Okay, so I wanted to mess around with this “LanceDB” thing and see if I could actually get it to work. I’ve been hearing about vector databases and how they’re supposed to be super fast for searching, so I figured, why not give it a shot? I decided to try using it with a “Lance” drive, basically trying to index some data on my disk and then search it quickly.
Getting Started
First, I had to install the thing. It was pretty straightforward, just a simple pip install command. Nothing too crazy there. I think it was something like `pip install lancedb`.
Dumping Some Data In
Then came the fun part, actually getting some data into the database. I created a dummy dataset. I didn’t want anything too complex, just some made-up stuff with some numbers and text, simulating data that has vector embeddings.
- I made a Python list of dictionaries.
- Each dictionary had an “id”, a “vector”, and maybe a “text” field. The vector was just a list of random floats.
I used the `lancedb` library to create a connection to where I wanted to store this thing (on my drive, in a folder). And then used the example code to create a table.
The Actual “Lance Drive” Part
The whole point was to use my hard drive, right? So, I made sure the LanceDB was saving the data to a specific location. I think it was in a folder I called “lance_data” or something, and gave this path to lancedb.
Query Time!
Now for the moment of truth: searching. I crafted a simple query, another vector that I made up. The idea was to find the closest vectors in my dummy data to this query vector, and get some kind of top results. This is the “nearest neighbor” search that vector databases are good at.
I used `*(query_vector).limit(5).to_pandas()` ,where the `query_vector` is the data I wanted to search.
Did it Work?
It did! I got back a few results. I think I told it to return the top 5 closest matches. It was pretty fast, even with my totally fake and unoptimized data. It printed out the IDs and distances of the matches, just like I expected. So, the basic setup was working.
It wasn’t super exciting because it was all fake data, It just looks for numbers close to the ones in my query.
Wrapping Up
So, that was my little adventure with LanceDB and a “Lance drive”. It was more about making sure the basic process worked than doing anything super useful. I can definitely see how this could be cool for searching large datasets of images, audio, or text, if you have the right kind of embeddings. My next step will probably be to try it with some real-world data and see how it performs.
