quese 0.1.2
Package that make easier the searching process in pyhton, through Embeddings and Semantic Similarity
License: MIT
Keywords: searching,search,embeddings,intelligent search,similarity search,embedding search,sentence transformer search,searcher,semantic similarity,sentence similarity

# QUESE

"Quese" allows you implement in an easy way a Search Algoritm, based on Embeddings and Semantic Similarity, in your Python apps.
The module provides a function called search_by_embeddings(), with several params to customize the searching process.

## INSTALLATION

You can install "quese" with pip:

```bash
pip install quese
```
## EXAMPLE WITH BY
```
from quese import search_by_embeddings

data_ = [
    {
        "title": "UX Designer",
        "tags": "Designer"
    },
    {
        "title": "Senior Accounter",
        "tags": "Accounter" 
    },
    {
        "title": "Product Manager",
        "tags": "Managment" 
    }
]

results = search_by_embeddings(data=data_, query="Manager", by="title")
#Results will return a LIST with the dictionaries whose title is Semantically Similar to the query: "Manager", so in this case, the last dictionary: "Product Manager".
print(results)
```
## PARAMS

#### __data__:
It's the first param, it's **REQUIRED**, and it must be a **list of dictionaries**.

#### __query__:
It's the second param, it's **REQUIRED** as well, and it represent the query you want to pass.<br>
Type: **string**

#### __by__:
It's the third param, it's **only REQUIRED if you don't pass the "template" param**, and it represent the value of your dictionaries that you are searching for.<br>
For example, if you want to search in  a list of products, your "by" param could be the prop "name" of each product.<br>
Type: **string**

#### __template__:
It's **only REQUIRED if you don't pass the "by" param**, and it's similar to "by", but allow you to search by a customized string for each dictionary in your data list.<br>
For example, if you want to search in a list of products, your "template" param could be a string like this: "{name}, seller: {seller}".
Notice that you have to define your props **between "{}"**, as you can see in the example with the variables **"name"** and **"seller"**.<br>
Type: **string**

#### __accuracy__:
It's **optional**, and it represents the similarity that the dictionary must have with the query to be considered a result.<br>
**The default value is 0.4**, wich works good with almost all the models. However, if you want to change it, we don't recommend to set vary high values or very low values, the range **0.3-0.6** should be enought.<br> 
Type: **float number between 0-1**

#### __model__:
It's **optional**, and it represents the **embedding model** you want to use.<br>
The default model is **'sentence-transformers/all-MiniLM-L6-v2'**. You can use an other model like 'sentence-transformers/all-mpnet-base-v2', but take care because **if the model don't work with sentence-transformers this package will not work with it**.<br>
Type: **string**

## EXAMPLE WITH TEMPLATE
```
from quese import search_by_embeddings

data_ = [
    {
        "title": "UX Designer",
        "tags": "Designer"
    },
    {
        "title": "Senior Accounter",
        "tags": "Accounter" 
    },
    {
        "title": "Product Manager",
        "tags": "Managment" 
    }
]

results = search_by_embeddings(data=data_, query="Manager", template="{title}, {tags}")
#Results will return a LIST with the dictionaries whose title and tags are Semantically Similar to the query: "Manager", so in this case, the last dictionary: "Product Manager".
print(results)
```