From CSV To GraphRAG Systems With Neo4j And LangChain | Knowledge Graphs RAG | Part 1

In this video, we'll dive into the world of GraphRAG (Graph Representation and Analytics) applications and learn how to build one using Python, Pandas, Neo4j, and the LangChain framework.

GraphRAG applications leverage the power of knowledge graphs to represent and analyze complex data, and then provide conversational interfaces to interact with that data. This approach offers numerous benefits, including improved data modeling, enhanced analytical capabilities, and more engaging user experiences.
Throughout the video, we'll cover the following topics:

Introduction to GraphRAG Applications: Understand what GraphRAG applications are, their use cases, and the advantages they provide.
Reading and Preprocessing CSV Files with Pandas: Learn how to use Pandas to read in a CSV file, clean and transform the data, preparing it for ingestion into the knowledge graph.

Designing the Knowledge Graph Schema: Discover the process of determining the entities, relationships, and properties that will make up the knowledge graph, and how to map the CSV data to this schema.
Inserting Data into the Neo4j Knowledge Graph: Explore the Neo4j graph database and the Neo4j Aura cloud instance, and write Cypher code to create the nodes and relationships in the knowledge graph.

Building a Conversational Interface with LangChain: Dive into the LangChain framework and see how to create a chatbot that can query and interact with the knowledge graph, providing valuable insights to users.

By the end of this video, you'll have a solid understanding of how to build a GraphRAG application using Python, Pandas, Neo4j, and LangChain, and how this approach can revolutionize the way you represent, analyze, and interact with your data.

Git Repo:
https://github.com/Princekrampah/neo4j_graphrag_tutorial
Medium Articles:
First video's article:
https://medium.com/ai-advances/building-a-graphrag-from-scratch-neo4j-csv-integration-step-by-step-3a1e5b43c239
Second video's article:
https://medium.com/ai-advances/building-an-extract-transform-and-load-pipeline-etl-for-neo4j-graphrag-application-part-2-123be950946e
Third video's article:
https://medium.com/ai-advances/learning-the-basics-of-langchain-for-neo4j-graphrag-application-part-3-d32ad8d47fc5
Forth video article:
https://medium.com/ai-advances/natural-language-to-cypher-with-agentic-ai-part-04-a269a959bb8c
Fifth video's article:
https://medium.com/ai-advances/agentic-graphrag-with-neo4j-and-langchain-ce03b344149c
Sixth video's article:
https://medium.com/ai-advances/graphrag-with-neo4j-building-containerized-chatbot-backend-api-service-4855e6b66d46

Next Video:
https://youtu.be/Y5-do648JHQ

💡 All Videos in Series:
https://www.youtube.com/playlist?list=PLU7aW4OZeUzxaHn6exh8aijLEf-src84S

Tags:
#GraphRAG #Python #Pandas #Neo4j #LangChain #KnowledgeGraph #DataAnalysis #ConversationalAI #Python #ai #graphrag #Pandas


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