Yes. A Lightroom catalog file is, after all, just a SQLite database. (Srsly, make a copy of your catalog file, rename it whatever.sqlite and use your favorite SQLite GUI to rip it open and look at the tables and fields). It's just storing the pathame to the RAW file for that file's record in the database. Source: about 1 year ago

I use visidata with a playback script I recorded to open the sheet to a specific Excel tab, add a column, save the sheet as a csv file. Then I have a sqlite script that takes the csv file and puts it in a database, partitioned by monthYear. Source: about 1 year ago

Use the most-used database in the world: https://sqlite.org/index.html. Source: over 1 year ago

With this in mind, I wrote a few versions of this post, but I hated them all. Then I realized that jodliterate PDF documents mostly do what I want. So, instead of rewriting MirrorXref.pdf, I will make a few comments about jodliterate group documents in general. If you're interested in using SQLite with J, download the self-contained GitHub files MirrorXref.ijs and MirrorXref.pdf and have a look. - Source: dev.to / almost 2 years ago

SQLite, by many estimates, is the most widely deployed SQL database system on Earth. It's everywhere. It's in your phone, your laptop, your cameras, your car, your cloud, and your breakfast cereal. SQLite's global triumph is a gratifying testament to the virtues of technical excellence and the philosophy of "less is more.". - Source: dev.to / almost 2 years ago

AgentCloud uses Qdrant as the vector store to efficiently store and manage large sets of vector embeddings. For a given user query the RAG application fetches relevant documents from vector store by analyzing how similar their vector representation is compared to the query vector. - Source: dev.to / 24 days ago

Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker. - Source: dev.to / about 1 month ago

I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours. - Source: dev.to / about 1 month ago

I'm currently looking to implement locally, using QDrant [1] for instance. I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2]. [1]. https://qdrant.tech/. - Source: Hacker News / 2 months ago

There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb. - Source: Hacker News / 3 months ago