Research Paper Q&A Tool

A powerful tool for document search and analysis using advanced language models. Upload PDFs, convert them to vectors, and query your documents with ease.

Demo.Recording.mp4

Features

• PDF Upload and Vectorization: Upload PDFs and convert them into vectors using Pinecone.
• Advanced Querying: Leverage the Ollama model for intelligent document querying.
• User-Friendly Interface: Built with Streamlit for a seamless user experience.

Quick Start

Prerequisites

• Python 3.10
• Docker
• Pinecone API Key
• Ollama Model (pre-configured in the application)

Setup

1. Create a Pinecone Account and API Key

1. Sign up for a Pinecone account at Pinecone.
2. Create an index and generate your API key.
3. Save your API key and index name, as you'll need them to run the application.

2. Configure the Models in the Code

1. Open backend/core/embedding_service.py.
2. Find the section where the models are defined:

   # Example configuration
   LLM_MODEL_NAME = "your_llm_model_name"
   EMBEDDING_MODEL_NAME = "your_embedding_model_name"

3. Replace "your_llm_model_name" and "your_embedding_model_name" with the actual names of the models you downloaded.

4. Build and Run

4.1. With Docker

1. Ensure Docker is installed on your system.
2. From the project root, run:

docker-compose up --build

3. Access the app at http://localhost:8501.

4.2. Without Docker

1. Install dependencies:

pip install -r backend/requirements.txt

2. Start the FastAPI server:

python backend/scripts/main.py

3. In a new terminal, start the Streamlit frontend:

streamlit run frontend/streamlit_ui.py

4. Open http://localhost:8501 to use the app.

5. Enter Pinecone API Key and Index to Use

After starting the Streamlit frontend, enter the Pinecone API key and index:

6. Upload your document pdf

• Push the buttom to convert your pdf to vector and store to the vectorDB
• Now, you may start to ask the llm question about your pdf