semvec/pypi-packages huggingface dataset:
optimize-query 0.0.1
Fine-tune BERT for vectorized query search
License: MIT

# Vector Query Optimizer

This project aims to use any pre-trained BERT model from the HuggingFace library 'transformers' as a custom embedding function. It is useful when your query results are sometimes not related to your input.

To see the improved results, it utilizes the open-source vector database ChromaDB.

The structure of the custom embedding model is simply adding another linear layer on top of the base BERT model of your choice, and freezing the original BERT model's parameters before fine-tuning the weights of the linear layer. By doing this, it preserves the original knowledge of the BERT model, thus only improving the search results without causing 'forgetting'.

# How to Use

Important: I'm having some problems with publishing this as a package, so you can just download the source code
and use the script 'vecquery_tune' under the folder 'vecquery_tune' as it is. I will release this 
as a package as soon as possible.

Here is an example usage:

```
from vecquery_tune import FineTune

# fine tune model
fine_tune = FineTune(
    data_path='data.json',
    model_name='bert-base-uncased',
    path_to_save_model='./'
)

fine_tune(
    epochs=20,
    batch_size=32,
    max_len=256,
    lr=2e-5
)
```

'data.json' file must be of the format:
```
[
    {
        'input': 'query input',
        'output': 'the desired output of the query'
    },
]
```

To see the improved results, you need to first create a database and add your data into it.
This package utilizes ChromaDB to run inference and see the results. Before using the 
'Inference' class, you need to use 'CreateDatabase' class and create a database.

Here is how you can use the 'CreateDatabase' class:

```
from vecquery_tune import CreateDatabase

# create database
create_database = CreateDatabase(
    data_path='data.csv',
    model_name='bert-base-uncased',
    model_weights_path='./model.pt',
    collection_name='collection',
    client_path='./'
)

create_database(
    metadata_column='time,author', # seperate metadata columns by either ';' or ','
    documents_column='docs',
    max_len=256
)
```

After creating the database, you can run inference via the 'Inference' class.
Keep in mind that if you supplied a collection name and a client path different than the 
default ones, you need to supply them here as well.

Here is how you can use the 'Inference' class:

```
from vecquery_tune import Inference

# inference
inference = Inference(
    model_weights_path='./model.pt',
    model_name='bert-base-uncased',
    collection_name='collection',
    client_path='./',
    num_results=5
)

inference(
    metadata_column='author',
    documents_column='docs',
    max_len=256
)
```

# What's Next
- Add JSON formatted data option for creating the database and running inference.
- Add flexibility regarding the JSON formatted data keys when fine-tuning.

# Issues with Installing ChromaDB

Please note that if you encounter issues while installing ChromaDB, you may need to install Visual Studio. You can take a look at [this SO answer](https://stackoverflow.com/a/76245995) for more information.