Bhakti

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An easy-to-use vector database.

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Powered by <a href="https://github.com/vortezwohl/Dipamkara">Dipamkara</a>

## Abstract

With the rapid development of big data and artificial intelligence technologies, the demand for effective processing and retrieval of vector data is growing. Against this backdrop, I have developed the Bhakti vector database, aiming to provide a lightweight and easy-to-deploy solution to meet the storage and semantic search needs of small and medium-sized datasets. Bhakti supports a variety of similarity calculation methods and a domain-specific language (DSL) for document-based pattern matching pre-filtering, facilitating migration of data with its portable data files, flexible data management and seamless integration with Python3. Furthermore, I propose a memory-enhanced large language model dialogue solution based on the Bhakti database, which can assign different weights to the question and answer in dialogue history, achieving fine-grained control over the semantic importance of each segment in a single dialogue history. Through experimental validation, my method shows significant performance in the application of semantic search and question-answering systems. Although there are limitations in processing large datasets, such as not supporting approximate calculation methods like HNSW, the lightweight nature of Bhakti gives it a clear advantage in scenarios involving small and medium-sized datasets.

## Citation

If you are incorporating `Bhakti` into your research, please remember to properly **cite** it to acknowledge its contribution to your work.

如果您正在將 `Bhakti` 整合到您的研究中，請務必正確引用它，以聲明它對您工作的貢獻.

```latex
@article{wu2025bhakti,
  author = {Zihao Wu},
  title = {Bhakti: A Lightweight Vector Database Management System for Endowing Large Language Models with Semantic Search Capabilities and Memory},
  journal = {arXiv preprint},
  year = {2025},
  eprint = {2504.01553},
  archivePrefix = {arXiv},
  primaryClass = {cs.DB},
  url = {https://arxiv.org/abs/2504.01553}
}
```

## Installation

- From [PYPI](https://pypi.org/project/bhakti/)

```shell
    pip install bhakti
    ```

- From [Github](https://github.com/vortezwohl/bhakti/releases)

Download .whl first then run

```shell
    pip install ./bhakti-X.X.X-py3-none-any.whl
    ```

## Quick Start

**Before all, make sure you've successfully installed Bhakti :)**

- ### Run Bhakti Server

- To begin, create a path for storing data

```shell
    mkdir -p /path/to/db
    ```
  
  - #### **Start server using shell command**

1. Create configuration file (.yaml)

```yaml
 # bhakti.yaml
 DIMENSION: 1024
 DB_PATH: /path/to/db
 DB_ENGINE: dipamkara # optional, default to dipamkara
 CACHED: false # optional, default to false
 HOST: 0.0.0.0 # optional, default to 0.0.0.0
 PORT: 23860 # optional, default to 23860
 EOF: <eof> # optional, default to <eof>
 TIMEOUT: 4.0 # optional, default to 4.0 seconds
 BUFFER_SIZE: 256 # optional, default to 256 bytes
 VERBOSE: false # optional, default to false
 ```

2. Run bhakti in shell

```shell
        # bash
        bhakti ./bhakti.yaml
        ```
    
  - #### Start server using Python

```python
      # main.py
      from bhakti import BhaktiServer
      from bhakti.database import DBEngine

if __name__ == '__main__':
 bhakti_server = BhaktiServer(
 dimension=1024, # required, only vectors with 1024 dimensions are acceptable
 db_path='/path/to/db', # required, path where stores data, portable
 db_engine=DBEngine.DIPAMKARA, # optional, default to dipamkara
 cached=False, # optional, default to false
 host='0.0.0.0', # optional, default to 0.0.0.0
 port=23860, # optional, default to 23860
 eof=b'<eof>', # optional, default to b'<eof>'
 timeout=4.0, # optional, default to 4.0 seconds
 buffer_size=256, # optional, default to 256 bytes
 verbose=False # optional, default to false
 )
 # run server
 bhakti_server.run()
 ```

- ### Interact With A Bhakti Client

**Currently, Python(>=3.10) is supported**

```python
  # main.py
  import asyncio
  import numpy as np
  from bhakti import BhaktiClient
  from bhakti.database import Metric
  from bhakti.database import DBEngine

async def main():
 client = BhaktiClient(
 server='127.0.0.1', # optional, default to 127.0.0.1
 port=23860, # optional, default to 23860
 eof=b'<eof>', # optional, default to b'<eof>'
 timeout=4.0, # optional, default to 4.0 seconds
 buffer_size=256, # optional, default to 256 bytes
 db_engine=DBEngine.DIPAMKARA, # optional, default to dipamkara
 verbose=False # optional, default to false
 )
 vector = np.random.randn(1024)
 await client.create(vector=vector, document={'age': 31, 'gender': 'male'})
 await client.create_index('age')
 await client.create_index('gender')
 results = await client.find_documents_by_vector_indexed(
 query='age <= 31 && gender != "female"', 
 vector=vector,
 metric=Metric.EUCLIDEAN_Z_SCORE, 
 top_k=3
 )
 print(results)

if __name__ == '__main__':
      asyncio.run(main())
  ```
  
- ### Projects Related
  
  - [Bhakti based memory retrieval dialogue solution](https://github.com/vortezwohl/bhakti-based-memory-llm)

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