semvec/pypi-packages huggingface dataset:
stelarys 2.0.0
Una herramienta god por q me mide 20cm
Requires Python: >=3.7

╔════════════════════════════════════════════════════╗
║                  STELARYS V2.0.0                   ║
╠════════════════════════════════════════════════════╣
║          · Autor: Pablo (p_a_b_l_0_xd)             ║
║          · Soporte: discord.gg/viperfinder         ║
║                                                    ║
║              ¡Gracias por usarlo!                  ║
╚════════════════════════════════════════════════════╝
receipt-statement-linker 0.2.1
Link receipts to bank statement transactions
Requires Python: >=3.12

# receipt-statement-linker

Python program that links receipts/invoices to bank statements, extracting data from both into a single json file.

Sample receipt:

<img width="856" height="738" alt="image" src="https://github.com/user-attachments/assets/a9e1d095-44b4-44f1-9b6b-fe4bd6897e6f" />

Sample bank statement transaction:

<img width="639" height="65" alt="image" src="https://github.com/user-attachments/assets/5542d8ac-cc42-42ff-b523-b39f36569579" />

Sample linker output:

<img width="1257" height="601" alt="image" src="https://github.com/user-attachments/assets/e7e73bf0-049b-49d4-ac68-2288b3c6f94a" />





# Command Line Options
- `--receipt-input receipt.jpg` - Receipt/invoice files to process
- `--statement-input statement.pdf` - Bank statement files to process
- `--receipt-output output.json` - JSON filepath for output
- `--categorize` - Turn categorization on
- `--categories category1 category2 ...` - Categories to use for categorization (optional)

# Config file
The config file can be found at `$XDG_CONFIG_HOME/receipt_statement_linker/config.toml`. The config file has the following fields:
- `transcription_model` - model used for receipt & statement transcription.
- `categorization_model` - model used for receipt & statement categorization, if enabled.
- `matching_model` - model used for matching receipts to statement transactions when multiple transaction prices match the receipt price.
- `categorization_notes` - notes to provide extra context to the model when categorizing

Models are litellm model strings. You can find them by going [here](https://docs.litellm.ai/docs/providers) and selecting a provider.

# Categorization
With categorization on, each entry will include a category for the transaction, and categorization for each item within the associated receipt. The default categories are:

```py
DEFAULT_CATEGORIES = [
  "GROCERIES",
  "EATING_OUT",
  "TRANSPORTATION",
  "HOUSING",
  "HEALTHCARE",
  "PERSONAL_CARE",
  "ENTERTAINMENT",
  "SAVINGS_AND_INVESTMENTS",
  "DEBT_PAYMENT",
  "UTILITIES",
  "MISC",
]
```

Custom categories can be specified using `--categories category1 category2 ...`.

Only labels may not be enough context for the categorization. To add more context to give to the model, you can add context in the config file like so:
```toml
categorization_notes = "'Liberty' is a theatre, so it falls under entertainment."
```

# Supported Filetypes
The filetypes that are supported are based on whatever model is selected. The defualt is Gemini 2.5 Flash, and supported filetypes for this model can be found [here](https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-flash).

# Example
A sample invoice and statement can be found under `example/`. The output of running the following command can be found at `example/example.json`:
```bash
uvx receipt-statement-linker \
          --receipt-input     example/invoice-sample.pdf \
          --statement-input   example/bank-statement-sample.pdf \
          --categorize \
          --receipt-output    example/example.json
```
micro-graph 0.3.1
A simple library to build compute graphs for logical flows like agents.
Requires Python: >=3.10
Keywords: ai, graph, low-code

# Micro-Graph

> If all the fancy langgraph etc. libraries are too heavyweight and complex for you, look no further.

A tiny library allowing you to build graphs for executing workflows.
It is focused purely on minimalism, simplicity and ease of understanding.
It does not need any dependencies.

## 🛠️ Installation

```bash
pip install micro-graph

# to also install `micro_graph.ai` dependencies
pip install micro-graph[ai]
```

## 👨‍💻 Usage

See the [examples](examples) or [tests](tests) for example uses.

## 👥 Contributing

Feel free to make this code better by forking, improving the code and then pull requesting.

However, the goal of this repo is to be the minimal functionallity required and implementing it without any external dependencies.
Please keep this in mind if you modify this repo.

Keep it simple!

### Setup

Install dev dependencies and pre-commit hooks

```bash
pip install -e .[dev,ai]
pre-commit install
```

This will automatically run `ruff check .` and `pytest` before each commit.

## ⚖️ License

Micro-Graph is licensed under the permissive MIT license -- see [LICENSE](LICENSE) for details.