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| title: Chat With Documents | |
| emoji: 🚀 | |
| colorFrom: purple | |
| colorTo: purple | |
| sdk: streamlit | |
| sdk_version: 1.13.0 | |
| app_file: app.py | |
| pinned: false | |
| --- | |
| # 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. | |
| --- | |
| ### 💡 **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. | |
| --- | |
| ### ✨ **Enjoy the App!** ✨ | |
| Now, go ahead and chat with your documents! 😄 | |
| --- | |