GITHUB: github.com/ron...
TELEGRAM: t.me/ttyoutube...
Welcome to Total Technology Zone! In today's tutorial, we'll explore how to create a Retrieval-Augmented Generation (RAG) system using Elasticsearch as a vector database with LangChain. This tutorial will guide you through the entire process, from deploying an Elasticsearch Cloud instance to writing the code for creating a RAG system. Let's dive in!
-
Introduction
Hey everyone, this is Ronnie! Welcome back to our channel, Total Technology Zone. In this 76th tutorial, we will focus on creating a simple RAG system using Elasticsearch as a vector database with LangChain. We've covered various advanced RAG techniques in previous videos, but today, we'll keep it straightforward and beginner-friendly. If you're familiar with Elasticsearch, this tutorial will be super helpful for you.
Steps Covered
Here's a breakdown of what we'll cover in this tutorial:
1. **Deploy Elasticsearch Cloud Instance**: We'll guide you through deploying a free Elasticsearch Cloud instance.
2. **Get API Key and Cloud ID**: Learn how to retrieve the API key and Cloud ID for your Elasticsearch instance.
3. **Write Code Using LangChain**: We'll write the code to create a vector index using LangChain's Elasticsearch utility.
4. **Load Documents and Validate Index**: Use LangChain's document loader to load documents and validate the created index.
5. **Create RAG System**: Implement the RAG system by integrating the vector index with a retrieval and generation model.
Detailed Steps
#### 1. Deploy Elasticsearch Cloud Instance
Visit [elastic.co](www.elastic.co/) and deploy your Elasticsearch instance. Select your region and create your instance. Ensure you understand the basics of Elasticsearch before proceeding.
#### 2. Retrieve API Key and Cloud ID
Navigate to Stack Management API Keys to create and retrieve your API key. Copy the Cloud ID from your Elasticsearch instance dashboard.
#### 3. Set Up LangChain
Import the necessary libraries and set up your text loader, splitter, and embeddings. Prepare your documents for indexing in Elasticsearch.
#### 4. Create and Validate Index
Use LangChain's Elasticsearch store to create a vector index and validate it using the Kibana interface. Ensure your documents are correctly indexed and ready for querying.
#### 5. Create RAG System
Integrate the indexed documents with a retrieval and generation model to create a functional RAG system. Use the prompt template and LangChain utilities to handle question-answering tasks efficiently.
Example Use Case
We'll use a set of documents containing a story about King John, his family, and his kingdom. We'll query the vector index to retrieve relevant information and generate coherent answers to various questions.
Conclusion
This tutorial provides a comprehensive guide to creating a RAG system using Elasticsearch and LangChain. By following these steps, you'll be able to handle and search through large datasets efficiently using vector embeddings. This powerful combination of Elasticsearch and LangChain can significantly enhance your data processing capabilities.
Call to Action
If you enjoyed this video, please subscribe to my channel, like the video, and share it with your friends and family. Your support helps me grow and motivates me to bring more exciting tutorials. Leave your comments and feedback below to let me know what you think and what you'd like to see in future videos.
---
*Tags:* #Elasticsearch #RAG #LangChain #AI #MachineLearning #TechTutorials #DataScience #TotalTechnologyZone
Thank you for watching! See you in the next tutorial. Happy learning!
Негізгі бет Build rag with elasticsearch using
Пікірлер: 8