正在加载视频...

视频加载失败

Can LLMs extract knowledge graphs from unstructured text? Introducing GraphGPT! Pass in any text (summary of a movie, passage from Wikipedia, etc.) to generate a visualization of entities and their relationships. A quick example:

578,837 次观看 • 3 年前 •via X (Twitter)

10 条评论

Varun Shenoy 的头像
Varun Shenoy3 年前

Here's GraphGPT creating a knowledge graph from the synopsis of Veep:

Varun Shenoy 的头像
Varun Shenoy3 年前

In case you want to play with it yourself, here's the repo. All you need is an OpenAI API key.

Varun Shenoy 的头像
Varun Shenoy3 年前

GraphGPT is on the front page of HN 🎉

Varun Shenoy 的头像
Varun Shenoy3 年前

Play with GraphGPT without having to do any of the dev setup. All you need is an OpenAI API key! Thanks @sfzhu for the idea + PR 🚀

amir 的头像
amir3 年前

is that localhost?

Varun Shenoy 的头像
Varun Shenoy3 年前

🚢 🚢 🚢

Tom Bielecki 的头像
Tom Bielecki3 年前

What!! This is crazy useful. Great that you can append and edit with further Input. It probably wouldn't be hard to extend this to explore any domain/market with web search/summarization @codexeditor

Stratos Kontopoulos 的头像
Stratos Kontopoulos3 年前

Great stuff, especially for generating small & focused visualizations! But the output is simply a graph, not a #KnowledgeGraph, since it's missing #semantics & schema information. Also, it would be great if the output was available in a machine-processible format as well.

Joel Fernandes 的头像
Joel Fernandes3 年前

Nice project. How does one go about building a gpt-like training with custom data (without using openAI) ? For eg I have say a million spreadsheets of unstructured data and I want to chat with my data asking it to draw plots, graphs , asking questions to my data in English ..

verumlotus 的头像
verumlotus3 年前

man's shipping velocity crazy 🫡

相关视频

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

169,184 次观看 • 1 年前