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From CSV To GraphRAG Systems With Neo4j And LangChain | Knowledge Graphs RAG | Part 1
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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:
Medium Article:
Next Video:
Tags:
#GraphRAG #Python #Pandas #Neo4j #LangChain #KnowledgeGraph #DataAnalysis #ConversationalAI #Python #ai #graphrag #Pandas
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Follow me on social media:
Hope you enjoy today's video. Please show your love and support by just liking and subscribing to the channel so we can grow a strong and powerful community. Activate the 🔔 beside the subscribe button to get the notification!📩 If you have any questions or requests feel free to leave them in the comments below.
Thank you for watching and see you in the next video!!
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:
Medium Article:
Next Video:
Tags:
#GraphRAG #Python #Pandas #Neo4j #LangChain #KnowledgeGraph #DataAnalysis #ConversationalAI #Python #ai #graphrag #Pandas
Buy me a coffee:
Follow me on social media:
Hope you enjoy today's video. Please show your love and support by just liking and subscribing to the channel so we can grow a strong and powerful community. Activate the 🔔 beside the subscribe button to get the notification!📩 If you have any questions or requests feel free to leave them in the comments below.
Thank you for watching and see you in the next video!!
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