Ask PDF is a web application designed to streamline document understanding by providing instant answers to user queries within PDF documents. Whether you're a student, researcher, or professional, Ask PDF simplifies the process of extracting valuable insights from lengthy documents.
- Upload PDF: Users can upload their PDF documents directly through the file input menu.
- Text Extraction: Ask PDF extracts text from the uploaded PDF, ensuring no information is missed during the process.
- Vector Database: The extracted text is stored in a vector database using embeddings, enabling efficient storage and retrieval of document content.
- Text Similarity Search: When a user asks a question, Ask PDF performs a text similarity search within the vector database to find relevant content.
- Output Generation: Leveraging the Language Model (LLM), Ask PDF generates and displays the final output, providing users with accurate and contextually relevant answers to their queries.
- Streamlit: Provides an intuitive interface for user interaction.
- EasyOCR: Ensures accurate text extraction from PDF documents.
- Langchain: Empowers document processing with features like text splitting and retrieval-based question answering.
- Hugging Face Models: Utilizes state-of-the-art transformer models for precise and context-aware answers.
- FAISS: Enables fast and efficient document retrieval, enhancing user experience.
To get started with Ask PDF, simply clone this repository and install the necessary dependencies. Run the application using Streamlit and upload your PDF documents to begin extracting insights instantly.
streamlit run app.pyContributions to Ask PDF are welcome! If you have any ideas for improvement or would like to report a bug, feel free to open an issue or submit a pull request.