LinkedIn Icebreaker Bot

An AI-powered tool that generates personalized icebreakers and conversation starters based on LinkedIn profiles. This project uses IBM watsonx.ai and LlamaIndex to create a tool that helps make introductions more personal and engaging.

Project Overview

Imagine you're heading to a big networking event, surrounded by potential employers and industry leaders. You want to make a great first impression, but you're struggling to come up with more than the usual, "What do you do?"

This AI icebreaker bot does the research for you. You input a name, and within seconds, the bot searches LinkedIn, generating personalized icebreakers based on someone's career highlights, interests, and even fun facts.

Features

• Extract LinkedIn profile data using ProxyCurl API or mock data
• Process and index the data using LlamaIndex and IBM watsonx embeddings
• Generate interesting facts about a person's career or education
• Answer specific questions about the LinkedIn profile
• Interact with the bot through a command-line interface or a Gradio web UI

Project Structure

icebreaker_bot/
├── requirements.txt           # Dependencies
├── config.py                  # Configuration settings
├── modules/
│   ├── __init__.py
│   ├── data_extraction.py     # LinkedIn profile data extraction
│   ├── data_processing.py     # Data splitting and indexing
│   ├── llm_interface.py       # LLM setup and interaction
│   └── query_engine.py        # Query processing and response generation
├── app.py                     # Gradio interface
└── main.py                    # Main script to run without Gradio

Getting Started

Prerequisites

• Python 3.11+
• A ProxyCurl API key (optional - you can use mock data)

Installation

1. Clone the repository:

git clone https://github.com/HaileyTQuach/icebreaker.git
cd icebreaker

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Add your ProxyCurl API key to config.py (optional)

Usage

Command Line Interface

Run the bot using the command line:

python main.py --mock  # Use mock data
# OR
python main.py --url "https://www.linkedin.com/in/username/" --api-key "your-api-key"

Web Interface

Launch the Gradio web interface:

python app.py

Then open your browser to the URL shown in the terminal (typically http://127.0.0.1:7860).

Development Tasks

This is a starter template with placeholder functions. Your task is to implement the following components:

1. In config.py:

   • Define the prompt templates for facts generation and question answering

2. In modules/data_extraction.py:

   • Implement the extract_linkedin_profile function

3. In modules/data_processing.py:

   • Implement the split_profile_data function
   • Implement the create_vector_database function
   • Implement the verify_embeddings function

4. In modules/llm_interface.py:

   • Implement the create_watsonx_embedding function
   • Implement the create_watsonx_llm function
   • Implement the change_llm_model function

5. In modules/query_engine.py:

   • Implement the generate_initial_facts function
   • Implement the answer_user_query function

6. Update modules/__init__.py to import your implemented functions

7. In main.py:

   • Implement the process_linkedin function
   • Implement the chatbot_interface function

8. In app.py:

   • Implement the process_profile function
   • Implement the chat_with_profile function

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• IBM watsonx.ai for providing the LLM and embedding models
• LlamaIndex for the data indexing and retrieval framework
• ProxyCurl for LinkedIn profile data extraction