mirror of
https://github.com/camel-ai/owl.git
synced 2026-03-22 05:57:17 +08:00
239 lines
7.4 KiB
Markdown
239 lines
7.4 KiB
Markdown
# 🦉 Interview Preparation Assistant
|
|
|
|
An intelligent multi-agent interview preparation system powered by the OWL framework that helps you prepare for job interviews with comprehensive research, tailored questions, and detailed preparation plans.
|
|
|
|
|
|
|
|
## ✨ Features
|
|
|
|
- **🔍 Company Research**: Automatically researches companies using real-time web data
|
|
- **❓ Interview Question Generation**: Creates tailored interview questions specific to your job role and target company
|
|
- **📋 Preparation Plans**: Builds comprehensive step-by-step interview preparation plans
|
|
- **🧠 AI-Powered Agents**: Leverages multiple AI agents to work together on your interview preparation
|
|
- **💻 Code Examples**: Provides code examples for technical roles with explanations
|
|
- **🔄 Real-time Progress**: Shows conversation process between AI agents as they prepare your materials
|
|
|
|
## 📋 Table of Contents
|
|
|
|
- [Requirements](#-requirements)
|
|
- [Installation](#-installation)
|
|
- [Quick Start](#-quick-start)
|
|
- [Usage Guide](#-usage-guide)
|
|
- [Configuration](#-configuration)
|
|
- [Troubleshooting](#-troubleshooting)
|
|
- [Project Structure](#-project-structure)
|
|
|
|
## 🛠 Requirements
|
|
|
|
- Python 3.10+ (tested on Python 3.10)
|
|
- Access to one of the following AI models:
|
|
- OpenAI API (GPT-4)
|
|
- OpenRouter API (Gemini models)
|
|
- Internet connection for web search and company research
|
|
- Minimum 8GB RAM
|
|
|
|
## 🚀 Installation
|
|
|
|
### 1. Clone the OWL Repository
|
|
|
|
First, clone the OWL repository, which this project depends on:
|
|
|
|
```bash
|
|
git clone https://github.com/camel-ai/owl.git
|
|
cd owl
|
|
```
|
|
|
|
### 2. Create a Virtual Environment
|
|
|
|
```bash
|
|
# Create a conda environment (recommended)
|
|
conda create -n interview_assistant python=3.10
|
|
conda activate interview_assistant
|
|
|
|
# OR using venv
|
|
python -m venv interview_env
|
|
source interview_env/bin/activate # On Windows: interview_env\Scripts\activate
|
|
```
|
|
|
|
### 3. Install OWL and Dependencies
|
|
|
|
```bash
|
|
# Install OWL
|
|
pip install -e .
|
|
|
|
# Install additional dependencies
|
|
pip install streamlit numpy pandas opencv-python
|
|
```
|
|
|
|
### 4. Configure API Keys
|
|
|
|
Create a `.env` file in the project directory with your API keys:
|
|
|
|
```bash
|
|
# Navigate to the Interview Preparation Assistant directory
|
|
cd community_usecase/new\ int/
|
|
|
|
# Create .env file
|
|
touch .env
|
|
```
|
|
|
|
Add your API keys to the `.env` file:
|
|
|
|
```
|
|
# OpenAI API (recommended for best results)
|
|
OPENAI_API_KEY=your_openai_api_key_here
|
|
|
|
# OR OpenRouter API (for access to Gemini models)
|
|
OPENROUTER_API_KEY=your_openrouter_api_key_here
|
|
|
|
# Optional: Google Search API for enhanced research (optional)
|
|
GOOGLE_API_KEY=your_google_api_key_here
|
|
SEARCH_ENGINE_ID=your_google_search_engine_id_here
|
|
```
|
|
|
|
## ⚡ Quick Start
|
|
|
|
The fastest way to get started is to use the Streamlit web interface:
|
|
|
|
```bash
|
|
# Navigate to the project directory
|
|
cd community_usecase/new\ int/
|
|
|
|
# Start the web application
|
|
streamlit run app.py
|
|
```
|
|
|
|
This will open a web browser window with the Interview Preparation Assistant interface where you can:
|
|
|
|
1. Enter your target job role (e.g., "Machine Learning Engineer")
|
|
2. Enter your target company name (e.g., "Google")
|
|
3. Generate interview preparation materials
|
|
|
|
## 📚 Usage Guide
|
|
|
|
### Web Interface
|
|
|
|
The web interface provides three main functions:
|
|
|
|
#### 1. Company Research
|
|
|
|
Click on "Research Company" to generate a comprehensive report about your target company including:
|
|
- Company background and culture
|
|
- Technical stack and technologies used
|
|
- Interview process and expectations
|
|
- Key products and services
|
|
|
|
|
|
#### 2. Interview Questions
|
|
|
|
Click on "Generate Questions" to create tailored interview questions for your role and company:
|
|
- Technical questions with code examples
|
|
- Behavioral questions specific to the company culture
|
|
- Role-specific questions to showcase your expertise
|
|
- Sample answers and solution approaches
|
|
|
|
|
|
#### 3. Preparation Plan
|
|
|
|
Click on "Create Preparation Plan" to receive a detailed day-by-day preparation guide:
|
|
- Structured preparation timeline
|
|
- Technical topics to review
|
|
- Practice exercises and code challenges
|
|
- Research and preparation tasks
|
|
- Interview day tips
|
|
|
|
|
|
### Command Line Usage
|
|
|
|
You can also run specific functions from the command line:
|
|
|
|
```bash
|
|
# Run company research
|
|
python -c "from main import research_company; result = research_company('Google', detailed=True); print(result['answer'])"
|
|
|
|
# Generate interview questions
|
|
python -c "from main import generate_interview_questions; result = generate_interview_questions('Machine Learning Engineer', 'Google'); print(result['answer'])"
|
|
|
|
# Create preparation plan
|
|
python -c "from main import create_interview_prep_plan; result = create_interview_prep_plan('Machine Learning Engineer', 'Google'); print(result['answer'])"
|
|
```
|
|
|
|
### Log Monitoring
|
|
|
|
You can view the logs in real-time in the "System Logs" tab of the web interface to monitor:
|
|
- AI agent conversations
|
|
- Progress of each request
|
|
- Any errors or issues that occur
|
|
|
|
## ⚙️ Configuration
|
|
|
|
### Customizing Parameters
|
|
|
|
You can adjust the following parameters in `main.py`:
|
|
|
|
1. **Round Limit**: Change the conversation round limit by modifying the `round_limit` parameter in function calls (default: 5)
|
|
|
|
2. **Model Selection**: Edit the model configuration in `construct_interview_assistant()` to use different models
|
|
|
|
3. **Output Directory**: Change `INTERVIEW_PREP_DIR` to customize where results are stored
|
|
|
|
### Environment Variables
|
|
|
|
In addition to API keys, you can customize behavior with these environment variables:
|
|
|
|
- `LOG_LEVEL`: Set to `DEBUG`, `INFO`, `WARNING`, or `ERROR` to control logging verbosity
|
|
|
|
## 🔧 Troubleshooting
|
|
|
|
### Common Issues
|
|
|
|
1. **API Key Errors**
|
|
- Ensure your API keys are correctly set in the `.env` file
|
|
- Check that you're using the correct format without quotes or extra spaces
|
|
|
|
2. **Model Errors**
|
|
- If using OpenRouter, ensure the model specified is available on your account
|
|
- Verify you have sufficient API credits for your requests
|
|
|
|
3. **Round Limit Not Working**
|
|
- The system enforces a strict limit of 5 conversation rounds to prevent excessive token usage
|
|
- You can adjust this in the code if needed, but may encounter higher API costs
|
|
|
|
4. **Memory Errors**
|
|
- Processing large contexts can require significant memory
|
|
- Try using a machine with more RAM or reducing model context sizes
|
|
|
|
### Getting Help
|
|
|
|
If you encounter issues not covered here:
|
|
|
|
1. Check the logs in the "System Logs" tab of the web interface
|
|
2. Examine the console output for error messages
|
|
3. File an issue on the GitHub repository
|
|
|
|
## 📂 Project Structure
|
|
|
|
```
|
|
community_usecase/new int/
|
|
├── app.py # Streamlit web interface
|
|
├── main.py # Core functionality and API connections
|
|
├── config/
|
|
│ └── prompts.py # Prompt templates for different tasks
|
|
├── interview_prep/ # Generated interview preparation materials
|
|
├── logging_utils.py # Logging utilities
|
|
└── README.md # This documentation
|
|
```
|
|
|
|
## 📝 License
|
|
|
|
This project is built on top of the CAMEL-AI OWL framework, which is licensed under the Apache License 2.0.
|
|
|
|
## 🙏 Acknowledgements
|
|
|
|
- This project is built on the [CAMEL-AI OWL framework](https://github.com/camel-ai/owl)
|
|
- Special thanks to the contributors of CAMEL-AI for making multi-agent AI systems accessible
|
|
|
|
---
|
|
|
|
Made with ❤️ for job seekers everywhere.
|