metadata

title: Chat With Documents
emoji: 🚀
colorFrom: red
colorTo: red
sdk: streamlit
sdk_version: 1.13.0
app_file: app.py
pinned: false

Here’s the updated README.md without the "How to Run Locally" part:

Chat With Documents 🤖📄

Welcome to the Chat with Documents app! 🚀 This Streamlit app allows you to upload PDF and PPT files, extract their content, store the extracted text in a vector store, and interact with it using natural language queries! 🤖💬

Built with LangChain, OpenAI, Streamlit, and Astra DB, this project leverages the power of LLMs (Large Language Models) to allow users to chat with their documents like never before. 🧠

🚀 Features

PDF & PPT Extraction: Upload PDF and PowerPoint files to extract text! 📄➡️📝
Vector Store: Automatically stores extracted text in a Cassandra vector store. 🔍📚
Ask Anything: Ask questions about the document, and get answers powered by OpenAI! 🤖❓

🛠️ Tech Stack

Streamlit: Frontend framework to interact with the app.
LangChain: For seamless document processing and querying.
OpenAI: For LLM integration to provide intelligent responses.
Astra DB: Database for storing and managing vectorized text data.
Python Libraries: PyPDF2, python-pptx, cassio, and more.

🌍 Deployment

This project is designed to be deployed on Hugging Face Spaces. Just upload your code, and it will run in the cloud! 🌩️

Make sure to configure the Secrets in Hugging Face Spaces for storing your sensitive API keys securely! 🔒

💡 How It Works

Upload a PDF or PPT file using the file uploader. 📤
The app will extract text from the file using PyPDF2 (for PDFs) or python-pptx (for PPTs). 📄➡️📝
The extracted text is split into manageable chunks using LangChain's CharacterTextSplitter. ✂️
The chunks are then added to Cassandra as vectorized data using OpenAI embeddings. 🔄
Ask any query about the content of your document, and the app will respond using the power of OpenAI! 🤖💬

🎯 Why Use This?

Make documents interactive: Easily explore the content of your documents by asking questions.
Quick retrieval: With the text stored in a vector store, you can query the content efficiently.
Secure API keys: API keys are securely managed using environment variables and Hugging Face Spaces Secrets. 🔑💼

✨ Enjoy the App! ✨

Now, go ahead and chat with your documents! 😄

This version now only focuses on the app’s features and deployment, making it more suited for hosting and sharing on Hugging Face Spaces!