semvec/pypi-packages huggingface dataset:
freeports-analysis 0.1.2
Script for dumping a csv from different finance reports about ethical aspects
Requires Python: >=3.8
Keywords: finance, pdf, ethic, economy, datascience

[![Freeports logo](https://www.freeports.org/assets/logo/complete-dark.svg)](https://www.freeports.org)
# Finance reports analysis ([Freeports](https://www.freeports.org))
This project is intended parse finance pdf reports and create `CSV` dataset.
The purpose of the project and related infos can be found at the [*official website*](https://www.freeports.org).

## Installation
There are for now 2 installation method:
- using `pip` *(Recommended)*
- from source
### Using `pip`
Install in a python virtual environment launching
```bash
pip install freeports_analysis
```
### From source
> Requirements:
> You need to have the python `build` package. 
> You can install it in your virtual environment with `pip install build`
1. Clone the repository:
```bash
git clone https://github.com/tvp-freeports/analysis_finance_reports.git
```
2. `cd` into the created directory
```bash
cd analysis_finance_reports
```
3. build the package
```bash
python -m build .
```
4. install local package
```bash
pip install .
```
5. enjoy


## Quickstart
To start use the command provided with the library call
```bash
freeports -h
```
to see the options. All the option can be provided as environment variables.
If you want to use `freeports` as a python library write in you code
```python
import freeports_analysis as fra
```

## Usage
To have a complete overview of the package look at the [full documentation](https://docs.freeports.org).
pylibsrd 1.3.4
pylibSrd is a library containing my modules I use repeatedly.

pyLibSrd 1.3.4
==================
Sam Davis

Commands
------------------
1. ```pylibsrd```
Displays the commands available in libsrd.  

2. ```mergepdfs```  
Will merge all pdf's found in the current directory, and save the result at: ./Output/Output.pdf  
  
3. ```imgconvert [args]```  
Will convert all images of ```InitalFormat``` in current directory to ```FinalFormat``` in ./Output/   

4. ```markhtml [args]```  
Will convert a markdown file to a html file.  

5. ```pdfresize```  
Will resize the all pdf's in current directory to a4.


Classes
---------------
1. Table  
A custom TSV reading and writing table class, that can read, write and parse.  

2. HtmlBuilder  

A very nice html building class, that is used to programatically build html files.
retrievify 0.1.4
Lightweight Retrieval-Augmented Generation (RAG) toolkit in 3 lines.
Requires Python: >=3.9
Keywords: rag, nlp, retrieval, embeddings

# retrievify

![PyPI](https://img.shields.io/pypi/v/retrievify)
![Python](https://img.shields.io/pypi/pyversions/retrievify)
![License](https://img.shields.io/github/license/MeerMagia/retrievify)
![Downloads](https://img.shields.io/pypi/dm/retrievify)

**Lightweight Retrieval-Augmented Generation (RAG) toolkit in 3 lines.**

```python
from retrievify import RAG
rag = RAG().fit("docs/", patterns=["*.pdf","*.md","*.txt"])
print(rag.ask("What are the core contributions?"))
```

## Why retrievify?
- ⚡ Fast local embeddings (MiniLM) by default
- 🧱 Smart chunking & FAISS/Annoy vector stores (Windows-friendly)
- 🧩 Optional LLM generation hook (OpenAI/Ollama)
- 🛠️ CLI for quick indexing and querying

## Install
```bash
pip install retrievify
# If FAISS is tricky on Windows, use Annoy:
pip install annoy
```

## Quickstart
```python
from retrievify import RAG
rag = RAG().fit("docs/")
res = rag.ask("What are the key limitations?", k=5)
print(res["evidence"][0])
```

## CLI
```bash
retrievify index ./docs --pattern "*.pdf,*.md"
retrievify query ./docs -q "evaluation pipeline" -k 5 --generate
```

## LLM (optional)
Set env var for OpenAI first:
```powershell
$env:OPENAI_API_KEY="sk-..."
```
Then:
```python
from retrievify import RAG
rag = RAG({"generation": True, "llm_backend": "openai"}).fit("docs/")
print(rag.ask("Summarize the paper")["answer"])
```

## Roadmap
- Cross-encoder re-ranking
- HTML/URL loaders & deduplication
- Simple retrieval eval (Recall@k, MRR, NDCG)