semvec/pypi-packages huggingface dataset:
graphreader_agentic_rag 0.1.1
Graphreader Implementation
Requires Python: <4.0,>=3.12

# Graphreader

Graphreader is a Python library that allows you to read and write data from a Neo4j database. It is built on top of the Langgraph/LangChain library, which provides a framework for building language models. It is based on Medium article [Implementing GraphReader with Neo4j and LangGraph](https://towardsdatascience.com/implementing-graphreader-with-neo4j-and-langgraph-e4c73826a8b7)




## Getting started

1. Install the library using pip:


    ```bash
    pip install graphreader_agentic_rag
    ```

2. Configure environment variables (.env):

    - OPENAI_API_KEY='your-openai-api-key'
    - OPENAI_MODEL_NAME='gpt-4o-mini'
    - NEO4J_URI="neo4j+s://xxxxxxxx.databases.neo4j.io"
    - NEO4J_USERNAME=neo4j
    - NEO4J_PASSWORD='your-neo4j-password'
    - ITERATION_LIMIT=12
    
Iteration limit is used to control the number of iterations that the agent will perform. This avoids infinite loops. If not specified, the default value is 10 iterations. Langgraph use 25 iterations. If the value higher than 25, it will cause an error.

3. Import data:

To import data, you can use the [import.ipynb](https://github.com/cccadet/graphreader_agentic_rag/blob/main/examples/import.ipynb) Jupyter notebook available in the `examples` folder of the repository. There, three methods for data import are provided:

- **Text:** Import text.  
- **A single file:** Import a specific file.  
- **All files in a folder:** Import all available files in a folder.  

Currently, only files in the `txt` format are supported. This allows you to reprocess your original files using the tool of your choice.


4. Run the agent:

To run the agent, use the [agent.ipynb](https://github.com/cccadet/graphreader_agentic_rag/blob/main/examples/agent.ipynb) Jupyter notebook available in the `examples` folder of the repository. 


## Roadmap

   - [] Memgraph integration
   - [] Customizable prompting for agents







   







## Add your files





## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.

from graphreader.chains.neo4j_utils import result_sources
getmyqr 1.0
A simple QR code generator using a free API
Requires Python: >=3.6

# GetMyQR

A simple Python package to generate QR codes using a free API.

## Installation

You can install the package using `pip`:

```bash
pip install getmyqr
```

## Usage

You can use the QR code generator in your Python script or from the command line.

### Example 1: Python Script

Generate a QR code with default size and high quality:

```python
from getmyqr import generate_qr_code

output_file = generate_qr_code("Hello, world!", "hello_world_default.png")

if "Error" not in output_file:
    print(f"QR code saved to: {output_file}")
else:
    print(output_file)
```

Generate a QR code with custom size and quality:

```python
output_file_custom = generate_qr_code("Custom QR code", "custom_qr_medium.png", size="400x400", quality="M")

if "Error" not in output_file_custom:
    print(f"QR code saved to: {output_file_custom}")
else:
    print(output_file_custom)
```

Generate a QR code with low quality:

```python
output_file_low = generate_qr_code("Low quality QR code", "low_quality_qr.png", size="500x500", quality="L")

if "Error" not in output_file_low:
    print(f"QR code saved to: {output_file_low}")
else:
    print(output_file_low)
```

### Example 2: Command Line

To generate a QR code from the command line, use the following command:

```bash
getmyqr "Hello, world!" --output "hello_world_qr.png"
```

To generate a QR code with custom size and quality:

```bash
getmyqr "Hello, world!" --output "hello_world_qr_custom.png" --size "400x400" --quality "Q"
```

## Parameters

- `--size`: Customizes the size of the QR code. Example: `400x400` (default is `300x300`).
- `--quality`: Customizes the error correction level. Options: `L` (low), `M` (medium), `Q` (quartile), `H` (high). Default is `H`.
- `--output`: The name of the output file. Default is `qrcode.png`.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
magvar-wayforward 1.0.0
Magnetic variation (aka declination) for aviation navigation
Requires Python: >=3.1

# The magvar module

This module provides the function

	`magnetic_variation(date, latitude, longitude, elevation)`

`date` must be a float - the Julian date.  Example: 2022.1 is in February.

`latitude` is in radians, positive for north, negative for south.

`longitude` is in radians, positive for east, negative for west.

`elevation` is in meters.

This returns what the geo-scientists would call 'declination' but what
aviators call 'magnetic variation'.

The result is in radians, and is negative for EAST "east is least" and
positive for WEST.

For example:

```
>>> from geomag import magnetic_variation
>>> from math import degrees, radians

>>> degrees(magnetic_variation(2022.05,
                               radians(25.7953611),
                               radians(-80.2901158),
                               3)
6.9474...
```

This shows the magnetic variation for the Miami Internation Airport (KMIA)
as 6.9 degrees WEST, which matches what the sectional chart for January 2022.



## Building from COF files

This distribution includes WMM2010.COF, WMM2015.COF, and WMM2020.COF.

These files were downloaded from the National Centers for Environmental
Information (NCEI), formerly known as the National Geophysical Data Center
(NGDC).

	[NCEI](https://www.ncei.noaa.gov/products/world-magnetic-model)

The file `wmm.c` contains static data compiled from these .COF files.

To generate the file from scratch, use the `compile-cof' script:

```
$ ./compile-cof WMM2010.COF WMM2015.COF WMM2020.COF >wmm.c
```

This incorporates all models, so you can get the magnetic variation
for any latitude and longitude from any date from January 1, 2010.

If you're tight on space, and only need the current magnetic model:

```
$ ./compile-cof WMM2020.COF >wmm.c
```


## Testing the Software

The `test` subdirectory has test values from the NCEI.

Test the Python:

```
$ cd test
$ python3 test_wmm2020_test_values.py
```

Test the C code:

```
$ cd test
$ make
$ ./test_geomag
$ make clean
```