semvec/pypi-packages huggingface dataset:
builtsimple-llamaindex 0.1.0
LlamaIndex readers for Built-Simple research APIs (PubMed, ArXiv, Wikipedia)
License: MIT
Requires Python: <4.0,>=3.9
Keywords: llama-index, research, pubmed, arxiv, wikipedia, academic, papers

# builtsimple-llamaindex

LlamaIndex readers for [Built-Simple](https://built-simple.ai) research APIs, providing semantic search over scientific literature.

[![PyPI version](https://badge.fury.io/py/builtsimple-llamaindex.svg)](https://pypi.org/project/builtsimple-llamaindex/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

> **Note**: This is the standalone Built-Simple package. For the official LlamaIndex integration, see [`llama-index-readers-builtsimple`](https://pypi.org/project/llama-index-readers-builtsimple/) published by the LlamaIndex team.

## Features

- **PubMed Reader** - 4.5M+ biomedical articles with hybrid semantic/keyword search
- **ArXiv Reader** - 2.7M+ preprints in physics, math, CS, and ML
- **Wikipedia Reader** - Semantic search over Wikipedia articles
- **No API key required** - Free tier available for all endpoints
- **Rich metadata** - Full citation info for all documents

## Installation

```bash
pip install builtsimple-llamaindex
```

## Quick Start

```python
from llama_index.readers.builtsimple import (
    BuiltSimplePubMedReader,
    BuiltSimpleArxivReader,
)

# Search PubMed for medical literature
pubmed_reader = BuiltSimplePubMedReader()
pubmed_docs = pubmed_reader.load_data("CRISPR gene therapy", limit=10)

for doc in pubmed_docs:
    print(f"Title: {doc.metadata['title']}")
    print(f"Journal: {doc.metadata['journal']}")
    print(f"Year: {doc.metadata['pub_year']}")
    print(f"URL: {doc.metadata['url']}\n")

# Search ArXiv for ML papers
arxiv_reader = BuiltSimpleArxivReader()
arxiv_docs = arxiv_reader.load_data("transformer architecture attention", limit=10)

for doc in arxiv_docs:
    print(f"Title: {doc.metadata['title']}")
    print(f"Authors: {doc.metadata['authors']}")
    print(f"ArXiv ID: {doc.metadata['arxiv_id']}\n")
```

## Build a RAG Index

```python
from llama_index.core import VectorStoreIndex
from llama_index.readers.builtsimple import BuiltSimplePubMedReader

# Load documents
reader = BuiltSimplePubMedReader()
documents = reader.load_data("immunotherapy cancer treatment", limit=20)

# Build index
index = VectorStoreIndex.from_documents(documents)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("What are the side effects of CAR-T therapy?")
print(response)
```

## Rate Limits

| Tier | Rate Limit | Notes |
|------|------------|-------|
| Free | 10 req/min | No API key needed |
| Pro | 100 req/min | Requires API key |

Get an API key at [pubmed.built-simple.ai](https://pubmed.built-simple.ai) or [arxiv.built-simple.ai](https://arxiv.built-simple.ai).

## Links

- [Built-Simple](https://built-simple.ai)
- [PubMed API Docs](https://pubmed.built-simple.ai/docs)
- [ArXiv API Docs](https://arxiv.built-simple.ai/docs)
- [LlamaIndex](https://llamaindex.ai)

## License

MIT License
pdf-gluer 0.1.1
Batch merge related PDF files in a directory
License: MIT
Requires Python: >=3.12

<div align="center">
  <img src="logo.png" alt="pdf-gluer" width="512"/>

  [![PyPI](https://img.shields.io/pypi/v/pdf-gluer.svg)](https://pypi.org/project/pdf-gluer/)
  [![Python](https://img.shields.io/badge/Python-3.12+-blue.svg)](https://www.python.org/)
  [![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)

  **📄 Intelligently find and merge related PDF files with a single command 🔗**

</div>

## Overview

pdf-gluer is a CLI tool that scans directories for PDF files and merges them together. It can combine all PDFs into one file or intelligently group related files by name pattern (e.g., "report-1.pdf", "report-2.pdf") and merge each group separately.

## Features

- **Smart grouping** - Automatically detects related PDFs by filename patterns (`doc-1.pdf`, `doc-2.pdf` → merged)
- **Two merge modes** - Merge all PDFs or only related file groups
- **Interactive prompts** - Optional confirmation before each merge operation
- **Preserves originals** - Source files are never modified or deleted
- **Progress tracking** - Visual progress bars for batch operations

## Quick Start

```bash
# Install from PyPI
pip install pdf-gluer

# Merge all PDFs in a directory
pdf-gluer /path/to/pdfs

# Merge only related file groups
pdf-gluer /path/to/pdfs --grouped
```

## Installation

Requires Python 3.12+.

```bash
# From PyPI (recommended)
pip install pdf-gluer

# Or with pipx for isolated install
pipx install pdf-gluer

# Or from source with uv
git clone https://github.com/tsilva/pdf-gluer.git
cd pdf-gluer
uv tool install .
```

## Usage

### Default Mode

Merges all PDF files alphabetically into a single `merged_all.pdf`:

```bash
pdf-gluer /path/to/directory
```

### Grouped Mode

Merges only files with similar names. Files like `invoice-1.pdf`, `invoice-2.pdf`, `invoice-3.pdf` become `invoice.merged.pdf`:

```bash
pdf-gluer /path/to/directory --grouped
```

### Interactive Mode

Add `--ask` to confirm before each merge:

```bash
pdf-gluer /path/to/directory --ask
pdf-gluer /path/to/directory --grouped --ask
```

### Example Output

```
Found potentially related PDFs for 'document':
  1. document-1.pdf
  2. document-2.pdf
  3. document-3.pdf

Would you like to merge these files? [Y/n]:
```

## Pattern Matching

The grouping algorithm matches files with trailing numeric suffixes:

| Files | Base Name | Output |
|-------|-----------|--------|
| `report-1.pdf`, `report-2.pdf` | report | `report.merged.pdf` |
| `doc_3.pdf`, `doc_4.pdf` | doc | `doc.merged.pdf` |
| `scan 1.pdf`, `scan 2.pdf` | scan | `scan.merged.pdf` |

Supported separators: `-`, `_`, and spaces.

## Programmatic API

You can also use pdf-gluer as a library:

```python
from pdf_gluer import merge_pdfs, merge_all_pdfs, batch_merge_pdfs_grouped

# Merge specific files
merge_pdfs(["/path/to/file1.pdf", "/path/to/file2.pdf"], "/path/to/output.pdf")

# Merge all PDFs in a directory
merge_all_pdfs("/path/to/directory")

# Merge grouped PDFs by name pattern
batch_merge_pdfs_grouped("/path/to/directory")
```

## License

[MIT](LICENSE)
sintellix 0.1.1
High-Performance Neural Network Framework with 3D Grid Architecture
License: MIT
Requires Python: >=3.8
Keywords: neural-networks, cuda, deep-learning, attention, vq-gan

# Sintellix Python API

PyTorch-like Python interface for Sintellix Neural Network Framework with HOT (Higher Order Thought) Architecture.

## Features

- **PyTorch-like API**: Familiar interface for PyTorch users
- **CUDA Acceleration**: High-performance CUDA kernels for all operations
- **Advanced Architecture**: Multi-head attention, SSM, RWKV, temporal attention, DDPM, and more
- **Tiered Storage**: Automatic GPU→RAM→Disk memory management
- **VQ-GAN Codec**: Semantic encoding/decoding with vector quantization
- **Easy Training**: Built-in trainer with checkpointing and logging

## Installation

```bash
pip install sintellix
```

Or install from source:

```bash
git clone https://github.com/sintellix/sintellix.git
cd sintellix/python
pip install -e .
```

## Quick Start

### Basic Usage

```python
from sintellix import NeuronModel, NeuronConfig

# Create model
config = NeuronConfig(dim=256, grid_size=(32, 32, 32))
model = NeuronModel(config)
model.initialize()

# Forward pass
import torch
input_tensor = torch.randn(1, 256, 256)
output = model(input_tensor)
```

### Training

```python
from sintellix import Trainer, TrainingConfig

# Create trainer
train_config = TrainingConfig(
    learning_rate=0.001,
    batch_size=32,
    epochs=100
)
trainer = Trainer(model, train_config)

# Train
trainer.train(train_loader, val_loader)
```

### Model Management

```python
from sintellix import download_model

# Download pretrained models
e5_path = download_model("e5-large")
vqgan_path = download_model("vqgan-codebook")
```

## Configuration

```python
config = NeuronConfig(
    dim=256,                    # Neuron dimension
    num_heads=8,                # Attention heads
    grid_size=(32, 32, 32),     # Neuron grid
    temporal_frames=8,          # Temporal history
    enable_multi_head=True,     # Enable modules
    enable_ssm=True,
    enable_rwkv=True,
    gpu_cache_size_mb=4096,     # Storage config
    ram_cache_size_mb=16384,
)
```

## License

MIT License - see [LICENSE](../LICENSE) for details
Topsis-Aishita-102303744 0.1.0
TOPSIS implementation as a command-line tool
License: MIT

# Topsis-Aishita-102303744

This Python package implements the **TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)** method.  
It provides a command-line tool to rank multiple alternatives based on multiple criteria.

---

## Installation

Install the package from PyPI using:

```bash
pip install Topsis-Aishita-102303744 
```

---

## Usage

After installation, run the TOPSIS command in the following format:

```bash
topsis <InputDataFile> <Weights> <Impacts> <OutputFileName>
```

---
Example

```bash
topsis data.csv "1,1,1,1,1" "+,+,+,-,+" result.csv 
```

---