embeddingsearch/README.md

embeddingsearch

Embeddingsearch is a python library that uses Embedding Similarity Search (similiarly to Magna) to semantically compare a given input to a database of pre-processed entries.

When first implementing the idea, it was conceptualized to only import files into the database.

How to set up

1. Install
2. Pull a few models using ollama (e.g. paraphrase-multilingual, bge-m3, mxbai-embed-large, nomic-embed-text)
3. Install the depencencies
4. Set up a local mysql database

How to run the example script

1. Start the script python3 dbtest.py
2. Generate the index. Type in index_folder and submit. Then target and submit. (This might take a while with no GPU acceleration - go get some coffee)
3. After the indexing is done, you may prompt searches using search

Installing the dependencies

Ubuntu 24.04

pip install mysql.connector apt install python3-magic

Windows

TODO

MySQL database setup

1. Install mysql: sudo apt install mysql-server and connect to it: sudo mysql -u root
2. Create the database CREATE DATABASE embeddingsearch; use embeddingsearch;
3. Create the user CREATE USER embeddingsearch identified by "somepassword!"; GRANT ALL ON embeddingsearch.* TO embeddingsearch;
4. Create the tables

CREATE TABLE searchdomain (id int PRIMARY KEY auto_increment, name varchar(512), settings JSON);

CREATE TABLE query (id int PRIMARY KEY auto_increment, id_searchdomain int, query TEXT, FOREIGN KEY (id_searchdomain) REFERENCES searchdomain(id));

CREATE TABLE entity (id int PRIMARY KEY auto_increment, name varchar(512), probmethod varchar(128), id_searchdomain int, FOREIGN KEY (id_searchdomain) REFERENCES searchdomain(id));

CREATE TABLE queryresult (id int PRIMARY KEY auto_increment, id_query int, id_entity int, result double, FOREIGN KEY (id_query) REFERENCES query(id), FOREIGN KEY (id_entity) REFERENCES entity(id));

CREATE TABLE attribute (id int PRIMARY KEY auto_increment, id_entity int, attribute varchar(512), value longtext, FOREIGN KEY (id_entity) REFERENCES entity(id));

CREATE TABLE datapoint (id int PRIMARY KEY auto_increment, name varchar(512), probmethod_embedding varchar(512), id_entity int, FOREIGN KEY (id_entity) REFERENCES entity(id));

CREATE TABLE embedding (id int PRIMARY KEY auto_increment, id_datapoint int, model varchar(512), embedding blob, FOREIGN KEY (id_datapoint) REFERENCES datapoint(id));

To-do

• Proper config file
• Add support for other databases?
• Add database setup script?
• Remove tables related to caching (It's not done on the sql server side anymore.)

Off-scope

• Support for other database types