Disclaimer

A part of this blog reflects my learnings from Augment your LLM Using Retrieval Augmented Generation by NVIDIA. The content is based on topics covered in the course and my understanding of them through some practical examples. If you are not sure, what RAG is, I would suggest you to check out my following blog. Also to understand this blog, as a prerequisite I would highly recommend you to read the previous blogs of this series.

Exploring RAG: Why Retrieval-Augmented Generation is the Future?

Ďēv Šhãh 🥑 ・ Oct 1

#rag #langchain #llm #vectordatabase

LangChain

LangChain is a framework for building applications that work with Language Models (LLMs) to perform tasks like answering questions, analyzing texts, and processing data. By using LangChain, developers can connect to different language models, such as ChatGPT, and access information from various sources, like databases or documents. This framework also allows developers to set up workflows with logic to help LLMs provide more accurate responses. LangChain makes it quicker and easier for developers to build powerful language-based applications.

LlamaIndex

LlamaIndex is a data framework for LLM applications designed to work with structured or private, domain-specific data. Simply put, this framework enables LLM applications to retrieve and process specific data, such as private or domain-specific information, to generate more relevant and up-to-date responses to user queries.

Simple Directory Reader

LlamaIndex includes a class called SimpleDirectoryReader, which can read a stored document from a specific directory location. This class automatically parses the document based on its file extension.

Once LlamaIndex retrieves the data, it provides a querying and chat engine, allowing users to interact with the data directly, such as asking questions about the content of a PDF.

Prompt Template

A Prompt Template is essentially a pre-defined structure for prompts that will be sent to Language Models to get responses. This template includes instructions and context to help the LLM understand the user’s request and provide more accurate responses. Think of it like a function in programming, where the template has predefined instructions and user-defined variables that are filled in based on specific inputs.

Example

Here’s an example of a prompt template for a travel assistant that provides itineraries:

"You are a travel assistant. Based on the user's preferences, create a detailed itinerary for their trip.


User Preferences:

- Destination: {destination}
- Duration: {duration} days
- Budget: {budget}
- Interests: {interests}"

This template for generating a detailed itinerary takes input from the user (like destination, duration, budget, and interests), inserts it into the template, and passes it to the LLM. The LLM then uses this template structure to create a response based on the user’s preferences.

Citation
I would like to acknowledge that I took help from ChatGPT to structure my blog and simplify content.