End to end RAG LLM App Using Llamaindex and OpenAI- Indexing and Querying Multiple pdf's

Summary

In this YouTube tutorial, Krishak demonstrates how to create a Retrieval-Augmented Generation (RAG) system using LangChain and OpenAI's API. The project involves querying multiple PDFs with the help of LangChain's LlamaIndex and OpenAI's API. Krishak guides viewers through setting up the environment, installing necessary libraries, and building a basic RAG system. The video covers reading PDFs, converting text into vectors, indexing, and retrieving information based on user queries. It also highlights how to customize the system for more advanced use cases, such as modifying the number of query responses and implementing similarity thresholds.

Takeaways

• 😀 The video introduces a project to create an LLM app using retrieval augmented generation, which queries multiple PDFs with the help of Llama Index and OpenAI.
• 🛠️ The project involves building a RAG system from scratch, starting with setting up a Python environment using `conda create` and installing necessary libraries like Llama Index, OpenAI, and PyPDF.
• 📂 The script demonstrates how to read PDFs from a 'data' folder, convert them into vectors, and index them using Llama Index for later retrieval.
• 🔍 The video explains the process of querying the indexed data using a query engine, which acts like a search engine to retrieve information based on user queries.
• 📊 The script showcases how to enhance the query engine by using a retriever and a similarity post-processor to filter results based on similarity scores and other parameters.
• 💾 The video also covers how to save the index to persistent storage on the disk, allowing the index to be loaded and queried without keeping it in memory.
• 🔑 Environment variables are used to securely handle the OpenAI API key, which is loaded using the `python-dotenv` library.
• 📝 The script provides a step-by-step guide on implementing the project, from setting up the environment to querying the index and displaying results.
• 🔄 The video emphasizes the importance of practice and implementation, encouraging viewers to try the project themselves and reach out if they encounter any issues.
• 📈 The presenter plans to build more advanced RAG systems in future videos, incorporating databases and Langchain for more complex applications.

Q & A

What is the main focus of the video by Krishak?

The video focuses on creating an LLM app that utilizes retrieval augmented generation to query multiple PDFs using Llama Index and OpenAI.

What does the acronym 'LLM' stand for as used in the video?

LLM stands for 'Large Language Model', which is a type of AI model that the video's project is based on.

What is the purpose of using Llama Index in the project?

Llama Index is used to convert text from PDFs into vectors and create an index that can be queried to retrieve information.

How does the video suggest to handle the OpenAI API key?

The video suggests creating an environment variable to securely handle the OpenAI API key without exposing it.

What are the three important libraries that the video mentions installing for the project?

The three libraries are Llama Index, OpenAI, and PyPDF.

What is the role of the 'simple directory reader' in the project?

The 'simple directory reader' is used to read PDFs from a directory and convert them into a format that can be processed by Llama Index.

How does the video demonstrate querying the created index?

The video demonstrates querying the index by asking specific questions and showing how the system retrieves answers from the indexed PDFs.

What is the significance of the 'similarity score' mentioned in the video?

The 'similarity score' indicates how closely a retrieved response matches the query, with higher percentages indicating better matches.

How can the number of responses retrieved be controlled?

The number of responses retrieved can be controlled by adjusting the 'similarity top K' parameter in the query engine.

What is the purpose of the 'similarity post processor' discussed in the video?

The 'similarity post processor' is used to filter responses based on a similarity threshold, only showing responses that meet or exceed a certain similarity percentage.

How does the video handle persistent storage of the index?

The video discusses storing the index as persistent storage in a directory on the hard disk using the storage context provided by Llama Index.