https://github.com/shubham-bendre/chat-with-multiple-pdfs
Effortlessly interact with your PDF files using the power of Google Generative AI! This app enables users to upload PDF files, process them into a searchable format, and ask detailed questions based on the PDF content.
https://github.com/shubham-bendre/chat-with-multiple-pdfs
faiss-vector-database gemini streamlit
Last synced: 3 months ago
JSON representation
Effortlessly interact with your PDF files using the power of Google Generative AI! This app enables users to upload PDF files, process them into a searchable format, and ask detailed questions based on the PDF content.
- Host: GitHub
- URL: https://github.com/shubham-bendre/chat-with-multiple-pdfs
- Owner: Shubham-Bendre
- License: mit
- Created: 2024-11-21T07:42:27.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-21T09:40:42.000Z (over 1 year ago)
- Last Synced: 2025-03-05T10:45:57.565Z (over 1 year ago)
- Topics: faiss-vector-database, gemini, streamlit
- Language: Python
- Homepage:
- Size: 139 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 📚 Chat with PDF using Gemini 💁
Effortlessly interact with your PDF files using the power of **Google Generative AI**! This app enables users to upload PDF files, process them into a searchable format, and ask detailed questions based on the PDF content.
---
## 🚀 Features
- 📄 **Upload Multiple PDFs**: Seamlessly process one or more PDF files.
- 🧠 **AI-Powered QA**: Get answers to your questions based on the PDF content using **Gemini Pro**.
- 🗂️ **FAISS Indexing**: Efficient text chunking and embedding storage for rapid responses.
- 🔐 **Secure Access**: Integrated with environment variables for secure API key handling.
---
## 📂 File Structure
- `main.py`: Core Streamlit app logic.
- `requirements.txt`: All dependencies required for the project.
- `.env`: Stores your `GOOGLE_API_KEY` for secure API access.
- `faiss_index/`: Directory where FAISS indexing data is stored locally.
---
## 🤝 How It Works
1. **Upload PDFs**: Drag and drop your PDF files in the sidebar.
2. **Process PDFs**: Click on **Submit & Process** to extract text and create searchable embeddings.
3. **Ask Questions**: Enter your query in the input box and get detailed responses powered by Gemini AI.
---
## 🧩 Key Technologies
- **Streamlit**: Interactive and user-friendly web interface.
- **PyPDF2**: Extracts text from PDF documents.
- **LangChain**: Manages conversational chains and embeddings.
- **Google Generative AI**: Provides embeddings and conversational capabilities.
- **FAISS**: Fast and efficient similarity search.
---
## 💡 Usage Tips
- For best results, ensure your PDF documents contain searchable text (scanned images may not work well).
- Questions unrelated to the PDF context will result in: _"Answer is not available in the context."_.
---
## 📸 Screenshots
### 1️⃣ Main Task Window
## 🚀 Usage
To run the **app.py**, execute the following command in your terminal:
```bash
streamlit run app.py
```
---
Feel free to 🌟 star this repo if you find it helpful! 🚀