💻
RAG and LLM Bootcamp
  • Welcome to the Bootcamp
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Kick-Off Session for the Bootcamp
  • Basics of LLMs
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of LLMs
    • Bonus Resource: Multimodal LLMs and Google Gemini
  • Word Vectors, Simplified
    • What is a Word Vector?
    • Word Vector Relationships
    • Role of Context in LLMs
    • Transforming Vectors into LLM Responses
    • Bonus: Overview of the Transformer Architecture
      • Attention Mechanism
      • Multi-Head Attention and Transformer Architecture
      • Vision Transformers (ViTs)
    • Bonus: Future of LLMs? | By Transformer Co-inventor
    • Graded Quiz 1
  • Prompt Engineering and Token Limits
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • For Starters: Best Practices
    • Navigating Token Limits
    • Hallucinations in LLMs
    • Prompt Engineering Excercise (Ungraded)
      • Story for the Excercise: The eSports Enigma
      • Your Task fror the Module
  • RAG and LLM Architecture
    • What is Retrieval Augmented Generation (RAG)?
    • Primer to RAG: Pre-trained and Fine-Tuned LLMs
    • In-context Learning
    • High-level LLM Architecture Components for In-context Learning
    • Diving Deeper: LLM Architecture Components
    • Basic RAG Architecture with Key Components
    • RAG versus Fine-Tuning and Prompt Engineering
    • Versatility and Efficiency in RAG
    • Key Benefits of using RAG in an Enterprise/Production Setup
    • Hands-on Demo: Performing Similarity Search in Vectors (Bonus Module)
    • Using kNN and LSH to Enhance Similarity Search (Bonus Module)
    • Bonus Video: Implementing End-to-End RAG | 1-Hour Session
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites (Must)
    • Docker Basics
    • Your Hands-on RAG Journey
    • 1 – First RAG Pipeline
      • Building with Open AI
      • How it Works
      • Using Open AI Alternatives
      • RAG with Open Source and Running "Examples"
    • 2 – Amazon Discounts App
      • How the Project Works
      • Building the App
    • 3 – Private RAG with Mistral, Ollama and Pathway
      • Building a Private RAG project
      • (Bonus) Adaptive RAG Overview
    • 4 – Realtime RAG with LlamaIndex/Langchain and Pathway
      • Understand the Basics
      • Implementation with LlamaIndex and Langchain
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Sample Projects and Additional Resources
    • Submit Project for Review
Powered by GitBook
On this page
  • What are Retrievers in LlamaIndex?
  • Pathway Retriever and its Integration with LlamaIndex
  1. Hands-on Development
  2. 4 – Realtime RAG with LlamaIndex/Langchain and Pathway

Understand the Basics

Previous4 – Realtime RAG with LlamaIndex/Langchain and PathwayNextImplementation with LlamaIndex and Langchain

Last updated 10 months ago

What are Retrievers in LlamaIndex?

Retrievers play a critical role in the LlamaIndex ecosystem. They are tasked with fetching the most relevant context for a given user query or message. This process involves:

  • Efficiently retrieving relevant context from an index based on the query.

  • Being a crucial component in query engines and chat engines for delivering pertinent information.

  • The possibility of building atop indexes or being defined independently underscores their versatility.

Pathway Retriever and its Integration with LlamaIndex

While so far we've used Pathway's LLM app, you might know that Pathway stands out as an open data processing framework, ideal for developing data transformation pipelines and machine learning applications that deal with live and evolving data sources. Interestingly it's the world's fastest framework for stream data processing. ()

Now, the integration with LlamaIndex is facilitated through the and . Here our focus is on which taps into Pathway's dynamic indexing capabilities to provide always up-to-date answers. This here is also quite comprehensive but let us give you a quick walkthrough once.

Key Features of the Integration:

  • Live Data Indexing Pipeline: Monitors various data sources for changes, parses and embeds documents using LLaMAIndex methods, and builds a vector index.

  • Simple to Complex Pipelines: While the basic pipeline focuses on indexing files from cloud storage, Pathway supports more sophisticated operations like SQL-like operations, time-based grouping, and a wide range of connectors for comprehensive data pipeline construction.

  • Ease of Setup: The integration process involves installing necessary packages, setting up environment variables, and configuring data sources to be tracked by Pathway.

😉
ArXiV paper
PathwayReader
PathwayRetriever
PathwayRetriever
linked documentation