How-To Create RAG Pipeline with Docling on PDF with Images and Tables

How-To Create RAG Pipeline with Docling on PDF with Images and Tables

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AI Summary

Video Title: Building an End-to-End RAG Pipeline Using DocLing and IBM Granite Models

Key Points:

1. Introduction to RAG:
   • RAG (Retrieval Augmented Generation) enables the use of personal documents with LLMs.
   • Common tools: DocLing, IBM Granite.
2. Integration Focus:
   • Integrating DocLing with IBM Granite models for building a RAG pipeline.
3. Setup Instructions:
   • Use Google Colab for setting up the pipeline without infrastructure concerns.
   • Install prerequisites: transformers, pillow, langchain, docling, and replicate.
   • Replicate API:
     • Sign up at replicate.com to create an API token.
4. Processing Documents:
   • Download and process PDF documents with DocLing to extract images, text, and tables.
   • Chunk documents for embedding modeling.
5. Embedding Model:
   • Use the embedding model from Hugging Face for numerical representation of documents.
   • Store processed data in a vector store like Milas for semantic search capabilities.
6. Querying Vector Store:
   • Perform similarity search to retrieve relevant information based on user queries.
   • Responses are grounded in the user’s data through effective prompt design.
7. Creating AI Applications:
   • Leverage the integration of text and images to develop AI applications that understand complex documents.
   • Use nested structures of Langchain and Granite to model responses based on retrieved context.
8. Conclusion:
   • The video illustrates how to build an end-to-end RAG pipeline effectively using available tools.
   • Highlights the efficiency and utility of using personal documents for powered applications.