darca-vector-db 0.1.0

Abstraction layer for vector databases.

A pluggable vector database client system with backend support. Currently, the only supported backend is Qdrant. This project provides a unified client interface to interact with vector databases. It is designed to be easily extensible to support additional backends in the future.

GitHub Repository:

Visit the official repository at: darca-vector-db on GitHub

Installation

The project uses Poetry for dependency management and virtual environment handling.

To set up the project locally, follow these steps:

1. Clone the repository: .. code-block:: bash

   git clone https://github.com/roelkist/darca-vector-db.git cd darca-vector-db

2. Install dependencies and set up the environment with a single command: .. code-block:: bash

   make install

This command will handle creating the virtual environment, installing Poetry, and installing dependencies.

Make Targets for Faster Iterations

You can use specific make targets for faster development cycles:

• make format : Formats the codebase using black and isort.

• make test : Runs the test suite with coverage reporting.

• make docs : Builds the documentation using Sphinx.

• make check : Runs all checks (formatting, testing, docs) before committing.

Always run make check before committing changes to ensure consistency and quality.

3. Running tests: .. code-block:: bash

   make test

4. Formatting the code: .. code-block:: bash

   make format

5. Building the documentation: .. code-block:: bash

   make docs

Features

• Unified client interface for vector databases

• Qdrant support

• Easy to extend for additional backends

• Structured logging and error handling

• Flexible CI and local environment support

• Automated testing and code formatting

Usage

To use the client, instantiate it with the desired backend (currently only Qdrant is supported):

from darca_vector_db import DBClient

client = DBClient(backend="qdrant", host="localhost", port=6333)
client.connect()
client.create_collection(name="my_vectors", vector_size=128, distance_metric="cosine")
client.insert_vector("my_vectors", vector_id=1, vector=[0.1] * 128, metadata={"label": "example"})
results = client.search_vectors("my_vectors", query_vector=[0.1] * 128, top_k=5)
print(results)

Contributing

Contributions are welcome! Please refer to the CONTRIBUTING.rst for detailed guidelines.

License

MIT License

Author

Roel Kist