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Building Data-Driven Applications with LlamaIndex

You're reading from   Building Data-Driven Applications with LlamaIndex A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835089507
Length 368 pages
Edition 1st Edition
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Author (1):
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Andrei Gheorghiu Andrei Gheorghiu
Author Profile Icon Andrei Gheorghiu
Andrei Gheorghiu
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Table of Contents (18) Chapters Close

Preface 1. Part 1:Introduction to Generative AI and LlamaIndex
2. Chapter 1: Understanding Large Language Models FREE CHAPTER 3. Chapter 2: LlamaIndex: The Hidden Jewel - An Introduction to the LlamaIndex Ecosystem 4. Part 2: Starting Your First LlamaIndex Project
5. Chapter 3: Kickstarting Your Journey with LlamaIndex 6. Chapter 4: Ingesting Data into Our RAG Workflow 7. Chapter 5: Indexing with LlamaIndex 8. Part 3: Retrieving and Working with Indexed Data
9. Chapter 6: Querying Our Data, Part 1 – Context Retrieval 10. Chapter 7: Querying Our Data, Part 2 – Postprocessing and Response Synthesis 11. Chapter 8: Building Chatbots and Agents with LlamaIndex 12. Part 4: Customization, Prompt Engineering, and Final Words
13. Chapter 9: Customizing and Deploying Our LlamaIndex Project 14. Chapter 10: Prompt Engineering Guidelines and Best Practices 15. Chapter 11: Conclusion and Additional Resources 16. Index 17. Other Books You May Enjoy

Hands-on – ingesting study materials into our PITS

It’s time for some practice. We now have everything we need to continue building our project. Let’s write the documend_uploader.py module.

This module will take care of ingesting and preparing our available study material. The user can upload any available books, technical documentation, or existing articles to provide more context to our tutor.

  1. First, we have the imports:
    from global_settings import STORAGE_PATH, CACHE_FILE
    from logging_functions import log_action
    from llama_index import SimpleDirectoryReader, VectorStoreIndex
    from llama_index.ingestion import IngestionPipeline, IngestionCache
    from llama_index.text_splitter import TokenTextSplitter
    from llama_index.extractors import SummaryExtractor
    from llama_index.embeddings import OpenAIEmbedding
  2. Next, we must define the main function that’s responsible for handling the ingestion process. You’ll notice that it uses an ingestion pipeline...
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