Explore Complex PDF with chat-gemini-

Table of Contents

• Features
• Technologies Used
• Setup Instructions
• Usage

💡 What is RAGIFY-ENGINE?

RAGIFY-ENGINE is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.

Features

• Table Extraction: Identify and parse tables to retrieve structured data, making it easier to answer data-specific questions.
• Text Extraction: Efficiently extract and process text from PDFs, enabling accurate and comprehensive information retrieval.
• Image Analysis: Extract and interpret images within the PDFs to provide contextually relevant information.

Technologies Used

• RAG (Retrieval-Augmented Generation): Combines retrieval and generation for more accurate answers.
• LangChain: Framework for building applications with language models.
• Streamlit: Framework for creating interactive web applications with Python.
• Poetry: Dependency management and packaging tool for Python.

Setup Instructions

Follow these steps to set up the project on your local machine:

1. Clone the Repository:

• Begin by cloning the repository to your local machine:

https://github.com/narendra-bluebash/chatgpt-clone-gemini-streamlit.git
cd chatgpt-clone-gemini-streamlit

2. Install project dependencies:

• Use Poetry to install the dependencies defined in your pyproject.toml file. This command will also respect the versions pinned in your poetry.lock file:

poetry install

This will create a virtual environment (if one does not already exist) and install the dependencies into it.

3. Activate the virtual environment (optional):

• If you want to manually activate the virtual environment created by Poetry, you can do so with:

poetry shell

This step is optional because Poetry automatically manages the virtual environment for you when you run commands through it.

4. Set Up Environment Variables:

• Create a .env file in the root directory of your project and add the required environment variables. For example:

GOOGLE_API_KEY = YOUR_API_KEY

5. Run Streamlit app

python -m streamlit run app.py