This article is in continuation of my previous article related to Journey of relational to distributed and LSM. This article will often contain below words. I will try to cover its actual meaning as they will come. This article also assumes that you know about LLM ( like chatgpt) models –
- Large language models
- TiDB (vector supported distributed database)
- high dimensional data
- vector
- semantic search
- knowledge retrieval
- RAG( Retrieval augmented generation )
- Euclidean distance
- cosine similarity
- ANN
- High dimensional indexes like HNSW
- methods to perform similar searches
Let us try to understand from a use case which I was discussing with my colleagues a few days ago, and then I thought to put it down in my article in a very simple language so that everyone can understand it.
Let’s think about Honda City of white colour. What things does my Honda City have?
- HP of engine : 1500
- Car colour : 0001 ( white colour)
- Car length : 75
- Car Width : 40
- Number of gears : 6
- Cruise feature : 1
- Maximum speed : 400 (Kmph)
- Pickup speed :60
- Seat colour : 010
- Ambience light : 101
- Airbags :6
- Speakers :4
- Twitter : 2
How I selected these attributes and allocated these numbers ?
Now how I selected these attributes and on what basis I allocated these numbers is a separate discussion, which is related to training models but for now you can think that it’s random and there is no such rule that only specific numbers can be provided to a specific attribute.
Going back to the topic, if I combine all these numbers, it looks like a list of numbers :
{1500,0001,75,40,6,1,400,60,010,101,6,4,2}
This becomes a vector representation of my Honda City, and every attribute refers to a dimension. If I keep on adding more attributes of the car, it becomes high-dimensional data, i.e it has got high number of attributes.
Do I need a separate data type for this ?
Yes, like varchar, we do have a VECTOR data type. See below. Please note that database I used here is TiDB
mysql> show create table cars;
+-------+------------------------------------------------------------------------------------------------------------------------------------------------------+
| Table | Create Table |
+-------+------------------------------------------------------------------------------------------------------------------------------------------------------+
| cars | CREATE TABLE `cars` (
`name` char(10) DEFAULT NULL,
`car_vector` vector DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_bin |
+-------+------------------------------------------------------------------------------------------------------------------------------------------------------+
1 row in set (0.00 sec)
Is this data type available in traditional databases ?
Storing these array or list of numbers or vector in traditional databases like Postgres/MySQL as varchar , is possible, but if I try to find similar products then I can’t perform a similarity search. One obvious question comes to mind about using “Like operator“, but that performs text-based searching on a specific pattern and can’t give similar data.
Let’s go into more detail on implementing it in traditional databases and understand why it won’t work-
Let’s add one more car. Say, you have a Red Hyundai Verna whose vector representation can be –
verna – {1500,0011,75,40,6,1,300,50,100,110,4,3,1}
and my city was – {1500,0001,75,40,6,1,400,60,010,101,6,4,2}
If we perform the euclidean distance ( to search for similar products ) then we will see that both items are quite close and thus can be called similar. I can use multiple mathematical functions to do this, but the real problem is SCALE. You cannot handle multiple such operations at the scale of such a million records.
mysql> insert into cars values ('city_sedan','[1500,1,75,40,6,1,400,60,10,101,6,4,2]');
Query OK, 1 row affected (0.02 sec)
mysql> insert into cars values ('vern_sedan','[1500,1,79,40,6,1,450,60,10,101,3,2,1]');
Query OK, 1 row affected (0.01 sec)
That’s where the vector database knocks in ? Can we use TiDB, which is a distributed database and supports vector datatype with HNSW, ANN , euclidean distance and cosine ?
They calculate the similarity search using multiple methods, but not limited to below.
- Search on the same 2D-3D axis or multi-dimensional axis.
- Magnitude search ( not only subtraction of numbers ) i.e cosine search
Let’s try to perform some search using euclidean distance in TiDB, which works on below fundamental.
(vectorA1- vectorA2)^2 + (vectorB1- vectorB2)^2
sqroot(vector A + vector B)
Think about a situation wherein you need to find a car, which is similar to your given vector input. I have decided to give an input of high end speed cars –
mysql> SELECT name,VEC_L2_DISTANCE(car_vector,'[5000,10,19,400,60,10,330,600,100,1001,30,20,10]') from cars;
+------------+--------------------------------------------------------------------------------+
| name | VEC_L2_DISTANCE(car_vector,'[5000,10,19,400,60,10,330,600,100,1001,30,20,10]') |
+------------+--------------------------------------------------------------------------------+
| city_sedan | 3674.4128782704865 |
| vern_sedan | 3675.8008651176956 |
+------------+--------------------------------------------------------------------------------+
2 rows in set (0.00 sec)
Above is the distance of the input from the available vectors. Now here we can do the filtering :
mysql> SELECT name,VEC_L2_DISTANCE(car_vector,'[5000,10,19,400,60,10,330,600,100,1001,30,20,10]') from cars where VEC_L2_DISTANCE(car_vector,'[5000,10,19,400,60,10,330,600,100,1001,30,20,10]') < 100;
Empty set (0.00 sec)
This means there is no car we have which is near 100 distance. This value of 100 is specific to the need that at what threshold we want to setup our similar search.
Not all databases which support vector datatype can be called as fully vector DB.
For example MySQL 9 supports VECTOR as a data type, but still it needs time to get mature as it can’t be completely called as vector database because it still cant scale well to millions and billions of records and also the absence of required indexes for vector search makes it less efficient for billions of search. Moreover, what MySQL supports as a part of the method to perform similarity search is Euclidean distance but not COSINE support ( which is good for DNA search ).
So what we learnt until now –
- What is similarity search ?
- How these searches are being implemented ?
- Why traditional databases are not efficient for these purposes even though they are supporting it ?
- What is vector and methods to do it ?
- Indexes for vector data types
Can’t we use B-tree indexes on these data types ?
We all worked on Btree, but it is not well suited for similarity search at such a large scale, and this is where HNSW search knocks in, and it is blazing fast because the amount of comparison is very, very less, and it doesn’t focus on exact match but similar by creating layers.
So now going back to our question “what databases are well suited for LLM” . LLM ( like chat gpt or deepseek) uses RAG to get more data from knowledge, and this knowledge needs to be stored, and this store can be vector.
In 2nd phase of this article, I will do the implementation on TiDB to see how it actually works.
SimplifieDB 
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