athens VS mindforger

You might be interested in how in Athens we modelled properties as named (map-like) block relationships: https://github.com/athensresearch/athens/blob/main/doc/adr/0026-properties.md

https://github.com/athensresearch/athens

The underlying in memory datalog style database that can run in the browser that enables these apps

There is a docker compose file on there github that made things easy to get started. Has a web interface and was very quick. You could have multiple users as well.

I need a guide how to install athens for installing previously downloaded go packages. I have created a binary file from athens https://github.com/athensresearch/athens and configure .toml with path to local storage, also change the storage parameter to [disk

Athens

Get the docker compose manager. Create a new stack and put in the docker Compose file from Athens. Change to the ports and volumes you want and you are good to go.

Has anyone successfully running a selfhosted version of Athens from athensreseach. There is a demo. They even have a document page with some help for docker.

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/