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 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!

💡 Don't forget to like, subscribe, and hit the notification bell to stay updated with the next parts of our GraphRAG application series.

#GraphRAG #LangChain #Neo4j #OpenAI #AITutorial #GraphDatabases #MachineLearning #neo4j #openai #knowledgegraph

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