Doc_QA_LangChain is a front-end only implementation of a website that allows users to upload a PDF or text-based file (txt, markdown, JSON, HTML, etc) and ask questions related to the document with GPT. The project uses Vue3 for interactivity, Tailwind CSS for styling, and LangChain for parsing documents/creating vector stores/querying LLM.

1. Parse a PDF or text-based file
2. Split the document into 1000-token chunks
3. Create embeddings of the document chunks with GPT API
4. Store the embeddings into an ephemeral document store
5. Create an embedding of the question to query relevant chunks from the document
6. Get a response from GPT API and display it in the text area

To run the project:

1. Clone the project
2. Run npm install
3. Run npm run dev

To use it with a hosted frontend:

Visit https://doc-qa-langchain.vercel.app/

Once the project is running, go to your web browser and navigate to the URL provided by Vite. You can then upload a PDF or text-based file and ask questions related to the document.

• Vue3
• Tailwind CSS
• LangChain

This project is licensed under the MIT License. See the LICENSE file for details.

If you have any questions about the project, please feel free to reach out to me at [email protected]