Cohere RAG APP

Conversational PDF Question Answering

This Streamlit application allows users to upload a PDF and ask questions about its content. The application uses Cohere's LLM, HuggingFace embeddings, and Chroma vectorstore to retrieve relevant information from the PDF and generate answers.

Features

• Upload a PDF file.
• Enter your Cohere API key.
• Ask questions about the uploaded PDF.
• Get answers based on the PDF content.

Requirements

• Python 3.8 or higher
• Streamlit
• PyPDF2
• LangChain libraries
• HuggingFace Embeddings
• Cohere API Key

Installation

1. Clone the repository:

   git clone https://github.com/meakshayraut/cohere-rag.git
   cd cohere-rag

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Run the Streamlit application:

   streamlit run app.py

2. Open your browser and go to http://localhost:8501.

3. Enter your Cohere API key in the sidebar.

4. Upload a PDF file using the file uploader.

5. Ask questions about the PDF in the chat input.

Project Structure

• app.py: Main application file.
• requirements.txt: List of required Python packages.

Example

1. After running the application, you'll see the interface to enter your Cohere API key and upload a PDF file.
2. Once the PDF is uploaded, you can type a question in the chat input and receive an answer based on the PDF content.

Screenshot

Troubleshooting

• Ensure that you have a valid Cohere API key.
• Ensure that the PDF file is correctly uploaded and contains readable text.
• If the application fails to start, check for any missing dependencies and install them.