Trying out vector embeddings

newsSum is a Google App Engine application that bundle articles from different news sources. To try out ML embeddings, I decided to add a suggestions service.

High-level idea

• There will be no changes to the backend of "newssum". The suggestion service "newssum-sug" will be implemented as a separate service
• The frontend of "newssum" will check if the suggestion service "newssum-sug" is available. If so, it will allow the user to expand an article and query the suggestion service for additional information to display

Implementation of the suggestion service

• Technically, "newssum-sug" could gather suggestions from any sources (e.g. Google search results, a Youtube video etc). But for now, it will process articles from selected "newssum" sources. So, there will be scheduled tasks to collect articles from "newssum" and prepare them for searching.
• Vector embeddings will be used to find similar articles. A machine learning model is used to turn a news headline into a vector of numbers. When a query comes in, an embedding will also be generated from that query. By comparing the distance between vectors, we could find articles that are related to the query.
• The embeddings generated during batch processing are stored in a vector database. The database will also provide the mechanism for searching vectors by distance.
• Since "newssum" is for current news only, embeddings will only be kept for 2 days.
• The suggestion service can also be used for free-text search. But for now, the frontend only uses it for article suggestions.

While "newssum" is open source, the "newssum-sug" service is still under development in closed source. But the basic functionality has been integrated and available on the demo site.

A machine learning model to identify music albums from photos

 

I was looking for a home automation project to select and play specific music album from stream services. There are similar ideas of using NFC tags. Basically, it means preparing some NFC tags with album/movie cover arts on them. And putting a tag on the reader will trigger the playback of that album/movie. While it brings the joy of handling and selecting physical collections, it costs money and time to prepare those NFC tags and I wanted to avoid that.

Since now we have those machine learning models and classifiers, I thought I can just train up a model to look at a webcam photo of a record / CD and tell me the Spotify link to play that album.

BTW, I know Microsoft co-pilot (or maybe OpenAI too) can do it without any special training, but then I don't want to pay extra for that and just wanted to host the model on my own machines.

I imagine it will be something like this:

I put an album in front of a webcam...

... and the model will tell me the Spotify URL to pass on to the music streamer

Long story short, my model can a identify my music collection with a 98% correctness (more on that later). If you are interested in the technical details and the scripts used to train the model, they are available on github: https://github.com/kitsook/AlbumSpotter

But eventually I didn't integrate this into my home automation, which is kind of related to the correctness. When I got a new CD / vinyl record, I always add that to my collection on Spotify. So I can just get the cover arts from Spotify to train my model. But then I discovered there are at least two problems that will affect the correctness:

• there are many editions of the same album. e.g. I could have a physical CD of the standard edition but my Spotify collection has the extended edition with a different song list
• nowadays artists tend to release an album with several "special" cover arts. My physical copy could look totally different from the one on Spotify

That means I will need to cleanup the data for a more accurate result. As procrastination kicks in, I am stopping the project with just the machine learning model and the home automation part will be a future project.

OpenSUSE Tumbleweed - another bug

 

Tumbleweed used to be great in the past eight years or so that I used it on my main desktop. But recently it is just bugs after bugs after every update.

Here is another one after updating today: the KDE screenlocker crashed with segfault and failed to unlock the screen. Seems to be pam library related.

Google cloud AppEngine deployment issue with cache

Notes to self: when deploying Google Cloud applications, if encounter the following "no such object" error, it is because the deployment process is looking for cache that doesnt exist.

"build": ERROR: failed to initialize analyzer: getting previous image: getting config file for image

To solve it, deploy with "--no-cache" parameter.

Segfault with bluez and A2DP

Currently (as of Feb 23, 2024) openSUSE Tubleweed with bluez 5.71-2.4 is experiencing segfault whenever I try to connect my Sony WH-H800 headphone (but fine with others).

After some digging, seems like it is plagued by a recent bug. Waiting for Tumbleweed to include the patch.

[Fri Feb 23 16:23:24 2024] bluetoothd[9971]: segfault at 5600bf0628b5 ip 00005605de5743c1 sp 00007ffee8f18f70 error 4 in blu
etoothd[5605de552000+d6000] likely on CPU 7 (core 1, socket 0)
[Fri Feb 23 16:23:24 2024] Code: 41 83 2c 24 01 0f 85 1b ff ff ff 4c 89 e7 e8 96 f4 fd ff e9 0e ff ff ff 90 41 55 41 54 55 5
3 48 83 ec 08 48 8b 2a 48 8b 7a 08 <48> 8b 45 20 4c 8b ad 88 00 00 00 4c 8b 20 48 85 ff 74 19 c7 47 08

$ sudo coredumpctl info 9971
          PID: 9971 (bluetoothd)
          UID: 0 (root)
          GID: 0 (root)
       Signal: 11 (SEGV)
    Timestamp: Fri 2024-02-23 16:23:24 PST (4h 2min ago)
 Command Line: /usr/libexec/bluetooth/bluetoothd
   Executable: /usr/libexec/bluetooth/bluetoothd
Control Group: /system.slice/bluetooth.service
         Unit: bluetooth.service
        Slice: system.slice
      Boot ID: 153713284dde4d7cba57f31e2956690d
   Machine ID: 5c42528e25094a3cb1af7e2c43a85357
     Hostname: linux-lct7
      Storage: /var/lib/systemd/coredump/core.bluetoothd.0.153713284dde4d7cba57f31e2956690d.9971.1708734204000000.zst (present)
 Size on Disk: 138.1K
      Message: Process 9971 (bluetoothd) of user 0 dumped core.
                
               Stack trace of thread 9971:
               #0  0x00005605de5743c1 n/a (bluetoothd + 0x463c1)
               #1  0x00005605de55f4d0 n/a (bluetoothd + 0x314d0)
               #2  0x00005605de55f5a8 n/a (bluetoothd + 0x315a8)
               #3  0x00005605de569767 n/a (bluetoothd + 0x3b767)
               #4  0x00007fc0b22daf30 n/a (libglib-2.0.so.0 + 0x5bf30)
               #5  0x00007fc0b22dcb58 n/a (libglib-2.0.so.0 + 0x5db58)
               #6  0x00007fc0b22dd42f g_main_loop_run (libglib-2.0.so.0 + 0x5e42f)
               #7  0x00005605de5555dc n/a (bluetoothd + 0x275dc)
               #8  0x00007fc0b1e2a1f0 __libc_start_call_main (libc.so.6 + 0x2a1f0)
               #9  0x00007fc0b1e2a2b9 __libc_start_main@@GLIBC_2.34 (libc.so.6 + 0x2a2b9)
               #10 0x00005605de5566b5 n/a (bluetoothd + 0x286b5)
               ELF object binary architecture: AMD x86-64

Logitech Media Server commands

While moving the music stream service from Tidal to Spotify on Home Assistant, I found I forgot how to get the playlist ID from Logitech Media Server (LMS).

Note to self: connect to LMS with "nc lms-address 9090" and issue the command "playlists 0 100 tags:E". The command doc is available at "lms-address:port/html/docs/cli-api.html"