A Streamlit application that extracts text from a PDF file and answers questions based on the extracted text.
This Python script utilizes several libraries and modules to create a Streamlit application for processing PDF files. It extracts text from the uploaded PDF, splits it into chunks, and builds a knowledge base for question answering. Users can ask questions about the PDF content, and the application provides answers based on the extracted text.
- Upload PDF files to extract text from them.
- Perform text extraction and chunking for efficient question answering.
- Utilize OpenAI embeddings for text processing.
- Use a question-answering chain for answering user questions.
- Display answers in the Streamlit app interface.
- Clone the repository:
git clone https://github.com/CodeThat/Langchain-Chat-PDF.git
- Change to the project directory:
cd your-repository
- Create a virtual environment (optional but recommended):
python -m venv venv
source venv/bin/activate
- Install the required dependencies:
pip install -r requirements.txt
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Make sure you have activated the virtual environment (if created).
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Run the script:
python script.py -
Open your web browser and navigate to the provided local URL (e.g., http://localhost:8501).
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Upload a PDF file using the file uploader in the Streamlit app.
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Enter your question about the PDF content in the provided text input.
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View the answer displayed in the app interface.
This project is licensed under the MIT License.
Add functionality to parse more doc types using langchain.
- Streamlit - The web application framework used for building the user interface.
- PyPDF2 - A library for reading PDF files.
- OpenAI - The language model and embeddings used in the script.
- LangChain - The library for text splitting, embeddings, vector stores, and question answering.
- FAISS a library that allows us to quickly search for multimedia documents that are similar to each other