Langchain: Integration of ElasticSearch Vector Store and RAG with Gemini

Overview

Explore integrating Langchain with ElasticSearch for RAG applications, using ElasticSearch as a vector store and retrieval component with Gemini.

Syllabus

• Introduction to Langchain and RAG
Overview of Langchain
Introduction to Retrieval-Augmented Generation (RAG) concepts
Use cases for Langchain and RAG
• Introduction to ElasticSearch
Overview of ElasticSearch functionality
ElasticSearch as a vector store
Integration possibilities with Langchain
• Setting Up ElasticSearch for RAG
Installing and configuring ElasticSearch
Creating and managing indices for vector storage
Vectorization concepts and vector store operations
• Introduction to Gemini
Overview of Gemini's capabilities
Role of Gemini in RAG systems
Benefits of using Gemini in conjunction with ElasticSearch
• Integrating Langchain with ElasticSearch
Connecting Langchain to ElasticSearch
Querying ElasticSearch through Langchain
Managing vector search and results ranking
• Implementing RAG with Langchain, ElasticSearch, and Gemini
Setting up the hybrid retrieval model
Utilizing Gemini for enhanced RAG workflows
Practical examples and implementation patterns
• Hands-On Exercises and Workshops
Exercise on configuring ElasticSearch as a vector store
Exercise on implementing RAG workflows with Langchain and ElasticSearch
Workshop on integrating Gemini into RAG systems
• Best Practices and Optimization
Optimizing ElasticSearch for performance in RAG
Managing and scaling vector data efficiently
Troubleshooting common integration issues
• Future Directions and Advanced Topics
Emerging trends in RAG applications
Advanced features of ElasticSearch and Gemini
Roadmap for further learning and exploration
• Final Project
Design and implementation of a complete RAG system using Langchain, ElasticSearch, and Gemini
Presentation and peer review of project work

Subjects

Data Science