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README.md

@@ -4,7 +4,75 @@ app_file: interface.py
 sdk: gradio
 sdk_version: 4.44.1
 ---
-python -m spacy download en_core_web_sm
+# Knowledge Graph Project README
 
-resources that helped
-https://www.analyticsvidhya.com/blog/2019/10/how-to-build-knowledge-graph-text-using-spacy/ (Knowledge Graph: Data Science Technique to Mine Information from Text (with Python code))
+## 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
+
+1. Clone the repository
+2. Install the required dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+3. Download the required spaCy model:
+   ```bash
+   python -m spacy download en_core_web_sm
+   ```
+
+## Usage
+
+Run the Gradio interface:
+```bash
+python interface.py
+```
+
+The interface allows you to:
+1. Select which news sources to include
+2. Generate a knowledge graph from the selected sources
+3. 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
+
+1. The application fetches recent articles from selected RSS feeds
+2. Content is processed and split into manageable chunks
+3. An LLM (GPT-4o) extracts entities and relationships from the text
+4. A directed graph is constructed from these relationships
+5. 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:
+- [Analytics Vidhya: How to Build Knowledge Graph from Text using spaCy](https://www.analyticsvidhya.com/blog/2019/10/how-to-build-knowledge-graph-text-using-spacy/)
+- [DataCamp: Knowledge Graph RAG Tutorial](https://www.datacamp.com/tutorial/knowledge-graph-rag)
+
+## 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.

requirements.txt

@@ -8,4 +8,5 @@ matplotlib
 pyvis
 plotly
 langchain
-langchain_experimental
+langchain_experimental
+langchain_openai