Interact with your complex PDF that includes images, tables, and graphs using Raptor RAG.

Table of Contents

• Introduction
• RAPTOR
• Features
• Technologies Used
• Setup Instructions
• Usage

Introduction

The RAG-RAPTOR-DEMO project simplifies the process of extracting and querying information from Complex PDF documents, including complex content such as tables, graphs, and images. Leveraging state-of-the-art natural language processing models and Unstructured.io for document parsing, as well as integrating RAPTOR, which introduces a novel approach to retrieval-augmented language models by constructing a recursive tree structure from documents for more efficient and context-aware information retrieval across large texts, and Raptor Rag for retrieve semantic chunk, the chatbot provides a user-friendly interface to interact with and retrieve detailed information from these documents.

RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

RAPTOR introduces a novel approach to retrieval-augmented language models by constructing a recursive tree structure from documents. This allows for more efficient and context-aware information retrieval across large texts, addressing common limitations in traditional language models.

For detailed methodologies and implementations, refer to the original paper:

• RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

• Github Repo
• Youtube link

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

• LangChain: Framework for building applications with language models.
• RAG (Retrieval-Augmented Generation): Combines retrieval and generation for more accurate answers.
• RAPTOR: Constructs a recursive tree structure from documents for efficient, context-aware information retrieval.
• Streamlit: Framework for creating interactive web applications with Python.
• Unstructured.io: Tool for parsing and extracting complex content from PDFs, such as tables, graphs, and images.
• 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/langchain-tech/Rag-raptor-demo.git
cd Rag-raptor-demo

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:

OPENAI_API_KEY=Your_OPENAI_API_KEY
POSTGRES_URL_EMBEDDINDS=YOUR_POSTGRES_URL,  like:-postgresql+psycopg://{db_user}:{db_password}@{db_host}:{db_port}/{db_name}
POSTGRES_URL=YOUR_POSTGRES_URL ,  like:- postgresql://{db_user}:{db_password}@{db_host}:{db_port}/{db_name}

5. Run Data ingestion file

• This command will insert data into your postgres database

python3 ingest/app.py

6. Start the Application:

Run the application using Streamlit:

streamlit run app.py

Examples