The "Work Experience Chatbot" is a dynamic project designed to assist hiring managers and recruiters in exploring the professional background and work experience of potential candidates. As a data scientist actively seeking employment, I developed this chatbot to showcase my technical skills and professional achievements in an interactive and engaging manner.
The main objective of this project is to create an interactive chatbot that provides detailed information about a candidate's professional background. This tool is intended to make the screening process more efficient for recruiters and hiring managers by delivering pertinent information through a conversational interface.
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Interactive Chat Interface: Users can ask questions and receive information about the candidate's work experience, skills, and educational background.
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Custom Queries Handling: The chatbot is equipped to handle a variety of questions, ensuring that users can get the specific information they are looking for.
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User-friendly Dashboard: Built with Streamlit, the dashboard offers a clean and intuitive user interface for interacting with the chatbot.
- Python: The primary programming language used for building the backend logic of the chatbot.
- Streamlit: An open-source app framework used to create the user interface for the chatbot.
- LangChain: Leveraged for orchestrating the chatbot's language model and managing conversation flow.
- Pinecone: Utilized for managing the vector database that powers the search capabilities of the chatbot.
- OpenAI: Utilizes APIs from OpenAI for advanced natural language processing capabilities.
To get a local copy up and running follow these simple steps.
- Python 3.11 or higher
- pip
- Clone the repo
git clone https://github.com/Prvargas/work-experience-chatbot.git
- Install required packages
pip install -r requirements.txt
To run the chatbot application:
streamlit run app.pyContributions are what make the open-source community such a powerful platform for learning, inspiring, and creation. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
- Phillip Vargas - DataScientistPhil@gmail.com
- LinkedIn - https://www.linkedin.com/in/prvargasds/
- Projects Portfolio - https://prvargas.github.io/Phillip_Portfolio/