🚀 Build a Knowledge Graph with LLMs – Full Hands-On Coding Walkthrough

🔗 Github: https://github.com/cocoindex-io/cocoindex

Hey devs! In this video, we walk through building a knowledge graph from real documentation using large language models (LLMs). You'll see how to extract entities, map relationships, and structure everything into a graph you can plug into powerful applications—especially AI agents.

🧠 Why knowledge graphs?
Knowledge graphs are foundational for context-aware AI agents. They give structure to otherwise unstructured information—letting agents reason, recall, and navigate complex domains. From personal assistants to enterprise copilots, graphs help LLMs act less like autocomplete and more like strategic thinkers.

🛠️ What you’ll learn:

- Parse documentation and preprocess text
- Use LLMs to extract entities and concepts
- Automatically identify relationships between those entities
- Export nodes + edges for visualization or use in graph databases like Neo4j

📂 We’ll use example docs (CocoIndex) to extract technical terminology and map connections—turning flat text into rich, queryable structure.

💡 Perfect if you're building:
- AI agents that need memory/context
- NLP pipelines for enterprise search
- Graph-powered apps (recommendations, discovery engines)
- LLM-RAG systems with deep structure

📎 Resources & Code:
https://cocoindex.io/blogs

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#KnowledgeGraph #LLM #PythonDev #SemanticAI #Neo4j #LangChain #AIEngineering #graphdatabases 

Timeline:
00:00 - Introduction
01:52 - CocoIndex 
02:38 - CocoIndex and Knowledge Graphs basics (Property Graph Targets)
04:20 - Project setup
04:48 - Start coding
05:13 - DataFlow and import source
07:32 - Create Document Node and use LLM to add summary to each document
09:26 - Relationship extraction with LLM 
13:42 - Collect Document Mention as Relationship
15:58 - Map data from collectors to Neo4j (export nodes) 
17:32 - Export `Document` Nodes to Neo4j
19:05 - Export Relationship to Neo4j
21:29 - Demo knowledge graph with Neo4j
22:08 - Thank you and star us!

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Community Picks

Explore the process of building a real-time knowledge graph using Koko Index and Neo4j. The tutorial walks through extracting relationships from documents with a Large Language Model, structuring the data into nodes and properties, and exporting them to Neo4j for visualization. It highlights the ease of data transformation and management with Koko Index, which supports real-time data pipelines beyond traditional SQL.

Code with me - Build Real-Time Knowledge Graph For Documents with LLM

<p>Thanks for sharing. At FalkorDB we’ve updated our browser for visualization as well, check it out in your next project, keen to hear feedback!
<a href="http://browser.falkordb.com" target="_blank" rel="noopener nofollow">browser.falkordb.com</a></p>
