Podcast
Questions and Answers
What is the primary purpose of the ETL pipeline in the RAG use case?
What is the primary purpose of the ETL pipeline in the RAG use case?
The TikaDocumentReader is used to extract text from PDF files only.
The TikaDocumentReader is used to extract text from PDF files only.
False
What is the purpose of the TextSplitter abstract base class?
What is the purpose of the TextSplitter abstract base class?
To divide documents to fit the AI model's context window
The ETL pipeline is composed of the following interfaces and implementations, as shown in the ___________ Class Diagram section.
The ETL pipeline is composed of the following interfaces and implementations, as shown in the ___________ Class Diagram section.
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What is the name of the implementation of DocumentWriter used to load data into a Vector Database?
What is the name of the implementation of DocumentWriter used to load data into a Vector Database?
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All PDF documents contain the PDF catalog.
All PDF documents contain the PDF catalog.
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Match the document readers with their corresponding functions:
Match the document readers with their corresponding functions:
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What is the name of the file that provides guidance on obtaining an OpenAI API Key and running the first AI RAG application?
What is the name of the file that provides guidance on obtaining an OpenAI API Key and running the first AI RAG application?
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Study Notes
ETL Framework in RAG
- ETL (Extract, Transform, and Load) framework is the backbone of data processing in Retrieval Augmented Generation (RAG) use case.
- The ETL pipeline transforms raw data sources into a structured vector store, making it optimal for retrieval by the AI model.
RAG Use Case
- RAG use case is designed to augment the capabilities of generative models by retrieving relevant information from a body of data.
- This enhances the quality and relevance of the generated output.
ETL Pipeline Components
- The ETL pipeline consists of three main components: Document, DocumentReader, and DocumentWriter.
- The Document class contains text and metadata, and is created from various document types via the DocumentReader.
- DocumentReader instances include:
- ParagraphPdfDocumentReader: splits input PDF into text paragraphs and outputs a single Document per paragraph using PDF catalog (TOC) information.
- TikaDocumentReader: uses Apache Tika to extract text from various document formats, such as PDF, DOC/DOCX, PPT/PPTX, and HTML.
DocumentWriter
- The DocumentWriter is responsible for preparing documents for storage and provides integration with various vector stores.
- VectorStore is an implementation of DocumentWriter.
ETL Pipeline Creation
- A simple ETL pipeline can be constructed by chaining together instances of each ETL type.
- A Java function style syntax can be used to perform basic loading of data into a Vector Database for use with the RAG pattern.
Additional Resources
- The generated README.md file provides guidance on obtaining an OpenAI API Key and running the first AI RAG application.
- The ETL Class Diagram section provides a detailed diagram of the ETL pipeline components.
- The Vector DB Documentation provides a comprehensive list of supported vector stores.
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Description
Learn about the ETL framework in the Retrieval Augmented Generation (RAG) use case, which processes raw data for AI model retrieval. Understand how it enhances the quality of generated output.
