jyut-dict VS mindforger

Shameless plug, if you’re on PC or Mac, Jyut Dictionary also has words.hk data + Wiktionary + CC-Canto in a Pleco-like interface!

Unaffiliated with this group, but there are a bunch of graded readers, romanized with Jyutping, freely available, and very high quality![1]

A dictionary program for Windows/Mac/Linux that I make [2], and another for mobile that I don’t make.[3]

Finally, a list of other good resources from Cantonese Alliance.[4]

[1] https://hambaanglaang.hk/

[2] https://jyutdictionary.com/

[3] https://www.pleco.com/

[4] https://cantonese-alliance.github.io/language.html

The database creation script is also available, so you can see how each of the tables are created.

Jyut Dictionary

Individual users can download it for themselves though! If you have some programming knowledge, you can try running the scripts on your own computer: https://github.com/aaronhktan/jyut-dict/tree/master/src/dictionaries/cuhk

Rear is a really interesting project with admirable goals. I believe this is just the beginning, but you have already done a great job!

I have been working on my note-taking application (https://github.com/dvorka/mindforger) for some time and wanted to go in the same direction. However, I gave up (for now). I used ggerganov/llama.cpp to host LLM models locally on a CPU-only machine with 32GB RAM, and used them for both RAG and note-taking use cases (like https://www.mindforger.com/index-200.html#llm). However, it did not work well for me - the performance was poor (high hardware utilization, long response times, failures, and crashes) and the actual responses were rarely useful (off-topic and impractical responses, hallucinations). I tried llama-2 7B with 4b quantization and a couple of similar models. Although I'm not happy about it, I switched to an online commercial LLM because it performs really well in terms of response quality, speed, and affordability. I frequently use the integrated LLM in my note-taking app as it can be used for many things.

Anyway, Reor "only" uses the locally hosted LLM in the generation phase of the RAG, which is a nicely constraint use case. I believe that a really lightweight LLM - I'm thinking about a tiny base model fine-tuned for summarization - could be the way to go (fast, non-hallucinating). I'm really curious to know if you have any suggestions or if you will have any in the future!

As for the vector DB, considering the resource-related problems I mentioned earlier, I was thinking about something similar to facebookresearch/faiss, which, unlike LanceDB, is not a fully-fledged vector DB. Have you made any experiments with similarity search projects or vector DBs? I would be interested in the trade-offs similar to small/large/hosted LLMs.

Overall, I think that both RAG with my personal notes as a corpus and a locally hosted generic purpose LLM for the use cases I mentioned above can take personal note-taking apps to a new level. This is the way! ;)

Good luck with your project!

Please share your suggestions, ideas or constructive criticism! You may install or update from GitHubreleases or PPA.

I finally managed to complete new MindForger release:

https://www.mindforger.com/NimbusnoteWikidpadBecause you mentioned writing:

Neat, the Similarity type reminds me of MindForger's Associations feature that also displays similarity scores between your current note and other existing notes

[Mindfrogger](https://github.com/dvorka/mindforger)

Mindforger: https://github.com/dvorka/mindforger/