_ai_config.llm_endpoint = "http://test-url"
+
+    request = httpx.Request("POST", "http://test-url/v1/chat/completions")
+    mock_openai_llm.return_value.chat_with_tools.side_effect = openai.APITimeoutError(
+        request,
+    )
+
+    client = AIClient()
+
+    with pytest.raises(LLMTimeoutError):
+        client.run_llm_query("test_prompt")
+
+
+def test_run_llm_query_httpx_timeout_raises_local_error(
+    mock_ai_config,
+    mock_ollama_llm,
+):
    mock_ai_config.llm_backend = "ollama"
    mock_ai_config.llm_model = "test_model"
    mock_ai_config.llm_endpoint = "http://test-url"

    mock_llm_instance = mock_ollama_llm.return_value
-    mock_llm_instance.chat.return_value = "test_chat_result"
+    mock_llm_instance.chat.side_effect = httpx.ReadTimeout("timed out")

    client = AIClient()
-    messages = [ChatMessage(role="user", content="Hello")]
-    result = client.run_chat(messages)

-    mock_llm_instance.chat.assert_called_once_with(messages)
-    assert result == "test_chat_result"
+    with pytest.raises(LLMTimeoutError):
+        client.run_llm_query("test_prompt")
@@ -19,6 +19,7 @@ def mock_ai_config():
        MockAIConfig.return_value.llm_embedding_endpoint = None
        MockAIConfig.return_value.llm_allow_internal_endpoints = True
        MockAIConfig.return_value.llm_context_size = 8192
+        MockAIConfig.return_value.llm_request_timeout = 120
        yield MockAIConfig


@@ -71,6 +72,7 @@ def test_get_embedding_model_openai(mock_ai_config):
            model_name="text-embedding-3-small",
            api_key="test_api_key",
            api_base="http://test-url",
+            timeout=120,
            http_client=ANY,
            async_http_client=ANY,
        )
@@ -92,6 +94,7 @@ def test_get_embedding_model_openai_prefers_embedding_endpoint(mock_ai_config):
            model_name="text-embedding-3-small",
            api_key="test_api_key",
            api_base="http://embedding-url",
+            timeout=120,
            http_client=ANY,
            async_http_client=ANY,
        )
@@ -393,6 +393,23 @@ class TestCompact:
            for c in held:
                c.close()

+    def test_force_compact_streams_rows_across_batches(
+        self,
+        store,
+        monkeypatch,
+    ) -> None:
+        """Rebuild must preserve every row when rows span multiple batches.
+
+        A tiny batch size forces several fetchmany()/executemany() cycles so a
+        regression in the streaming loop (dropped tail, off-by-one) surfaces.
+        """
+        monkeypatch.setattr("paperless_ai.vector_store.COMPACT_BATCH_SIZE", 3)
+        store.add([make_node(f"n{i}", "1", seed=float(i)) for i in range(10)])
+        store.compact(force=True)
+        ids = {n.node_id for n in store.get_nodes(filters=_in_filter(["1"]))}
+        assert ids == {f"n{i}" for i in range(10)}
+        assert self._bloat_ratio(store) == pytest.approx(1.0)
+

 class TestDbFile:
    def test_single_db_file_in_index_dir(self, store, tmp_path: Path) -> None:
@@ -42,6 +42,11 @@ SCHEMA_VERSION = 1
 # a rebuild copies the live rows into a fresh table.
 COMPACT_BLOAT_RATIO = 2.0

+# compact(): number of rows copied per executemany() when rebuilding the file.
+# Rows are streamed from the source cursor in batches of this size rather than
+# materialized all at once, keeping memory bounded regardless of index size.
+COMPACT_BATCH_SIZE = 500
+
 # Filterable vec0 metadata columns. _build_where() only ever receives filter
 # keys we construct ourselves, but allowlisting keeps SQL identifiers safe by
 # construction.
@@ -500,24 +505,28 @@ class PaperlessSqliteVecVectorStore(BasePydanticVectorStore):
                value = self._meta_get(key)
                if value is not None:
                    self._meta_set_on(new_conn, key, value)
-            rows = self._conn.execute(
+            src_cursor = self._conn.execute(
                "SELECT id, document_id, modified, node_content, embedding "
                "FROM " + DEFAULT_TABLE_NAME,
-            ).fetchall()
-            new_conn.execute("BEGIN IMMEDIATE")
-            new_conn.executemany(
-                self._INSERT,
-                [
-                    (
-                        r["id"],
-                        r["document_id"],
-                        r["modified"],
-                        r["node_content"],
-                        bytes(r["embedding"]),
-                    )
-                    for r in rows
-                ],
            )
+            new_conn.execute("BEGIN IMMEDIATE")
+            # Stream rows from the source cursor in batches instead of
+            # materializing the whole table in memory, so a large index does
+            # not cause an OOM during routine maintenance compactions.
+            while batch := src_cursor.fetchmany(COMPACT_BATCH_SIZE):
+                new_conn.executemany(
+                    self._INSERT,
+                    [
+                        (
+                            r["id"],
+                            r["document_id"],
+                            r["modified"],
+                            r["node_content"],
+                            bytes(r["embedding"]),
+                        )
+                        for r in batch
+                    ],
+                )
            # Reset the cumulative counter: after compact, total_inserts == live.
            self._meta_set_on(new_conn, "total_inserts", str(live))
            new_conn.execute("COMMIT")
@@ -27,7 +27,7 @@ wheels = [

 [[package]]
 name = "aiohttp"
-version = "3.13.4"
+version = "3.14.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "aiohappyeyeballs", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
@@ -36,85 +36,92 @@ dependencies = [
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    { name = "multidict", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
    { name = "propcache", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+    { name = "typing-extensions", marker = "(python_full_version < '3.13' and sys_platform == 'darwin') or (python_full_version < '3.13' and sys_platform == 'linux')" },
    { name = "yarl", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
 ]
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 [[package]]
Author	SHA1	Message	Date
stumpylog	badd791e4c	Few more before	2026-06-22 07:27:48 -07:00
stumpylog	1941a018b9	Even more vulns	2026-06-19 10:05:41 -07:00
Trenton H	7bf2a9ff82	Fix (beta): Stream chunks during compaction to prevent oom on smaller installs (#13014 )	2026-06-19 15:40:16 +00:00
Trenton H	bb5d7438b1	Documentation (beta): Updates documentation for new v3 features (#13033 ) Co-authored-by: shamoon <4887959+shamoon@users.noreply.github.com>	2026-06-18 16:20:31 -07:00
Trenton H	a009ea1f04	Chore(beta): Update suggested broker to Valkey + Redis agnostic documentation (#13032 ) Co-authored-by: upmcplanetracker <219436948+upmcplanetracker@users.noreply.github.com>	2026-06-18 19:54:15 +00:00
shamoon	0cdf718d9f	Fix (beta): truncate embedding queries for small chunk size (#13028 )	2026-06-18 16:15:40 +00:00
shamoon	262183e848	Enhancement (beta): support LLM timeout config (#13002 )	2026-06-18 08:35:11 -07:00
Trenton H	b8f10269a7	Fix(beta): Workaround a Tantivy panic in more like this searching (#13026 )	2026-06-17 15:43:48 +00:00
shamoon	bcf5d2cffc	Chore: set tool_required to opena-like llm calls (#13025 )	2026-06-17 06:24:38 -07:00
shamoon	8bd620d8ab	Enhancement: ignore diacritics, support multiple substring matching for UI filtering (#13021 )	2026-06-17 05:58:55 -07:00
Trenton H	ad1b54ce88	Fix (beta): Catch consumer files created during watcher re-creations (#13013 )	2026-06-15 19:23:54 -07:00