Welcome to RAG in Memory! This application allows you to upload a PDF, create a FAISS-based search index, and get answers to specific questions related to the content of the PDF using the power of OpenAI's AI.
- Intuitive Interface: Uses a simple and intuitive Streamlit-based user interface.
- PDF Upload: Upload a PDF from your local machine.
- Text Extraction: Extract text from the uploaded PDF.
- Create FAISS Index: Create a FAISS index from the extracted text chunks.
- Semantic Search: Perform semantic searches within the PDF using OpenAI.
- Python 3.7 or higher
- OpenAI key
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Clone the repository:
git clone https://github.com/tuo-username/rag-in-memory.git cd rag-in-memory -
Create a virtual environment (optional but recommended):
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
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Install the dependencies:
pip install -r requirements.txt
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Set your OpenAI API key:
Open the
.envfile (create the file if it does not exist) and add your OpenAI API key:OPENAI_KEY=sk-your-api-key-here
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Start the Streamlit application:
streamlit run app.py
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Upload a PDF via the user interface.
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After uploading the PDF, wait for the FAISS index to be created.
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Ask a question about the content of the PDF and get an immediate answer!
Upload a PDF directly from the user interface. The PDF will be processed and the text will be extracted automatically.
Enter a specific question regarding the content of the PDF and get an accurate answer by leveraging the power of OpenAI's language model.
Contributions are welcome! If you have suggestions or improvements, feel free to open a pull request or create an issue.
This project is distributed under the MIT license. See the LICENSE file for more details.
We hope you find this application useful! If you have any questions or need support, please feel free to contact us.