Lanchang Pinecone QnA Overview Lanchang Pinecone QnA is a demonstration project that showcases the integration of AI with APIs to provide a seamless experience for extracting information from video content and answering queries based on that information. The project is built using the MERN stack, with a backend that handles video transcription and data storage, and a frontend that provides an interactive interface for users.
Project Structure Backend: Located in the server directory. Frontend: Located in the my-app directory.
Features Video Transcription: Transcribe video content using the OpenAI Whisper API. Data Storage: Store the transcript in MongoDB and convert it into vector embeddings using Pinecone. Query Answering: Use the OpenAI API to answer user queries based on the vector embeddings stored in Pinecone. Setup and Installation To set up this project locally, follow these steps:
- Environment Variables Create a .env file in both the server and my-app directories with the following variables:
MongoDB Connection String: MONGODB_URI OpenAI API Key: OPENAI_API_KEY Pinecone API Key: PINECONE_API_KEY Pinecone Environment Key: PINECONE_ENVIRONMENT_KEY
- Backend Setup Navigate to the server directory: cd server
Install dependencies: npm install
Start the server: npm start
The backend server will be running and listening for API requests.
- Frontend Setup Navigate to the my-app directory:
bash Copy code cd my-app Install dependencies:
bash Copy code npm install Start the React application:
bash Copy code npm start The frontend application will be running and accessible at http://localhost:3000.
Usage Video Upload and Transcription:
Use the frontend interface to input a video URL. The backend will transcribe the video using the OpenAI Whisper API.
Data Storage: The transcript is stored in MongoDB as text. Vector embeddings of the transcript are stored in Pinecone.
Querying: Enter queries related to the video content on the frontend. The backend will use the stored vector embeddings to provide answers through the OpenAI API.
Contributing Feel free to submit issues, feature requests, or pull requests. Contributions are welcome!
Contact For any questions or support, please contact [email protected]
Thank you for checking out Lanchang Pinecone QnA! We hope this project demonstrates the powerful integration of AI technologies and provides a useful tool for extracting and querying information from video content.