Welcome to ChatCV, an innovative and interactive tool designed to present your curriculum vitae (CV) in a unique and engaging format.

ChatCV leverages the power of Retrieval-Augmented Generation (RAG) and ChatGPT to create a dynamic, conversational experience for showcasing your professional profile. Unlike traditional personal webpages, ChatCV offers a distinctive approach to presenting your information, making your CV more interactive and accessible. This project features LangChain, LangServe, and LLama-index libraries.

1. Interactive Chat Interface: Engage viewers with a conversational format that can answer questions and provide detailed information about your career and qualifications.
2. Enhanced Presentation: Move beyond static text and typical layouts by offering a more engaging way to explore your professional background.
3. Customizable Content: Tailor the chat responses to highlight key aspects of your experience, skills, and accomplishments.
4. User-Friendly: Easy to set up and use, allowing you to focus on what matters most – your career journey.

1. Stand Out: Differentiate yourself from others by using a modern and interactive method to present your CV.
2. Increased Engagement: Potential employers and network connections are more likely to interact with and remember your profile.
3. Accessibility: Provide information in a conversational manner that can be more intuitive and accessible to a wider audience.

To install ChatCV, follow these steps:

1. Clone the repository.
2. Add your CV, publications, etc., to the chatcv/media directory. These files will be used as retrieved context for the chat. All types of textual files can be uploaded.
3. Create a GPT key. You can create a .env file to use this key for debugging. Remember to keep this key private!
4. Deploy the app. This app can be easily deployed on Railway. Alternatives are suggested by LangServe.
5. Add the API URL to the frontend/streamlit_frontend.py file. Update the template questions of the frontend template_questions = []

Here's how you can use ChatCV:

1. Process your media files and embed them for retrieval. To recalculate embeddings, use https://API/compute_embeddings.
2. Run the Streamlit chat with streamlit run frontend/streamlit_frontend.py. The Streamlit front-end can be deployed for free in Streamlit
3. To add new files or update existing ones, upload new files to the media folder and reprocess the embeddings via the API.

[Lorenzo Baraldi] © [2024]. [Apache 2.0 License].