semvec/pypi-packages huggingface dataset:
llmtree 1.0.1
CLI tool for preparing project data for LLM context
Requires Python: >=3.8

# LLM Tree

**Docs:** [🇺🇸 English](README.md) | [🇷🇺 Русский](docs/RU.md) | [🇩🇪 Deutsch](docs/DE.md) | [🇯🇵 日本語](docs/JA.md) | [🇨🇳 中文](docs/CH.md)

A CLI tool for preparing project data to be used in LLM context. It generates a markdown file with the project structure and source file contents.

## Installation

```bash
pip install -e .
```

Or for global installation:

```bash
pip install llmtree
```

## Quick Start

```bash
# Process the current directory
llmtree .

# Process a specific folder
llmtree /path/to/project

# Use a specific profile
llmtree . -p python

# Configure profiles
llmtree --config
```

## Key Features

### Profiles

* **default** – a universal profile suitable for most projects
* **python** – optimized for Python projects
* Create custom profiles via an interactive menu

### Interactive Usage

When running `llmtree .`:

* **Enter** – generates the `4llm.md` file
* **Space** – opens the settings menu

### Profile Settings

* File inclusion patterns
* Exclusion patterns
* Include tree structure
* Max file size
* Line numbering
* Custom header and footer

## Configuration Structure

Settings are stored in `~/.llmtree/config.json`:

```json
{
  "default": {
    "name": "default",
    "include_patterns": ["*.py", "*.js", "*.md"],
    "exclude_patterns": ["node_modules/*", ".git/*"],
    "include_tree": true,
    "max_file_size": 100000,
    "encoding": "utf-8",
    "add_line_numbers": false,
    "include_hidden": false,
    "tree_depth": 3,
    "custom_header": "",
    "custom_footer": ""
  }
}
```

## Usage Examples

### Creating a Profile for Frontend

```bash
llmtree --config
# Select "Create new profile"
# Name: frontend
# Include patterns: *.js,*.jsx,*.ts,*.tsx,*.vue,*.css,*.scss,package.json
# Exclude patterns: node_modules/*,dist/*,build/*
```

### Profile for Documentation

```bash
llmtree --config
# Create a profile named "docs"
# Include patterns: *.md,*.rst,*.txt
# Tree: Yes
# Add line numbers: No
```

## Command Line

```
usage: llmtree [-h] [-p PROFILE] [-o OUTPUT] [--config] [path]

positional arguments:
  path                  Target directory path (default: current directory)

optional arguments:
  -h, --help           Show this help message and exit
  -p, --profile PROFILE Profile to use (default: default)
  -o, --output OUTPUT  Output file name (default: 4llm.md)
  --config             Run interactive configuration
```

## Output Format

The generated `4llm.md` includes:

1. Custom header (if specified)
2. Project structure (tree)
3. Source file contents with syntax highlighting
4. Custom footer (if specified)

Example output:

```markdown
## Project Structure

```

.
├── src/
│   ├── main.py
│   └── utils.py
├── tests/
│   └── test\_main.py
└── README.md

````

## Source Files

### src/main.py
```python
def main():
    print("Hello, World!")
````

### README.md

```markdown
# My Project
```

## License

MIT License
llama-index-agent-llm-compiler 0.3.2
llama-index agent llm compiler integration
Requires Python: <4.0,>=3.9

# LlamaIndex Agent Integration: LLM Compiler

This Agent integration implements the [LLMCompiler agent paper](https://github.com/SqueezeAILab/LLMCompiler).

A lot of code came from the source repo, we repurposed with LlamaIndex abstractions. All credits
to the original authors for a great work!

## Usage

First install the package:

```bash
pip install llama-index-agent-llm-compiler
```

```python
# setup pack arguments

from llama_index.core.agent import AgentRunner
from llama_index.agent.llm_compiler.step import LLMCompilerAgentWorker

agent_worker = LLMCompilerAgentWorker.from_tools(
    tools, llm=llm, verbose=True, callback_manager=callback_manager
)
agent = AgentRunner(agent_worker, callback_manager=callback_manager)

# start using the agent
response = agent.chat("What is (121 * 3) + 42?")
```
rajan-nse 1.3.0
The rajan_nse package is a comprehensive toolkit designed for analyzing and visualizing stock market data from the National Stock Exchange (NSE) of India. This package provides various modules to facilitate technical analysis, identify candlestick patterns, create visual representations, and develop trading strategies.
Requires Python: >=3.8

# rajan_nse
Python package for nse calculations
Read the documentation [https://rajan-nse.readthedocs.io/](https://rajan-nse.readthedocs.io/)

# requirements
- alive_progress
- json
- matplotlib
- numpy
- pandas
- re
- requests
- seaborn
- sklearn
- tqdm

# commands

```bash

# run test cases and ignore print statements
 python3 -m pytest tests

# run test cases with print statements
 python3 -m pytest -s tests
```
Flask-Opsgenie 0.5.1
Opsgenie extension for Flask
Keywords: Alerting, flask, web, Reliability, DevOps


Flask-Opsgenie
---------------

An easy to use Opsgenie extension for flask. Allows user
to raise an opsgenie alert on unwanted response status code,
increased response latency and on unhandled exception thrown
by routes.
With flask-opsgenie, you will no more have to add alerting
logic to your code manually, rather all you need to do is configure
this extension with different alert conditions and attributes.
ezfastq 0.1.1
Scan directories for FASTQ files and associate with sample names
Requires Python: >=3.10

# ezfastq

Search a source directory for FASTQ files matching a list of given sample names and copy to a destination directory.


```
# Shell
ezfastq /path/to/data/ sample1 sample2 sample3 --workdir=path/to/dest/
ezfastq /opt/seq/ samplenames.txt --workdir=out/

# Python
import ezfastq
samples = ["sample1", "sample2", "sample3"]
source = "/path/to/seqs/"
dest = "new/path/"
ezfastq.api.copy(samples, source, workdir=dest)
```