metadata

title: 06-knowledge-graph
app_file: interface.py
sdk: gradio
sdk_version: 4.44.1

Knowledge Graph Project README

Overview

This project creates an interactive knowledge graph visualization from RSS feeds. It extracts entities and relationships from news articles using NLP and LLM-based techniques, then visualizes the connections in an interactive graph.

Features

Fetches and aggregates content from multiple RSS news feeds
Processes text using LLM-based knowledge extraction
Builds a directed graph of entities and their relationships
Provides an interactive visualization using Plotly
Allows selection of specific news sources

Installation

Clone the repository
Install the required dependencies:
```
pip install -r requirements.txt
```

Download the required spaCy model:

python -m spacy download en_core_web_sm

Usage

Run the Gradio interface:

python interface.py

The interface allows you to:

Select which news sources to include
Generate a knowledge graph from the selected sources
View the aggregated feed content and interactive graph visualization

Project Structure

interface.py: Main Gradio application with the UI and visualization logic
fetch.py: Functions for retrieving and parsing RSS feeds
sources.py: List of available RSS feed URLs
requirements.txt: Required Python packages
tutorials/: Example notebooks showing the knowledge graph extraction process

How It Works

The application fetches recent articles from selected RSS feeds
Content is processed and split into manageable chunks
An LLM (GPT-4o) extracts entities and relationships from the text
A directed graph is constructed from these relationships
The graph is visualized using Plotly with interactive features

Dependencies

spaCy: For NLP processing
Gradio: For the web interface
NetworkX: For graph data structures
Plotly: For interactive visualizations
LangChain: For LLM-based graph transformations
OpenAI API: Powers the LLM graph transformer

Acknowledgements

This project was inspired by techniques from:

Part of 30 Agents in 30 Days

This project is #6 in the 30 Agents in 30 Days series, which provides practical AI agent workflows for different stages of product development including research, development, testing, marketing, and sales.