Multimodal RAG: Chat with PDFs (Images & Tables) [latest version]

Introduction

This tutorial will guide you through building a multimodal Retrieval-Augmented Generation (RAG) pipeline using LangChain and the Unstructured library. You will learn how to create an AI-powered system capable of querying complex documents, such as PDFs with text, images, tables, and plots. By leveraging advanced Language Learning Models (LLMs) like GPT-4 with vision, you'll expand the capabilities of document intelligence beyond just text.

Step 1: Set Up the Unstructured Library

To begin, you need to install and configure the Unstructured library, which is essential for parsing and pre-processing various document types.

• Installation:

  pip install unstructured
  

• Usage:

  • Import the library in your Python environment.
  • Use it to read and process documents by converting them into a structured format, suitable for further analysis.

Step 2: Create a Document Retrieval System

Next, you will build a document retrieval system that utilizes both textual and visual data.

• Integrate LangChain:
  • Install LangChain:

    pip install langchain
    

  • Set up a retrieval function that can handle different document formats.
  • Ensure the retrieval system can access both text and images from your documents.

Step 3: Partition the Document

Once your system is set up, you need to partition the documents into manageable chunks.

• Chunking Process:
  • Load the document using the Unstructured library.
  • Split the document into smaller sections based on logical breaks (e.g., paragraphs, tables).
  • This makes it easier for the model to process and analyze each piece.

Step 4: Summarize Each Chunk

After partitioning, summarize each chunk to create a concise representation.

• Summarization Techniques:
  • Use LLMs to generate summaries for each chunk.
  • Aim for clear and informative summaries that capture the essence of the content.

Step 5: Create the Vector Store

Now, create a vector store to hold the processed data for efficient retrieval.

• Setting Up the Vector Store:
  • Use a library like FAISS or Annoy to create a vector index.
  • Store the embeddings (numerical representations) of the summarized chunks.
  • This allows for quick searching and retrieval based on user queries.

Step 6: Build the RAG Pipeline

With all components ready, integrate them into a cohesive Retrieval-Augmented Generation pipeline.

• Pipeline Integration:
  • Connect the retrieval system, summarization, and vector store.
  • Ensure that when a query is made, the system retrieves relevant chunks, summarizes them, and generates a coherent response using the multimodal LLM.

Conclusion

In this tutorial, you learned how to build a multimodal RAG pipeline that can handle complex documents by leveraging the Unstructured library and LangChain. By setting up document parsing, retrieval, summarization, and integrating these components into a functional pipeline, you can create an intelligent document querying system.

For further enhancements, consider exploring different LLMs or optimizing your vector store for better performance. Happy coding!