| # VideoChad🗿 : RAG based Youtube Video Conversational Chat-Bot 📤📺 | |
| ### *Got Bored watching Long Youtube Videos ? Here's a Full Stack App that* | |
| - ⭐ **Generates Smart Summary** ⭐ | |
| - ↪ **Provides BackLinks (Reference) to the Video (No hallucination)** ↪ | |
| - 🗣 **(ChatBot) Enables you to have Conversation with Video** 🗣 | |
| - 🧠**Generates MindMap** 🧠 | |
| ## Demo | |
| [](https://www.youtube.com/watch?v=_fflcGaQjBM) | |
| ## Features | |
| - **Automated Video Summarization**: The application generates concise summaries of video content, allowing users to grasp key concepts efficiently and identify areas requiring deeper focus. | |
| - **Real-time Chat Interaction**: Users can engage in conversation with the video content, fostering a deeper understanding of the subject matter by asking questions and receiving instant responses. | |
| - **Video Backlinking**: The application incorporates a backlinking feature that enables users to seek relevant timestamps in the video player by clicking on provided reference links. | |
| - **MindMap**: Generates a interactive mindmap using the important keywords from the video content's essence! | |
| - **Transcript Download**: Users can download a text file containing the transcript of the processed video for future reference. | |
| ## Technologies Used | |
| - **Flask**: A lightweight Python web framework used for building the backend API. | |
| - **React**: A JavaScript library for building the user interface on the frontend. | |
| - **Large Language Models (LLMs)**: Specifically, the OpenAI ChatGPT 3.5 (gpt-3.5-turbo) model is employed for generating contextual responses. | |
| - **Retrieval-Augmented Generation (RAG)**: This approach combines a retriever and a language model, allowing for efficient retrieval of relevant information from the video transcript. | |
| - **LangChain**: A framework for building applications with large language models, facilitating the integration of the RAG approach. | |
| - **Vector Database (Chroma)**: A vector database used for storing and efficiently searching the embeddings of the video transcript. | |
| - **OpenAI Embeddings API**: Utilized for converting textual data into high-dimensional vector representations. | |
| - **YouTube API**: Employed for fetching video transcripts and metadata. | |
| ## Getting Started | |
| To get started with this application, follow these steps: | |
| 1. Clone the repository: | |
| ``` | |
| git clone https://github.com/foolmalhar/VideoChad.git | |
| ``` | |
| 2. Install the required dependencies: | |
| ``` | |
| cd VideoChad | |
| pip install -r requirements.txt | |
| ``` | |
| 3. Set up the necessary environment variables: | |
| - `OPENAI_API_KEY`: Your OpenAI API key for accessing the language models. [OpenAi Platform](https://platform.openai.com/account/api-keys) | [Account Setup](https://platform.openai.com/docs/quickstart/account-setup) | |
| 4. Start the Flask backend: | |
| ``` | |
| python app.py | |
| ``` | |
| 5. In a separate terminal, start the React frontend: (optional) | |
| ``` | |
| cd VideoChad | |
| npm install | |
| npm run dev | |
| ``` | |
| 6. Access the application in your web browser at `http://localhost:5000`. | |
| ( if you don't use static pre-built files and are running node on the VideoChad frontend, then port might be :3000, check terminal ) | |
| ## Usage | |
| 1. Enter a valid YouTube video link in the provided input field. (Link must have English Transcript available on Youtube ) | |
| 2. The application will fetch the video transcript and generate a summary. | |
| 3. Interact with the video content by asking questions in the chat interface. | |
| 4. Click on the provided reference links to seek relevant timestamps in the video player. | |
| 5. explore ! | |
| ## Contributing | |
| Contributions to this project are welcome! If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request. | |
| ## License | |
| This project is licensed under the [MIT License](LICENSE). | |
| ## Acknowledgments | |
| - [DeepLearning.ai Short Course](https://www.deeplearning.ai/short-courses/langchain-chat-with-your-data/) Understand RAG with Langchain and Chromadb | |
| - [LangChain](https://www.langchain.com/) The Tool! | |
| - [OpenAI](https://openai.com/) for their powerful language models and APIs. | |
| - [Chroma](https://www.trychroma.com/) for their vector database solution. | |