semvec/pypi-packages huggingface dataset:
llama-index-llms-langchain 0.7.2
llama-index llms langchain integration
Requires Python: <4.0,>=3.9

# LlamaIndex Llms Integration: Langchain

## Installation

1. Install the required Python packages:

   ```bash
   %pip install llama-index-llms-langchain
   ```

## Usage

### Import Required Libraries

```python
from langchain.llms import OpenAI
from llama_index.llms.langchain import LangChainLLM
```

### Initialize LangChain LLM

To create an instance of `LangChainLLM` with OpenAI:

```python
llm = LangChainLLM(llm=OpenAI())
```

### Generate Streaming Response

To generate a streaming response, use the following code:

```python
response_gen = llm.stream_complete("Hi this is")
for delta in response_gen:
    print(delta.delta, end="")
```

### LLM Implementation example

https://docs.llamaindex.ai/en/stable/examples/llm/langchain/
freeplay-python-adk 0.2.3
Freeplay integration for Google ADK
Requires Python: >=3.9

This folder contains the Freeplay Python ADK, which provides integration between Freeplay and Google ADK.

You can use it to instrument your Google ADK agents and send traces to Freeplay to observe and analyze your agent's behavior.

If you choose, you can also move your prompts into Freeplay for centralized management and versioning, which can help
you run experiments efficiently and let your whole team collaborate on your agent's prompts, even for users that don't feel comfortable writing code.

# Setup

## Setup ADK
First, make sure you can run a simple agent [Python Quickstart for ADK](https://google.github.io/adk-docs/get-started/python/).
If you already have an agent that you want to observe using Freeplay, you can move on to the next step.

## Sign up for Freeplay
Sign up for an account on [Freeplay](https://freeplay.ai). It's free to get started.

Once you've signed up and created a project in Freeplay, copy the project ID from the URL. For example, if your project has the URL
https://app.freeplay.ai/projects/532982fa-a847-4e87-9c44-7e79b98cc965/sessions, your project ID would be `532982fa-a847-4e87-9c44-7e79b98cc965`.

Create an API key on the [Freeplay API Access page](https://app.freeplay.ai/settings/api-access).

Set the project ID, API URL and API key in your environment file:
```
FREEPLAY_PROJECT_ID=
FREEPLAY_API_URL=https://app.freeplay.ai/api
FREEPLAY_API_KEY=
```

If you are using a private Freeplay instance, set the `FREEPLAY_API_URL` to your instance's URL, for example: `https://my-company.freeplay.ai/api`.

## Install the library
You can install the Freeplay Python ADK using pip:

```bash
pip install freeplay-python-adk
```

Or uv:

```bash
uv add freeplay-python-adk
```

## Instrument your agent
Instrument your code to use the Freeplay Python ADK library.

We recommend doing this in the config.py file that runs before your agent is initialized.

```python
from freeplay_python_adk import FreeplayADK

FreeplayADK.initialize_observability()
```

Add the FreeplayObservabilityPlugin to your app's plugins:

```python
from freeplay_python_adk.freeplay_observability_plugin import FreeplayObservabilityPlugin
from google.adk.apps import App

app = App(
    name="my_agent_app",
    root_agent=my_agent,
    plugins=[FreeplayObservabilityPlugin()],
)
```

And run your app! You should see traces show up in the Freeplay application.

You can run your app from this directory like so: `uv run adk run examples`.
safe-push 0.1.4
An educational CLI tool to prevent accidental sensitive data exposure.
Requires Python: >=3.8

# safe-push 🛡️

An educational, beginner-friendly Python CLI tool that teaches you about accidental sensitive data exposure while helping you keep your repositories clean.

## 🌟 Why safe-push?

Accidentally pushing a `.env` file or a hardcoded API key to GitHub is a rite of passage for many developers—but it's also a major security risk! `safe-push` helps you identify these risks *before* you push, explaining **why** certain files shouldn't be shared.

Perfect for "vibe coders" and beginners who want to stay safe while learning.

## 🚀 Features

### 🆓 Free Tier (Always)
- **Scan Current Directory**: Detects common mistakes like `.env` files, `__pycache__`, and `venv` folders.
- **Basic Secret Detection**: Finds generic API keys and tokens.
- **Educational Insights**: Explains the danger of each finding so you learn as you go.
- **No Data Leaves Your Machine**: Your code stays local. Always.

### 💎 Premium Features
- **Advanced Secret Scanning**: Deep detection for AWS, Stripe, OpenAI, Firebase, and more.
- **Auto .gitignore Generator**: Quickly create a recommended `.gitignore` for your Python projects.
- **Pre-commit Hook Template**: Automatically scan your code every time you try to `git commit`.
- **Verbose Mode**: See exactly what's happening under the hood.

## 🛠️ Installation

```bash
pip install safe-push
```

## 📖 How to Use

Simply run the tool in your project's root directory:

```bash
safe-push
```

### Options
- `safe-push --verbose`: Show a detailed breakdown of the scan.
- `safe-push --unlock`: Learn how to unlock premium features.
- `safe-push --generate-gitignore`: (Premium) Create a recommended `.gitignore`.
- `safe-push --install-hook`: (Premium) Install a Git pre-commit hook.

## 🔓 Unlocking Premium (The Solana Way)

We use a simple, decentralized verification system. No accounts, no credit cards.

1. Donate at least **0.005 SOL** (~$2) to the recipient wallet shown when you run `safe-push --unlock`.
2. Enter your wallet address and the Transaction ID (TXID).
3. The CLI verifies the transaction directly on the Solana blockchain and unlocks your features locally!

## 🔒 Security & Privacy
- **Local Only**: This tool scans only your local directory.
- **Privacy First**: No data, code, or telemetry is ever sent to any server.
- **Transparency**: The only network call made is to a public Solana RPC endpoint for donation verification.

## 📜 License
MIT License. Feel free to use, learn, and share!

---
*Stay safe and keep coding!* 🚀
dxt-explorer 0.4
DXT Explorer is an interactive web-based log analysis tool to visualize Darshan DXT logs and help understand the I/O behavior.
Requires Python: >=3.8
Keywords: dxt-explorer

<p align="center">
  <img src="https://github.com/hpc-io/dxt-explorer/raw/main/docs/source/_static/images/dxt-explorer.png" alt="DXT Explorer"/>
</p>

DXT Explorer is an interactive web-based log analysis tool to visualize Darshan DXT logs and help understand the I/O behavior of applications. Our tool adds an interactive component to Darshan trace analysis that can aid researchers, developers, and end-users to visually inspect their applications' I/O behavior, zoom-in on areas of interest and have a clear picture of where is the I/O problem. 

### Documentation

You can find our complete documentation at [dxt-explorer.readthedocs.io](https://dxt-explorer.readthedocs.io).

### Citation

You can find more information about DXT Explorer in our PDSW'21 paper. If you use DXT in your experiments, please consider citing:

```
@inproceedings{dxt-explorer,
   title = {{I/O Bottleneck Detection and Tuning: Connecting the Dots using Interactive Log Analysis}},
   author = {Bez, Jean Luca and Tang, Houjun and Xie, Bing and Williams-Young, David and Latham, Rob and Ross, Rob and Oral, Sarp and Byna, Suren},
   booktitle = {2021 IEEE/ACM Sixth International Parallel Data Systems Workshop (PDSW)},
   year = {2021},
   volume = {},
   number = {},
   pages = {15-22},
   doi = {10.1109/PDSW54622.2021.00008}
}
```
---

DXT Explorer Copyright (c) 2022, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.

If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov.

NOTICE.  This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights.  As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.
macrotools 0.1.7
Employ America tools for pulling and graphing U.S. macroeconomic data.
Requires Python: >=3.9

# MacroTools

A Python package providing flexible tools to work with macroeconomic data and create Employ America-style time series graphs.

## Installation

`pip install macrotools`

## Features

- Download Flat Files and individual series easily
    - Caches flat files by default for easy retrieval
- Create professional time series graphs with matplotlib in EA style
    - Support for dual y-axes for comparing different data series
    - Flexible formatting options
- Includes a few useful tools to work with time series macro data (compounded annual growth rates, rebasing)

## Examples

See [this notebook](https://github.com/PrestonMui/macrotools/blob/main/examples/macrotools_guide.ipynb) for examples on how to use Macrotools

## Roadmap and Development

Currently stored at [GitHub](https://github.com/PrestonMui/macrotools.git).

Some features I am working on.

[] Wrapper for FRED API -- allow for pulling multiple series