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☁️ Backend

The backend uses the following technologies:

  • Python
  • Django
  • Pytorch

✨ Code Standards​

In order to keep the code consistently structured, the backend has a pre-commit hook. Run pre-commit install from apps/backend/ and the linter and the formatter will check your work before you commit.

We use ruff for both linting and formatting, configured in apps/backend/pyproject.toml. You can also run it by hand with ruff check . and ruff format ..

🐛 Debugging​

Usually nothing goes as planned, and you need to debug your shiny new feature. In the next paragraph, I will explain to you the two tools we have for debugging:

Using pdb​

Add the following in the python code where you want a breakpoint:

import pdb; pdb.set_trace()

Attach to the backend service:

docker attach $(docker ps --filter name=backend -q)

Debug as normal in pdb!

When you're done debugging, continue execution (c) and press Ctrl-P followed by Ctrl-Q to detach from the container without stopping it.

Using silk​

In order to debug queries, start the backend container in dev mode. Then you can access silk under /api/silk. Silk is a live profiling and inspection tool for the Django framework. Silk intercepts and stores HTTP requests and database queries before presenting them in a user interface for further inspection.

🏙️ Structure​

There are a lot of folders in our backend. Here is a quick rundown on where you can find what. The Django project itself — settings (librephotos/settings/), URL routing (librephotos/urls.py) and wsgi.py — lives in the librephotos/ folder, with supporting pieces in scripts/, chunked_upload/ and nextcloud/.

Django​

Most of the application code is within the API folder using Django. In the following section, I will explain where you can find what

management​

This exposes all the commands that you can use via the command line. If you want a new command, that's the place to add it.

migrations​

Every time we change our models, we have to migrate the database. We use Django migration feature to create migration files to migrate without the headaches.

models​

Here are the actual data types. If you want to figure out how a photo works or how faces are connected to persons, then this is your folder.

views​

Here you can find our API implemented. They are separated, similar to the models. Views that expose the photos will be here in photos too.

serializers​

You have your python model and want to somehow convert that to JSON. That's what the serializer does!

Services​

Not everything runs inside Django. The heavy machine-learning work lives in standalone Flask processes that Django talks to over plain HTTP on localhost. Seven of them sit under service/ — thumbnail, face_recognition, clip_embeddings, image_captioning, llm, exif and tags — and image_similarity/ is a separate top-level folder. Each is served by a gevent WSGIServer on a fixed port; the ports are defined in the SERVICES dict in api/services.py (image_similarity 8002, thumbnail 8003, face_recognition 8005, clip_embeddings 8006, image_captioning 8007, llm 8008, exif 8010, tags 8011).

You start them with python manage.py start_service, which also schedules api.services.check_services in django-q2 to poll each service's /health endpoint and restart any that have gone stale or died. The llm service additionally needs certain CPU features (avx, sse4_2) and is skipped on hardware that lacks them.

Because these are separate processes, the docker attach + pdb trick above does not reach them — to debug a service, check its log under /logs/ or add logging in the service's own main.py.

Machine Learning​

We use as a base framework PyTorch. If you find a cool machine learning model with PyTorch, we sure can add that too.

Image Captioning​

Captions are generated on demand by the image captioning service (service/image_captioning/, port 8007). The model is chosen with the Captioning Model site setting (im2txt by default, or none to turn caption generation off):

  • im2txt — the original PyTorch captioning model. It still works, though its output is fairly basic.
  • blip_base_capfilt_large (BLIP) — a newer captioning model. It is not a separate service; it lives inside the image captioning service at service/image_captioning/api/im2txt/blip/ and is selected by passing blip=True through to the model.
  • moondream — a visual LLM. This one does not go through the captioning service at all: api/image_captioning.py routes it to the llm service (port 8008).

Whichever model runs, the caption is stored under the "im2txt" key in PhotoCaption.captions_json, and if an LLM is enabled it can post-process the caption before it is saved. For the user-facing comparison of these models, see the image captioning guide.

Face Recognition​

We use InsightFace to detect and recognise faces, running on ONNX Runtime: an SCRFD detector finds the faces and an ArcFace model turns each one into a 512-dimension embedding. This replaced the previous dlib / face_recognition pipeline (which produced 128-dimension encodings). The model is selectable via the Face Recognition Model site setting (buffalo_sc by default). Unknown faces are grouped with automatic clustering, and a separate classification step matches faces against already-labelled people.

Tagging Models​

The tags service (service/tags/) generates auto-tags for photos. Two models are available, selectable via the Tagging Model site setting:

  • places365 — Scene classification using the Places365 CNN. Generates scene category tags (e.g. "kitchen", "beach").
  • siglip2 (service/tags/siglip2/) — Google's SigLIP 2 vision-language model running as ONNX. Uses zero-shot classification by computing cosine similarity between image embeddings and a curated vocabulary of 900+ text tag embeddings. Tag embeddings are cached to disk after the first run. Returns the top 10 tags.

Tags from each model are stored independently in PhotoCaption.captions_json under their model key (e.g. "places365", "siglip2"), so switching models does not require regeneration.

Here you can find the code which allows us to search semantically for images like "trees in a valley".