From CSV To GraphRAG Systems With Neo4j And LangChain | LangChain Essentials | Part 3
🔍 Building GraphRAG Applications: LangChain Essentials (Part 3)
Welcome to Part 3 of our comprehensive series on constructing GraphRAG (Graph Retrieval-Augmented Generation) applications using Neo4j, LangChain, and OpenAI! In this crucial installment, we're diving deep into LangChain fundamentals that will serve as the backbone for our GraphRAG system.
🏗️ Series Progress:
Part 1: Introduction to GraphRAG: • From CSV To GraphRAG Systems With Neo...
Part 2: Setting up the ETL Pipeline: • Building a Fully Automated ETL Pipeli...
Part 3: LangChain Basics (You are here!)
🔑 In this video, you'll learn:
How to set up LangChain for GraphRAG applications
Integrating OpenAI models with LangChain
Crafting effective prompts for graph-based queries
Using LangChain Expression Language (LCEL) for flexible GraphRAG workflows
Leveraging LangChain Agents for dynamic graph interactions
Creating custom tools for Neo4j graph operations
Implementing tool calling for efficient graph traversal and query execution
This tutorial provides hands-on experience with LangChain components essential for building powerful GraphRAG applications. By mastering these concepts, you'll be well-equipped to create AI-powered systems that leverage the strengths of graph databases and large language models.
🚀 Ready to enhance your GraphRAG development skills? Hit play and let's build something amazing!
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