semvec/pypi-packages huggingface dataset:
dappier-py 0.2.0
Dappier Python SDK for interacting with Dappier APIs
Requires Python: >=3.6

# Dappier Python SDK

Python SDK for interacting with Dappier's API's.

## Overview

`dappier-py` provides a straightforward way to interact with Dappier's API's, which allows for real-time data search on the internet and other datamodels from the marketplace. The library is designed to be easy to use and integrate into existing Python projects.

## Installation

To install the package, run:

```bash
pip install dappier

```

Alternatively, you can clone the repository and install the dependencies:

```bash
git clone https://github.com/DappierAI/dappier-py
cd dappier-py
pip install -r requirements.txt
```

## Initialization

You can get your API key from your [Dappier account](https://platform.dappier.com).

```bash
from dappier.dappier import DappierApp

app = DappierApp(api_key='your_api_key')

```
## Real-Time Search

You can perform a real-time search by providing a query. This will search for real-time data related to your query.

```bash
result = app.realtime_search_api("When is the next election?")
print(result.response['response']["results"])

```

## AI Recommendations
The AI Recommendations feature allows you to query for articles and other content using a specific data model. 
You can pick a specific datamodel from [marketplace](https://marketplace.dappier.com/) 

### Default Options:

```python
ai_result = app.ai_recommendations(query="latest tech news", datamodel_id="dm_02hr75e8ate6adr15hjrf3ikol")
print(ai_result.results)
```

### Custom Options:
You can pass custom parameters such as `similarity_top_k`, `ref` and `num_articles_ref`:

```python
ai_custom_result = app.ai_recommendations(
    query="latest tech news", 
    datamodel_id="dm_02hr75e8ate6adr15hjrf3ikol", 
    similarity_top_k=5, 
    ref="techcrunch.com", 
    num_articles_ref=2
)
print(ai_custom_result.results)

```

## Search API
You can also perform a search using a specific datamodel_id. This method allows users to input custom queries and retrieve data based on the datamodel provided.
```python
search_result = app.search(
    query="Latest Microsoft News",
    datamodel_id="dm_01htjq2njgecvah7ncepm8v87y",
    similarity_top_k=6,
    ref="familyproof.com",
    num_articles_ref=3
)
print(search_result.results)

```


Checkout (example.py)[https://github.com/DappierAI/dappier-py] in this repository for a working example.
zipschema 1.0.12
CLI tool for validating zipschema files and generating documentation.
Requires Python: >=3.6

# Zipschema

Zipschema is a CLI tool for validating zipschema YAML files, validating 
ZIP file contents against these schemas, and generating documentation (Markdown or AsciiDoc).

Zipschema was written in order to allow for validation of "meta formats" which use the
zip file as a container.  While mostly these are loosely defined, this Zipschema was created
to allow for validation of those files, and the files they contain.



## Features

- **Schema Validation**: Validate the structure of your zipschema YAML file.
- **ZIP Validation**: Validate the contents of a ZIP file against a zipschema.
- **Documentation Generation**: Generate Markdown or AsciiDoc documentation from a zipschema.

## Installation

To install zipschema, run:

```bash
pip install zipschema
```

## Usage

### Validate a zipschema YAML file:

```bash
zipschema validate-schema path/to/zipschema.yaml
```

### Validate a ZIP file against a zipschema:

```bash
zipschema validate-zip path/to/zipschema.yaml path/to/zipfile.zip
```

### Generate documentation from a zipschema:

```bash
zipschema generate-docs path/to/zipschema.yaml --format markdown
```

## License

This project is licensed under the MIT License.