semvec/pypi-packages huggingface dataset:
jgtfxcon 0.6.125
PDS Services
Requires Python: >=3.7



# jgtfxcon

just getting prices from fxconnect


## Installation
```sh
pip install -U jgtfxcon
```

## Example

```py

    >>> import pandas as pd
    >>> import jgtfxcon
    >>> df=jgtfxcon.getPH('EUR/USD','H4')
    >>>
    >>> # retrieve 3000 periods and generate from the DF
    >>> df=jgtfxcon.getPH('EUR/USD','H4',3000,with_index=False)
    >>> dfi=jgtfxcon.createFromDF(df)
    >>>

```

## More


### Enhancements Idea

#### -l (for --timeline)

* --@STCGoal An easy way to snap a moment in time and save it to our store.

```sh
jgtfxcli -i "SPX500" -t "H1,H4,H8,D1,W1,M1" -c 500 -l "22101313"

```

```
$JGTPY_DATA/pds  NORMAL
$JGTPY_DATA/pdl/  L Item

```
smithers-llm 1.0.10
An LLM-powered CLI that simulates job interviews based on your resume.
Requires Python: >=3.13

# Smithers

An LLM-powered CLI that simulates job interviews based on your resume.

## Example

```bash
pipx run smithers-llm [RESUME_PATH] --role=[ROLE]
```

## Installation

With pip:

```bash
pip install -g smithers-llm
```

With pipx (automatically adds smithers to PATH):

```bash
pipx install smithers-llm
```
 
Please notice that the user is expected to run the model separately as well.

So far the only model supported is Llama3.1

You can easily self-host it with [Ollama](https://ollama.com). After installing it, pull the model and keep it running on the background with the command below:

```bash
ollama run llama3.1
```

## Usage

With smithers installed on the current env:

```bash
python -m smithers-llm [RESUME_PATH] --role=[ROLE]
```

With smithers installed globally and defined on PATH:

```bash
smithers-llm [RESUME_PATH] --role=[ROLE]
```

With pipx:

```bash
pipx run smithers-llm [RESUME_PATH] --role=[ROLE]
```

## Learnings

- LangChain
  - Structured Outputs
- LangGraph
  - Human in the Loop
  - Checkpointer
- Python packaging with SetupTools and Build
  - Differences between project configurations files such as the modern pyproject.toml and the battle-tested setup.py and setup.cfg
- Publishing to PyPI and TestPyPI with Twine
- Creating CLIs with Click
  - Handling different kinds of options: parameters and arguments
- Experimenting with Jupyter Notebooks
  - Research whether it was worth commiting the notebooks, although they might be reasonable for documentation purposes, they weren't for the way I used them in this project, to scratch the general ideas up right in the beginning
- Self-reflection if RAG was necessary here, the answer I came to was NO. The files are too small.
- Creating a GitHub Pages workflow to publish project to PyPI on pushing tagged commits to GH.

## Fun fact

The repo was named after a character from the show "The Simpsons".

Smithers conducts a job interview with Homer on the episode ["I Married Marge"](https://www.youtube.com/watch?v=rG6w0IAoT4U).
jgtfx2console 0.5.15
PDS Services
Requires Python: >=3.7
Keywords: data



# jgtfx2console

just getting prices from fxconnect


## Installation
```sh
pip install -U jgtfx2console
```

## Example

```py

    >>> import pandas as pd
    >>> import jgtfx2console
    >>> df=jgtfx2console.getPH('EUR/USD','H4')
    >>>
    >>> # retrieve 3000 periods and generate from the DF
    >>> df=jgtfx2console.getPH('EUR/USD','H4',3000,with_index=False)
    >>> dfi=jgtfx2console.createFromDF(df)
    >>>

```

## More


### Enhancements Idea

#### -l (for --timeline)

* --@STCGoal An easy way to snap a moment in time and save it to our store.

```sh
jgtfxcli -i "SPX500" -t "H1,H4,H8,D1,W1,M1" -c 500 -l "22101313"

```

```
$JGTPY_DATA/pds  NORMAL
$JGTPY_DATA/pdl/  L Item

```
llmscout 0.1.0
An LLM-powered tool for discovering and analyzing research papers
Requires Python: >=3.8

# LLMScout

An LLM-powered tool for discovering and analyzing research papers. LLMScout helps researchers efficiently search, analyze, and manage academic papers from arXiv, leveraging the power of large language models.

## Features

- 🔍 Smart keyword generation using LLM
- 📚 Automated paper search on arXiv
- 📊 Intelligent paper analysis and summarization
- 📥 Batch paper downloading
- 📝 Detailed logging and progress tracking
- ⏸️ Resume capability for interrupted operations

## Installation

```bash
pip install llmscout
```

Or install from source:

```bash
git clone https://github.com/cafferychen777/llmscout.git
cd llmscout
pip install -e .
```

## Quick Start

1. Set up your environment variables:
```bash
# Copy the example environment file
cp .env.example .env

# Edit .env and add your OpenAI API key
OPENAI_API_KEY=your-api-key-here
```

2. Use in Python:
```python
from llmscout import ResearchPipeline

# Initialize the pipeline
pipeline = ResearchPipeline()

# Run the complete analysis
pipeline.run(
    topic="watermark attack language model",
    max_results=10,
    date_start="2023-01-01"
)
```

3. Or use the command-line interface:
```bash
llmscout --topic "watermark attack language model" --max-results 10
```

## Environment Variables

The following environment variables can be configured in your `.env` file:

```bash
# Required
OPENAI_API_KEY=your-api-key-here

# Optional
OPENAI_MODEL=gpt-4              # Default: gpt-4
OPENAI_TEMPERATURE=0.7          # Default: 0.7
OPENAI_MAX_TOKENS=1000          # Default: 1000

# Output directories
OUTPUT_DIR=./results            # Default: ./results
DOWNLOAD_DIR=./papers          # Default: ./papers
LOG_DIR=./logs                 # Default: ./logs
```

## Documentation

For detailed documentation, visit [our documentation site](https://llmscout.readthedocs.io/).

## Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Citation

If you use this tool in your research, please cite:

```bibtex
@software{llmscout,
  title = {LLMScout: An LLM-Powered Tool for Research Paper Discovery and Analysis},
  author = {Caffery Chen},
  year = {2025},
  url = {https://github.com/cafferychen777/llmscout}
}