semvec/pypi-packages huggingface dataset:
markitdown-advanced-pdf-llm-plugin 0.1.1
3-rd party plugin for markitdown library. It is to be used for converting a pdf to markdown purely based on llm's capability
Requires Python: >=3.10

# markitdown-advanced-pdf-llm-plugin

## Overview
markitdown-advanced-pdf-llm-plugin is a plugin for the [MarkItDown](https://github.com/microsoft/markitdown) library, specifically engineered for extracting the knowledge out of complex multi-modal PDF documents which is non-text heavy. This plugin addresses the challenges of reduced LLM output quality on large multi-modal documents by leveraging higher intelligence Large Language Models (LLMs) to interpret/extract knowledge out of these documents.

## Why MarkItDown
MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. Markdown is extremely close to plain text, with minimal markup or formatting, but still provides a way to represent important document structure. Mainstream LLMs, such as OpenAI's GPT-4o, natively "speak" Markdown, and often incorporate Markdown into their responses unprompted. This suggests that they have been trained on vast amounts of Markdown-formatted text, and understand it well. As a side benefit, Markdown conventions are also highly token-efficient.

## Why markitdown-advanced-pdf-llm-plugin
- Token efficiency: When involving Multi-Modal document in RAG, text only capabilities consume less token than multi-modal capabilities
- RAG output quality: The quality of LLMs output degrades as input token increases. Passing several pages of multi-modal document at once can lead to poor LLM summarization than several pages of text documents 
- Latency: Text only input has lesser latency than multi-modal input

## What it does?
### SimpleLLMKnowledgeExtractor
- Converts each PDF page to both a standalone PDF and an image
- prompts LLM (presently only OpenAI Client) to extract all text, tables, and picture information
- Processes pages concurrently for improved performance
- Generates a complete markdown document with both extracted content and adds page image

### Future Extractor (In Progress)
Current approach of sending full pages to LLMs sometimes results in incomplete extraction or information loss. Future extractor's goal is to 
- Make the extraction more deterministic by extracting each element (text, table and picture) using libraries like PyMuPDF4LLM
- Validate the deterministic extraction performed well using LLM
- Apply LLM extraction if needed

### Example page from a document where this plugin is beneficial
![Screenshot 2025-05-04 184336](https://github.com/user-attachments/assets/080d24fd-a849-475a-919d-f28eba498dbe)

## Installation
The plugin is available for installation through pip

```
pip install markitdown-advanced-pdf-llm-plugin
```

## Usage
```
from markitdown import MarkItDown
from openai import OpenAI

client = OpenAI(api_key="openai-key")

md = MarkItDown(enable_plugins=True, llm_client=openai_client, llm_model="prefered-model") # Incase custom prompt, use arg 'llm_prompt'
result = md.convert("doc.pdf")

markdown_content = result.markdown
```
organizze_mcp 0.1.4
Add your description here
Requires Python: >=3.11

# Organizze MCP

A Python-based integration for Organizze financial management platform using FastMCP.

## Description

This project provides a Multi-Call Protocol (MCP) server for the Organizze financial management platform, allowing operations like viewing transactions and managing categories.

## Features

- List transactions within a date range
- List available categories
- Set categories for transactions

## Installation

This project uses Poetry for dependency management.

```bash
# Clone the repository
git clone https://github.com/yourusername/organizze-mcp.git
cd organizze-mcp

# Install dependencies
poetry install
```

## Configuration

Create a `.env` file in the root directory with the following variables:

```
ORGANIZZE_EMAIL=your_organizze_email
ORGANIZZE_TOKEN=your_organizze_api_token
```

You can obtain your API token from your Organizze account settings.

## Usage

Run the MCP server:

```bash
poetry run python src/organizze-mcp/main.py
```

## Requirements

- Python 3.12+
- fastmcp 2.3.3+
- requests 2.32.3+

## License

[Add your license here]

## Author

Diego (dccarbone@gmail.com)
bayesnest 1.0.0
Lightweight log-space nested sampling for Bayesian inference
Requires Python: >=3.8
Keywords: bayesian, nested, sampling, inference, posterior, likelihood

# BayesNest

**BayesNest** is a lightweight, log-space nested sampler for Bayesian inference, supporting complex posterior structures including multimodal, curved, and non-Gaussian distributions. It supports multiple constrained sampling strategies including:

- Naive rejection sampling
- Ellipsoidal sampling (MultiNest-style)
- MCMC-constrained sampling
- Slice sampling
- Diffusive sampling (tempered MCMC)

BayesNest computes both posterior samples and Bayesian model evidence

---

## Features

- Log evidence computation
- Posterior sampling with log-weights
- Support for multimodal and curved posteriors
- Corner plot visualization of parameter distributions
- Multiple constrained samplers (`uniform`, `ellipsoid`, `mcmc`, `slice`, `diffusive`)
- Convergence diagnostics via log-evidence trace plots
- Minimal, dependency-light design

---

## Installation

```bash
pip install bayesnest
```

## Examples
All examples are located in the examples/ directory of the repo.
### 1: Linear Gaussian Model


```bash
python examples/linear_gaussian_example.py
```
This example:

- Simulates data from a linear Gaussian model y = ax + b + noise

- Use nested sampling to estimate the posterior distribution over
slope `a` and intercept `b`

- Print the posterior mean and log evidence

- Generate a 2D corner plot of the posterior distribution plot

- Plots the convergence of the log-evidence estimate

### 2: Multimodal Posterior
A synthetic example with two separated Gaussian modes is also found in the github repo

```bash
python examples/multimodal_example.py
```
This synthetic example defines a 2D likelihood with two well-separated Gaussian peaks and:

- Sample using ellipsoidal decomposition

- Reveal both modes clearly in the posterior distribution plot

- Demonstrate ellipsoid nested sampling’s strength in handling multimodal distributions

Expected output:

- A bimodal posterior in the corner plot

- A log-evidence trace that reflects both modes properly being integrated

### 3: Rosenbrock

```bash
python examples/rosenbrock_example.py
```
This classic benchmark stresses the sampler with a narrow, curved valley in 5D.
- Uses slice sampling to effectively explore curved likelihood regions
- Computes posterior samples and log-evidence
- Generates a pairwise corner plot

Expected output:
- The expected 2D plot between theta_0 and theta_1 should show a tight, curved valley, starting from -1 to 1
- Posterior mean near theta_i = 1 for all i