RAG PDF Helper

Overview

The RAG PDF Helper is an application that enables users to ask questions about a PDF document loaded into the system. Utilizing LangChain for document processing and OpenAI’s powerful language model, this tool allows for dynamic interactions with the content of the PDF, providing responses based on the information extracted from the document.

Features

• Load PDF documents and process their content.
• Retrieve information from the PDF using natural language queries.
• Seamless integration of advanced language models for accurate and context-aware responses.
• Uses embeddings for improved retrieval of relevant information.

Requirements

To run this application, you need the following:

• Python 3.7 or higher
• Required Python packages:
  • chainlit
  • langchain
  • openai (for embeddings and models)
  • pdfplumber (for PDF loading)
  • chromadb (for vector store functionality)

RAG Architecture

Image credits - dynatrance

The RAG (Retrieval-Augmented Generation) architecture combines the advantages of retrieval-based and generation-based approaches. In this architecture:

1. Retrieval Component: This part is responsible for fetching relevant documents or excerpts from the loaded PDF based on the user’s query. The retrieval is powered by the embeddings generated from the content, ensuring that only the most pertinent information is accessed.

2. Generation Component: After retrieving the relevant information, the generation component uses an advanced language model (like OpenAI’s models) to construct coherent and contextually relevant responses based on the fetched data.

3. Integration of Tools: The architecture utilizes tools such as LangChain for document processing, pdfplumber for loading PDF content, and Chromadb for efficient storage and retrieval of vector embeddings.

This cohesive structure allows users to interact effectively with the content of PDFs, facilitating smooth question-and-answer sessions.

Github Link