From CSV To GraphRAG Systems With Neo4j And LangChain | Knowledge Graphs RAG | Part 1
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- Опубликовано: 4 фев 2025
- 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:
github.com/Pri...
Medium Articles:
First video's article:
/ building-a-graphrag-fr...
Second video's article:
/ building-an-extract-tr...
Third video's article:
/ learning-the-basics-of...
Forth video article:
/ natural-language-to-cy...
Fifth video's article:
/ agentic-graphrag-with-...
Sixth video's article:
/ graphrag-with-neo4j-bu...
Next Video:
• Building a Fully Autom...
💡 All Videos in Series:
• GraphRAG Systems
Tags:
#GraphRAG #Python #Pandas #Neo4j #LangChain #KnowledgeGraph #DataAnalysis #ConversationalAI #Python #ai #graphrag #Pandas
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Thank you for watching and see you in the next video!!
Can we automatically identify columns and relationships between them and load into the graph?
Yes, you can. I talked about it in the demo section. Search for an LLM Builder for LangChain. If it's structured data, i suggest yiu do it yourself, that's my personal take anyway.
I found part 3 and 4 but not 2. where is part 2?
Hello, kindly ignore the labeling. Just follow the Series as they are in the right order. I'll clean thus up
@@CodeWithPrince thanks, which series?, There are a lot of videos in your channel.
which one is part 2 ....i found 1,3,4,5,scratch
Can you provide your mail id I have a csv data but cypher query is not working. Can you help me with that please.
Hey, my contacts are on the channel page and in the video description. You can reach me on LinkedIn @Prince krampah
Man do some voice over... sorry, but I'm striving with your accent