semvec/pypi-packages huggingface dataset:
pdfconduit 4.7.3
PDF toolkit for preparing documents for distribution.
Requires Python: >=3.8


A Pure-Python library built as a PDF toolkit.  Prepare documents for distribution.

Features:
- Watermark: Dynamically generate watermarks and add watermark to existing document
- Label: Overlay text labels such as filename or date to documents 
- Encrypt: Password protect and restrict permissions to print only
- Rotate: Rotate by increments of 90 degrees
- Upscale: Scale PDF size
- Merge: Concatenate multiple documents into one file
- Slice: Extract page ranges from documents
- Extract Text and Images
- Retrieve document metadata and information
zipsar 0.1.0
A Python package for Zipsar.
Requires Python: >=3.6

# Zipsar

[![PyPI version](https://badge.fury.io/py/zipsar.svg)](https://pypi.org/project/zipsar/)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/zipsar/zipsar/blob/main/LICENSE)

**Zipsar** is a Python package designed to [describe package functionality here].  
It provides [brief feature summary].  

## ðŸš€ Installation

You can install **Zipsar** directly from PyPI:

```sh
pip install zipsar
databridge-client 0.3.0
DataBridge Python Client
Requires Python: >=3.8

# DataBridge

A Python client for DataBridge API that enables document ingestion and semantic search capabilities.

## Installation

```bash
pip install databridge-client
```

```python
from databridge import DataBridge

# Initialize client
db = DataBridge("your-api-key")

# Ingest a document
doc_id = await db.ingest_document(
    content="Your document content",
    metadata={"title": "Example Document"}
)

# Query documents
results = await db.query(
    query="Your search query",
    filters={"title": "Example Document"}
)

# Process results
for result in results:
    print(f"Content: {result.content}")
    print(f"Score: {result.score}")
    print(f"Metadata: {result.metadata}")
```