Neo4j LLM Knowledge Graph Builder - Extract Nodes and Relationships from Unstructured Text (PDF, YouTube, Webpages)

The Neo4j LLM Knowledge Graph Builder is an online application for turning unstructured text into a knowledge graph, it provides a magical text to graph experience.

It uses ML models (LLM - OpenAI, Gemini, Llama3, Diffbot) to transform PDFs, web pages, and YouTube videos into a graph of entities and their relationships, which it stores in your Neo4j database.

The front-end is a React Application and the back-end a Python FastAPI application. It uses the llm-graph-transformer module that Neo4j contributed to LangChain.

Here is a quick demo:

Functionality Includes

Connect to a Neo4j (Aura) instance
Provide your PDF files, Youtube URLs, Wikipedia Keywords or S3/GCS buckets
Construct Graph with LLM / Diffbot
Visualize Knowledge Graph in App
Chat with your data with GraphRAG
Open Neo4j Bloom for further visual exploration
Use the constructed knowledge graph in your applications

Login, or create an account at https://console.neo4j.io
Under Instances, create a new AuraDB Free Database
Downloads the credentials file
Wait until the instance is running
Drop the credentials file on the connect dialog for the LLM Graph Builder

How it works

Uploaded Sources are stored as Document nodes in the graph
Each document (type) is loaded with the LangChain Loaders
The content is split into Chunks
Chunks are stored in the graph and connected to the Document and to each other for advanced RAG patterns
Highly similar Chunks are connected with a SIMILAR relationship to form a kNN Graph
Embeddings are computed and stored in the Chunks and Vector index
Using the llm-graph-transformer or diffbot-graph-transformer entities and relationships are extracted from the text
Entities are stored in the graph and connected to the originating Chunks