Hybrid Search RAG With Langchain And Pinecone Vector DB

Introduction

Hello, everyone! My name is Krishak, and welcome to my YouTube channel. In this specific video, we are going to implement an amazing hybrid search RAG application. Initially, we will go ahead with a theoretical understanding of hybrid search and then proceed with developing an amazing end-to-end project. We will create a vector database in the cloud using Pinecone and see how we can implement a hybrid search on a specific vector database. All these things will be covered in this particular video, so let's go ahead and enjoy this video.

Before proceeding, here is a quick announcement on three amazing courses that I have recently launched on Udemy at a very affordable price—399 rupees INR. You'll find the link in the description of this particular video. Here are the courses:

• Complete Generative AI course with LangChain and Hugging Face.
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• Building Generative AI App with 12+ Hands-on projects with Generative AI Pro.

Understanding Hybrid Search

In this video, we are going to discuss something called hybrid search. This kind of search is specifically used in RAG applications.

The Traditional Approach

Let's say we have a document. We usually divide this document into chunks. For example: