149 lines
4.7 KiB
Markdown
149 lines
4.7 KiB
Markdown
# **Automatic LinkedIn Answer AI**
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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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---
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## **Features**
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- **Automated LinkedIn Messaging**: Replies to recruiter messages in real-time.
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- **Document-Based Answers**: Uses your resume and professional documents to generate responses.
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- **Precision with RAG**: Retrieves relevant information from your documents for accurate answers.
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- **Memory for Context**: Remembers previous interactions for consistent responses.
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- **Easy Setup**: Simple configuration and no need for LinkedIn API credentials.
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---
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## **Use Cases**
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1. **Job Seekers**: Automatically respond to recruiters with tailored answers based on your resume.
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2. **Busy Professionals**: Save time by letting the bot handle initial recruiter interactions.
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3. **Networking**: Maintain professional communication without manual effort.
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---
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## **Installation**
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1. **Clone the Repository**:
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```bash
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git clone https://github.com/iklobato/automatic-linkedin-answer-ai.git
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cd automatic-linkedin-answer-ai
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```
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2. **Install Dependencies**:
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```bash
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uv install
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```
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3. **Set Up Environment Variables**:
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- Create a `.env` file:
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```bash
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OPENAI_API_KEY=your_openai_api_key
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```
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- Replace `your_openai_api_key` with your OpenAI API key.
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4. **Prepare Documents**:
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- Place your resume and other professional documents in the `docs/` directory.
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- Supported formats: `.txt`.
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5. **Configure `config.yaml`**:
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```yaml
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linkedin:
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username: "your@email.com"
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password: "your_password"
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deepseek:
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api_key: "your_openai_api_key"
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model: "gpt-4o"
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documents_dir: "docs/"
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bot_disclaimer: "🤖 Automated Response: This is an AI assistant. I'll respond based on my resume and experiences."
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```
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---
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## **Usage**
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1. **Run the Bot**:
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```bash
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python bot.py
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```
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2. **How It Works**:
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- The bot logs into LinkedIn using your credentials.
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- It navigates to your messages and checks for new recruiter messages.
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- For each new message, it retrieves relevant information from your documents and generates a response using OpenAI's GPT model.
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- The bot sends the response with a disclaimer indicating it's an automated reply.
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3. **Monitor Logs**:
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- The bot logs all actions (e.g., new messages, replies, errors) for debugging.
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- Logs are saved in the console with timestamps.
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---
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## **Customization**
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### **1. Add More Documents**
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- Place additional `.txt` files in the `docs/` directory.
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- The bot will automatically include them in its knowledge base.
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### **2. Adjust Response Style**
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- Modify the `bot_disclaimer` in `config.yaml` to change the bot's tone.
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- Update the `system_prompt` in the `generate_response` function for custom instructions.
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### **3. Fine-Tune for Precision**
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- Use OpenAI's fine-tuning API to train a custom model on your specific dataset.
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- Replace the `ChatOpenAI` model with your fine-tuned model.
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---
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## **Example Workflow**
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1. **Recruiter Message**:
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```
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Hi, I came across your profile and noticed your experience with Python. Can you tell me more about your projects?
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```
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2. **Bot Response**:
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```
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🤖 Automated Response: This is an AI assistant. I'll respond based on my resume and experiences.
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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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```
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---
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## **Troubleshooting**
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### **1. Login Issues**
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- Ensure your LinkedIn credentials in `config.yaml` are correct.
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- If LinkedIn blocks the login attempt, try using a browser session to verify your account.
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### **2. No Responses**
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- Check the logs for errors.
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- Ensure your documents in the `docs/` directory contain relevant information.
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### **3. Rate Limits**
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- If OpenAI API rate limits are hit, reduce the bot's polling frequency by increasing the `time.sleep()` duration.
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---
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## **Contributing**
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Contributions are welcome! Follow these steps:
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1. Fork the repository.
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2. Create a new branch (`git checkout -b feature/your-feature`).
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3. Commit your changes (`git commit -m 'Add some feature'`).
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4. Push to the branch (`git push origin feature/your-feature`).
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5. Open a pull request.
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---
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## **License**
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This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
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---
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## **Acknowledgments**
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- [browser-use](https://github.com/browser-use/browser-use) for browser automation.
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- [LangChain](https://langchain.com) for RAG and memory integration.
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- [OpenAI](https://openai.com) for the GPT models.
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