Author-Co-Author Network Navigator

This project provides a web-based interface to explore a network of academic authors and their co-authors. It features a semantic search engine to find authors based on their research abstracts and a 3D force-directed graph to visualize the co-authorship network.

Features

• Semantic Search: Find authors based on natural language queries related to their research.
• AI-Powered Explanations: Get AI-generated explanations of why an author is a good match for your search query.
• 3D Network Visualization: Explore the co-authorship network in an interactive 3D graph.
• Flask-Based API: A simple and extensible API for search and data retrieval.

Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.7+
• pip (Python package installer)

Setup

1. Clone the repository:

   git clone <repository-url>
   cd <repository-folder>
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Configure API Key:

   • Rename the config.env.example file to config.env.
   • Open config.env and add your Google Gemini API key:

     GOOGLE_API_KEY=your_google_api_key
     

Data Preparation

The search engine and graph visualization rely on pre-processed data files.

1. Input Data: The primary data source is a JSON file containing author information, including abstracts and co-author relationships. The project expects this file at nicolasdata/author_abstracts_5.json.

2. Generate Vector Database: Run the embedding_database.py script to create the vector database from your input data. This will generate the static/vectorbig.json file by default.

   python embedding_database.py load nicolasdata/author_abstracts_5.json
   

   You can specify a different path for the database:

   python embedding_database.py --db /path/to/your/vector_database.json load nicolasdata/author_abstracts_5.json
   

3. Generate Force Graph Data: Run the convert_author_abstracts_4_to_graph.py script to create the data for the 3D visualization. This will generate the static/forcegraph_data_3.json file. Provide the input and output file paths as arguments.

   python convert_author_abstracts_4_to_graph.py nicolasdata/author_abstracts_5.json static/forcegraph_data_3.json
   

Running the Application

Once the data preparation is complete, you can start the web server:

python search_api.py

By default, the server runs on 0.0.0.0:5000 and uses the vector database at static/vectorbig.json. You can change the host, port, and database path using command-line arguments:

python search_api.py --host 127.0.0.1 --port 8080 --db /path/to/your/vector_database.json

The application will be available at http://<host>:<port>.

• The 3D graph visualization will be at the root URL: http://<host>:<port>/
• The search API is available at the /search endpoint.

API Endpoints

/search

• Method: POST
• Description: Performs a semantic search for authors based on a query.
• Body:

  {
      "query": "your search query here"
  }
  

• Response: A JSON object with a list of matching authors.

/explain_match

• Method: POST
• Description: Generates an AI-powered explanation for why an author matches a search query.
• Body:

  {
      "query": "your search query here",
      "author_id": "author_id_from_search_results"
  }
  

• Response: A JSON object with the explanation text.