Automatic LinkedIn Answer AI

Automatically respond to LinkedIn messages from recruiters using AI. This bot leverages your resume and professional documents to generate precise, context-aware answers. It uses browser automation, Retrieval-Augmented Generation (RAG), and memory to ensure accurate and personalized responses.

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Features

• Automated LinkedIn Messaging: Replies to recruiter messages in real-time.
• Document-Based Answers: Uses your resume and professional documents to generate responses.
• Precision with RAG: Retrieves relevant information from your documents for accurate answers.
• Memory for Context: Remembers previous interactions for consistent responses.
• Easy Setup: Simple configuration and no need for LinkedIn API credentials.

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Use Cases

1. Job Seekers: Automatically respond to recruiters with tailored answers based on your resume.
2. Busy Professionals: Save time by letting the bot handle initial recruiter interactions.
3. Networking: Maintain professional communication without manual effort.

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Installation

1. Clone the Repository:

   git clone https://github.com/iklobato/automatic-linkedin-answer-ai.git
   cd automatic-linkedin-answer-ai
   

2. Install Dependencies:

   uv install 
   

3. Set Up Environment Variables:

   • Create a .env file:

     OPENAI_API_KEY=your_openai_api_key
     

   • Replace your_openai_api_key with your OpenAI API key.

4. Prepare Documents:

   • Place your resume and other professional documents in the docs/ directory.
   • Supported formats: .txt.

5. Configure config.yaml:

   linkedin:
     username: "your@email.com"
     password: "your_password"
   deepseek:
     api_key: "your_openai_api_key"
     model: "gpt-4o"
   documents_dir: "docs/"
   bot_disclaimer: "🤖 Automated Response: This is an AI assistant. I'll respond based on my resume and experiences."
   

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Usage

1. Run the Bot:

   python bot.py
   

2. How It Works:

   • The bot logs into LinkedIn using your credentials.
   • It navigates to your messages and checks for new recruiter messages.
   • For each new message, it retrieves relevant information from your documents and generates a response using OpenAI's GPT model.
   • The bot sends the response with a disclaimer indicating it's an automated reply.

3. Monitor Logs:

   • The bot logs all actions (e.g., new messages, replies, errors) for debugging.
   • Logs are saved in the console with timestamps.

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Customization

1. Add More Documents

• Place additional .txt files in the docs/ directory.
• The bot will automatically include them in its knowledge base.

2. Adjust Response Style

• Modify the bot_disclaimer in config.yaml to change the bot's tone.
• Update the system_prompt in the generate_response function for custom instructions.

3. Fine-Tune for Precision

• Use OpenAI's fine-tuning API to train a custom model on your specific dataset.
• Replace the ChatOpenAI model with your fine-tuned model.

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Example Workflow

1. Recruiter Message:

   Hi, I came across your profile and noticed your experience with Python. Can you tell me more about your projects?
   

2. Bot Response:

   🤖 Automated Response: This is an AI assistant. I'll respond based on my resume and experiences.
   
   I have worked on several Python projects, including a machine learning model for predicting customer churn and a web scraper for data analysis. You can find more details in my resume.
   

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Troubleshooting

1. Login Issues

• Ensure your LinkedIn credentials in config.yaml are correct.
• If LinkedIn blocks the login attempt, try using a browser session to verify your account.

2. No Responses

• Check the logs for errors.
• Ensure your documents in the docs/ directory contain relevant information.

3. Rate Limits

• If OpenAI API rate limits are hit, reduce the bot's polling frequency by increasing the time.sleep() duration.

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Contributing

Contributions are welcome! Follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a pull request.

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License

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

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Acknowledgments

• browser-use for browser automation.
• LangChain for RAG and memory integration.
• OpenAI for the GPT models.