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Documentation (beta): Updates documentation for new v3 features (#13033)

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Co-authored-by: shamoon <4887959+shamoon@users.noreply.github.com>
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Trenton H
2026-06-18 16:20:31 -07:00
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@@ -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