semvec/pypi-packages huggingface dataset:
pycronet 0.1.0
Python cffi bindings for Cronet
Requires Python: >=3.9

# pycronet (cffi)

This package provides minimal Python bindings to Cronet using cffi.

## Build

The build expects a local Cronet build under `../build/dist` (relative to this
folder). You can override paths using environment variables:

- `CRONET_BUILD_DIR`: GN build directory (default: `../build`)
- `CRONET_DIST_DIR`: Dist directory containing `include/` and `lib/`
  (default: `../build/dist`)

Example:

```sh
python -m pip install .
```

## Runtime

The bindings expose the raw `ffi` and `lib` objects plus a small Engine wrapper.

```python
import pycronet
print(pycronet.engine_version())
```
ragent-core 0.2.1
Add your description here
Requires Python: >=3.11

# RAGent

RAGent is a research project focused on training language models to perform agentic search through reinforcement learning. The goal is to train models that autonomously learn to interact with search systems, whether they are Elasticsearch indexes, vector databases, BM25 engines, file systems, SQL databases, or hybrid combinations of these.

## Project Structure

The project is organized into three main development areas:

### 1. Synthetic Data Generation

A pipeline for generating question-answer pairs of varying complexity given a specific data source. The current approach uses an **explorer agent pipeline** (`ragent_core/pipelines/explorer_agent/`), inspired by the agentic search synthesis methodology described in [DeepSeek-V3.2](https://arxiv.org/abs/2512.02556). The pipeline:

- Extracts key concepts from documents in the corpus
- Assigns breadth and depth parameters to each concept based on occurrence frequency
- Uses specialized agents (breadth, depth, synthesis) to generate progressively complex QA pairs
- Leverages retrieval tools during generation to ground questions in actual document content

A graph-based approach for QA generation, inspired by [WebShaper](https://arxiv.org/abs/2507.15061)'s formalization-driven synthesis framework, is currently under development.

### 2. Environment Development

The environment layer (`environments/`) handles the search system setup, curriculum learning, and reward functions. Each environment provides:

- **Search Engine Layer**: The retrieval backend (BM25, vector search, hybrids, etc)
- **Tool Interface**: Actions available to the agent (`search(query)`, `read(doc_id)`, etc.)
- **Data Source**: The knowledge base (a collection of documents to search over)
- **QA Dataset**: Question-answer pairs used for training and evaluation
- **Reward Functions**: LLM-based judges and other reward signals (e.g., format compliance, tool usage) for evaluating agent trajectories

Current environments:
- `bm25/` — Lightweight lexical search environment using BM25s.

### 3. Training

Training is performed using the [verifiers](https://github.com/PrimeIntellect-ai/verifiers) library and [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl) library.

## Data Sources

QA pairs have been generated from the following data sources for now:

- GitLab Handbook
- IETF RFCs
- Python Enhancement Proposals (PEPs)
- DevDocs.io developer documentation
- OWASP Cheat Sheets
- Rust RFCs
- Domain-specific corpora (marine biology, space exploration, mythology) extracted from Wikipedia.

## Current Development

Training is starting with approximately 2,500 QA pairs generated from the data sources listed above using the explorer agent pipeline. The initial focus is on lexical search over diverse technical documentation.
gastimeter 0.2.6
Extract text from an image using Azure Cognitive Services.
Requires Python: >=3.8

[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/gkunz/gastimeter/badge)](https://securityscorecards.dev/viewer/?uri=github.com/gkunz/gastimeter)
[![REUSE status](https://api.reuse.software/badge/github.com/gkunz/gastimeter)](https://api.reuse.software/info/github.com/gkunz/gastimeter)

# Gastimeter

`gastimeter` is a simple tool to obtain readings from a gas meter by means of OCR (Optical Character Regonition). As OCR is not 100% accurate, the tool effectively provides a guestimate of the gas meter reading - hence the name. 

## Installation

Clone the repository and install `gastimeter` using pip - optionally in a virtually environment.

```
  git clone https://github.com/gkunz/gastimeter.git
  cd gastimeter
  pip3 install .
```

## Known issues

`gastimeter` uses [OpenCV](https://opencv.org/) to capture images from a connected camera. If running `gastimeter` on a RaspberryPi on Debian Bullseye (or higher), note that [OpenCV does not work (yet)](https://github.com/opencv/opencv/issues/22820#issuecomment-1339283736) with the new libcamera subsystem. There are two possible workarounds:


### Use the legacy camera subsystem based on V4L2

Switch to the old camera subsystem using the following:

1. `sudo raspi-config`
1. Select ` Interface Options`
1. Select `Legacy Camera`

Note that this camera subsystem is deprecated and will be removed in the future.

### Use libcamera tooling

Use `libcamera-jpeg` to capture an image and feed this into `gastimeter` using the `--image` option.


## License
Gastimator is licensed under the [MIT license](https://spdx.org/licenses/MIT.html).