semvec/pypi-packages huggingface dataset:
keepass2-merger 0.0.2
A python package to interactively merge two KeePass2 databases.
License: MIT
Requires Python: >=3.9
Keywords: copier, template, python

KeePass2 Merger
===============

![PyPI](https://img.shields.io/pypi/v/keepass2-merger?logo=pypi&color=brightgreen)
![Python Version](https://img.shields.io/pypi/pyversions/keepass2-merger?logo=python)
![Tests](https://img.shields.io/github/actions/workflow/status/Karol-G/keepass2-merger/workflow.yml?branch=main&logo=github)
![Copier Template](https://img.shields.io/badge/copier-template-blue?logo=jinja)
![License](https://img.shields.io/github/license/Karol-G/keepass2-merger)

A python package to interactively merge two KeePass2 databases.

## Installation

You can install keepass2-merger via [pip](https://pypi.org/project/keepass2-merger/):
```bash
pip install keepass2-merger
```

## Usage

Merge two KeePass databases interactively:

```bash
keepass2-merger /path/to/db1.kdbx /path/to/db2.kdbx /path/to/output.kdbx
```

You will be prompted for both database passwords (securely via `getpass`) and then shown each change
needed to merge new or updated groups/entries from the second file into the first. A full-screen curses
UI is used to avoid leaving scrollback residue; press the on-screen keys to apply or skip each change.
No deletions are performed; the tool recommends newer versions when conflicts arise. The recycle bin is
ignored. Use `--no-curses` to fall back to a plain prompt UI.

## Contributing

Contributions are welcome! Please open a pull request with clear changes and add tests when appropriate.

## Issues

Found a bug or have a request? Open an issue at https://github.com/Karol-G/keepass2-merger/issues.

## License

Distributed under the MIT license. See `LICENSE` for details.
demo-rag 0.0.1
A demo project for RAG using FastAPI and LlamaIndex
License: MIT
Requires Python: >=3.13

# Readme

This is a demo project for Retrieval-Augmented Generation (RAG) service based on LlamaIndex and FastAPI.
It includes an example of how to set up and use the RAG service with a MinIO storage backend, MongoDB for metadata storage, and Qdrant for vector storage.

## Prerequisites
- Docker and Docker Compose installed on your machine.
- Python 3.13 or higher installed.
- Required Python packages listed in `pyproject.toml`.

## Setup
1. Clone the repository to your local machine.
2. Install the required Python packages using Pip:
   ```bash
   pip install .
   ```
   or if you are using uv:

   ```bash
    uv sync
   ```
3. Start the MinIO, MongoDB and Qdrant service using Docker Compose:
   ```bash
   cd example
   docker compose up
   ```

## Usage
1. Modify the configuration in `example/config.yaml` as needed, especially the LLM settings.

   Note: **we use the OpenAI-compatible models.**
   
   Here is an example configuration for the embedding and generation models:

   ```yaml
    rag:
        embedding_model:
            model_name: text-embedding-v4
            api_base: https://dashscope.aliyuncs.com/compatible-mode/v1
            api_key: <YOUR_API_KEY>


        generation_model:
            model_name: qwen3-next-80b-a3b-instruct
            api_base: https://dashscope.aliyuncs.com/compatible-mode/v1
            api_key: <YOUR_API_KEY>
            other_parameters:
                is_chat_model: true
   ```

2. Start the RAG service by running the `example/main.py` script:
   ```bash
   cd example
   python main.py
   ```

## Test

- The `test.py` file contains simple tests for the RAG service functionalities. You can run it:
   ```bash
    python test.py
    ```

- The `test_rag_service.py` file contains unit tests for the RAG service. You can run it using pytest:
   ```bash
    python test_rag_service.py
    ```